ISSN 2465-8391

2nd ICIECT + DigiCon 2016

The 2nd International Conference on Information, Electronics, and Communication Technology + Digital Contents Society 2016

The Korea  Institute of Information, Electronics,

and Communication Technology

Digital Contents Society

■ Technical Program Chairman's Message

I am greatly honored to give the welcoming address at Da Nang, Vietnam, June 29~July 2, 2016. This conference somewhat differs from most others conferences covering broad overview and current assessment of global and multidisciplinary trends of our common interests every year. Especially, this year the Korea Digital Contents Society thankfully joins us together.

There are several reasons for organizing this conference. Research on convergence is entering a period of mushrooming growth. Therefore, It was highly desirable that interested scientists gather to report new advances, review progresses, discuss conflicting findings and divergent interpretations, and make more meaningful plans for the future phases and directions of research.

Finally, I would like to thank all the attendees, track and session chairs, reviewers and local organizing directors for their valuable contributions for ICIECT 2016. It would be the good fortune of getting to know personally many of the active and experienced researchers in this field.

Thanks you.

June 2016

The Korea  Institute of Information, Electronics,

and Communication Technology

President of the Society

Cheol-Soo Bae

■ General Chairman's Message

The Korea Digital Contents Society(KDCS), founded in 2000, has grown into a major national and international digital contents society in the field of IT (Information Technology) that currently has over 1,200 academically active members.

With the expansion of the internet and other technologies, digital contents technology has been evolving and developing rapidly and has received recognition as a key technology of the 21st century.   Accordingly, the KDCS has been organizing various academic publications, presentations and exhibits in the field of digital contents.

Around 120 research papers are annually published through the journal of digital contents society bi-monthly. In addition, the KDCS has recently hosted several international conferences through "DigiCon"(International Conference on Digital Contents) and is continually contributing to its internationalization.

The KDCS has received recognition for the excellence of the quality of published papers and continues to accomplish impressive growth.

Please contribute your attention, love and active participation to the KDCS.

I promise to do my best for the development of the KDCS with all members.

Thank you.

June 2016

President of  The Korea Digital Contents Society

Soon-Gohn Kim

■ Organization(Committee)

￭ General Chair

․ Cheol-Soo Bae, Catholic Kwandong University, Korea

․ Soon-Gohn Kim, Joongbu University, Korea

․ Han-Kyung Yun, Korea University of Technology abd Education, Korea

․ Sung-Yong Son, Gachon University, Korea

. Young-Cheol Park, Yonsei University, Korea

․ Rizwana Mouly, Manav Rachna International University, India

￭ Program Co-chairs

. Ik-Ho Song, KAIST, Korea

․ Eung-Nam Ko, Baekseok University , Korea

. Jin-Sul Kim, Chonnam National University , Korea

․ Eun-Jee Song, Namseoul University,  Korea

. Yong-Kap Kim, Wonkkwang University, Korea

. Sang Sik Lee, Catholic Kwandong University, Korea

․ Karlygash Mukhatarova, Ai-Farabi Kaxakh National University, Kazkhstan

￭ Publication Co-chairs

. Tae-Sun Choi, GIST, Korea

. Gu-Min Jung, Kooknim University, Korea

․ Hwa-Jin Park, Sookmyung Women's University, Korea

. Seok-Chan Kim, Pusan University, Korea

. Young-Jun Yoon, Hanyang University, Korea

․ Su Xinolin, Shangdong University of Fiance and Economics, China

￭ Publicity Co-chairs

․ In -Sik Choi, Hannam University, Korea

. Yang- Hoei Do, Jeju University, Korea

. Ki Young Lee, Catholic Kwandong University, Korea

․ Faisal Multagin, University of Indonesia Kampus Ul Bara, Indonesia

￭ International Advisory Board

․ Orazaly Sabden, Uinion of Scientists of Kazakhstan, Kazakhstan

․ Lavauya Puttan, Nimra Institute of Science & Technology, India

․ Khang Heesam, Trinity International University, USA

․ Kim Dongil, Pusan National University, Korea

￭ Program Committee

․ Zang Shuy, Sangdong Instutute of Commerce and Technology, China

․ Anel A. Kireyeva, Institute of Economics of the Ministry of

Education and Science, Kazkhstan

․ Sun Libu, Sangdong University, China

․ Siva Kumar Challa, Nimra Institute of Science & Technology, India

․ Xiuo Wu, Tsinghua University, China

․ Ji-Hong Jang, Jungwon University, Korea

․ Byung-Kwan Lee, Catholic Kwandong University, Korea

․ Hae-Yeon Lee, Kumoh Nationl Institute of Technology, Korea

․ Geun Chang Kwak, Chosun University, Korea

․ Hyun Chul Kim, Ulsan University, Korea

￭ Publication Committee

․ Sunjian Quan, Shandong University of Political Science and Law, China

․ Sun Xiaolin, Shandong University of Finance and Economics, China

․ Chen Xiaolong, Shandong University of Finance and Economics, China

․ Rizal Edy Halim, University of Indonesia Kampus UI Baru, Indonesia

․ Faisal Muttaqin, University of Indonesia Kampus UI Baru, Indonesia

․ Jae-Kwang Lee, Hannam University, Korea

․ Young-Hee Kim, Changwon University, Korea

․ Ki-Rae Kim,, Daeduk University, Korea

․ Kwan-Hyoung Lee, Daejin University, Korea

￭ Publicity Committee

․ Katta Ashok Kumar, Saveetha University, India

․ Elham Shadkam, Isfahan University of Technology, Iran

․ Saken S. Yespayev, Institute of Economics of the Ministry of

Education and Science, Kaxakhstan

․ Yeon-Ho Jung, Pupyoung University, Korea

● Day : Jun. 29 ~ Jul. 02, 2016

● Location : Grand Mercure Danang Hotel (Crystal & Jade room)

● conference : First day - June 29

Day	Time	Activity	Location
Jun.29	14:00 ≀ 15:00	Registration
	15:00 ≀ 16:00	▷ Opening ceremony MC : Han-Kyung Yun (Korea University of Technology and Education) ․ the Pledge of Allegiance ․ opening address: President of the society(Cheol-Soo Bae) ․ welcoming address:	Grand Mercure Danang Hotel Crystal room
	16:00 ≀ 17:00	▷ guest lecture Young-June Yoon(Hanyang University): Role of biomechanics in mechanical biological	Grand Mercure Danang Hotel Crystal room

● conference : Second day - June 30

Day	Time	Activity	Location
Jun.30	09:00 ≀ 09:20	-Opening Ceremony MC: Han-Kyung Yun(Korea) -Opening address: President of the society(Cheol-Soo Bae: (KIIECT) -Welcoming address: President of the society Soon-Gohn Kim(KDCS)	Grand Mercure Danang Hotel Crystal room
	09:30 ≀ 12:00	▷ Oral Session 1	Crystal room
		▷ Oral Session 2	Jade room
		▷ Poster exhibit	Lobby
	12:00 ≀ 13:00	▷ lunch time	Hotel Chinese Restaurant
	13:00 ≀ 17:00	▷ Oral Session 1	Crystal room
		▷ Oral Session 2	Jade room
		▷ Poster exhibit	Lobby
	17:00 ≀ 18:00	▷ Dinner party

● conference : Third day -  July 01

Day	Time	Activity	Location
Jul.01	09:30 ≀ 12:00	▷ Oral Session 1	Crystal room
		▷ Oral Session 2	Jade room
		▷ Poster exhibit	Lobby
	12:00 ≀ 13:00	▷ lunch time
	13:00 ≀ 17:00	▷ Oral Session 1	Crystal room
		▷ Oral Session 2	Jade room
		▷ Poster exhibit	Lobby

Session 1. Oral Presentation

Senior person present : Takeo Sugimoto(Ritsumeikan University Kusatsu, Japan),

Han-Kyung Yun(Korea University of Technology and Education)

Young-Kab Kim(Wonkwang University)

Time : Jun. 29 ~ Jul. 2,  09:30 ~ 12:00

Location : Crystal room

(Presentation time : 09:30)

□ Extraction of Effective Observation Area from Fisheye lens image

Sun-Min Whang, Han-Kyung Yun(Korea)

(Presentation time : 09:50)

□ Analysis of Power Load Patterns using Fuzzy Clustering

Keon-Jun Park, Sung-Yong Son(Korea)

(Presentation time : 10:10)

□ Design for Low Voltage DC Distribution Network Testbed

Sung-Won Park, Sung-Yong Son(Korea)

(Presentation time : 10:30)

□ Design for Power System Big Data Service Index

Sung-Won Park, Sung-Yong Son(Korea)

(Presentation time : 10:50)

□ Timestamp based Key Exchange Protocol

Young-Seok Lee, Yeon-Sung Choi(Korea)

(Presentation time : 11:10)

□ Big data based Log Management System for Wireless Sensor Networks

Hyeop-geon Lee, Young-woon Kim, Ki-young Kim, Eun-Chul Park(Korea)

(Presentation time : 11:30)

□ Rits Eye: A Software-Based System for Realtime Face Detection and Tracking Using Pan-Tilt-Zoom Controllable Camera

Gang Xu, Takeo Sugimoto(Ritsumeikan University Kusatsu, Japan)

Session 1. Oral Presentation

Senior person present : Sharad K. Ghimire(Tribhuvan University, Nepal)

Young-Gyu Choi(Korea National University of Transportation)

Kwang-Chae Park(Chosun University)

Time : Jun. 29 ~ Jul. 2,  13:00 ~ 17:00

Location : Crystal room

(Presentation time : 13:00)

□ A Study on NoSQL Database for the Big Data Processing

Hong-Jin Park(Korea)

(Presentation time : 13:20)

□ SQLite Recovery Method Using CST+-Tree

S. S. Han, S. J. Cho(Korea)

(Presentation time : 13:40)

□ Motion-based recognition of pedestrians

B. Heisele, C. Wohler(Singapore)

(Presentation time : 14:00)

□ Recognition of Facial Expressions Using Muscle-Based Feature Models

Hiroshi OHTA，Hitoshi SAJI, Hiromasa NAKATANI(Shizuoka University, Japan)

(Presentation time : 14:20)

□ Application of wavelet analysis and multi-resolution decomposition for high quality compression and reconstruction

Fahim-ul-Haq, Azra Khalid, Saad Umer Baig(NED University, Pakistan)

(Presentation time : 14:40)

□ Adaptive Radio Resource Management Algorithm for MIMO-OFDMA System

Mr. Pradip Paudyal,Prof. Laszlo Pap (PhD)(Budapest University, Hungary),

Prof. Andras Gabor (PhD)(Corvinurs University, Hungary),

Asst. Prof.Sharad K. Ghimire(Tribhuvan University, Nepal)

(Presentation time : 15:00)

□ Active Contour Model Based in Mathematical Morphology

Akira Asano, Tohru Yamashita(Hiroshima University, Japan),

Shunsuke Yokozeki(Kyushu Institute of Technologylizuka)

(Presentation time : 15:20)

□ Eye Tracking and Animation for MPEG-4 Coding

Stefan Bemogger, LijunYin, AnupBasu(University of Alberta, Canada),

AxelPinz(GrazUniversity of Technology, Austria)

(Presentation time : 15:40)

□ Implementation of A WWW interactive 3D Graphics Exploratorium

Edmond C. Prakash(Nanyang Technological University, China)

(Presentation time : 16:00)

□ Analysis of Asynchronous selective reject aloha with considering propagation delay

Gole-Jong Jaw, Heng-Dao Lin, Ching-Pio Hong

(Telecommunication Laboratories, Taiwan)

(Presentation time : 16:20)

□ A new generation computer and the future Telecommunications systems

Y.Z. Sheng(Beijing university, China)

Session 2. Oral Presentation

Senior person present : Y.Z. Sheng(Beijing university, China)

Soon-Gohn Kim(Joongbu University, Korea)

Ho-Joon Kim(Jeonju University)

Time : Jun. 29 ~ Jul. 2,  09:30 ~ 12:00

Location : Jade Room

(Presentation time : 09:30)

□ Design of Non-Fuzzy Prediction Based on K-Medoids Clustering

D. Y. Lee, K. J. Park(korea)

(Presentation time : 09:50)

□ Reversible data hiding algorithm using surface characteristics of image

S. M. Jung(korea)

(Presentation time : 10:10)

□ Correcting Forward Head Posture using Computer Vision Methodologies	Dong-young Kim, Chung-Hee Lee(Korea)

(Presentation time : 10:30)

□ Development of Kinetic interactive art using dynamixel control –Shadow Mobile-

Sung-Dae Hong, Ok-Hue Cho(korea)

(Presentation time : 10:50)

□ A Study on Disease Pattern with Lifestyle Risk Factors Using CP of EMR for Healthcare Promotion Plan

Young-Sung Cho, Song-Chul Moon(korea)

(Presentation time : 11:10)

□ A Study on the Development of Change Blade using Curved Precision Process Technology

Jeong-Beom Kim, Soon-Gohn Kim (korea)

(Presentation time : 11:30)

□ Process separation model for reliability and efficient memory management and sustainable software architecture

Woon-Yong Kim, Soon-Gohn Kim (korea)

Session 2. Oral Presentation

Senior person present : Da-yong zhao(Academia Sinica, China)

Soon-Gohn Kim(Joongbu University, Korea)

Young-Geil Kim(Korea National University of Transportation, Korea)

Time : Jun. 29 ~ Jul. 2,  13:00 ~ 17:00

Location : Jade Room

(Presentation time : 13:00)

□ An Implementation of the effective service operation model for a Lift Digital Signage Platform based on Web Socket

Soon-Gohn Kim, Seok-Gyu Park, Woon-Yong Kim (korea)

(Presentation time : 13:20)

□ Implementation of the Moving Objects Detection System of Spherical Panorama

Soon-Gohn Kim, Kwang-Man Ko (korea)

(Presentation time : 13:40)

□ A study on music video analysis by photographer

H. K. Kang (korea)

(Presentation time : 14:00)

□ Comparison of Sensor Programming Schemes in MSRDS

Jong-In Chung (korea)

(Presentation time : 14:20)

□ FADECT: A Middleware Platform for Automatic Fault Detection of Building Energy Consumption

Jong-Wook Kim(korea)

(Presentation time : 14:40)

□ A Scheme for Privacy Preservation in Ubiquitous Computing

Arijit Ukil(TCS Kolkata, India)

(Presentation time : 15:00)

□ integrated services switching system

J.H.M.Abu Baker, M-Abbas(University Tecknology Malasia, Malasia)

(Presentation time : 15:20)

□ A design of an Optimized Indoor Positioning System for tracing a patient’s current location in a general hospital

Yusrina Tifani, Byung-Kwan Lee, Eun-Hee Jeong(korea)

(Presentation time : 15:40)

□ Performance of Pragmatic 16-state 8-PSK TCM Codes in Non-Linear Satellite Channel

VK Dubey, NK Lim, E Gunawan(Nanyang Technological Institute, Singapore)

(Presentation time : 16:00)

□ A Grey-Scale refinement coding scheme for progressive transmission of images

Da-yong zhao (Academia Sinica, China)

Session 3. Poster Presentation

Poster

Senior person present : Song-Chul Moon(Namseoul University)

Jong-Wook Kim(Sangmyung University)

Time : Jun. 29 ~ Jul. 2,  09:30

Location : Hotel Lobby

(Presentation time : )

□ Design and Measurement of Logic eFuse OTP Memory

Ri-jun Jin, Heon Park, Yong-hee Kim, Pan-bong Ha(korea)

Yongxu Ren(IP Development Team, Design Service division, Shanghai Huali Microelectronics Corporation Shanghai, China)

(Presentation time : )

□ Design and Measurement of NMOS-Diode eFuse OTP Memory

Heon Park, Ri-jun Jin, Pan-bong Ha, Yong-hee Kim, Seung-hoon Lee(korea)

(Presentation time : )

□ Analysis of Biofeedback Signals Algorithm for Face Muscle Motion

Sang-sik Lee, Ki-young Lee(korea)

(Presentation time : )

□ Dental colorimetry system studies using smartphone

Sang-sik Lee, Jin-hyoung Jeong, Chang-dong Lee(korea)

(Presentation time : )

□ Maximum Strength and Isokinetic Exercise of Biceps

Sang-sik Lee, Jin-hyoung Jeong, Jee-hun Jang,

Ki-young Lee, Yoon-jeong Kim(Korea)

(Presentation time : )

□ Research on Influence of Job Characteristics of Social Workers at Welfare Institution for the Disabled on their Emotional Labor

Jong-Pil Kim, Sung-je Cho(korea)

(Presentation time : )

□ Research on the Job Stress and Job Satisfaction of Social Workers at Welfare Institution for the Disabled	Jong-Pil Kim, Sung-je Cho(Korea)

(Presentation time : )

□ Human Bio-Sensor Monitoring for Autonomous Navigation System of Power Wheelchair

Moon-Shu Jung, Kuk-Se Kim, Seong-Soo Ahn, Joon Lee(Korea)

(Presentation time : )

□ Bridge Resistance Deviation-to-Period Converter for Resistive Biosensors

Chang-su Won, Hoon Kim, Won-Sup Chung, Sang-Hee Son(Korea)

(Presentation time : )

□ Vocal Tract Modeling Based Unfixed Sectionlength Acoustic Tubes

Dong-jun Kim(Korea)

(Presentation time : )

□ RGB Motion Segmentation using Background Subtraction based on AMF

Alla Kim, Yoon-Ho Kim(Korea)

(Presentation time : )

□ Osteonal Inclination Alters The Bone Fluid Pore Pressure

Young-June Yoon(Korea)

(Presentation time : )

□ Autonomous Navigation System of Power Wheelchair using Distance Measurement Sensors

Kuk-Se Kim, Moon-Shu Jung, Seong-Soo Ahn, Joon Lee(Korea)

Session 3. Poster Presentation

Senior person present : Jong-In Chung(Kongju National University)

Soo-Mok Jung(Sahmyook University)

Time : Jun. 29 ~ Jul. 2,  09:30

Location : Hotel Lobby

(Presentation time : )

□ Hybrid Type Wireless Power Transfer Systemfor Structural Health Monitoring System

Ji-min Kim, Joon-seon Ahn, Ho-sik Park, Min-seok Han(Korea)

(Presentation time : )

□ High Accuracy Gradient Magnitude Caculator Using Angle Correlation of Neighborhood Pixels for Energy Efficient Edge Detector

Han-bin Kim, Ju-seong Lee, Seok-Geum Son, Ho-Myoung An(Korea)

(Presentation time : )

□ Type-2 Fuzzy Model for Emotion Reasoning

Keon-Jun Park, Ji-Seong Kim, Won-Kyu Lee, Yong-kab Kim, Ho-Jun Kim,

Geun-Chang Hoang(Korea)

(Presentation time : )

□ Design of Emotional Lighting Suited for relaxation and Meditation by EEG

Jong-Hyeok Jang, Ji-Seong Kim, Won-Kyu Lee, Keon-Jun Park,

Yong-kab Kim, Geun-Chang Hoang(Korea)

(Presentation time : )

□ Analysis of EEG of Left Brain and Right Brain to Identify the Effects of Emotional Lighting in Meditation

Jong-Hyeok Jang, Ji-Seong Kim, Keon-Jun Park, Yong-Kab Kim,

Geun-Chang Hoang, Byun-Gon Kim(Korea)

(Presentation time : )

□ Study on the Distance Calculation Revision Algorithm Using RSSI Measurement Result Filtering

Ji-Seong Kim, Jong-Hyeok Jang, Keon-Jun Park, Yong-Kab Kim

Geun-Chang Hoang, Kwan-Woong Kim(Korea)

(Presentation time : )

□ A FAST ALGORITHM FOR REGION-ORIENTED TEXTURE CODING

Young-Gyu Choi, Cheol-Soo Bae(Korea)

(Presentation time : )

□ ADDRESS PREDICTIVE COLOR QUANTIZATION IMAGE COMPRESSION FOR MULTIMEDIA APPLICATIONS

Chong-Hwan Choi, Kwang-Chae Park, Young-Geil Kim, Cheol-Soo Bae(Korea)

(Presentation time : )

□ Fire Detection System using Arduino Sensor

Joong-Chang Chun, You-sik Hong, Cheon-shik Kim

Cheol-Soo Bae (Korea)

(Presentation time : )

□ License Plate Recognition Method for Inclined Plates

Young-gyu Choi, Han-kyung Yun, Hyeop-geon Lee(Korea)

(Presentation time : )

□ A Hand Tracking System Based On the Kalman Filter

Seok-Han Lee(Korea)

(Presentation time : )

□ A Study on Measurements of Hydrogen Permeation Time using the Pressure Sensor and Wavelet Threshold

Tae-Soo Kim, Joong-Chang Chun(Korea)

(Presentation time : )

□  Object Detection Based on Corresponding Points Estimation

Dong-wook Kim(Korea)

Guest Lecture

Smart Appliance Interface for Demand Response

Professor: Sung-Yong Son

(Gachon University)

Extraction of Effective Observation Area from Fisheye lens image

For Quantifying Cloud Cover from Whole Sky Image

Sun-Min Whang*, Han-Kyung Yun**

*School of Computer Science and Engineering Korea University of Technology and Education

Chonansi, Chungnam, Korea

smhwang@koreatech.ac.kr

**School of Computer Science and Engineering Korea University of Technology and Education

Chonansi, Chungnam, Korea

hkyun@kut.ac.kr

Abstract

The cloud amount is one of major variables to decide amount of sunshine or solar irradiance. Due to the eye observation of cloud amount, the result may depend on the individual difference and the limitation of human vision. Instead of the eye observation, computer aided automated observation might be applied to get the cloud amount or cloud cover. A rising issue is how to get the cloud area since the boundary of cloud generally is ambiguous. Some of studies related cloud generally used values of RGB or those ratio such as Red Green Ratio and Red Blue Ratio etc. Previous studies reported that RGB value in the cloud bitmap image could be different among pixels even the region of cloud. The threshold value to get a detailed edge is artificially used to overcome the ambiguity. It might be included some errors since the atmosphere condition and surrounding often changes according to time and location. Furthermore, the boundary of sky may not easy to be decided since the circumference of whole sky image generally includes some obstacles such as natural or artificial object. The proposed edge detection of the observed area is more precise and convenient. The exact area of sky easily gets than the currently used method.

Keywords: whole sky image; colud; fisheye image; sky edge detection

1. Introduction

The cloud amount affects meteorological variables as well as weather forecast.  It is an absolute variable for determining solar radiation and sunshine on ground. In addition, it is highly used in an industry using solar energy and air traffic as well as meteorological areas. However, cloud amount lacks an objective evaluation due to visual observation on the ground at present, which has low spatiotemporal resolution. To overcome the limitations of bare-eye observation, satellite data are employed in calculating the cloud amount but they are not identical to those measured from ground and there is still a problem to get objective data. Therefore, WSI (Whole Sky Imager) and TSI (Total Sky Imager) are invited to improve the calculation of the cloud amount, but few studies related to it have been conducted domestically [1, 2]. The present study developed algorithm of calculating the effective sky zone to get the cloud amount with the whole sky image data taken the image data taken the automatic cloud observation system operated by the Metrological Administration in Korea. To evaluate the performance of the algorithm, comparison was made with the current used method, since the result of the current method has not given reliable data compared naked eye observation. The whole sky image, obtained the automatic cloud observation system in Daikwanryoung weather observation station at in Korea, has been analyzed [Fig. 1]. The result is shown that the proposed method has more reliable and accurate. More detailed analysis, seasonable a monthly analysis were required to get a universal commercialized method.

Figure 1. Automatic cloud observation system and obtained images.

A. Sky and cloud Charateristic

The change of sky color at sunrise or sunset (red nearest the sun, blue furthest away) is caused by Rayleigh scattering by atmospheric gas particles which are much smaller than the wavelengths of visible light. The grey/white color of the clouds is caused by Mie scattering such as water droplets, yellow dust or Asian dust, volcanic ash, and sea salt particles which are of a comparable size to the wavelengths of visible light [Fig.2].

Figure 2. Permiabilty related frequancy and major absorbtion gas.

B. Whole sky iamge analysis

The cloud amounts, one of the basic important parameter in atmospheric observation, have been observed by naked eyes of observers, which are affected by the subjective view. In order to ensure reliable and objective observation, an algorithm to retrieve cloud amount was constructed using true color images composed of red, green and blue (RGB)[1,2,3,4]. The true color image is obtained by the automated cloud observation system as shown in the figure 1, The principle of distinguishing cloud from sky lies in a spectrum analysis that the spectral characteristics of light scattering is different for air molecules and cloud.

2. Sky Zone Extraction

A. Circular fisheye whole sky image

The circular fisheye lenses take in a 180° hemisphere and projected this as a circle within the sensor area. These have a 180° vertical angle of view, and the horizontal and diagonal angle of view are also 180°. Most circular fisheye lenses cover a smaller image circle than rectilinear lenses, so the corners of the frame will be completely dark as shown in the figure 3[5].

Figure 3. Whole sky image obtained from the cloud observation system.

B. Optical chracteristics of sky and cloud

The principle of separation between clear sky zone and cloud zone is the spectral characteristics difference. Molecules in the air induce scattering. Therefore the cloud amount could be obtained using true color images composed of red, green and blue since there are differences among RGB as shown figure 4. The clear sky shows different intensities depended on RGB colors, however the cloudy zone shows that the intensity of each color fluctuates with time but their intensities are not much different.

Figure 4. Whole Sky Image Analysis(Region A: clear sky, Region B: cloud) Ave. of Brightness of RGB(top), Comparison of Ave. of RBR and RGR(middle), Ave. of Standard Deviation of Brightness of RGB.[7]. Effective sky zone extraction

The current surrounding of the observation system has problem since the border of image circle includes the obstacle such as building, trees, electric power line and some utility poles. The current solution is that the image is periodically obtained and revised manually to make a filter to extract the sky region, but the proposed method calculates automatically using constraint conditions which are included in the whole sky image and system parameters. The figure in below shows the difference that the left image is processed by the manual works. The street light and its pole are disappeared and the top of trees at the middle of right are new obstacles. The right image is processed automatically using the proposed method and the inside edge is exactly the observed sky zone. By scanning from the center to the edge, the sky zone is able to get the number of pixels of the sky zone [6, 7].

Figure 5. The sky zone of the current method(left) and the proposed method(right).

The previous study shows the separation of clear sys and cloud is more precise and convenient if the HIS (hue, intensity and saturation) system is applied as shown in the figure 6. It may be applied to be observed for the cloud type and the cloud height which are also required parameters for the cloud observation. The right image in the figure shows that the brightness in the image is free and the sun’s influence is less than the original image.

Figure 6. The comparision between the original image and the processed negative intensity image.

3. Result and Future Work

This groundwork study is part of developing an intelligent cloud observation system for the Meteorological Agency in Korea. The effective observation sky area is defined and the obtained sky area does not require extra manual work to make filter to get the sky area. It is obtained automatically from the exactly same original image. The proposed cloud amount measuring solution is shown in Figure 7, which is the block diagram of the proposed solution for cloud amount observation. It will be extended to measure the cloud type and height.

Figure 7. The block diagram of the proposed solution.

At the present time, the result of a pilot solution is shown in below and the solution requires some improvement. It will be tested after modification and packaging. However, the obtained cloud amount or cover using the proposed solution is better reliable and precise than the current automatic cloud observation system at the Meteorological Agency as shown in below.

Figure 8.The result of the Proposed solution.

Acknowledgment

Korea University of Technology and Education and AND3D Co. Ltd. (sponsors)

References

[1] Yun mi Kim, Jhoon Kim, and Hi Gu Cho, “Development of objective algorithm for cloudiness using all-sky digital camera”, Atmosphere, Korean Meteorological Society, vol. 18(1), pp. 1–14, March 2008.

[2] Boo Yoo Kim, Cloud amount calculation using Skyview image data. Dissertation, Gangneung-Wonju National University, 2014.

[3] Seungok Kim, et el, “A comparative study of total cloud amount and cloud base height between cloud automatic observation system and visual observations, automatic observations”, Research Report of Meteorological Research  Institute, Koera Meteorological Administration, evol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 2013.

[4] Heinle, A., Macke, A., and Srivastav, A., “Automatic cloud classification of whole sky images”, Atmospheric Measurement Techniques Discussions, 3(1), 269-299,

[5] Shields J., Karr M., Johnson R., and Burden A., “Day/night whole sky imagers for 24-h cloud and sky assessment: history and overview”, Applied Optics, 52, 1605-1616, 2013.

[6] Shields J., Karr M., Burden A., Johnson R., Hodgkiss W., “Research toward Multi-Site Characterization of Sky Obscuration by Clouds”, Technical Note, University of California San, 274, 2009.

[7] Shields J., Karr M., Burden A., Johnson R., and Hodgkiss, W., “Continuing support of cloud free line of sight determination including whole sky imaging of clouds“, University of California San Diego, Technical Note 273, 2007.

Analysis of Power Load Patterns Using Fuzzy Clustering

Keon-Jun Park*, Sung-Yong Son**

*Smart Green Home Research CenterGachon University Seongnam-si, South Korea

kjpark@gachon.ac.kr

**Dept. Electrical Engineering Gachon University Seongnam-si, South Korea

xtra@gachon.ac.kr

Abstract

As the smart grid is getting the limelight, there is growing interests in power demand forecasting. In this paper, we propose an analysis method for power load patterns using fuzzy clustering to forecast the power load of a particular building. Data collected from a target building are analyzed through the fuzzy clustering according to the attributions of data, mainly seasonal and hourly patterns. And the representative load pattern of each cluster is drawn for the target building and analyzed by the cluster cells. This approach is expected to be of use in the load pattern analysis.

Keywords: power load, load patterns, fuzzy clustering, representative load pattern, forecast

1. Introduction

Smart grid is the next generation of the power grid to optimize energy efficiency by exchanging real-time information to power suppliers and consumers in both directions by combining the information technology to the existing power grid. As the industrial development, energy consumption has been increasing, and the awareness of the energy efficiency has been improving. In this regard, the studies on techniques for managing energy efficiently have been in progress. In addition, there is going research on the technologies for predicting the power demand to respond to the power demand that increases every year [1-8].

Power demand forecasts are generally performed through the analysis by separating into the time periods such as annual, seasonal, and monthly periods and weekday/holidays using electricity demand data obtained from a specific target. These power demand data is to form a variety of load patterns over time periods; the clustering algorithm methods are used to extract and analyze the features of the power load patterns [1-8].

Various methods such as k-mean clustering, hierarchical clustering, and density-based methods have been used for the clustering. They are based on partitioning, tree-type data structuring, and density-based classification, respectively [9-12]. Among them, the k-mean clustering method has been widely used to derive the load patterns [1-5].In this paper, we introduce an analysis method for power load patterns using fuzzy clustering to derive the power load pattern of a particular building. Data collected from a target building are analyzed through the fuzzy clustering with membership degree of each data according to the seasonal and hourly patterns. And the representative load patterns of each cluster are drawn for the target building.

2. Data of Power Load Pattern

An office building in Guro, Korea is used for the load pattern analysis. The building consists of 157 separately owned offices that are called as 'cells' in this paper. Data for load pattern analysis are acquired in the form of the actual hourly power consumption data of the entire days of 2014. The data include calendar information such as year, month, day, and the day of the week and are divided into weekdays and holidays. The collected data are refined by removing missing values and outliers. And 157 cells of the target building are finally selected for the load pattern analysis.

3. Fuzzy c-means Clustering Algorithm

Clustering is useful to find the pattern of the data and is an unsupervised method that aims at partitioning a given set of data (objects) into subgroups (clusters) based on similarity. Fuzzy c-means clustering is a data clustering algorithm in which each data belongs to a cluster with a degree specified by a membership grade. Fuzzy c-means clustering partitions a collection of vectors into c fuzzy groups and finds a cluster center in each group. The main difference between fuzzy c-means and hard c-means is that fuzzy c-means clustering employs fuzzy partitioning where a given data can belong to several groups with membership grades between 0 and 1. This algorithm divides the space into the clusters and is aimed at the formation of ‘c’ clusters.

Consider the set X={x1,x2,…,xN}, xp□ Rn treated as vectors in n-dimensional Euclidean space. In clustering, patterns xp□X are assigned into c clusters, which are represented by prototypes (centers) vi□ Rn. The assignment to each cluster is expressed in terms of the partition matrix U = [uip] where

	(1)
	(2)

The objective function Q leading the clustering is expressed as a sum of the distances of each data from the centers to v1, v2, ∙∙, and vc,

	(3)

Here, ‘m’ is a fuzzification coefficient, mi>1.0 and denotes the Euclidean distance.

The minimization of the objective function Q is conducted through successive iterations by adjusting both the entries of the partition matrix and the prototypes; that is min Q(U, v1, v2, …, vc). The corresponding equations used in an iterative method read as follows.

	(4)
	(5)

In order to find the load patterns of a particular building, power consumption data are grouped on the basis of the average consumption. Load patterns are derived by the average consumption of the cells belonging to each cluster.

4. Analysis of Power Load Patterns

As mentioned earlier, in this paper, we first performed cluster analysis on weekdays for a year on the basis of 24 hours per day by using the data collected from 157 cells of the target building. Then cluster analysis was performed on weekdays divided into summer and winter. Cluster analysis was performed using a fuzzy c-mean clustering algorithm, and the number of clusters was set to five, fuzzification coefficient was set to 2.5. The number of clusters and the fuzzification coefficient were determined by repeated experiments by changing the values to find reference load patterns.

Figure 1 shows the results of clustering for the annual weekdays on the basis of 24 hours a day. Four cells in this building were included in the first cluster labeled Level 5 which has the highest average consumption. Figure 2 illustrates the load patterns of the annual average power consumption by each cluster shown in Figure 1. The Level 5 cluster shows the pattern of increasing the average power consumption from 6 am before the rush hour and consuming maximum average power at 1:00 pm.

Figure 1. Clustering results of annual weekdays.

Figure 2. Load patterns of annual weekdays.

The clustered results for weekdays in summer on the basis of 24 hours a day is shown in figure 3. In this case, the first cluster has 6 cells which have the highest average consumption. Figure 4 draws the load patterns of the average power consumption for weekdays in summer by each cluster shown in figure 3. The Level 5 cluster shows the pattern of increasing the average power consumption from 6 am before the rush hour and consuming maximum average power at 1:00 pm. In summer, it can be seen that the average consumption pattern is similar to the annual average consumption patterns.

Figure 3. Clustering results of weekdays in summer.

Figure 4. Load patterns of weekdays in summer.

Figure 5 shows the results of the clustering for weekdays in winter on the basis of 24 hours a day. Two cells are included in the Level 5 cluster which has the highest average. Figure 6 illustrates the load patterns of weekdays in winter. The Level 5 cluster shows the pattern of increasing dramatically the average power consumption from 6 am to 8 am before the rush hour and consuming maximum average power at 8:00 am. It can be seen that the average consumption pattern on weekdays in winter is different from the pattern of annual average consumption. Thus, the result means that the customized management of the load pattern is needed on weekdays in winter.

Figure 5. Clustering results of weekdays in winter.

Figure 6. Load patterns of weekdays in winter.

5. Conclusion

In this paper, we analyzed the power load patterns using fuzzy clustering to predict the power load of a building. For this purpose, the data collected from the target building were analyzed by fuzzy clustering, depending on the seasonal and hourly patterns. In addition, representative load patterns were derived for each cluster for the target building.

From the result, load pattern of the target building showed that while a load pattern in Level 5 cluster on weekdays in summer shows a pattern similar to the annual average load pattern, a load pattern in Level 5 cluster in winter shows a rapidly increasing pattern different to the annual average load pattern. As a result, the load pattern models are required to be developed according to the season.

Acknowledgment

This work is supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20142020104320).

References

[1] L. Hernandez, C. Baladron, J.M. Aguiar, B. Carro, A.J. Sanchez-Esguevillas, J. Lloret, and J. Massana, “A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings,” IEEE Communications Surveys & Tutorials, Vol. 16, No. 3, pp. 1460-1495, 2014.

[2] Y.I. Kim, H. Choi, "A Study for Load Profile Generation of Electric Power Customer using Clustering Algorithm," KIPS Spring conference, Vol. 15, No. 1, May 2008.

[3] J.H. Park, J.H. Shin, M. Piao, H. G. Lee, and K. H Ryu, "3D Cube Mining and Calendar Pattern Based Temporal Mining for Analyzing Power Load Pattern," KIPS Spring conference, pp.200-203, May 16, 2008.

[4] S.S. Park, H.S. Shon, D.G. Lee, E.M. Ji, H.S. Kim, and K. H. Ryu, "Short-term Power Load Forecasting using Time Pattern for u-City Application," Journal of Korea Spatial Information System Society, Vol.11, No.2, pp. 177 - 181, 2009.

[5] B.G. Koo, C.H. Kim, J.H. Park, and H.S. Lee, "Daily electric load classification using data mining", KIEE summer conference, pp.111 - 112, July 14, 2009.

[6] H.G. Lee and Y.H. Shin, "Forecasting Electric Power Demand Using Census Information and Electric Power Load," Journal of the Korea Industrial Information Systems Research, Vol. 18 No. 3, pp. 35-46, 2013.

[7] H.M. Hwang, S.H. Lee, J.B. Park, Y.G. Park, and S.Y. Son, "Load Forecasting using Hierarchical Clustering Method for Building", The Transactions of the Korean Institute of Electrical Engineers, Vol. 64 No. 1, pp. 41-47, 2015.

[8] J.S. Shin, and J.O Kim, "A Study on Modeling of Users a Load Usage Pattern in Home Energy Management System Using a Copula Function and the Application," The Transactions of the Korean Institute of Electrical Engineers, Vol. 65 No. 1, pp. 16-22, 2016.

[9] J. C., Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.

[10] P. N. Tan, M. Steinbach, and V. Kumar, Introduction to data mining, Pearson Addison Wesley, 2006.

[11] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, Elsevier, 2011.

[12] C.C. Aggarwal and C.K. Reddy, Data clustering: algorithms and applications,, CRC Press, 2013.

Design for Low Voltage DC Distribution Network Testbed

Sung-Won Park*, Sung-Yong Son**

*Department of Electrical EngineeringGachon University Seongnam-si, Gyeonggi-do

truming@gachon.ac.kr

**Department of Electrical Engineering Gachon University Seongnam-si, Gyeonggi-do

xtra@gachon.ac.kr

Abstract

Recently various studies for low voltage DC (LVDC) distribution networks have been conducted to improve power distribution system efficiency, quality, and reliability. DC distribution networks have different characteristics from that of AC distribution networks. Especially, studies on the power quality(PQ) of DC distribution networks have not been performed enough. In this study, the issues of applying AC PQ standards to DC are reviewed. In addition, a simulation and monitoring testbed for LVDC distribution network is designed. The testbed is expected to be of useful in the selection of monitoring parameters and detection of unusual phenomena of LVDC distribution networks.

Keywords: LVDC, DC distribution, testbed, power quality

1. Introduction

Low voltage DC (LVDC) distribution network technology is gaining popularity as the next generation distribution network due to its improved features of syncing frequency and conversion efficiency. As such, many studies are conducted on the adoption of the LVDC distribution network that can offer improved distribution efficiency of electricity, high power quality(PQ) and confidence [1][2]. LVDC distribution network, unlike the existing AC distribution network, has different characteristics in voltage and power. In particular, the PQ of an LVDC distribution network is an important factor in the distribution of LVDC systems, but studies on this subject are limited. As such, there are difficulties in applying existing PQ measurement standards for the monitoring, analysis and status diagnosis of LVDC distribution networks.

This study reviewed the issues that may occur when the PQ measurement standard designed for AC distribution networks are applied to LVDC distribution networks. Based on the review results, the major measurement parameters for PQ in LVDC distribution networks are selected. In addition, an experimental environment for LVDC PQ monitoring is developed`.

2. Power Quality of LVDC Distribution Network

PQ refers to the electrical changes that cause errors or faults in the final adoption equipment [3]. In AC systems, the standardized definition such as IEC 61000-4-30/4-15, IEEE1159/1549 and EN50160 are used to manage the power quality. The indices for the major PQ in accordance with the afore-mentioned standard can be categorized as transient, interruption, sag/under-voltage, swell/overvoltage, distortion, voltage fluctuation, and frequency variation. In general, most PQ measurement parameters used in the AC distribution network are required even in the LVDC distribution network excluding some parameters that are only related to AC systems. The fundamental frequency that is usually 50Hz or 60Hz only holds meaning in AC networks.

LVDC distribution network consists of various power conversion devices that use power switching devices such as a converter, switching power supply, and battery charger. Therefore, the effects on disturbance such as the inrush current, harmonics, and electromagnetic interference(EMI) appear to be high. And these disturbances need to be considered as a major PQ measurement parameters in LVDC distribution networks [4][5][6][7]. Table 1 shows the list of major PQ types and their necessity to measure for LVDC monitoring.

Table 1. Major Measurement Parameters for PQ in The LVDC Distribution network

Type		Measurement	Details
Volt (V)	Interruption	O.	System error and protection equipment operation etc.
	Dip/sag, swell	O	Operation or stoppage of heavy loads
	Variations	O	Damage or shortened lifespan of electrical-equipment
Current (A)		O	-
Phase (deg.)		X	AC measurement factor
Fundamentals		X	AC measurement factor
Crest Factor		X	AC measurement factor
Form Factor		X	AC measurement factor
Watt (W)		O	-
Volt-Amps Reactive (VAR)		O	Distortion: Harmonics of Volt and current
			Modulation: periodic load fluctuations
Volt-Amps (VA)		O	-
Power Factor (%)		O	Power factor generated by Harmonics
Unbalance		O	Lack of equilibrium in the lines and loads
Flicker		O	Transformer tap changers, Motor starting, Intermittent loads etc.
Transient		O	Utility switching operations, operation and stoppage of heavy loads
Harmonics		O	Switching power supply, non-linear loads
Total Harmonic Distortion (THD)		O	Harmonics
Telephone Influence Factor(TIF)		O	Harmonics
Total Rated-Current Distortion (TRD)		O	Harmonics
Total Demand Distortion (TDD)		O	Harmonics
EMI		O	Harmonics
Inrush Current		O	Operation and stoppage of heavy loads

3. Design For LVDC Distribution Network Testbed

In this study, an LVDC distribution network testbed is developed to monitor PQ issues and phenomena. The LVDC distribution network testbed is designed to use 48VDC downscale voltage and consists of up/down power convertors, S-connectors and T-connectors. S-connectors serve as the switches of the distribution network allowing topology changes. T-connectors play the role of the branches of the distribution network and link the distribution line and power convertors. The measurement of the distribution network is performed by the sensors embedded in S- and T-connectors. As for the current sensor, it is designed to measure the current of all branches including the ground. The measured values of voltage and current of the distribution network are collected through RS-485 with 2 kHz sampling rates by using a 32 channel data acquisition (DAQ) board. The collected data are transmitted through TCP/IP to the main data collection unit (mDCU). Table 2 shows major design specifications of the designed testbed.

Table 2. Major Design Specifications of The LVDC Distribution Network

Category	Details
Operation voltage/ maximum current	48V / 10A
Consisting equipment	Up Converter	Input:12V, 20A
		Output:48V, 5A
	Down Converter	Input: 48V 5, 5A
		Output:12V 10,20A
	T-Connector	3 line connection
		2 Voltage sensor
		6 Current sensor (GND included)
	S-Connector	2 line connection
		1 Voltage sensor
		2 Current sensor (GND included)
Data Collection specifications	2kHz/32 channel(sample/node)
	RS 485 & TCP/IP Communication

Figure 1 shows the schematic diagram of the designed testbed while figure 2 shows the real image of the developed LVDC testbed. Each component is modularized for later ease of configuration changes and expandability.

Figure 1. Schematic diagram of designed LVDC distreibution network testbed.

Figure 2. Real image of developed LVDC distribution networks testbed.

Using the developed LVDC distribution network testbed, the effect of a load change is monitored. The load is changed the distribution network current from 0% to 32% (0~3.5A) of the maximum current to verify the phenomena that occur in the distribution network by the load changes. The distribution network voltage varies between 50.3~42.4V as shown in figure 3. The voltage changes would be reduced with the additional voltage control algorithms later.

Figure 3. Voltage measurement results obtained from the LVDC testbed.

4. Conclusion

This study proposes the major measurement parameters of the PQ in the LVDC distribution network. PQ measurement standard originally designed for AC distribution networks are reviewed to apply for LVDC distribution networks. Moreover, an LVDC testbed is designed to imitate and monitor unusual PQ phenomena in the LVDC distribution network. The study is expected to provide insight into the PQ issues in the LVDC distribution networks. As a follow-up study, a study on identifying issues and development of control algorithms for LVDC distribution networks will be conducted.

Acknowledgment

This research was supported by KESRI (15517), which is funded by Korea Electric Power Research Institute.

References

[1] A. Lana, A. Pinomaa, P. Nuutinen, T. Kaipia and J. Partanen, “Control and monitoring solution for the LVDC power distribution network research site” IEEE First International Conference on DC Microgrids (ICDCM), June 2015 pp. 7-10

[2] Lee Seung Yun : "KEPCO's DC Power Distribution Efforts to Promote New Power Industry Markets", Journal of the Electric Webzine, pp.38-43

[3] Electromagnetic compatibility (EMC), part 1: General, Section 1: Application and interpretation of fundermetnal definitions and terms, IEC 61000-1-1

[4] P. Nuutinen, A. Pinomaa, Juha-P. Ström, T. Kaipia, and P. Silventoinen, “On Common-Mode and RF EMI in a Low-Voltage DC Distribution Network” IEEE Trans. SMART GRID, vol. 5, no. 5, September 2014

[5] McGrath, B.P.; Holmes, D.G. A general analytical method for calculating inverter DC-link current harmonics. IEEE Trans. Ind. Appl. 2009, 45, 1851–1859

[6] Graham, A.D. The importance of a DC side harmonic study for a DC distribution system. In Proceedings of the 6th IET International Conference on Power Electronics, Machines and Drives (PEMD 2012), Bristol, UK, 27–29 March 2012; pp. 1–5.

Design for Power System Big Data Service Index

Sung-Won Park, Sung-Yong Son

Department of Electrical Engineering Gachon University Seongnam-si, Gyeonggi-do

E-mail: {truming, xtra}@gachon.ac.kr

Abstract

Recent increase in distributed energy resource deployment brings complex issues in operating a power grid and market, and using big data has been studied to solve these issues. However, big data are generated in various areas, and it is not easy to handle and share. In this work, data modeling to implement big data based services for a power system is studied. And we proposed to use indices for efficient information sharing among differently owned data sets.

Keywords: Power system, Big data, Index, Data modeling, Distribution network

1. Introduction

Recent increase in distributed energy resource (DER) deployment brings complex issues in operating a power system including a power market. There have been some efforts on utilizing big data to make a power system more reliable, economical, and secure in the perspective of a utility, aggregator, and customer [1][2][3]. However, the big data produced and managed in different service areas tend to be independently acquired. Therefore, there are issues in integrating these data to implement a big data based service. Here, service areas indicate various areas of services such as a power system, weather, and traffic, etc. In order to have compatibility and convenience in utilizing the big data obtained in different service areas, common data models are required. And introducing indices can be a good approach for efficient information sharing instead of sharing the entire data.

In this study, services related to DERs in power systems and their service requirements are drawn. Based on the service requirements the relations among the big data are analyzed to design data sharing models. Service indices are also designed for the services enabling ease of information sharing not providing excessive data. Hereupon, it has become feasible to provide service indices that acquired consistency and convenience of big data in DER service areas.

2. Power System Big Data Index Development Concept

In order to develop effective indices for power system services, it is required to organize requirements based on the major parameters in each service field. It is also needed to identify big data available other service areas through correlation analysis based on requirements and target service models. The target service is in need of data modeling for acquiring convenience of use as well as compatibility and shall be provided to users in the form of service indices. Figure 1 represents the power system big data index development concept.

Figure 1. Power system big data index developmet concept

3. Issues of Power System Service Model

For the development of power system big data service indices, it is firstly required to derive issues for the target service area. As for issues in the area of the distribution network, there are issues about reliability, economic feasibility, and stability in power system and market operation caused by the adoption of DERs[4][5]. In the perspective of aggregator or customer, there are issues about economic feasibility from energy production and consumption including participation in the power market or peak management. In addition, there are issues related to the advanced metering infrastructure (AMI) and internet of things(IOT) services. Table 1 represents classification and common data models in power system area.

Table 1. Data Models of Power System Services

Classification	Issue	Data Model
Distribution network	DER	Calculate the acceptable level of DR
		Predict distributed resources
		Types of electronic devices, location of installation, and calculation of capacity
		Operation in the environment where distributed resources are adopted (transformer, electronic device, etc.)
	Design & Sizing	Acquire flexibility and stability of power area from establishment of distributed resources
		Types of resources in consideration of multiple restrictions including benefit and cost and calculate capacity (multi-object problems)
	AMI	Measures for utilizing AMI information
		Connect with IOT service
	Property management	Issues about property management including transformer and electronic devices, etc.
Power market	Prosumer	Design and operate electronic transaction market
	Settlement	Customer Base Line(CBL)
Aggregator	Collection of resources	Select resources in consideration of economic profits
	Operation of resources	Operation in consideration of uncertainty and non-quantifiable elements

4. Development of Indices of Power System Big Data

A. Power Quality Index

When adopting DERs, there can be issues in power system stability in the perspective of utility due to the aggregators’ power market participation and customers’ peak management. Therefore, it is necessary for a utility company to plan for operating distribution network considering power quality (PQ) indices such as power flow as well as stability for each time. For this, distribution automation system (DAS) and metering data can be utilized. Power flow analysis can be used to model PQ indices and obtain quantitative values. Figure 2 and Table 2 shows data models and indices to describe PQ.

Figure 2. Power quality index concept

Table 2. Data Model and InDex for Power Quality

Type	Details
Major Issue	Occurrence of issues about stability in power areas in time period from adoption of distributed resources
Purpose	Derivation of PQ indices in each time according to characteristics of operation for power areas
Utilization	Utility
DATA	Collect	DATA
	Utility	DATA data(15min) →P, Q, V, I, 𝜃
		Measurement location information
	Aggregator	Electronic measuring devices (sec or min) →△P, △Q, △V, △I
		Distributed resource information, loading information, and location information
		Renewable forecasting information
Major indices	PQ Index in each period (Voltage stability and electronic power flow)
Index Modeling Method	Power Flow Analysis Method
Expected effect	Convenient in establishing operation plans for utility and aggregator
	Improve reliability of supply provision in utility

B. Renewable Forecasting Index

Renewable resource power output shall be predicted to operate DERs efficiently for customers, aggregators, and utilities. Weather information is important to estimate renewable energy output because most DERs’  power outputs related to weather. However, weather forecast information provided by a weather station is frequently not precise enough. Therefore, weather forecast indices that are related to renewable resource output can be good for customers, aggregators, and utilities. Regional and history based statistical modeling can be additionally applied based on weather forecasting information. Figure 3 and Table 3 shows the modeling concept and indices for renewable forecasting.

Figure 3. Renewable forecasting concept

Table 3. Data Model and Index for Renewable Forecasting

Items	Contents
Major Issue	Lack of predicting data for new generation of weather forecasting information and major forecasting parameters
Purpose	Provide renewable and major forecasting parameters
Utilization	Utility, Aggregator/Customer
DATA	Collect	DATA
	Meteorological Administration	Weather forecast statistics information in each area and weather forecasting information
	Utility and Aggregator	Types of renewable, location of installation, capacity, efficiency
Major indices	Predictive data and major forecasting parameters in each area and time period
Index Modeling Method	Renewable forecasting technique based on the area and time period
Expected effect	Improve operation reliability of utility and aggregator/customer

5. Conclusion

In this study, big data based services for a power system are analyzed and modeled considering the relationships among data. The model is designed especially for power quality and renewable resource output forecasting. It is also proposed to use indices that represent sufficient enough information for efficient data sharing. As for the future research, it is planned to conduct a study in dealing with modeling techniques to design additional service models and indices.

Acknowledgment

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea (No. 20151210200240).

References

[1] Talin Sokugawa,Matt Shawver, “Big Data for Reneable Intergration at Hawaiian Electric Utility”,DistribuTech Conference & exhibition, Feb 2016.

[2] DONG Energy, “Daily Operational Data in a Planning Perspective”,DistribuTech Conference & exhibition, Feb 2016.

[3] PG&E, “Distributed Generation Production and Advanced Metering Infrastructure Data Fusion ”,DistribuTech Conference & exhibition, Feb 2016.

[4] Chun-Lien Su, Hwei-Ming Chung, Chien-Yu Wei, Chao-Kai Wen, “Optimal VPP Operation Strategy in Liberalized Electricity Markets” , Environment and Electrical Engineering (EEEIC), 2015 IEEE 15th International Conference, June 2015, pp:478-481.

[5] Mahesh S Narkhede, S.Chatterji, Smarajit Ghosh, “Optimal dispatch of Renewable Energy Sources in smart grid pertinent to virtual power plant” , Green Computing, Communication and Conservation of Energy (ICGCE) 2013, Dec 2013, pp:525-529.

Timestamp based Key Exchange Protocol

Young-Seok Lee*, Yeon-Sung Choi**

*Department of Information & Telecommunication EngineeringKunsan National UniversityKunsan, South Korea

E-mail: leeys@kunsan.ac.kr

**Department of Information & Telecommunication EngineeringKunsan National UniversityKunsan, South Korea

E-mail: yschoi@kunsan.ac.kr

Abstract

To prevent cyber-attacks in satellite communication, Key Exchange protocol is conducted between NCC (Network Control Centre) and RCST (Return Channel Satellite Terminal). This paper suggests the protocol which can prevent Man-in-the-middle attack using Timestamp, and compare the proposed protocol with existing protocols through performance analysis, showing its resource management and the efficiency of communication. The procedure of preventing Man-in-the-middle attack of the suggested protocol is examined through safety analysis, and it can prevent Replay attack with addition of ID parameter.

Keywords:  Diffie-Hellman, Key Exchange Protocol, Timestamp, Satellite Communication

1. Introcution

The satellite communication has the advantages of sending mass data. However, the current satellite communication is easily exposed to attacks like eavesdropping of information, abnormal sending of packet, reusing of message, forgery/alteration of data etc. To prevent these attacks, the satellite communication has inside/outside network security policies, and these policies should be able to offer security services including authentication, confidentiality of data, availability, integrity, and unmanned prevention.

Figure 1. Reference mode for the Satellite Interactive Network

A way used to satisfy above matters is encryption/decryption and authentication of data using the key[1-4]. For safe key exchange, there exist Key Exchange protocol presented from EN 301 790 the standard of ETSI(European Telecommunication Standards Institute) and Certificate-based protocol made by improving previous protocols[5-7].

Fig. 1 shows the satellite reference model for broadband network. NCC (Network Control Centre) means the center of network maintenance, and RCST (Return Channel Satellite Terminal) means the satellite terminal of the user. Key Exchange protocol is conducted between NCC and RCST [5].

As existing Key Exchange protocols have risks to be exposed to attacks, however, it is hard to see that they are safe. The basic Key Exchange protocol of EN 301 790 standardized in the International Standard Institution ETSI is Main Key Exchange (MKE). It conducts exchanging keys through cookie-based user authentication. Exposure of data at the early time when NCC and RCST exchange cookies, however, brings Man-in-the-middle attack [8].

It also brings Replay attack because it doesn’t make any encryption during data transmission. By improving these disadvantages, Certificate-based Key Exchange protocol was suggested [6-8]. Even though it can make reliable user authentication because of using the certificate, it is an inappropriate way in the satellite communication based on wireless communication. Another authentication method is Key Exchange protocol using password [9-10]. However, NCC of the satellite environment should manage a majority of RCST, which brings the problem of password resources NCC manages.

This paper suggests a Key Exchange protocol for preventing Man-in-the-middle attack by introducing timestamp to improve the disadvantages of existing protocols. Like MKE protocol, because it uses Diffie-Hellman key exchange method, it requires the calculation process of the public key in NCC and RCST. NCC creates timestamp during transmitting/receiving data, and identifies Man-in-the-middle attack by using calculation time of the public key of NCC and RCST. Plus, its usage of timestamp whose data size is small makes itself free from the problem of resources against existing protocols.

Section II of this paper examines the conducting methods and disadvantages of existing protocols, and Section III describes the protocol suggested in this paper. Section IV compares both existing protocols and the suggested protocol in terms of memory usage, computation, and communication, and shows the efficiency of the suggested protocol. Section V offers conclusion and the direction of further study.

2. Related Studies

This paper focuses on the standard MKE protocol, presents the protocol suggested to prevent Man-in-the-middle attack. This section examines MKE protocol and Certificate-based protocol improving MKE protocol in the basis of the certificate.

A. Main Key Exchange (MKE) Protocol

MKE is the basic Key Exchange protocol of EN 301 790 standard [5]. It uses Diffie-Hellman algorithm for sharing the secret value between NCC and RCST, and uses the cookie value so that RCST can make the user authentication to NCC. It is the protocol conducted every time a new session is set, and uses selectively the shared value made newly for renewal of the cookie value. Finally it derives the shared secret key to create cipher text for processing payload stream data.

Figure 2. Main Key Exchange (MKE) Protocol

Fig. 2 presents the performance procedure of the protocol, and ‘||’ means connection of data, ‘(UC)x’ the transformation procedure of x value into unsigned char type, ‘ ’ empty string, and nonce the random number. Total operation steps can be examined simply in three steps.

1) After deciding Diffie-Hellman prime numbers a, q, and the random number nonce1, NCC sends RCST the public value X created using the private value x.

2) Receiving a, q, X, and the random number of NCC nonce1, RCST creates the public value Y using its own private value y, and then creates the random number nonce2. After creating the authentication value auth(r) using the received random number of NCC, its own random number, and the cookie value, it transmits its own random number nonce 2, the public value Y, and the authentication value auth(r) to NCC. Then, after creating the secret key s using the public value X of NCC and its own public value Y, RCST creates temp by using the secret key s, nonce1, and nonce2. Using the created temp value, it completes renewal of cookie and key generation.

3) NCC receiving the random number of RCST nonce2, the public value Y, and the authentication value auth(r) from RCST creates the authentication value auth(n) in the same way as that of RCST, and then compares their coincidence. If they are coincided, considering it the right terminal, NCC finishes renewal of cookie and key generation after creating temp in the same way as that of RCST.

In MKE protocol, there can be a Man-in-the-middle attack and Replay attack. At the early time the cookie value is setting if an attacker transmits and authenticates the public value between NCC and RCST using the obtained cookie value after gaining the cookie value from an adversary, the attacker can share both secret values of NCC and RCST. Therefore NCC and RCST exchanging cookies at the first implementation of session are exposed to Man-in-the-middle attack. Plus, though it exchanges cookies safely, the number of cookie NCC must manage increases according to the number of RCST, leading to rapid increase of memory usage. So it can’t be seen as an efficient protocol overall. When it comes to Replay Attack, because RCST transmits without encrypting such data as the random number of NCC(nonce1), the random number of RCST(nonce2), and the authentication value using Hash function(auth(r)) for authentication, the attacker can disguise as RCST through retransmitting the obtained data in session after gaining data.

B. Certificate-based Protocol

In order to overcome disadvantages of MKE protocol examined above, Certificate-based protocol was suggested [6-8]. Because it uses the public value for a long time using certificates, it introduces the random number for the freshness of secret. Unlike MKE protocol, for reliability of the secret key changed by every session, it uses the second authentication value. The second authentication value calculates the secret value every session by performing existing Diffie-Hellman algorithm using the arbitrary random number value and the public value of adversary. So if the arbitrary random number value changes, the private value also changes, and the freshness of secret can be offered. Also key reliability can be guaranteed because the authentication procedure is offered in the protocol.

Figure 3. Certificate-based Protocol

Fig. 3 shows the performance procedure of Certificate-based protocol. h(n) is the second authentication value of NCC, h(r) the second authentication value of RCST, Cert(N) the certificate of NCC, Cert(R) the certificate of RCST, rn the random number NCC creates, and rr the random number RCST creates. Each public value is included in the certificate.

After generating the random number rn, NCC transmits it to RCST with the certificate Cert(N).

RCST gains the public value X by using the received certificate of NCC Cert(N) after generating the random number rr. Using the random number of NCC rn, its own random number rr and the cookie value, RCST generates the authentication value auth(r).  RCST generates the secret value s using the random number and the public value, thereby generating the second authentication value h(r). After transmitting its own second authentication value h(r), the authentication value auth(r), its own certificate Cert(R), and the random number to NCC, RCST performs renewal of the cookie and key generating procedure after creating the temp value.

After generating the authentication value auth(n), NCC proceeds the authentication procedure using the authentication value of RCST auth(r). If the authentication procedure is correct, NCC generates the second authentication value h(n) after creating the secret key s. By comparing h(r) transmitted to RCST with its own second authentication value h(n), NCC proceeds the final authentication procedure.

However, this can’t be seen an effective method in wireless communication which has lots of restriction unlike cable communication.

3. The Suggested Protocol

C. Protocol Procedure

This paper suggests a protocol which can identify the attack without data shared by NCC and RCST, and have less amount of communication and computation of the device than when the certificate is used. It uses Diffie-Hellman algorithm for generating the secret key and applies timestamp for preventing Man-in-the-middle attack. Never using the fixed public value, the suggested protocol can have freshness of the key and guarantee the reliability of the key thanks to offering its authentication procedure. Fig. 4 represents the flowchart of the suggested protocol, which is performed in the total eight-step procedure.

The suggested protocol has the assumption particulars as following.

1) The calculation time of the public value of Diffie-Hellman algorithm is larger than the transmission time from NCC to the satellite.

2) All devices in the protocol have the same performance.

3) The protocol assumes that the pre-computed public value is excluded during the

real-time key exchange.

Table 1. Protocol Parameters

Notation	Descriptions
a, q	Prime numbers for Diffie-Hellman operation
x	Private value of NCC
y	Private value of RCST
X	Public value of NCC
Y	Public value of RCST
S	Secret key created by NCC and RCST
TC	Computation time to create public value X of NCC
TR	Round-trip time between NCC and Satellite
TN-S	Transmission time from NCC to Satellite
TT	Round-trip time between NCC and RCST
TH	Round-trip threshold time between NCC and RCST
T0	Time value before public value computation of NCC
T1	Time value after public value computation of NCC
T2	Time value when satellite receivers values of NCC
T3	Time value when NCC receives time-stamp of satellite
T4	Time value when NCC receives public value Y of RCST

Figure 4. Proposed Protocol

1) After deciding Diffie-Hellman prime numbers a, q, NCC generates timestamp T0. After calculating the public value X with the private value x and prime numbers a, q, NCC transmits prime numbers a, q, and the public value X to satellite, generating timestamp T1. For arithmetic operation time of the public value TC, TC= T1-T0 is calculated.

2) The satellite receives a, q, X and transmits them to RCST.

3) After calculating timestamp T2 receiving a, q, X, the satellite transmits it to NCC.

4) NCC receives timestamp T2 and generates timestamp T3 to confirm time receiving T2. In order to confirm the round-trip time between NCC and the Satellite (TR), TR= T3-T1 is calculated, and then TN-S = TR/2 is gained.

5) RCST receiving a, q, X generates the public value Y using its own private value y and prime numbers a, q. After sending the public value Y to the satellite, RCST calculates the secret key S using the received public value X of NCC and its own public value Y.

6) The satellite receiving the public value Y from RCST transmits it to NCC.

NCC receiving the public value Y generates T4 to confirm reception time, and then TT = T4-T1 is calculated for round-trip time between NCC and RCST. To obtain Round trip threshold time between NCC and RCST, TH = (4*TN-S)+TC+α is calculated. Here, α as variable time should not be set larger than calculating time of the public value (TC).

7) In order to confirm Man-in-the-middle attack, NCC judges its existence by using timestamp.

D. Safety Analysis

(1) Man-in-the-middle attack

The disadvantage of Diffie-Hellman algorithm is that it can be exposed to Man-in-the-middle attack. For preventing this, most approaches apply certificates, but it is not appropriate in the satellite environment.

Man-in-the-middle attack in the broadband satellite access network can be divided into two types of attack;

1) Man-in-the-middle attack between NCC and the satellite

2) Man-in-the-middle attack between RCST and the satellite.

The suggested protocol introduces timestamp to prevent Man-in-the-middle attack, and conducts two-step judgment using this. With the first judgment (TC > TN-S), Man-in-the-middle attack between NCC and the satellite can be checked, and so can Man-in-the-middle attack between RCST and satellite with the second judgment (TT < TH). Through the flowchart, immunity of Man-in-the-middle attack of the suggested protocol is also examined. The following table II represents the additional parameter for the flowchart of Man-in-the-middle attack.

Table 2. Additional Parameters For man-in-the-middle attack Flow Diagram

Notation	Descriptions
b, p	Prime numbers generated by Man-in-the-middle
M	Public value created from b, p
N	Public value created from a, q
SNM	Secret Key created by NCC and Man-in-the-middle
SMR	Secret Key created by RCST and Man-in-the-middle
TM	Time value when Man-in-the-middle modify the time value T2

Fig. 5 shows the flowchart of Man-in-the-middle attack between NCC and satellite. It is performed with thirteen-step procedure to check whether Man-in-the-middle attack is true or not.

Figure 5. Man-in-the-middle attack between NCC and Satellite

1) After deciding Diffie-Hellman prime numbers a, q, NCC generates timestamp T0. After calculating the public value X using the private value x and prime numbers a, q, NCC sends the satellite prime numbers a, q and public value X, generating timestamp T1. For calculating time of the public value TC, TC = T1- T0 is calculated.

2) Man in the Middle intercepts prime numbers a, q and the public value X that NCC sends.

3) After deciding prime numbers b, p for the Diffie-Hellman process between RCST and Man in the Middle, using its own secret value m, attacker creates the public value M for RCST. Man in the Middle disguised as NCC sends prime numbers b, p and the public value M to RCST. After sending them and calculating the public value N for creating the secret key with NCC using prime numbers a, q of NCC, Man in the Middle generates the secret key SNM by using the public value N and the public value X of NCC.

4) The satellite receives b, p, M and transmits them to RCST.

5) After calculating timestamp T2 receiving b, p, M, the satellite sends it to Man in the Middle disguised as NCC.

6) Man in the Middle receiving T2, Timestamp sent from the satellite transmits either T2 just as it is or TM, Timestamp modifying T2 to take an advantage of situation for itself to NCC.

7) NCC receiving T2, Timestamp sent from the satellite and TM, Timestamp modified by Man in the Middle creates Timestamp T3 or T3’ to confirm received time. To confirm Round-trip time between NCC and satellite (TR), TN-S = TR / 2 is gained after calculating TR = T3 - T1 or TR = T3’ - T1.

8) RCST receiving b, p, M generates the public value Y using its own private value y and prime numbers b, p. After sending the public value Y to the satellite, RCST calculates the secret key SMR using the received public value M and its own public value Y. Though SMR is the secret key between Man in the Middle and RCST, RCST mistakes it for the secret key with NCC.

9) The satellite receiving the public value Y from RCST sends it to Man in the Middle disguised as NCC.

10) Man in the Middle receiving the public value Y transmits the public value N for generating the secret key with NCC created in the previous process to NCC.

11) After sending the public value N to NCC, Man in the Middle creates the secret key SMR with RCST using the public value Y sent from RCST and the public value M for creating the secret key with RCST.

12) NCC receiving the public value N generates Timestamp T4 to confirm the received time, and then for confirming Round Trip time between NCC and RCST, TT = T4-T1 is calculated. In order to gain round-trip threshold time between NCC and RCST, TH = (4*TN-S)+TC+α is calculated. Here, α as variable time should not be set larger than calculating time of the public value (TC).

13) To confirm Man-in-the-middle attack, as the first judgment step, it should be checked whether the calculating time of the public value TC is larger than transmission time between NCC and satellite TN-S (TC > TN-S). Because Man in the Middle has additional calculating time of the public value M for creating the secret key with RCST in step 3, the time of TN-S is delayed. So calculating time of the public value TC becomes smaller than transmission time between NCC and the satellite TN-S. As a result, it is judged that NCC has Man-in-the-middle attack, so the protocol process ceases.

Fig. 6 shows the flowchart of Man-in-the-middle attack between RCST and satellite. It is conducted with the total twelve-step procedure to check the attack.

Figure 6. Man-in-the-middle attack between RCST and Satellite

1) After deciding Diffie-Hellman prime numbers a, q, NCC generates timestamp T0. After calculating the public value X using the private value x and prime numbers a, q, NCC sends prime numbers a, q and the public value X to the satellite, generating timestamp T1. For computation time of the public value TC, TC =T1 - T0 is calculated.

2) The satellite receives a, q, X and transmits them to RCST.

3) After calculating timestamp T2 as soon as the satellite receives a, q, X, the satellite sends T2 to NCC.

4) NCC receives timestamp T2 and creates timestamp T3 to confirm time receiving T2.  To confirm round-trip time between NCC and the satellite (TR), TN-S = TR   / 2 is gained after calculating TR = T3 - T1.

5) Man in the Middle intercepts prime numbers a, q, and the public value X transmitted from the satellite. After Man in the Middle decides prime numbers b, p for the process of Diffie-Hellman with RCST, it creates the public value M for RCST using its own private value m. And Man in the Middle disguised as NCC transmits prime numbers b, p and the public value M to RCST. After sending them, Man in the Middle calculates the public value N for creating the secret key with NCC using prime numbers a, q of NCC and generates the secret key SNM by using the public value N and the public value X of NCC.

6) RCST mistakes Man in the Middle for NCC, and receives prime numbers b, p and the public value M.

7) RCST generates the public value Y using its own private value y and prime numbers b, p. After sending it to the satellite, RCST calculates the secret key SMR using the received public value M and its own public value Y. Though SMR is the secret key between Man in the Middle and RCST, RCST mistakes it for the secret key with NCC.

8) Man in the Middle intercepts Y, the public value of RCST.

9) Man in the Middle receiving the public value Y transmits the public value N for creating the secret key with NCC created from the previous step to the satellite. The satellite mistakes it for the public value sent from RCST, and transmits the public value N to NCC.

10) After sending the public value N to NCC, Man in the Middle generates SMR  the secret key with RCST using the public value Y from RCST and the public value  M for creating the secret key with RCST.

11) After NCC receiving the public value N generates timestamp T4 for confirming the received time, TT  = T4-T1 is calculated, to check round-trip time between NCC and RCST. As for round-trip threshold time between NCC and RCST, TH = (4* TN-S)+TC+α is calculated. Here, α as variable time should not be set larger than calculating time of the public value(TC).

12) To check Man-in-the-middle attack, NCC judges its existence using timestamp. First, for confirming Man-in-the-middle attack between NCC and satellite, it should be checked whether calculating time of the public value TC is larger than TN-S, transmission time between NCC and satellite(TC > TN-S). Because calculating time of the public value of Man-in-the-middle attack doesn’t affect TN-S, it is confirmed that TC is larger than TN-S, which is judged an attack. Second, to confirm Man-in-the-middle attack between satellite and RCST, it should be checked whether round-trip time between NCC and RCST TT is smaller than round-trip threshold time TH (TT < TH). Because computation time of creating the public value M in the procedure 5) affects total protocol round-trip time, timestamp TT becomes larger than threshold timestamp TH. It is judged that NCC has Man-in-the-middle attack, so the protocol process ceases.

(2) Replay attack

By gaining prime numbers and the public value NCC and RCST exchange with each other, retransmission can be conducted. To prevent this, there is a way to confirm the sender of data. To confirm the sender of data from the suggested protocol, if the suggested protocol is modified so that the sender’s own ID which NCC and RCST can distinguish may be attached during transmitting data, replay attack can be prevented.

4. Performance Analysis

This section compares the performance analysis of three protocols- MKE protocol of EN 301 790 of ETSI standard, Certificate-based protocol improving MKE, and the protocol suggested from this paper- in terms of usage, communication, and computation.

E. Comparison of Memory Usage

When it comes to storage that NCC should store in MKE protocol, the following storage amount is needed: log(a) + log(q) + log(Pncc) + log(Prcst) + log(Rncc) + log(Rrcst) + log(Hncc) + log(Hrcst) + log(s) + log(t) + log(Cold) + log(Cnew) + log(K). Here, a and q are prime numbers needed for creating the public value based on Diffie-Hellman algorithm, Pncc is the public value created by NCC, Prcst is the public value RCST creates, Rncc is the random number NCC creates, Rrcst is the random number RCST creates, Hncc is the authentication value of NCC, Hrcst is the authentication value of RCST, s is the secret value, t is the temp value, Cold is the existing cookie value, Cnew  is the newly-created cookie value, and K is the secret key.

RCST in MKE protocol needs the amount of storage of log(a) + log(q) + log(Pncc) + log(Prcst) + log(Rncc) + log(Rrcst) + log(Hrcst) + log(s) + log(t) + log(Cold) + log(Cnew) + log(K) The meaning of symbols is the same thing above-mentioned.

NCC in Certificate-based protocol needs the amount of storage of log(a) + log(q) + log(Incc) + log(Pncc) + log(Prcst) + log(Rncc) + log(Rrcst) + log(Hncc) + log(Hrcst) + log(Ancc) + log(Arcst) + log(s) + log(t) + log(Cold) + log(Cnew) + log(K) + log(Certncc). Prime numbers a and q, Incc, is the private value of NCC, Pncc, is the public value created by NCC, Prcst is the public value RCST creates, Rncc is the random number NCC creates, Rrcst is the random number RCST creates, HNCC is the authentication value of NCC, HRCST is the authentication value of RCST, ANCC is the second authentication value of NCC, ARCST is the second authentication value of RCST, s is the secret value, t is the temp value, Cold is the existing cookie value, Cnew is the newly-created cookie value, K is the secret key, and Certncc is the certificate of NCC.

RCST in Certificate-based protocol needs the amount of storage of log(a) + log(q) + log(Incc) + log(Pncc) + log(Prcst) + log(Rncc) + log(Rrcst) + log(Hrcst) + log(Arcst) + log(s) + log(t) + log(Cold) + log(Cnew) + log(K) + log(Certncc). Here, the remains are the same above except that Certrcst is certificate of RCST.

NCC in our protocol needs the amount of storage of log(a) + log(q) + log(Pncc) + log(Prcst) + log(T0) + log(T1) + log(T3) + log(T4) + log(TC) + log(TR) + log(TN-S) + log(TT) + log(TH) + log(K). The meaning of symbols are found in Table I. RCST in proposed protocol needs the amount of storage of log(a) + log(q) + log(Pncc) + log(Prcst) + log(K).

Table 3. Comparison of Memory Usage

	MKE Protocol	Certificate-based Protocol	Proposed Protocol
NCC	log(a q Pncc Prcst Rncc Rrcst Hncc Hrcst s t Cold Cnew K)	log(a q Incc Pncc Prcst Rncc Rrcst Hncc Hrcst Ancc Arcst s t Cold Cnew K Certncc)	log(a q Pncc Prcst T0 T1 T3 T4 TC TR TN-S TT TH K)
RCST	log(a q Pncc Prcst Rncc Rrcst Hrcst s t Cold Cnew K)	log(a q Incc Pncc Prcst Rncc Rrcst Hrcst Arcst s t Cold Cnew K CertRCST)	log(a q Pncc Prcst K)

F. Comparison of Communication Performance

As for communication performance of MKE protocol, the following is calculated: log(a) + log(q) + log(Pncc) + log(Rncc) + log(Prcst) + log(Rrcst) + log(Hrcst). The Certificate-bases protocol computes the communication performance by log(Rncc) + log(Rrcst) + log(CERTncc) + log(CERTrcst) + log(Hrcst) + log(Arcst). The communication performance of proposed protocol needs the amount of log(a) + log(q) + log(Pncc) + log(Prcst) + log(T2). The meaning of symbols in this section can be found on Table I and Section IV.A.

Table 4. Comparison of Communication Performance

	MKE Protocol	Certificate-based Protocol	Proposed Protocol
NCC-RCST	log(a q Pncc Rncc Prcst Rrcst Hrcst)	log(Rncc Rrcst Hrcst Arcst CERTncc CERTrcst)	log(aqPnccPrcstT2)

G. Comparison of Computation Performance

As for computation performance of NCC in MKE and Certificate-based protocol, computation of r+m+e+h is needed. Here, r means computation of random number occurrence of NCC, m is Modulation computation, e is exponential computation, and h is computation of Hash function. In MKE and Certificate-based protocols, computation performance of RCST is the same to that of NCC. In proposed protocol, NCC and RCST need the computation amount of m+e, which includes Modulation computation, and exponential computation.

Table 5. Comparsion of Computation Performance

	MKE Protocol	Certificate-based Protocol	Proposed Protocol
NCC	r+2m+2e+4h	r+m+3e+5h	2m+2e
RCST	r+2m+2e+4h	r+m+3e+5h	2m+2e

5. Conclusion

Security policies used in the satellite network should be able to provide security services such as authentication, confidentiality of data, availability, integrity, and unmanned prevention, and, in order to satisfy this, they use encryption/decryption of data and the authentication method using keys. For safe Key Exchange, EN 301 790 of ETSI standard offered MKE protocol, but because NCC and RCST have exposure of data during exchanging keys at the early time, it is vulnerable to Man-in-the-middle attack.

Though Certificate-based protocol was suggested to prevent Man-in-the-middle attack, it is not appropriate for the wireless communication-based satellite environment. In case of applying these protocols, however, because NCC which has to manage lots of RCST should control much data, the problem of resources can occur.

Plus, because the satellite environment doesn’t send data through several hops, the Key Exchange method using the key chain is hard to be available. This thesis suggested the protocol which can prevent Man-in-the-middle attack using Timestamp, and compared this with existing protocols through capacity analysis/evaluation, showing its resource management and the efficiency of communication resources. The procedure of preventing Man-in-the-middle attack of the suggested protocol was examined through safety analysis, and it was represented that it can prevent Replay Attack with addition of ID parameter. If the suggested protocol is applied in accordance with the operational environment, it can be expected to be a very efficient protocol in the satellite communication using existing NCC and RCST.

Acknowledgment

This research was supported by ICT Promising Technology Development Program of Institute for Information & Communications Technology Promotion(IITP)  funded by the Ministry of Science, ICT & Future Planning (Grant R6910-15-1102).

References

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[3] Jeong-Woo Hong, et al. "A New Efficient Key Agreement Scheme for VSAT Satellite Communications Based on Elliptic Curve Cryptosystem," Information Technology and Control, Vol. 40, No. 3, pp.252-259, 2011.

[4] Ya-Fen Chang, Chin-Chen Chang. "An efficient authentication protocol for mobile satellite communication systems," ACM SIGOPS Operating Systems Review, Vol. 39, No. 1, pp.70-84, January 2005.

[5] ETSI EN 301 790 v. 1.5.1, Digital video broadcasting (DVB); interaction channel for satellite distribution systems, May 2009. http://www.etsi.org

[6] Ayan Roy-Chowdhury, John S. Baras, "A Lightweight Certificate-Based Source Authentication Protocol for Group Communications in Hybrid Wireless/Satellite Networks," IEEE GLOBECOM 2008, Global Telecommunications Conference, pp.1-6, Nov. 30 2008.

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[8] H. R. Oh and H. Y. Youm, “Key exchange protocols for domestic broadband satellite access network,” Korea Institute of Information Security and Cryptology(KIISC), Vol. 14, No. 3, pp. 13-25, June. 2004.

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Big data based Log Management System for Wireless Sensor Networks

Hyeop-geon Lee*, Young-woon Kim**, Ki-young, Kim***, Eun-Chul Park****

*Dept. of Data Analysis Seoul Gangseo Campus of Korea Polytechnic Seoul, Korea

hglee67@kopo.ac.kr

**Dept. of Data Analysis Seoul Gangseo Campus of Korea Polytechnic Seoul, Korea

luckkim@kopo.ac.kr

***Dept. of Computer SoftwareSeoil University Seoul, Korea

ganet89@seoil.ac.kr

****Dept. of Electrical and Computer Engineering Chungbuk National University

Cheongju-si, S. Korea

peus0@naver.com

Abstract

The wireless sensor networks is a network technique for the implementation of smart computing environment. It is the wireless network environment that consists of the many sensors of the lightweight and the low-power. And then the wireless sensor networks applied to various fields such as the internet of thing (IoT), the logistics, the environmental control and the home network. The sensor node more increasing, the sensing information also increases gradually. Thus, we need a log management system for managing the sensing information. In this paper, we propose big date based log management system for wireless sensor networks. The log management system performs the log management, security, store, and data analysis.

Keywords: Management System, Distributed File System, Hadoop, Gluster File System, Cassandra File System

1. Introduction

In recent years, the term Big Data has emerged to describe a new paradigm for data applications. New technologies tend to emerge with a lot of hype, but it can take some time to tell what is new and different. While Big Data has been defined in a myriad of ways, the heart of the Big Data paradigm is that is too big (volume), arrives too fast (velocity), changes too fast (variability), contains too much noise (veracity), or is too diverse (variety) to be processed within a local computing structure using traditional approaches and techniques. The technologies being introduced to support this paradigm have a wide variety of interfaces making it difficult to construct tools and applications that integrate data from multiple Big Data sources. This report identifies potential areas for standardization within the Big Data technology space[1].

The wireless sensor networks[2] is a network technique for the implementation of smart computing environment. It is the wireless network environment that consists of the many sensors of the lightweight and the low-power. And then the wireless sensor networks applied to various fields such as the internet of thing (IoT), the logistics, the environmental control and the home network. The sensor nodes are used by wireless sensor nodes to capture data from their environment. And then sensor nodes send to PAN coordinator the sensing information. Therefore, the sensing information is more increasing. And current log Management Systems are not efficient in the log management.

In this paper, we propose big data based log management system for wireless sensor networks. The log management system is composed of three servers: an operation server, a security server and a log management distributed file system. The log management system performs the log management, security, store, and data analysis.

The structure of the paper is organized as follows: Section 2 describes the distributed file systems; Section 3 describes the proposed log management system, and Section 4 concludes this paper.

2. DISTRIBUTED FILE SYSTEM

A. Hadoop Distributed File System

The HDFS (Hadoop Distributed File System) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware. HDFS provides high throughput access to application data and is suitable for applications that have large data sets. HDFS relaxes a few POSIX requirements to enable streaming access to file system data. HDFS was originally built as infrastructure for the Apache Nutch web search engine project. HDFS is now an Apache Hadoop subproject[3]. Figure 1 shows the HDFS Architecture.

Figure 1. The HDFS architecture

B. Gluster File System

GlusterFS (Gluster File System) is an open source, clustered file system capable of scaling to several petabytes and handling thousands of clients. GlusterFS can be flexibly combined with commodity physical, virtual, and cloud resources to deliver highly available and performant enterprise storage at a fraction of the cost of traditional solutions[4].

GlusterFS clusters together storage building blocks over Infiniband RDMA and/or TCP/IP interconnect, aggregating disk and memory resources and managing data in a single global namespace. GlusterFS is based on a stackable user space design, delivering exceptional performance for diverse workloads[4].

GlusterFS is designed for today's high-performance, virtualized cloud environments. Unlike traditional data centers, cloud environments require multi-tenancy along with the ability to grow or shrink resources on demand. Enterprises can scale capacity, performance, and availability on demand, with no vendor lock-in, across on-premise, public cloud, and hybrid environments.Figure 2 shows the GlusterFS architecture[4].

Figure 2. GlusterFS  Architecture

C. Cassandra File System

The CFS (Cassandra File System) was designed by DataStax Corporation to easily run analytics on Cassandra data. Now implemented as part of DataStax Enterprise, which combines Apache Cassandra, and Solr™ together into a unified big data platform, CFS provides the storage foundation that makes running Hadoop-styled analytics on Cassandra data hassle-free[5].

In contrast to a master-slave architecture like HDFS, CFS is based on Cassandra, so the implementation is peer-to-peer and “masterless.” A user is able to create a cluster that seamlessly stores real-time data in Cassandra, performs analytic operations on that same data, and also handles enterprise search operations. Cassandra’s built-in replication transparently takes care of replicating the data among all realtime, analytic, and search nodes. A user may configure any type of cluster they desire[5].

Figure 3. Simple DataStax Enterprise Cluster

3. PROPOSAL LOG MANAGEMENT SYSTEM

Log management can benefit an organization in many ways. It helps to ensure that computer security records are stored in sufficient detail for an appropriate period of time. Routine log reviews and analysis are beneficial for identifying security incidents, policy violations, fraudulent activity, and operational problems shortly after they have occurred, and for providing information useful for resolving such problems. Logs can also be useful for performing auditing and forensic analysis, supporting the organization’s internal investigations, establishing baselines, and identifying operational trends and long-term problems. Figure 3 shows the proposal log management system Architecture.

The log management system is composed of three servers: an operation server, a security server and a log management distributed file system. The log management system performs the log management, security, store, and data analysis. An operation server is consist of an operation profiles and an operation process module. An operation profiles stored variable values for log management setting. An operation process module performs log management and data analysis using operation profiles. A security server is consist of a security profiles and a security process module. A security profiles stored variable values for security setting and polices. A security process module performs log analysis and system security (firewall, IDS and IPS) using security profiles. A log management distributed file system is consist of a hive, a HBase, a MapReduce and a HDFS. And a log management distributed file system is a Hadoop based system for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. A log management distributed file system is running on the same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where data is already present, resulting in very high aggregate bandwidth across the cluster

Figure 4. The proposal log management system  architecture

4. CONCLUSIONS

In this paper, we propose big data based log management system for wireless sensor networks. The proposal big data based log management system performs the log management, security, store, and data analysis. The big data based log management system is adapted for storing the number of data distributed from the sensor nodes.

Acknowledgment (Heading 5)

The preferred spelling of the word “acknowledgment” in America is without an “e” after the “g”. Avoid the stilted expression, “One of us (R. B. G.) thanks . . .”  Instead, try “R. B. G. thanks”. Put sponsor acknowledgments in the unnum-bered footnote on the first page.

References

[1] ISO/IEC JTC 1, Information technology, Big data, 2014

[2] IEEE Std 802.15.4: “Wireless Medium Access Control(MAC) and Physical Layer(PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs)”, 2003

[3] D. Borthakur, "HDFS architecture guide", HADOOP APACHE PROJECT http://hadoop. apache. org/common/docs/current/hdfs design. pdf, 2008

[4] GlusterFS Developers: Gluster File System 3.3.0 Administration Guide.

[5] Comparing the Hadoop Distributed File System (HDFS) with the Cassandra File System (CFS) White Paper BY DATASTAX CORPORATION August 2013

Rits Eye: A Software-Based System for Realtime Face Detection and Tracking Using Pan-Tilt-Zoom Controllable Camera

Gang Xu*, Takeo Sugimoto*

*Computer Vision Labolatory, Computer Science Department, Ritsumeikan University

Kusatsu, Shiga 525-8577, Japan

Email: {xu,takeo}@cv.cs.ritsumei.ac.jp

Abstract

This paper describes a working system that can robustly detect and track human faces with unknown sizes and positions in complex backgrounds from color images in real time. Skin color is modeled as and detected by a 3D Gaussian distribution in the RGB color space among different persons within the same race. Detected skin pixels are grouped into regions whose centroids and areas are recorded. If dark regions for eyes, eyebrows and mouth are also detected at certain positions within these regions, they are regarded as faces. Once the faces are detected, the system starts to track one of them using a pan-tilt-zoom controllable camera. Without any special hardware, the system achieves a speed of 0.2 sec for face detection and 0.1 sec for face tracking, on a Pentium 266 MHz PC, including the image acquisition time

1. Introduction

Human face detection is a key issue in human interface. It is not only important

in its own light, but is also vital for other vision tasks such as security check, face identification, model-based video coding and facial expression recognition, as most algorithms require that the faces be first detected and localized.

Quite a few face detection studies have been reported in the literature. Some use monochromatic images[8, 4, 6]. and some use color images[1, 3, 7]. Many assume that the faces to be detected are in the frontal poses[8, 4, 6], and that the sizes of faces to be detected are within a certain range[4, 6, 1, 7]. Some use pattern matching, with the patterns being prepared manually or by learning[6, 2, 1, 7, 5]. Since the face size and position are not known a priori, one gas to prepare and test the patterns in many position and in many sizes, consuming long computation time. For example, Wuet al. 's system consumes 2 seconds on a SGI Indigo2 machine[1, 7], and Rowley et al. 's system takes even longer time[2].

In this paper we describe a realtime face detection and tracking system. Without any special hardware, we achieved a speed of 0.2 sec per frame for face detection and 0.1 sec per frame for face tracking on a Pentium 266MHz PC. The face tracking is implemented using a pan-tilt-zoom controllable camera. Once a face is detected, the camera is controlled to pull back the face to the image center. We use 3D Gaussian for skin color modelling and detection, and without any "template matching" we achieve better speed.

Similar results have been reported by Yang et al.[9]. They use a 2D Gaussian for skin color modelling and achieve comparable speed for tracking.

Though we have not done any experiments on faces of other races than East Asians, the work reported by Yang  et al. and Sobottka et al. shows that similar approaches should work for people with other skin colors[9, 3].

2. Skin Color Modeling and Detection

In our approach, we use RGB color space and model the distribution of skin color within a particular race as a 3D Gaussian distribution, enabling segmentation of skin color from non-skin color by the Mahalanobis distance from the center of the Gaussian distribution. A general 3D Gaussian disitibution is expressed by

To learn the center and covariance matrix defined in a 3D Gaussian distribution, we first take sample images, and extract the skin pixels in it by hand. The center of the distribution  and the covariance matrix  are respectively computed by

,   (1)

where .

if a color vector whose Mahalanobis distance to the center of the Gaussian distribution, expressed by

,

is less than a predetermined threshold, then it is declared as a skin color; otherwise it is declared as a non-skin color.

The images we used as samples are all of East Asians. The total number of skin pixels is 437440, the computed center is  , and the computed covariance matrix is . Fig.1 shows

the distribution of skin color, and the computed center and the covariance matrix are shown by an associated ellipsoid. The specular reflections are excluded, which appear above the ellipsoid.

Figure 1. The 3D Gaussian distribution for the skin colors obtained from sample faces is shown by an associated ellipsoid.

For East Asians, the hair, eyes and eyebrows are black. These pixels can be detected by a simple thresholding of (R+G+B).

3. Detection of Faces

By the Mahalanobis distance and brightness thresholding, we can detect skin color pixels and dark pixels. Now we group neighboring skin color pixels into skin regions, and dark pixels to dark regions, each of which ih represented by its area and center. For each skin color region, we fit a square with its center superimposed with the center of the region. and its length of each side to be the square root of the region`s area. It is further divided into 4 small squares as shown in the middle figure in Fig2. If there are small dark region whose areas are within a certain percentage of the skin region area, and whose centers are in both of the upper small squares, then they are regarded as eyes and/or eyebrows, and in the lower rectangle, then it is regarded as a mouth, and the skin regions as faces. To cope with faces which are oriented toward left or right, and which have fronto-parallel rotations, we also shift the upper small squares to the left and right, as shown in the left and right figures in Fig.2, and if dark regions for eyes and eyebrows are found in both of them, they are regarded as faces. Experimental results show this greatly improves the performance

Figure 2. A face must find eyes and/or eyebrows in both the areas indicated in either of the 3 figures

This simple criterion is employed with the following merits: (1) it does not depend on hair style, which varies from person to person, and between sexes; (2) it allows faces to be in different orientations as far as the eyes and eyebrows are within this range; (3) it allows faces to be of different sizes as far as the eyes and eyebrows are visible; (4) it allows multiple faces to be detected altogether as far as they do not overlap; and (5) it is robust against glasses

4. Face Tracking

In the tracking mode, once a face is detected in the first image frame, the pan and tilt of the camera is controlled to bring the face back to the image center As the face is always near the image center, the search area for the next frame does not need to be the whole image, but only a smaller area around the image center, in our case, one fourth of the image size (of course, this depends on the face motion speed.) This reduces the face detection time, enabling the system to track the face with a higher moving speed.

Once the face is newly detected, the camera pan and tilt are controlled to pull back the face to the image center. Suppose that the face position is at  at time . We can predict its position at time  by assuming constant velocity v and constant acceleration a using the previous three position (Fig.3).

Note that these quantities include the camera motions which are known.

Figure 3. Prediction of face position

Now we want to control the camera pan and tilt such that at time , the face center is at the image center. Suppose that the focal length  is known. Then we can compute the orientation of the predicted face center as

(5)

and  are actually the changes in pan and tilt needed to move the face center to the image center. Accordingly, the change in image position due to change in pan and tilt can also be computed by

(6)

The actual face velocity is the sum of the observed velocity in the image frame and the velocity due to the camera motion,

(7)

where  denotes the observed face position.

Figure 4. The Hits Eye system: the pan-tilt controllable camera (left in black), the computer (Pentium266MHz PC), and the face.

5.Implementation and Experimental Results

The implementation is done with special attention paid to reducing the number of memory accesses, and to using as many integer operations as possible. Many experiments have been done. In our lab, Rits Eye welcomes visitors by taking their color images, locating their faces in realtime and tracking their motions. Fig.5 shows the results respectively for a face at a distance from the camera, multiple faces and faces with glasses, a face oriented to the left, a face oriented to the right and a face with a fronto-parallel rotation The lefthand figures show the original images superimposed with squares indicating the positions of detected faces, and the righthand figures show the regions for skin color (in gray), for dark (in black) and for other colors (in white). One can see from the righthand figures that though not every skin color pixel is detected as skin color due to reflectance or other factors, it does not affect the overall performance. From these examples, one can see that the algorithm is robust against various kinds of changes that one may encounter in actual situations

We achieved a total success rate of 49/53. The face detection takes about 0.2 seconds and face traking takes about 0.1 seconds, both including the image acquisition time (33ms), on a Pentium266MHz PC without any special hardware As for the background, if it does not have skin-like colors, then there is no problem. If it does have skinlike colors, and it happens to have some dark regions within the skin-like color regions, then they are also detected as faces. Note that this rarely happens in a room environment. But it sometimes happens with the skin-like colored cloth.

Figure 5. Experimental Results

6. Conclusion

We have in this paper described Rits Eye, a working system that can detect human faces from color images in realtime, without using any special hardware. The face tracking is realized by controlling the camera pan and tilt. We achieved a speed of 0.2 sec per frame for face detection and 0.1 sec per frame for face tracking. This simple criterion has the merits: (1) it does not depend on hair style, which varies from person to person, and between sexes; (2) it allows faces to be in different orientations as far as the eyes and eyebrows are within this range; (3) it allows faces to be of different sizes as far as the eyes and eyebrows are visible; (4) it allows multiple faces to be detected altogether as far as they do not overlap Though we have not done any experiments on faces of other races than East Asians, the same algorithm is expected to work by relearning the skin color model

References

[1] Q. Chen, H. Wu, and M. Yachida. Face detection by fuzzy pattern matching. In Proc. Fifth Int'l Conf. Corn-put. Vision, pages 591-596, Boston, 1995. IEEE.

[2] H. Rowley, S. Baluja, and K. T. Human face detection in visual scenes. Technical Report CMU-CS-95-158R, School of Computer Science, Carnegie Mellon Univer-sity, 1995.

[3] K. Sobottka and I. Pitas. Extraction of facial region and features using color and shape information. In Proc. 10th Int'l Conf. Pattern Recog., pages C421-425, Vi-enna, 1996.

[4] X. Song, C. Lee, G. Xu, and S. Tsuji. Extracting facial features with partial feature template. In Proc. First Asian Conf. Comput. Vision, pages 751-754, Osaka, 1993.

[5] K.-K. Sung and T. Poggio. Example-based learning for view-based human face detection. IEEE Trans. PAMI, 20(1):39-51, 1998.

[6] A. Tsukamoto, C. Lee, and S. Tsuji. Detection and tracking of human face with synthesized template. In Proc. First Asian Conf. Comput. Vision, pages 183-186, Osaka, 1993.

[7] H. Wu, Q. Chen, and M. Yachida. Face detection from color images by fuzzy pattern matching. IEICE Trans.JDII, 80(7):1774-1785, 1997. in Japanese.

[8] G. Yang and T. Huang. Human face detection in a scene. In Proc. IEEE Conf. Comput. Vision Pattern Recog., pages 453-458. IEEE, 1993.

[9] J. Yang and A. Waibel. A realtime face tracker. In Proc. of 3rd IEEE Workshop on Applications of Computer Vision, pages 142-147, 1996.

A Study on NoSQL Database for the Big Data Processing

Hong-Jin Park*

*School of Computer, Information and Communications, Sangji University, 83 Sangjidae-gil, Wonju, Gangwondo, Korea

E-mail: hjpark1@sangji.ac.kr

Abstract

As the quantity and quality of information is explosively increasing nowadays, interest on big data processing is also increasing. Various NoSQL databases are appearing to process big data. In this paper, the performance of HBase, Cassandra, Mongo, and Redis which are representative NoSQL databases are comparatively evaluated. 12 PCs were connected through a switching hub for fully-distributed processing in the study. YCSB was used as a benchmarking tool in which read and write were each performed 200,000 and 1,000,000 times 50% for evaluation. Cassandra showed the most outstanding transaction

Keywords: component; HBase, Cassandra, Mongo, Redis, YCSB, NoSQL

1. Introduction

Due to the innovative development of high speed internet network and wireless internet network, data is no longer restricted to time and space, and is being generated, transmitted, and shared. The quality and quantity of information has rapidly developed especially due to the commercialization of smart devices in which big data related technologies are quickly developing. Big data is distinguished in data volume, velocity, and variety aspects. Data volume is defined as mass data including physical scale and conceptual range. The second feature of big data which is velocity is defined as real time distribution speed or high speed. Lastly, variety includes traditional structural tangible data and also intangible data such as photos or videos. Conventional database systems are based on structural data and use data consistency and standardization to provide high performance. However, non-structural data processing and 100 tera-level big data processing is very complicated or difficult. The technology researched to solve big data and secure the weaknesses of existing databases. The performance of various NoSQL databases are evaluated in this paper. HBase, Cassandra, Mongo, and Redis were the NoSQL databases comparatively evaluated in this paper. 12 PCs were connected through a switching hub for fully-distributed processing in the study. YCSB was used as a benchmarking tool in which read and write were each performed 200,000 and 1,000,000 times 50% for evaluation.

2. Based rearch

A. HBase

HBase which is used by Facebook and eBay is an open source NoSQL database for Hadoop platform. HBase which was developed to efficiently use mass data operated on HDFS which is a distribution file system by Hadoop. The largest merit of HBase is that it saves and manages massive data in distributed environment to secure consistency. If consistency is not guaranteed, data consistency must be guaranteed by program codes that the complexity of coding increases. Efficient compression is required to economically save massive data. The basic unit of HBase based on columns has more efficient data compression than row based databases. Data is saved together standard to columns for column based databases that the data compression rate is higher than row based databases due to small difference between close data values. Also, database management and monitoring functions are provided through web consoles. The disadvantage is that it is more complicated to understand compared to other NoSQL databases and the total service must be stopped if a defect occurs in Namenode that manages the file information.

B. Cassandra

Cassandra that is performed by Apache project and developed by Facebook is a key-value type NoSQL database that has a row key as the index. It is a database suitable to efficiently manage massive data at several distributed servers. The advantage of Cassandra database is the it conducts load balancing by consistent hashing method. The server load can be reduced by transmitting many requests in a server to a server with less requests. Therefore, it prevents requests from being biased to a particular server and can increase performance. Also, load management efficiently takes place by considering load balancing when a new server is added. There is an advantage that the service can be easily added without the service being stopped when adding a new server to the distribution system. The disadvantage of Cassandra database is that searching in complicated condition is impossible. Only the index on the row key and columns is possible that it is applicable to simple services. Data update and input atomoic processing is difficult.

C. Mongo

Mongo database is a document type database. The largest feature of Mongo database is that there is no need to define the schema differently from the existing relation type database. It saves in BSON(binary JSON) including data structure information when inputting data that the schema does not need to be separately defined. Mongo database does not define the schema, but complicated searching on random key values is possible along with relation type databases. It has quick response speed compared to relation type databases and the processing can be made quicker by adding indexes like relation type databases. JOIN or transaction processing is impossible in Mongo databases. Also, data update and input is not directly written on the disk. Disk writing is asynchronous that data may be lost at cases.

D. Redis

Redis database saves data in kay and value form. The largest feature of Redis database is that data is saved in memory in which processing speed is fast. Redis has the function called Snapshot of saving data in the disk that the current situation of the database can be saved. I/O of the total data occurs at the time of the snapshot, but I/O is processed by sequential access that load due to I/O is not large.

3. Performance evaluation

E. Environment

The performance evaluation environment to evaluate the NoSQL databases HBase, Cassandra, Mongo, and Redis are shown in Fig. 1. As shown in Fig. 1, the operating system Ubuntu 12.04 LTS was installed. After installing Ubuntu on 12 computers, HBase, Cassandra, Mongo, and Redis were installed in each computer. YCSB the performance evaluation tool was installed to measure the performance of the NoSQL databases.

Figure 1. The Structure of Performance Evaluation

F. Performance evaluation

In this paper, 12 PCs were connected with a switching hub to evaluate the performances of fully-distributed HBase, Cassandra, Mongo, and Redis. Also, YCSB the benchmarking tool of NoSQL was used. Fig. 2 to Fig. 4 shows the results of 200,000, 400,000, 600,000, 800,000, and 1,000,000 times processing of read 50%, write 50% after installing each NoSQL database. As shown in Fig. 2, Cassandra shows the most outstanding transaction processing per second. Especially, it can be known that it shows about 2.5 times faster transaction compared with Mongo in 1,000,000 times processing.

Figure 2. Transaction Numbers per Second

Fig. 3 shows the number of performed processes per unit time. Mongo DB shows the best results in this case. Radis was second, and Cassandra and Hbase showed similar performances.

Figure 3.Throughput

Lastly, Fig. 4 shows the average delay time and higher delay time shows lower performance. Cassandra shows 3.5 times higher average delay time compared to other NoSQL databases. Hbase, Redis, and Mongo show similar average delay time results.

Figure 4. Average Latency

In the aspect of easy distributive saving and processing data in several servers and server processing competence, NoSQL databases are more suitable than scale-out method using several servers that have comparatively low price. The performances of NoSQL databases HBase, Cassandra, Mongo, and Redis were compared.

4. Conclusion

NoSQL databases were distributively installed in a total of 12 PCs. YCSB, which is the benchmarking tool of NoSQL, was used in which read 50%, write 50% were performed 200,000 times to 1,000,000 times to compare the performance by number of transaction processing per second and average delay time. Cassandra showed most outstanding number of transaction processing per second and Mongo DB showed outstanding processing rate. Also, Cassandra showed 3.5 higher average delay time compared to other NoSQL databases. Hbase, Redis, and Mongo showed similar average delay time in read 50%, write 50%.

References

[1] Jing Han, “Survey on NoSQL database”, Proceedings of the International Conference on Pervasive Computing and Applications (ICPCA), 2011

[2] Bogdan George Tudorica, “A comparison between several NoSQL databases with comments and notes”, Proceedings of the International Conference on Networking in Education and Research, 2011

[3] Mehul Nalin Vora, “Hadoop-HBase for large-scale data”, Proceedings of the  International Conference on Computer Science and Nework Technology, 2011

[4]Guoxi Wang ; Sch. of Software Eng., Tongji Univ., Shanghai, China ; Jianfeng Tang, “The NoSQL Principles and Basic Application of Cassandra Model” (CSSS),  Proceedings of the International Conference on Computer Science & Service System , 2012

[5] Jorge Bernardino, “NoSQL databases: MongoDB vs cassandra”, Proceedings of the International Conference on Computer Science and Software Engineering, 2013

SQLite Recovery Method Using CST+-Tree

S. S. Han*, S. J. Cho**

*School of Information and Telecommunication,Bucheon University, Korea

**School of Arts and Cultural Contents, Dongbang Graduate University, Korea

Abstract

SQLite is mainly used as database management system in smartphones and embedded devices. It is a compact relational database management system (RDBMS), which operates without a server system. To minimize the losses due to transaction failure, SQLite uses recovery technique such as roll back journal and WAL technique. These techniques use B-tree, which uses index that stores data in record level. Using B-tree may result in performance degradation due to frequent operation of the tree node. The performance evaluation has confirmed that the proposed method improved the recovery performance and the superior performance of the proposed method compared to the existing methods.

Keywords: SQLite, Database, B-tree, CST+-tree, Recovery

1. Introduction

SQLite is used in management system of smart phones and embedded devices, as well as small size DB programs. With recent increase in the use of portable and embedded devices, its use has increased rapidly. SQLite is a type of RDBMS database system like MySQL and Oracle, but it is an application, rather than a server, which is lighter and faster than other types of database and is able to store and manage simple data in a more systematic manner [1].

SQLite processes unit of logical work by grouping into transactions; DBMS has to satisfy ACID (Atomic, Consistent, Isolate, Durable) properties to ensure safe performance of transaction [2]. However, data loss may occur due to the failure of transaction from abnormal termination and DBMS attempts to recover data to minimize the loss. The recovery method used in such circumstance is one of the core parts of DBMS, which recovers it to the point before failure and helps maintain consistency of database [2].

SQLite normally uses Rollback journal and WAL (Write-Ahead Logging) as the recovery technique. These two techniques log the entire page, which has been changed, therefore they result in large sized logs and management cost of the log files. Unlike other DMBS, SQLite stores the entire database in a single file for management. B-trees store data and compose pages. However, using B-tree can degrade performance due to frequent operation.

In order to address the problem, the present study proposes recovery method, which uses index of CST*-tree. It was found that the proposed technique improved the recovery performance, as compared to B-tree, and through performance evaluation, the proposed technique demonstrated superior performance.

2. Theoretical Background and Relevant Studies

There are several different recovery methods of DBMS. First method is physical logging, which stores the entire page before and after change. Such physical logging enables fast undo/redo process, but results in large log size. Second method is logical logging, which does not store data as log but stores operation as opcode. Because it only stores the input data used in the operation, the size of log file is much smaller than that of the physical logging. However, because recovery has to be performed on each task, the speed is rather slow.

Unlike other DBMS, SQLite stores the entire database in a single file for management and each table is composed of B-tree. SQLite file consists of multiple pages, which become the smallest unit of SQLite I/O.

2.1. SQLite Recovery Method

2.1.1. Recovery Method Using Rollback Journal File

When modifying a specific page of database, rollback journal method backs up the original page and stores it in a separate file (“<Database file name>-journal”) before it modifies the page. Modified page is incorporated into the DB file completely when transaction is committed, at which point the journal file is deleted. By analyzing the journal file, it recovers the deleted SQLite database record [3].

2.1.2. WAL (Write-Ahead Logging) Recovery Method

WAL method has the opposite qualities to rollback journal. WAL When modifying a specific page by using WAL method, the modified page is put into the WAL frame and stored as a WAL file (“<Database file name>-WAL”). When there is a request submitted to view the modified page, WAL file is retrieved, rather than the actual file in the DB. WAL, in order to preserve atomicity of the transaction, writes the log record (WAL frame) on the disk before the specific page is incorporated into the disk. Then, in order to satisfy consistency of the transaction, it writes all relevant log records in the disk before transaction is committed.

WAL does not modify database file but writes WAL frame from the memory into the WAL file. The WAL file is incorporated into the database file during checkpoint.

2.2. CST-Tree

CST-tree (Cache Sensitive T-tree) was proposed to improve efficiency of cache and has the following characteristics. First, it uses the most possible amount of the data to be transferred to cache. Second, it eliminates use of pointer and composes tree in array. Third, it shows the best performance when the size of node block equals the size of cache block

Fig. 1. Structure in CST-tree.

[Figure 1] shows CST-tree structure, which uses pointer to connect node blocks. Key values within the node blocks have the maximum value of the data node. Therefore, each node within the node block has pointer that goes to the corresponding data node.

In CST-tree, there is no cache miss during binary search in the root node group until it reaches the terminal node group. Cache miss only occurs when accessing the child node group and data node.

CST-tree showed superior performance in point search, as compared to other index structures. For range search, however, it resulted in higher cost because it searches through node block traversal. During range search, it incurs in a larger traversal cost than T*-tree [4].

3. SQLite Recovery Method Using CST+-Tree

This chapter proposes CST+-tree, which is a variation of CST-tree and is known to have the best performance in main memory and database area. CST+-tree shows similar point search performance as CST-tree but has improved performance for range search. Using CST-tree, which has superior performance than B-tree used in the existing SQLite DBMS, it designed the CISLD (CST-tree Index Segment Log Directory) technique and proposes search, insert and delete algorithm of CST+-tree [5].

Fig. 2 Recovery Structure in CISLD Method.

[Figure 2] is composed of CST-tree index and log directory. This technique enables fast recovery by using Redo Log to perform quick search when there is a failure in a particular SQLite DBMS. Because CST-tree uses front and rear pointers, performance of point search, insert and delete functions is similar but the performance of range search improved by 600~1000% [5].

3.1. CST+-Tree Algorithm

Data nodes in CST-ISLD algorithm are connected as a linked list, in order to overcome the fact that the existing CST-tree is inefficient in range search.

3.1.1. Search Algorithm

Search algorithm of CST+-tree [5] is done in two stages:

Stage 1: searches the minimum key value within the search range

Stage 2: uses linked list to find the key value within the range from the value searched from stage 1

In stage 1, search key value and the maximum key value are compared. If search key value is greater than maximum key value, search is performed in the right-hand side sub-node block. If it is smaller than the maximum value, search key value exists in the current node or search key exists in the left-hand side sub-node block. Hence, mark the current node before searching the left-hand side sub-node block. If the value is not found by the time terminal node is reached, perform binary search in the data node of the node marked last.

Second search is done within the range provided by the data node, which contains the key value found from stage 1.

Search will be terminated if the searched key value is greater than or equal to the maximum key value within the range.

Algorithm1. CST+ Tree Search Phase 1

Algorithm2. CST+ Tree Search Phase 2

3.1.2. Insert Algorithm

Insert algorithm of CST+-tree is as follows [5]. After searching a node, it will insert a node, if it can be inserted. If there is no room for insert, it will delete the minimum value of data node and insert the value. The deleted minimum value will be inserted to the left-hand side sub-node block. If there is no sub-node block on the left, a new node block is created and node balancing will be checked. When a node block is created in the tree, the current data node will be marked and the new node block and marked data node will be connected with a linked list.

Algorithm3. CST+ Tree Insert

3.1.3. Delete Algorithm

Delete algorithm of CST+-tree [5] is performed by searching the data node, where the key value to be deleted exists. Then the key value and the corresponding record ID will be deleted in the data node.  The following explanation will not consider record ID in the data node but will only assume that the key value exists. If there is an underflow (number of keys is less than 1/2) in data node due to deletion, the maximum key value from the left-hand side sub-node will be inserted to the current data node and deleted from the sub-node. If the left sub-node does not exist, then the minimum value of the right sub-node will be inserted to the current data node and it will be deleted from the right sub-node. If the data node disappears due to deletion, replace the rear pointer of the connected data node with the rear point value of the deleted data node. If the node disappears from deletion, check tree balancing within the terminal node. If the node block disappears, check the balancing of entire CST+-tree [5].

3.1.4. Recovery Algorithm

In the current study, data recovery (reading the key value) and data insert (building index) are processed in parallel to combine the advantages of sequential insert and batch insert [5].

Algorithm5. CST+-Tree Recovery-Reading Data

Algorithm6. CST+- Tree Recovery-filling Data to

Data Node

4. Performance Test

In the current chapter, performance of the B-tree technique and the proposed technique is compared. The existing SQLite technique uses B-tree for recovery and the proposed technique uses CISLD, which is composed of CST+-tree. The two techniques are compared in terms of the performance.

4.1. Measure Experimental Performance

In this experiment, C language was used to measure the cache miss count and tree height for both B-tree technique and CISLD technique, which uses CST+-tree. For the performance test, Core i7- 4790(4GHz, 8M Cache), 8GB RAM was used.

4.2. Compare and Evaluate Performance

<Figure 3> shows cache miss count according to the page numbers randomly created between 5,000 and 53,000 by using B-tree technique and CISLD technique, which uses CST+-tree. The proposed technique resulted in smaller number of cache miss count because the proposed technique searches data node and then uses connecting pointer to search the next data node. This reduced number of cache miss; hence it is faster than B-tree method.

Fig. 3 Cache Miss Count of Page Count

Similar to <Figure 3>, <Figure 4> shows created random number of pages and analyzed the height of trees according to the page count.

CISLD technique had smaller tree height than B-tree technique because CST+-tree searches nodes one time to find the minimum key value within the search range. CST+-tree is faster than B-tree because it takes less time to find minimum key value due to the difference in the tree height.

Fig. 4 Tree Height of Page Count

In overall, the proposed technique showed improved performance than the existing technique. By using connecting pointer to search the next node, the proposed technique resulted in less number of cache miss; the difference in the tree height resulted in faster time to search minimum key value within the search range.

5. Conclusion

Use of SQLite is rapidly increasing due to the increase in the use of portable and embedded devices. The existing SQLite database recovery techniques can be divided into rollback journal and WAL, both of which uses B-tree. The present study proposed CISLD technique, which uses CST+-tree for having superior performance than the existing techniques. For B-tree technique and CST+-tree technique were compared for performance test. As a result, the proposed technique had lower number of cache miss count and faster time to retrieve the minimum key value. It was found that the use of connecting pointer reduced the cache miss count and difference in the tree height reduced retrieval time in the proposed method. In the future, we plan to study simultaneity of the SQLite DBMS, which uses CST+-tree.

References

[1] SQLite, http://sqlite.org/.

[2] S. Abraham, in Database System Concepts 6th edition,  p628, (2011).

[3] Mike Owens, Michael Owens. In The definitive guide to SQLite, Apress, May.(2006)

[4] D.H. Kang et al., Effective Range Search of CST-Tree, Korea Computer Conference Journal (2006), Vol. 33, No. 1(C )

[5] J.W. Lee et al., CST+-Tree Index Structure for Range Search, Journal of Information Science: Database (2008), Vol. 35, No. 1

[6] K.W. Lee et al., Removal and Overflow Data Recovery of SQLite Database, Korea Institute of Information Technology (2012), Vol. 10, No. 11

[7] Joonhee Lee, Mincheol Shin, Yongil Jang, Sanghyun Park, New SQLite Recovery Technique of Logical Logging, Korea Institute of Information Technology Journal 12(11) (2014), pp.181-192

Motion-based recognition of pedestrians

B. Heisele, C. Wohler*

*Daimler-Benz AG, Research and Technology Image Understanding, FT3/ABP.O. Box 2360, 89013 Ulm, Singapore

E-mail: { Heisele, Woehler}@DB AG. Ulrri.DaimlerBenz.Com

Abstract

In this paper we present an algorithm for recognizing walking pedestrians in sequences of color images taken from a moving camera. The recognition is based on the characteristic motion of the legs of a pedestrian walking parallel to the image plane. Each image is segmented into region-like image parts by clustering pixels in a combined color/position feature space. The proposed clustering tecnique implies maching of corresponding clusters in consecutive frames and therefore allows clusters to be tracked over a sequence of images Based on the observation of clusters which most likely represent the legs of pedestrians. A fast polynomial classifier performs a rough preselection of clusters by evaluating temporal changes of a shape-dependent cluster feature the final classification is done by a time delay neural network (TDNN) with spatio-temporal receptive fields.

1. Introduction

our application domain is vision-based driver tance in the inner city. . In this application early recognition and tracking of pedestrians is essential for collision In the following we briefly review some approaches for reconizing pedestians.

due to the high variability in the appearance fo pedestrians in outdoor scenes, there are only few approaches trians in outdoor scenes, there are only few approaches which do not rely on motion information. In [8] wavelet templates are used to extract frontal and rear views of pedestrians in single images. Most approaches use motion to extract moving pedestrians form a stationary background and perform a classiciaction based on the human gait – a survey about human motion analysis can be found in [3]. Segmentation and tracking of walking persons is discussed in [10.11]. A real-time system for detecting and tracking a single person in an arbitrary scene is presented in [14]. The system generates Gaussian models of the background and the person based on color and image location. In [6，7] patterns generated by walking persons are detected in the XT plane [6] and XYT space [7】. A method which does not require a fixed camera is proposed in [9]. For each independently moving object they extract a temporal sequence of image regions which are normalized in size. The motion pattern in such a sequence is classified based on optical flow.

In contrast to [9] our method does not require the detection of independently moving objects. Each image is segmented into a set of clusters by grouping pixels with similar color and position in the image plane. Tracking of cluclusters over time is implicitly done by the proposed clustering technique. A polynomial classifier preselects clusters which most likely correspond to the legs of pedestrians. For each selected cluster we extract a sequence of regions containing the cluster. The regions are normalized in size and finally classified by a time delay neural network (TDNN)

The outline of the paper is as follows: In Section 2 we briefly describe the segmentation by clustering and the tracking of clusters. The polynomial classifier is explained in Section 3, the TDNN in Section 4. We then present our results in Section 5 and give a summary of the paper in Section 6

2. Segmentation and Tracking

The proposed segmentation method assumes that pixels of similar color which are close to each other in the image belong to the same physical object and thus can be grouped together. Each pixel n in the image is described by a feature vector fn,containing its color in the RGB space and its position in the image plane: fn= (Rn, Gn  Bn ,xn, yn),where xn is the horizontal and yn the vertical position of pixel n in the image plane. The task of clustering is to find a given number R of prototypes pr, which minimize the sum of quantization errors: , where pr(n)is the prototype closest to the feature vector fn in the color/ position feature space.

For the first image of a sequence we use a divisive clustering technique [5] which partitions the image into a preselected number of clusters. In the consecutive frames the prototypes of the previous frame are shifted in the feature space by parallel k-means clustering [2] to fit the new image data. Thus we obtain consistent segmentation results over time. Moreover, no explicit matching of corresponding clusters is required. Assuming a continuous motion behavior of clusters, prediction techniques are used to increase the robustness of tracking. A detailed description of the applied clustering techniques can be found in [4]

Fig. 1 shows the results of clustering in a scene with a crossing pedestrian. In all sequences investigated in this paper the number of clusters was set to 128 which leads to reasonable segmentation results. However, the requirement of a fixed number of clusters may lead to problems when dealing with sequences where the complexity of the observed scene is changing rapidly over time

Figure 1. Clustering in the combined color/position feature space. Dark lines represent the boundaries of the clusters.

3. classification of Temporal shape Variations

Usually both legs or at least parts of both legs of a pedestrian are combined into the same cluster due to their similarity in color. In the case of a pedestrian walking parallel to the image plane, temporal changes in the shape of such a cluster can be used to distinguish it from clusters belonging to other parts in the scene To approximate the shape of a cluster we use a rectangular box which bounds the eigenvectors of the covariance matrix of the corresponding cluster in the image plane (see Fig. 2). Only the box width is taken as an input feature fo: the classifier As shown in Fig. 3 the periodicity in the human gait is reflected in the time signal of the

Figure 2. Temporal changes in a cluster belonging to the legs of a pedestrian The box used for approximating the shape of the cluster and the trajectory of the centroid of the cluster are drawn as dark lines

box width. To extract the dominant frequency a Fast Fourier Transform with a time window of fixed size is applied to the signal. The width of the time window was set to 16 frames (= 0.64 sec at 25 frames/sec) which is roughly the duration of one step of a pedestrian walking at medium speed. Only the first two lines of the spectrum at 1.56 Hz and 3.125 Hz are used for classification. We adapted a complete quadratic polynomial classifier with a sample set generated from 24 clusters representing moving legs and 40 clusters representing background The spectra have been calculated by shifting the time window continuously over the time signal. This lead to 668 spectra of pedestrians nad 777 spectra representing background.

About 16 % of the spectra belonging to legs of pedestrians could not be classified correctly . The main reason is an inaccurate segmentation due to low color contrast between the legs and the surrounding image parts Problems also occur when pedestrians are walking at sharp angles (< 50 deg) to the optical axis which significantly reduces the change in amplitude in the time signal. However, since a pedestrian occurs about 30 to 40 times in an image sequence, the probability of its detection is still sufficiently high. The necessity of a more sophisticated second stage classifier follows from the high number of false alarms: about 7 % of the background clusters have been classified as belonging to pedestrians. Since each image contains 128 clusters there are about 9 false alarms per image. They are caused by the motion of the camera which leads :〇 changes in the shapes of clusters belonging to the background

Figure 3. The upper diagram shows the box width of a cluster belonging to the legs of a pedestrian. The lower diagram shows the spectrum calculated by a FFT (window size = 32) and normalized by the direct component.

4. Final Classification with a TDNN

The typical motion pattern of the pedestrian’s legs is a very stable and characteristic feature. We perform the final classification of the regions possibly containing a pedestrian’s legs delivered by the polynomial classifier by means of a time delay neural network (TDNN) that has specially been designed for processing grayscale image sequences, simultaneously performing an object recognition and motion analysis on them. The classical application domain of TDNNs is speech recognition (see e. g. [12])； a TDNN specially adapted to motion detection is described in [1]. A detailed description of the TDNN architecture we use for pedestrian recognition can be found in [13]; here, only the basic concept will be outlined (see Fig. 4):

The regions of interest around the color clusters selected by the polynomial classifier as possible pedestrians are cropped and scaled to the size  pixel with (the index (1) refers to the first network layer). The image sequence to be classified was chosen to contain  frames. The raw grayscale values  of the  image se

quence serve as an input to the three-dimensional input layer. To take into account the three-dimensionality of the input and the locality of the object features, we make use of the concept of spatio-temporal receptive fields. This means that each neuron in the second layer is not connected to the complete input layer like e. g. in the multi layer perception architecture but only to a limited region of it, sized RxxRyxRtpixels. This region is called the receptive field of the neuron. Neighboring layer 2 neurons “see” neighboring regions of the input layer; the distance of the centers of two neighboring receptive fields in the three different dimensions is given by Dx,Dy, and Dt. The network consists of Nrf different branches as shown in Fig. 4. Each branch possesses one set of receptive fields and therefore extracts one spatio-temporal feature from the input sequence. We use shared weights within each network branch, i. e.，each layer 2 neuron of a certain branch s receives input from its respective receptive field by means of the same configuration of receptive field weights rsmnp,l<m<Rx, 1 < n < Ry.I<p<Rt, and 1 < s < Nrf. The parameter s denotes the network branch. The neurons are of the McCullough-Pitts type such that the output ^.t of the neuron at position (i,j, t)in branch number s in the second network layer amounts to

with   = tanh(x) as a sigmoidal activation function and 6s as the respective threshold value.

The receptive fields thus act as spatio-temporal filters and produce one “filtered” version of the input sequence per branch s in neuron layer 2. The receptive field weights, i. e.，filter coefficients, are adapted during the training process; the features to be extracted are therefore learned from the training examples instead of being imposed a priori. The second neuron layer is then connected to the third one by purely temporal receptive fields the weights of which are denoted by i. e.，a neuron in layer 3 is not connected to the complete underlying filtered sequence but only to Rh subsequent frames of it. In the spatial dimensions, however, neuron layers 2 and 3 are fully connected. To each branch s and each output class k of the network there belongs one such temporal receptive field; they produce the activations.

93(x) = tanh(x), in neuron layer 3. In the case of pedestrians, we only have to deal with K = 2 output classes, i. e., “pedestrian” and “garbage”. The output neurons perforin a classwise temporal integration of the activations of neuron layer 3, resulting in the output values

of the network

The network is trained by a simple gradient descent rule. In each training step, one image sequence is chosen at random from the training set. Defining a quadratic error measure with, for a pattern of class c ,  for  close to 1, the variations of the network weights in this training step are proportional to the derivative of the error measure  with respect to them,

The weight parameters and threshold values are initialzed by small positive and negative random numbers.A typical image sequence used as a training example is shown in Fig.4.Large overlapping receptive fields between the first two network layers yield a high size. and displacement tolerance; we therefore use the net-work parameter configuration Rx=Ry= 11, Rt = 5. Nrf = 2, Rh = 3, Dx=Dy= 6, and Dt = 1 Like the polynomial pre-classifier, the TDNN was trained on a set of 628 example sequences containing a pedestrian’s legs and 777 garbage patterns. As in this first step the rate of false positive recognitions was still quite high (about 10 % on several independent test sets), the network trained in this manner was applied to a number of further image sequences containing no pedestrians at all to produce a set of 1091 incorrectly classified garbage patterns to be added to the training set. After this bootstrapping step, at least 90 % of the pedestrian examples are correctly classified on several independent test sets, while the false positive rate now lies well below 2 %.

5. Results

Experiments were carried out on traffic scenes. A 3chip CCD camera, connected to a digital video recorder

(Y：U：V, 720 x 576 pixels, 25 frames/sec.), was used for taking images. The resolution of the images was

reduced to half the number of rows and column (360 x 288 pixels). All images have been partitioned into 128 clusters which took approximately 4 seconds on a Sun SPARC-20. The polynomial pre-classification is performed within a few milliseconds for all clusters of an image, while the verification carried out by the TDNN then takes about 50 milliseconds per pedestrian candidate

The effective probability that a pedestrian is correctly recognized by the two combined classifiers is about 75 % at a certain time step. This means in practice that in scenes of a length of some seconds, the pedestrians are recognized with a high stability 一 there may be a drop-out on every fourth or fifth image 一 while in false positive recognition occurs. The false positive can easily be distinguished from the correctly recognized pedestrians as they randomly occur at arbitrarypositions, while the pedestrian is always represented by the same cluster such that the recognition result is confirmed if a certain cluster ha5 been determined to represent a pedestrian at several time steps In Fig. 5 a-d, we present some example image sequences, all of them taken by a non-stationary camera The first two examples (a-b) show a single pedestrians.

crossing the street. The classification also works in the presence of strong disturbances (c). It is even possible

to recognize single pedestrians in a crowd crossing thestreet (d).

6. Summary

in this paper, we propose a method to perform the detection and recognition of pedestrians in color image sequence taken by a moving camera. Each image segmented into clusters by grouping pixels in a combined color/position feature space. The clustering implies a tracking of clusters over time. In order to determined if a cluster belongs to the legs of a pedestrian a quadratic polynomial classifier checks for periodicity in the temporal shape variation of a cluster. Due to background motion the false positive rate of this preclassification is quite high. Therefore a second stage classification is carried out by a TDNN with adaptable spatio-temporal receptive fields. The input data of the ire temporal sequences of gray-valued image regions selected by the polynomial classifier. As we have shown in experiments, the proposed method yields a stable and robust recognition and tracking of pedestrians even under difficult conditions (i, e, strong egomotion, partial occlusions, sudden changes in illumination). A future extension is adaption the of the initial clustering algorithm to grayscale image. This will significantly accelerate the segmentation rocogniton will be possible on standard hardware.

References

[1] S. Ambellouis and F. Cabestaing. Motion analysis with a time delayed neural network. In sympsium on Robotics and Cybernetics, CESA’96 IMACS Multiconferece, Computational Engineering in systems Applications, pages 328-332, Lille, France, 1996

[2] R.O. Duda P.E Hart. Pattern classification and scene analysis. Willey-Interscience, 1973

[3] D.M Gavrila The visual analysis of human movement a survey Accepted for publication in Computer vision Image Understanding.

[4] B, heisele U Kressel, and W, Ritter. Tracking non-rigid moving objects based on color cluster flow. In Proc. Computer Vision and Pattern. Recognition, pages 253-257, San Juan, 1997

[5] Y. Linde, A. Buzo, and R. Gray. An algorithm for vector quantizer design. IEEE Transactions on Communications, 28(l):84-95, 1980.

[6] S. A. Niyogi and E. H. Adelson. Analyzing and recognizing walking figures in IEEE Conference on Computer Vision and Pattern Recognition, pages 469-474, 1994

[7] S. A. Niyogi and E. H. Adelson. Analyzing gait with spatiotemporal surfaces In IEEE Workshop on Motion of Non-Rigid and Articulated Objects pages 64 –69 Austin, 1994

[8] M. Oren, C. Papageorgiou, P. Sinha, E. Osuna, and T. Poggio. Pedestrian detection using wavelet templates In IEEE Conference on Computer Vision and Pattern Recognition、pages 193-199, San Juan, 1997

[9] R. Polana arid R. Nelson. Low level recognition of human motion. In IEEE Workshop on Motion of Non-Rigid and Articulated Objects，pages 77-82, Austin, 1994

[10] J. Segen and S. Pingali. A camera-based system for tracking people in real time. In International Conference on Pattern Recognition, pages 63-67, Vienna, 1996

[11] S. Shio and J. Sklansky. Segmentation of people in motion. In IEEE Workshop on Visual Motion, pages 325-332, 1991

[12] A. Waibel, T. Hamazawa, G. Hinton, K. Shikano, and K. Lang. Phoneme recognition: Neural networks versus hidden markov models. In Proc. of the IEEE Int. Conf. on Acoust., Speech, Signal Processing, pages 107-110 1988

[13] C. Wohler and J. K. Anlauf. A Time Delay Neural Network Algorithm for Estimating Image-pattern Shape and Motion. Submitted to Image and Vision Computing Journal

[14] C. R. Wren, A. Azarbayejani, T. Darrell, and A. P. Pentland. Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):780-785,1997

Figure 5. Example image sequences displaying pedestrians. The images shown have already been processed by the clustering algorithm. The clusters recognized as pedestrians are marked by their respective circumscribing bounding boxes.

Recognition of Facial Expressions Using Muscle-Based Feature Models

Hiroshi OHTA*，Hitoshi SAJI*, Hiromasa NAKATANI*

*Department of Computer Science, Shizuoka University, Hamamatsu 432-8011，Japan

E-mail: {h-ohta, nakatani, saji} @cs.inf.shizuoka.ac.jp

Abstract

We present a technique for recognizing facial expressions from image sequences. The technique uses muscle- based feature models for tracking facial features. Since the feature models are constructed with 公 small number of parameters and are deformable in the limited range and directions, each search space for a feature can be limited. The technique estimates muscular contractile degrees for classifying six principal facial expressions. The contractile vectors are obtained from the deformations of facial muscle models. Similarities are defined between those vectors and representative vectors of principal expressions and are used for determining facial expressions

1. Introduction

Facial image processing has been an important research topic in both the computer vision and computer-human interaction communities. Many researchers have tackled such difficult problems as modeling， detection, tracking， and recognition of facial features and expressions【1，2, 3, 4). However, there is no recognition technique that uses the anatomical knowledge on relations between the facial expressions and facial muscle movements

In this paper, we present a technique for recognizing facial expressions using such anatomical knowledge [5】. The technique uses the muscle-based feature models for tracking facial features and estimates muscular contractile vectors for recognizing facial expressions

2. Model structures

The facial feature models consist of control points C, starting points O, muscular contractile vectors V, and muscular contractile parameters s. The model is outlined by node points that are interpolated between control points

The points O are located manually from a facial image at a neutral expression. The vector V is determined by a position and a direction of facial muscle, and the point

Figure 1. Eye model and eyebrow model

moves in the direction of vector V, from points Ot. The value of parameter st represents a contractile degree of a muscle associated with vector Vt. The model is deformed by varying the values of parameters s. When \si\ = 1.0, the muscle of a vector V,

2.1 Eye and eyebrow models

Figure 1 shows structures of the eye and eyebrow models. The eyebrow model has three control points, three starting points, and three muscular contractile vectors. The parameterrepresents the muscular contractile degree in the direction of either  or . when 0 the direction is of , and when  the direction is of . Then, the locations of control points  are represented by

and a weight w represents the magnitude of the motion at each control point.

The eye model has four control points, four starting points, and two muscular contractile vectors. The parameters  and  represent the muscular contractile degree in the direction of  and , respectively. Control points

Figure2. Facial expressions

2.2 Mouth model

The mouth model has six control points, six starting points, and ten muscular contractile vectors (Figure 2). When the tip of a jaw moves, the control points on the both mouth corners and the lower lip move in the same direction. Therefore, the location of the tip  is necessary for obtaining muscular contractile degrees.

We use the same parameters  to represent the muscular contractile degrees of mouth corners , in the directions of  and . Then, the locations of control points of mouth corners , , are represented by

The locations of control points of an upper and lower lip are represented by

3. Model matching

A set of parameters {s} determines a shape of a facial feature. By changing those parameter values and finding the best-fit shape of a feature, we match the models to facial images.

Let , , be the locations of node points in a neutral expression, where  is the total number of

Figure 3. Facial expressions

node pointspoints. Let I’(x,y) be the intensity value of an image of a neutral expression. Let (x(i),y(i)) and I(x,y) denote the values after an expression is appearing. The locations (x(i),y(i)) are determined by parameters {s}.

To track the feature motions, we use the dissimilarity between images with and without expressions. The dissimilarity of eye or eyebrow images is defined as

Facial features can be detected from an image by searching for such parameter values that minimize the dissimilarity.

Since the nodes of the mouth model are located on the outside edge of a lip, we use dI(x,y), the derivative of I(x,y). We also use the intensity at (,) inside the model outline and at () outside, so that the model should not match to the inside edge of the lip but the outside. Therefore, the dissimilarity of mouth images is defined as

4. Facial expression recognition

We define a facial expression space for recognizing facial expressions. The space is spanned by muscular contractile degrees s. In the space, we determined six vectors ,  for representing the six principal expressions by considering the relations between facial expressions and muscle contractions (Table 1). The six principal expressions we deal with in this paper are surprise, fear, disgust, anger, happiness, and sadness [6]. Here we do not use the contractile degrees concerning eyes for recognition purpose

Figure 4. Transitions of plausibilities of facial expressions.

to disregard the blinking motion, though we use them for tracking purposes.

Let  be a facial expression vector obtained form input images. Then projection P1 on E1 is represented by

,

and is used for estimating the plausibility of an expression

5. Experimental results

We take the six typical facial expressions in time- sequence images using a video camera and we track motions of facial features. Figure 3 shows such facial expressions and depicts our models by white lines. Transitions of the plausibility of each expression are shown in Figure 4. The vertical axis is the plausibility P. In all Figures 4 (a)-(f)， the maximum plausibility shows the correct expression. In ‘‘anger’’，however，the difference between expressions cannot be detected clearly because there is little movements of facial features. By thresholding the plausibility and finding the beginning and end of a certain expression, we can partition an image sequence into segments of expressions

6. Conclusions

In this paper, we presented a technique for recognizing facial expressions from image sequences. The technique uses muscle-based feature models for tracking facial features and estimates muscular contractile vectors for classifying six principal expressions

Experimental results show that the muscular contractile vectors show the changes of facial expressions effectively and that expression can be recognized by those vectors

References

[1]A. L. Yuille, P.W. Hallinan, and D. S. Cohen. Feature Extraction from Faces Using Deformable Templates. Int. J. Computer Vision, 8(2):99-111, 1992.

[2]D. Terzopoulos. and K.Waters. Analysis and Synthesis of Facial Image Sequences Using Physical and Anatomical Models. IEEE Trans. PAMI. 15(6):569-579, 1993.

[3]I. A. Essa and A. P. Pentland, "Coding, Analysis, Interpretation, and Recognition of Facial Expressions", IEEE Trans. PAMI, 19(7):757-763, 1997.

[4]M. J. Black and Y. Yacoob, "Tracking and Recognizing Rigid and Non-Rigid Facial Motions using Local Parametric Model of Image Motion", Proc. ICCV, 374-381, 1995.

[5]H. Ohta, H. Saji, and H. Nakatani. Muscle-Based Feature Models for Analyzing Facial Expressions. Proc. 3rd Asian Conference on Computer Vision, 2:711-718, 1998.

[6]P. Ekman, and W. V. Friesen. Unmasking the Face. Prentice-Hall Inc, 1975.

Application of wavelet analysis and multi-resolution decomposition for high quality compression and reconstruction

Fahim-ul-Haq, Azra Khalid, Saad Umer Baig

(fahim.ulhaque@hotmail.com, azra.Khalid@yahoo.com, saadisno1@hotmail.com)

NED University of Engineering and Technology - Pakistan

Abstract

With growing demand of various imaging applications, storage requirements of digital imagery are increasing explosively. Efficient representation of image data, their storage and transmission through limited communication bandwidth is a crucial and active area of development today and calls for efficient storage and transmission requirements. Wavelet transform provides analysis details on different levels with much less space as compared to the original data, resulting in high quality image compression. Wavelet transformation separates images into scales through a feature known as multiresolution analysis. The paper describes wavelet analysis using Daubeucies wavelet as the basis function for approximations. Here a novel multiresolution analysis based on Daubeucies wavelet is investigated which results in greater compression at low bit-rates and high quality reconstruction

Keywords: Wavelet transform, image compression, JPEG2000, multi-resolution analysis, discrete wavelet transform, quantization, encoding

1. INTRODUCTION

In the current era, the use of multimedia applications has greatly increased and the transmission of large images through constrained bandwidth is still a hot topic of research these days. For transferring of images, compression techniques are adopted to save both transmission time and crucial transmission bandwidth. Image compression techniques like JPEG, GIF, JPEG2000 minimizes the size in bytes without degrading the image quality to an unacceptable level and thus reduces the time required for images to be transmitted, e.g. over the internet or to be downloaded from a webpage

The JPEG2000 Image compression encoder consists of three closely connected components:

1.Source encoder transforms the image from the space or time domain to a new domain where the inter-pixel redundancies are reduced

2.Quantizer reduces the number of bits needed to store the transformed coefficientby reducing the redundancies of those values and represent it with appropriate method

3.Entropy coder flirther compresses the quantized values to give a better overall compression.

The wavelet based JPEG2000 standard provides improved compression efficiency at low bit-rates. The architecture supports both lossless and lossy compression. Because of their efficiency, wavelets are being vastly utilized in approximation theory, signal processing, region of interest extraction, as well as compression. Wavelet transform based coding does not require the image to be split into blocks. The image is broken into tiles and each tile is wavelet transformed. The basis functions have variable length. As a result the reconstructed image is much smoother without the blocky appearance at high compression rate

2. Wavelets and Multi-Resolution Analysis

Wavelet analysis is a windowing technique with variable-sized regions; i.e. the basis flinctions have variable length. The Wavelet Transform, at high frequencies, gives good time resolution and poor frequency resolution, while at low frequencies; the Wavelet Transform gives good frequency resolution and poor time resolution

The wavelet is a small wave with energy concentrated in time. The 2D- wavelet transform decomposes an image into sub-bands with different frequencies. The details which are very small can be set to zero without affecting much the information. The greater the number of zeros, the higher will be the compression

3. Daubechies Wavelet Analysis

The basis function chosen for image analysis is Daubechies wavelet, which decomposes the image in different subbands depending on the frequency. The image content corresponds to frequency regions over a range of scales. A true color, i.e. an RGB image is taken for analysis, though the basis function works equally efficiently with indexed, binary and intensity images

4. Conclusion

The paper represents a successful and smooth image compression based on Daubeucies wavelet. The quantization scheme adopted is the same as used in JPEG 2000 standard. For future work, other coding schemes like Arithmetic coding, Run length coding can be used for faster efficient computation. Further more, different step sizes can be explored and analyzed. Depending upon degree of compression, multi-level decomposition can be investigated

References

[1]Brendon J. Woodford, Nikola K. Kasabov and C. Howard Wearing. Fruit Image Analysis using Wavelets

[2]Kainrul Hasan TaJukder and Koichi Harada. Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image. I A ENG International Journal of Applied Mathematics, 36:1, IJAM_36_1_9

[3]M. Kutila and J. Viitanen. Parallel Image Compression and Analysis with Wavelets. World Academy of Science, Engineering and Technology 2 2014. pages 168:171.

Adaptive Radio Resource Management Algorithm for MIMO-OFDMA System

Mr. Pradip Paudyal***, Prof. Laszlo Pap (PhD)*, Prof. Andras Gabor (PhD)**

Asst. Prof.Sharad K. Ghimire***

* Dept. of Telecommunication, Budapest University of Technology and Economics, Budapest, Hungary,

**Dept. of Information Systems, Corvinurs University of Budapest, Hungary

*** Dept. of Computer and Electronics Engineering, Tu. I.O.E., Pulchowk Campus, Nepal

E-mail:pradipmailin@hotmail.com

Abstract

A real time QoS aware adaptive radio resource management algorithm for downlink of multi-cell and multi user MIMO-OFDMA system is presented. This proposed scheduling algorithm takes into account queue status, transmission rates, channel state information and priority of the user for subcarrier, power and bit allocation in MU-MIMO-OFDMA system. Subcarrier selection is based on the priority of the user and eigenvalue product criterion and “water-filling algorithm” is adapted for bit loading and power allocation problem. Simulation results show that this algorithm can not only increase the data rate of the system, but also QoS of the user. This algorithm can able to maintain required QoS for each user

Keywords: MIMO, OFDMA, SVD, RRM, eigenvalue sub-channels, product criterion, water-filling algorithm, adaptive modulation, packet loss rate and priority of the user

1. Introduction

Now the customer demands for wireless communication is increasing and the existing networks will not be able to fulfill the high data rate and low latency requirements of future communication services. Therefore, the spectral efficiency of future wireless networks needs to be further improved allowing for increased flexibility to serve a large number of simultaneous users and different services

Orthogonal Frequency Division Multiple Access (OFDM) can be used in conjunction with “Multiple-Input Multiple- Output” (MIMO) transceiver to increase diversity gain and/or system capacity by exploiting spatial domain. Because the OFDM system effectively provides numerous parallel narrow band channels, MIMO system is considered as a key technology in high data rate and reliable transmission system. MIMO-OFDMA based cellular systems are currently being standardized These suboptimal algorithms have good performance even though they can’t guarantee the required QoS among the users and high data rate of the system. Already proposed cross layer scheduling algorithms [12] can guarantee the QoS among the user by considering the priority of the user but this algorithm can’t ensure the maximum achievable data rate in the system. The adaptive resource allocation algorithm [8] can consider the fairness among the users and gives the optimal data rate of the system but this algorithm can’t guarantee the required QoS for each user.

In this paper, we investigate the subcarrier, power and bit allocation mechanism in MIMO-OFDMA system with maintaining required QoS for each user. We concentrate on the QoS aware radio resource management technique in MIMO-OFDMA system to maximize the capacity and QoS of the system

The rest of paper is organized as follows: in section II the multi-cell MIMO-OFDMA system model is considered. Priority calculation, subcarrier allocation, power and bit loading problem is done by using “water-filling algorithm” and these are described in section III. The evaluation of proposed algorithm is presented in section IV by the help of simulations. Finally we give the conclusion in section V. elements [eqn: [H _{k} ^{n} ] _{i,j}] represents the channel response from the [eqn: j _{th}] transmit antenna to [eqn: i _{th}] receive antennas.

Figure 1: System block diagram of downlink MiNO-OFDMA system

2. Conclusion

We have proposed a Qos aware dynamic resource allocation scheme for downlink multiuser MIMO- OFDMA system to improve the QoS and data rate with considering queuing state information of the user in the queue, channel state information, channel quality information, required BER, required transmission rate, required QoS for each user and available maximum power. Simulation result shows that proposed scheme achieves better QoS and data rate than other proposed radio resource management algorithm

References

[1]“IEEE 802.16e Mobile Wireless MAN Standard: Physical and medium access control layers for combined fixed and mobile operation in licensed bands

[2]“IEEE 802.16e Mobile Wireless MAN Standard: Physical and medium access control layers for combined fixed and mobile operation in licensed bands

[3]Holma H and Toskala A，“HSDPA/HSUPA for UMTS: High Speed Radio Access for Mobile Communications” yo/m Wiley and Sons, first edition, 2014

[4]“The WINNER (Wireless World Initiative New Radio) Project: development of a radio interface for system beyond 3G”， International Journal of Wireless Information Networks 14 (2): 67-78

[5]Third Generation Partnership project Long Term Evolution (LTE), official web sites: http:/八vww.3gpp.org/niahli 요 hts/LTE/LTE.htrn

[6]Ibrahim Y. Abualhaol and Mustafa M. Matalgah, “Subchannel-Division Adaptive Resource Allocation Technique for Cooperative Relay-Based MIMO/OFDM Wireless Communication System’’， IEEE Communication Society, 2012

Active Contour Model Based in Mathematical Morphology

Akira Asano*, Tohru Yamashita, Shunsuke Yokozeki**

*Division of Mathematical and Information Science, Faculty of Integrated Arts and Sciences, Hiroshima University

Higashi-Hiroshima, Hiroshima 739-8521 JAPAN

E-mail: asano@mis.hiroshima-u.ac.jp

**Department of Mechanical System Engineering, Kyushu Institute of Technologylizuka, Fukuoka 820-8502 JAPAN

E-mail: yokozeki@mse.kyutech.ac.jp

Abstract

A novel method of the active contour model is proposed by introducing the mathematical morphological operations on the “contour object”, which is a binary image object whose outline is the contour. The contours are shrunk and expanded by simply applying the mathematical morphological operations on the contour objects. The split and fusion of contour are also carried out by the mathematical morphological operations, and no additional procedure for split or fusion is required

1. Introduction

Tracking of moving objects using the active contour model is realized by fitting a flexible contour around a moving object. The snakes model [1] sets an initial contour that surround a target object, and solves the energy minimization problem that describes the process of shrinkage of the contour towards the outline of the object. However, since the energy function is defined that neighborhood pixels on the contour pull each other to make the contour approach toward the outline, complex or concave outlines are difficult to be extracted and additional operations to split the contour are required if more than one objects are surrounded by the initial contour [2]. Moreover, the computational cost of the energy minimization  problem is too high for realtime motion processing.

We propose in this paper a novel method of the active contour model by introducing the mathematical morphological operations [3] on the “contour object," which is a binary image object whose outline is the contour. The contours are shrunk and expanded by simply applying the mathematical morphological operations on contour objects. The split and fusion of contours are also carried out by the mathematical morphological operations, and no additional procedure for the split and fusion is required. Mathematical morphological operation are based on maximum and minimum of neighborhood pixel values, so that the computational cost is very low.

2. Method

2.1 Outline

If we assume that the background does not vary during tracking, the area where the pixel values are not zero in the differential image between consecutive two frames is equivalent to the locus of the moving object. Our algorithm achieve the tracking of the target object by moving the concur on the target in the first frame to that in the second as follows: The contour object over that target object in the first frame is expanded to cover the locus by dilation. Then the contour object over the locus is shrunk to fit to the lar- get object in the second frame by erosion

2.2 How to expand contour objects to cover the locus

The contour object is expanded to cover the locus by the following procedure

1.Overlapping the differential image(denoted by , whose pixel values are the absolute values of the difference between the first frame and the second, and the contour object image (denote by ). where  is a pixel position.

2.Determining whether each pixel within the contour object is dilated or not by the following condition:

then dilation       (1)

where integer  is initially set to .

3.Executing dilation on the pixels selected by the previous step.

4.Iterating steps 2 and 3 with increasing  until .

When  is small, the contour object is expanded along the area where the difference of pixel values is significant between the first and the second frame. While  increases, the contour object expands around the area over which it is expanded when  is small.

2.3 How to shrink contour objects to fit only to the target objects

To fit the contour object only to the target object, the intermediate contour object created in the previous subsection should be eroded properly. We define in this subsection the concepts of “edge energy” and "histogram energy.” The erosion at each pixel on the edge of contour object is accepted if the edge energy decreases and the histogram energy increases after this erosion. The edge energy indicates the degree of the contour object's tendency to approach to the edge of the target: The histogram energy indicates the degree of preservation of the histogram of pixels within the contour object.

The edge energy is defined at each pixel xe on the edge of the contour object by the following procedures:

1. Overlapping the expanded contour object over the "distance image" that is created by the following subprocedure

1-1. Applying erosion at every pixel of the second frame.

1-2. Creating a differential image between the original second frame and the eroded one.

1-3. Binarizing the above differential image to 0 and 255 by a threshold .

1-4. Dilating the above binarized image iteratively for n times with a grayscale structuring element whose value is 0 at the center and negative otherwise. This dilated image is the distance image.

2. Suppose a 3x3-pixel square neighborhood centered on . Calculating the edge energy  by the following:

(2)

where  is the distance image,  is the set of pixels on the edge of the contour object within the neighborghood, and  is the number of pixels in .

3. Eroding temporarily the contour object at , and calculating the edge energy again, denoted .

The edge energy decreases if the erosion makes the contour object, denoted

(3)

where  is the histogram of the pixel values within the contour object in the first frame, and  is a tolerance factor to compare the histogram.

4. The histogram energy after the expansion of the contour object, denoted , is defined as follows:

(4)

where  is the similar histogram after the expansion in the second frame.

Using the edge energy and the histogram energy. it is determined whether the erosion on  is accepted or not, by the following condition:

and  then erosion (5)

where  is the tolerance factor: In case , the erosion strictly makes the contour approach to the outline of the target. In case , the erosion is accepted even if the edge energy increases, in case that the histogram energy increases. The nonzero tolerance factor prevents that the contour object approaches to fault edges extracted by the procedure to create the distance image.

3. Experimental results

The first experiment shows the tracking of objects splitting during the tracking. In this experiment two rolling tennis balls are tracked as shown in Fig. 1. The contour is shown by white thick lines. At the first frame two balls are overlapped and are separated in the final frame. Our method tracks this object successfully.

The second experiment shows the tracking of complex-shaped objects. In this experiment the process of clenching a first is tracked. Figure 2 shows the result of our method. The clenching fist is successfully tracked.

4. Conclusions

We have proposed in this paper a novel method of the active contour model for the tracking of moving objects. Using the operations based on the mathematical morphology, the tracking of splitting objects and complex-shaped object are realized without any other additional methods

Acknowledgement

The authors would like to thank Inoue Foundation of Science for its support to the presentation of this work.

Reference

[1]M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active Contour Model. Int. J. Comput. Vision, 1(3): 321-331, 1988.

[2]S. Araki et al. A new splitting active contour model based on crossing detection. Proc. ACCV '95. Second Asian Conference on Computer Vision. , 2: 346-350, 1995.

[3]J. Serra. Image Analysis and Mathematical Morphology. Academic Press, London, 1982.

Fig. 1. Tracking of two rolling tennis balls. From left to right: First frame; Overlapped image of the second frame and the outline of intermediate expanded contour object over the locus; Second frame; Third frame. The contours are enhanced to show clearly

Fig. 2. Tracking of the clenching of fist. Each image corresponds to each of Fig. 1.

Eye Tracking and Animation for MPEG-4 Coding

Stefan Bemogger*, LijunYin*, AnupBasu*, AxelPinz**

*department of Computing Science University of Alberta I Edmonton, Alberta, Canada T6G 2H1

E-mail: {bernoegg,lijun,anup}@cs.ualberta.ca

**institute for Computer Graphics and Vision Graz University of Technology A-8010Graz,Austria

E-mail: {bernoegg,pinz}@icg.tu-graz.ac.at

Abstract

Accurate localization and tracking of facial features are crucial for developing high quality model-based coding (MPEG-4) systems. For teleconferencing applications at very low bit rates, it is necessary to track eye and lip movements accurately over time. These movements can be coded and transmitted to a remote site, where animation techniques can be used to synthesize facial movements on a model of a face. In this paper we describe and simple heuristics which are effective in improving the results of well-known facial feature detection and tracking algorithms. Animation models are also presented, along with experimental results to demonstrate the system being developed. We focus our discussion only on the detection, tracking and modeling of eye movements

1.Introduction

from the image analysis point of view, images can be considered as having structural features or objects such as contours and regions. These image features or objects have been exploited to encode image at very low bit rates. Research on this approach, known as model-based coding, which is related to both image analysis and computer graphics, has recently intensified. Up to now, most of the contributions to 3D model-based coding have been proposed [3, 9], automatic facial feature detection and tracking along with facial expression analysis and synthesis still poses a big challenge to the problem of finding accurate feature and their motion. a variety of approaches have been proposed for detection of facial features.

These include deformable temlate matching [4, 6, 14], Hough transforms, and color image processing [2, 13]. Matching deformable templates requires a fairly accurate initial localization of the template because the energy minimization process only finds a local minimum. Other problems are caused by using several energy terms and weighting factors during the different epochs of matching. Because of the definition of the energy terms in [14] the template also inclines to shrink. In this paper we overcome some of these difficulties by improving the initial localization process. We show that simple processing on color images coupled with Hough transform and deformable template matching can produce very accurate results.

Another important component in model-based coding is synthesizing facial movements and expression at a remote site, using the motion parameters detected on an actual face image and animation on a model of this face. To represent a facial expression，several approaches have been proposed relying on feature detection [1，14】，facial expression analysis [11，12】，and facial expression synthesis [7，10]. However, little work has been done specifically on the eye expression synthesis. Because the eye is one of the most significant organs contributing to a vivid face expression， subtle changes of the eye movement can result in a different expression. Therefore accurate eye expression analysis and synthesis are necessary. In this paper, we present an approach to synthesize the eye movement by using the extracted eye features to compute the deformation of the eyes of the 3D model. After creating an individualized 3D face model，we map this model to the first frame of the face sequence. Based on the extracted eye features we apply the deformation parameters to the 3D model to synthesize the eye movement in successive frames

The remainder of this paper is organized as follows: In Section 2, we describe the approach proposed for accurate eye feature detection and tracking. In Section 3, animation techniques for presenting eye movement are described, Experimental result are shown in Section4. Finally, concluding remarks are given in Section 5.

2. Robust detection and tracking of eye features

Our approach to detecting the eyes is similar to [4] in that it uses Hough transform and deformable template matching, however, it also exploits color information to extract the eyes accurately. The algorithm can be outlined as follows:

●Determine two coarse regions of interest for the eyes.

●Search the iris of the eyes using a gradient based Hough transform.

●Determine a fine region of interest for extracting the boundaries of the eyes.

●Using color information to get an initial approximation for the eye lids.

●Localize the eye lids using deformable templates.

After detecting the face region two coarse regions of interest in the upper left and upper right half of an image can be defined to detect the eyes. Also a coarse range for the size of the eyes can be derived.

Since the iris is the most significant feature of the eye and has a simple circular shape, it is detected first by using a gradient-based Hough transform for circles [5, 8】

Figure 1. Extraction of circle (image and result, edge image, Hough space)

The necessary information position, magnitude and direction of significant edges is extracted by convolving the intensity image with a Sobel kernel, and performing a non-maximum suppression by removing all edges with a magnitude lower than 30% of the maximum magnitude. Figure 1 shows the robustness of the Hough transform. In spite of the large amount of edgels which are not produced by the contour of the iris the Hough space shows a significant maximum at the exact position of the center of the iris.

After extracting the circles the deformable templates for the eye lids have to be initialized. The model, along with all the parameters used is shown in Figure 2; with the parameters being set as follows:

where is the radius of the extracted circle. The orientation ɑ is determined by the center points of the two circles. The initialized deformable template is also shown in Figure 2.

Legend:

w    halfwidth

h1   upper height

h2   lower height

ɑ    orientation

(xc.yc) centerpoint

Figure 2. Deformable template (model, initialization)

For the matching of the deformable template two difln ent types of image information are used to create potenthil fields, one in each epoch. The image information extractol from a typical eye is shown in Figure 3

Figure 3. Image fields used for computing the potential energy (image, saturation, edge)

The first step in localizing the eye lids is to approximate the position of the deformable template relative to the in*. This is done by minimizing the following energy (Esat) which is similar to the valley energy in [14]:                 (2)

Aw is the area inside the parabolas but not inside the circle of the iris and  is the inverted saturation value of the color image. Since only the location (not the size) is changed this method does not have the shrinking effect.

The next step is to approximate the parameters h1 and h2. Especially the parameter h1 is important because of the larger movement of the upper eye lid. In order to estimate the parameters, two regions of interest on both sides of the iris are defined (see Figure 4). The parameters of these regions are set as follows:

wROI=5   hROI=6riris  dROI=riris +5     (3)

Depending on the positon of the iris inside the deformable template only the left or the right region of interest is used for the further computation.

By using the horizontal integral projection([1]), and by detecting the two most significant opposite gradients in the projection, the position of a point on the upper and lower eye lid can be

detected(see Figure 4). The parameters h1 and h2 of the template are updated as follows:

and  are the y-coordinates of the detected points inside the region of interest of the upper and lower eye lid.  and  are the height of the actual parabolas inside the region of interest.

Figure 4. Integral projections beside iris (model and example).

The last step is to match the deformable template accurately to the eye lids by minimizing the following energy():

(5)

is the boundary of the parabolas and  is the edge magnitude. During this minimization every parameter of the deformable template (location, orientation, height, width) can be changed.

The tracking of eye features is similar to detection of eye feature with the following differences:

● The region of interest as well as the possible size and therefore the Hough space for the extraction of the iris can be restricted by using the position and the size of the extracted eye in the previous frame

● Instead of using the deformable template shown in Figure 2, the matched template of the previous frame is used for the initialization of the new template

3. Animation of eye movements

The eye detection and tracking algorithms extract the contours of the iris and eye lids in each frame of the image sequence. These eye features are used to synthesize the real motions of the eye on a 3D facial model. The main Procedure is described as follows:

● Based on the 3D eye model which is part of our existing 3D facial model, 11 feature vertices are defined for each eye as shown in Figure5. Once the eye lid contour and the iris are detected in the image sequence the corresponding feature points in the template can be also determined simply by computing the points on the boundary of the parabolas and by using the center of the circle.

Figure 5. Defined eye feature vertices

● For Iris anumation: After the iris is detected in the image sequence, the center of the iris in the eye model can be matched with the iris center in the eye image. The size of the iris model can be adjusted to the circle of the iris template.

● For eye lid animation: After the eye lids are detected in the image sequence, the feature vertices on the eye model can be fitted to the feature points on the eye image. The displacements of the feature vertices and feature points consist of a set of motion vectors (we call them feature motion vectors (FMV)). To deform and animate the eye movement, the set of motion vectors of non-feature vertices has to be found as well. They can be derived by interpolating the feature motion vectors which are around the non-feature vertices. Actually, in the interpolation procedure, the newly obtained motion vector of the non-feature vertex is also put into the set of FMV. Therefore the size of the FMV is increased dynamically. The motion vector of a non-feature vertex on the eye model (denoted as v) can be derived by the following equation:

where  is the distance between the non-feature vertex (v) and the feature vertex , n = 1,2, . . . ,N;(...,)are arranged in an increasing order, and      where        (7)

Based on these vertices, a deformed eye model can be built for each frame. The resulting eye model will show us the synthesized eye movement (see also Figure6)

● Finally, the animated eye model can be texture mapped by the original image(i.e.first frame) to synthesize the real eye expression completely.

4. Experimental results

To evaluate the algorithms developed, experiments with various color images of different eyes and eye sequences were made. The preliminary experiments have shown that the Hough transform, which is the first and therefore one of the most important steps, is very robust against noise as well as edges which are not produced by the contour of the iris. The results on tracking and animating of the iris and the eye lids in one image sequence are shown in Figure6.

Figure 6. Sequence (Frame 1, 24, 68, 169) showing eye movement. Original image (top); extracted eye tamplate (middle); matched and animated 3D model (bottom).

Eye movements are synthesized as follows: First, eye movements are modeled as deformations of the individualized wire frame model (see Figure 7)(top). Then, the wire frame models in the successive frames are texture-mapped by using the first frame of the sequence (see Figure 7)(bottom).

Figure 7. Animated wire frame model (top) and texture-mapped (bottom) for different eye expressions. From left to right: individual model and original image (first frame), deformed model and synthesized images looking left, right, and straight respectively.

From the preliminary experimental results, we can see that the algorithms proposed here behaves well for synthesizing eye movements. For the experiments in this paper, a high resolution facial model with 3118 patches is used instead of the low resolution model used in[11]. However, the time complexity of modeling, analysis and synthesis increase significantly as a result.

5. Conclusion

In this paper we discussed robust methods for detecting and tracking eye movements, a strategy for eye movement animation, and resulting application in model-based low bit-rate coding.

In future reports we will discuss extensions of the method to robust detection of lip movements and network strategies for real-time demonstration of the system.

References

[1] R. Brunelli and T. Poggio. Face Recognition: Features versus Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10):1042-1052,Oct. 1993.

[2] T. Chang and T. Huang. Facial Feature Extraction from Color Images. In Proc. 12 th IAPR Int. Conf. on Pattern Recognition, Jerusalem, Israel, volume II, pages 39-43. IEEE Computer Society Press, October 9-13 1994.

[3] C. Choi and T. Takebe. Analysis and Synthesis of Facial Image Sequences inModel-Based Image Coding. IEEE Transactions on Video Technology, 4:257-275, June 1994.

[4] G. Chow and X. Li. Towards a System for Automatic Facial Feature Detection. Pattern Recognition, 26(12):1739-1755, Dec. 1993.

[5] E. Davies. A Modified Hough Scheme for General Circle Location. Pattern Recognition, 7:37-43, Jan. 1988.

[6] J. Deng and F. Lai. Region-Based Template Deformation and Masking for Eye-Feature Extraction and Description. Pattern Recognition, 30(3):403-419,Mar. 1997.

[7] P. Ekman and W. Friesen. Facial Action Coding System. New York: Consulting Psychologists Press, 1977.

[8] P. Kierkegaard. A Method for Detection of Circular Arcs Based on the Hough Transform. Machine Vision and Applications, 5:249-263, 1992.

[9] D. Pearson. Developments in Model-Based Video Coding. Proc. of the IEEE, 83(6):892-906, June 1995.

[10] S. Platt and N. Badler. Animating Facial Expressions. Computer Graphics, 13(3):245-252, 1981.

[11] L. Tang and T. Huang. Quantifying Facial Expressions: Smiles. In Proc. of the Int. Workshop on Coding Techniques for Very Low Bit-rate Video, pages 345-350, 1994.

[12] D. Terzopoulos and K. Waters. Analysis and Synthesis of Facial Image Sequences Using Physical and Anatomical Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(6):569-579, June 1993.

[13] H. Wu, T. Yokoyama, D. Pramadihanto, and M. Yachida. Face and Facial Feature Extraction from Color Image. In Proc. 2 nd IEEE Int. Conf. on Automatic Face and Gesture Recognition, Killington, Vermont, USA, pages 345-350,October 14-16 1996.

[14] A. Yuille, P. Hallinan, and D. Cohen. Feature Extraction from Faces Using Deformable Templates. International Journal of Computer Vision, 8(2):99-111, 1992.

Implementation of A WWW interactive 3D Graphics Exploratorium

Edmond C. Prakash*

*software Division, School of Applied Science Nanyang Technological University, Singapore 639 798

E-mail: ASprakash@ntu.edu.sg

Abstract

Can students contribute in building a 3D graphics web? This article describes the experience gained by participation of all the students in the undergraduate computer graphics subject taught at the Nanyang Technological University at Singapore. The webware module called a Distributed Computer Graphics Exploratorium has been designed to give students an understanding of the fundamental principles and techniques used at the heart of modern graphics sys-tems, Each section in the courseware corresponds to taught lecture plus tutorial work, lab work and self directed study.

1. Traditional Graphics Web

The traditional approach for web based instruction involves the Course Web Page. Nomally links are provided for the following: i)Course Hand Outs

ii)Announcements  iii)Grades Online  iv)Demo Online  v)Graphics Images and Movies

2. The Contemporary Graphics Web-A Distributed Graphics Exploratorium

Currently there are several efforts to build a network based exploratorium [1,2]. Our goal is to develop a distributed exploratorium. Two areas have been addressed:

● Tutorials Online using Graphics Applets

- Implementation of Algorithms: It has been the procedure to implement selected algorithms to gain insight into the complexity of an algorithm. A hands-on with the implementation and testing gives a solid foundation to experience what happens inside an algorithm. It is similar to jump into the swimming pool to learn swimming. Unfortunately due to the time limit and the growing suite of graphics algorithm forces the student to embark on the implementation of a very small subset of the algorithms.

- Instructor distributing the Source Code/Library Another traditional practice is to distribute the appropriate code which reduces the time to prototype an implementation. This method suffered a lot due to the wide variety of versions of the software/hardware and the time required for the student to download, modify, compile, and execute the code. Most of such legacy systems and libraries are still available in C.

- Ubquitous Graphics: No compilation, no hardware software constraint, no location constraint, no day/night constraint would have been ideal. Such a system permits the student to do graphics investigation at the lab, home and any where in the world.

- Distributed Exploratorium Instead of developing all possible solutions for the students, we investigated on a distributed approach to identify. evaluate and then provide the guidance to investigate a wide variety of graphics algorithms on the web. This resulted in a virtually distributed exploratorium.

- The Interactive Applet Approach Graphics algorithms need interactive input specification, positioning of objects, view, and lights. This has been achieved by the mouse-controlled interaction that could be easily exploited by the use of graphics applets.

● Assignment Submission As Web Pages and Assessment Online Creating content for the web is the most challenging job. The approach we followed was that every student in the class should be part of the content creation process. To achieve this every student of the course should set up their own assignment page and create the graphics images and animation. Every participant can contribute in the development of :

- Web page

- Image

- interactive Applet

- Animated GIF

- Movies in MPEG

- VRML World

The assessment will be done online. To reduce ambiguities, a face to face assessment to clarify part of the code was also done.

3. Achievements

The following are the achievements of this ongoing project:

● Learning Goals: This helped students to learn on the web a variety of algorithms much more than what could have been done in their class or lab.

● Audience Characteristics/Needs: Learning can be done from their home at their own place.

● Social, cultual and Ethical Issues: And the idea is to promote individual values on how they can make a big impact in a world wide world community.

● Development Process/Team and Institutional: The institution provides adequate resources for maintaining tge local exploratorium with online access even in tutorial rooms.

● Issues: Main issue is the time constraint faced by students to allocate adequate time for exploration. The other issue that has been already addressed is the development of the web by all the students in your classroom and how to achieve total participation?

● Technology Issue: Understanding the latest development of technology such as web, jave, html, url, etc are considered buzz words. Creating the mindset for the change required in every student to adapt the new distributed exploratorium for graphic is what we would like to achieve at the end of the course.

● Tracking Effectiveness: This part is still debatable. And of course from an exam point of view, if the student gains enough knowledge from the exploratorium, he or she will be able to present material in an appropriate manner for the terminal exams. A web based tracking would be our ultimate aim.

4. Implementation and Results

Several sample scenes were modeled and shared using the distributed exploratorium. Figure 1. shows objects that are shown in wireframe and shaded display. Figure 2-4 show snapshots of the ray-cast images from the distributed exploratorium.

5. Summary

We have designed, developed and explored several aspects of a web based distributed graphics exploratorium. The results have been encouraging with enthusiastic participation from all students and the web community. Our next goal is to expand this for a much larger audience.

References

[1] Daniel L. Gould, Rosemary M. Simpson, and Andries vanDam. Granularity in the design of interactive illustration. Proceedings of SIGCSE '99,1999.

[2] J.E. Beall. New java technologies and a java-based framework for interactive illustration development. Master of sciences in the Brown University Department of Computer Science, August 1997.

Analysis of Asynchronous selective reject aloha with considering propagation delay

Gole-Jong Jaw*, Heng-Dao Lin*, Ching-Pio Hong*

*Telecommunication Laboratories, Ministry of Communications

P,O. Box 71, Chung-Li, Taiwan, R.O.C.

Abstract

An analytical performance evaluation model and design algorithm of asynchronous selective reject (SREJ) ALOHA system with considering propagation (acknowledgement) delay are firstly proposed for implementing a system in which a large number of inexpensive interactive remote VSAT`S (Very Small Aperture Terminals) Communicate with a central hub station over a single 56 K bit/s satellite link. Usually, on an ordinary SREJ ALOHA application, the propagation delay is very small and can be neglected or accounted as part of the retransmission delay. However, on the application of satellite communication channel, the performance of the SREJ ALOHA system implemented by this approach will be very far from the optimum. In addition to the "ready" and "backlogged” states, we introduce inventively a "waiting" state which corresponds to the propagation delay in a satellite channel, into our analytical performance evaluation model

1. Introduction

Fig.l and Fig.2 are the block diagrams of a system in which a large number of interactive remote VSAT’S communicate with a central hub station over a single 56 K bit/s satellite link. Fig.3 shows the approach of the ordinary analytical model that the propagation delay is neglected [l]-[8]. In the state diagram of this approach, a "ready" state in which new messages are generated and a ’backlogged' state in which unacknowledged packets are retransmitted are employed in the analytical model. We introduce firstly a third state-the "waiting" state in which VS AT terminals wait for acknowledgement due to propagation delay to establish a new performance analysis and evaluation model of SREJ ALOHA system as shown in Fig.4

As illustrated in Fig.5, in the asynchronous SREJ ALOHA accessing protocol, messages are formatted into a variable number of contiguous Tp second subpackets, each with Tj seconds of data and T〇v seconds of overhead for signal acquisition and addressing. This kind of accessing method is just like the pure ALOHA access scheme in which the messages are generated from - various VS AT terminals randomly and transmitted through a single satellite channel and many of them may collide together during the accessing process. For the SREJ ALOHA approach, only parts of colliding messages, called unacknowledged subpackets, will be retransmitted with random delay until all subpackets of a message are received

2. Analytical Model

Fig.6 shows the new finite user analytical model of asynchronous SREJ ALOHA system with considering the propagation delay. New messages generated from VSAT’S may have varying length and are assumed to have exponential distribution in this paper for convenience. The length distribution of the retransmission messages depends on the length distribution of the new messages and the channel load and it can be proved to be approximately exponential by simulation [4] when the new message length is assumed to be exponentially distributed. Similarly as derived in [4], two fundamental equations can be derived based on the condition that total input flow rate and output flow rate of the channel are statistically equal in the steady state of the system. Therefore., the ’’message flow" equation, equating total input and output message flow rates, can be derived as

[eqn: (N-m-n) lambda a=(N-m-n) lambda a bar{P} a+n lambda r bar{P} r] (1)

and the "subpacket flow” equation, equating total input and output subpacket flow rates, can be also derived as

[eqn: (N-m-n) lambda a=(N-m-n) lambda a bar{P} a+n lambda r bar{P} r] (2)

3. Stability

Stability becomes the most important factor for designing a asynchronous SREJ ALOHA system in which many VSAT terminals communicate with a central hub station through a single satellite link to assure the proper operation of the system without breaking down due to traffic jam. The criterion for assuring the system stability is that the input message flow rate will be always statistically less than the output message flow rate whenever the instaneous number [eqn: (0 LEQ n LEQ N)] of VSAT staying in the back- logged state varies beyond the VSAT number of being in equilibrium

4.Conclusions

A firstly-proposed state-the 'waiting' state which corresponds to propagation delay is introduced into the model of the asynchronous SREJ ALOHA system for performance analysis and evaluation. This new approach can improve the stability and performance of the designed system, we would like to mention and emphasize that the system will always become unstable no matter which value of retransmission rate we choose when the traffic level of the channel exceeds 24% ,which is a constraint of channel load in SREJ ALOHA system and a very important factor for designing a SREJ ALOHA system to assure its stability

Reference

[1]H. Kobayashi, Y. Onozato, and D. Huynh, "An approximate method for design and analysis of an ALOHA system," IEEE Trans, Commun., PP.148-157, Jan. 2013

[2]L. Kleinrock and S.S. Lam, "Packet switching in multiaccess broadcast channels--Performance evaluation," IEEE Trans. Commun., PP.410-423. Apr. 2012.

[3]A Carlical and M. Hellman, "bistable behavior of ALOHA-type system, "IEEE Trans. Commun., PP. 401-423,Apr.2014.

[4]D. Raychaudhuri, "Stability, throughput, and delay of asynchronous selective reject ALOHA," IEEE Trans Commun., PP.767-772, July 2014.

[5]M.J. Ferguson, "An approximate analysis of delay for fixed and variable length packets in an unslotted ALOHA channel," IEEE Trans. Commun., PP.644-654, July 2014

[6]S. Bellini and F. Borgonovo, "On the throughput of an ALOHA channel with variable length packets," IEEE Trans. Commun., PP. 1932-1935, Nov. 2012.

[7]D. Raychaudhuri, " ALOHA with multipacket messages and ARQ-type retransmission protocols," IEEE Trans. Commun., PP. 148-154, Feb. 2013

[8]L.G Roberts, " ALOHA packet system with and without slots and capture," ARPA Network Inform. Cen., Stanford Res. Inst., CA, A.S.S. Note 8, June 2013

A new generation computer and the future Telecommunications systems

Y.Z. Sheng*

Beijing university of posts & Telecommunications 100088,China

Abstract

A new generation computer is growing burgeoning proliferation the SPC switching system s will face a challenge In this paper, a prospect to the application of the artificial intelligence(AT)in the field of the switching systems will be considered A new idea to improve the Man-Machine communications of SPC switching systems may be proposed.

1. A New Challenge

In general the telecommunications maybe implemented by cable, optical-cable, radio ,satellite, telegraph, telephone, or TV,...,and so on. However, the modern communications have been related closely to the technology of computers, For an example, up to this , the existing variant SPC switching systems had been made by the conventional computer technology such that, next generation computers not only will urge the evolution of switching systems, but also the global telecommunications systems may face a new challenge, As we have known that the SPC digital switching systems and their telephone networks play an important role in the area of the telecommunications systems, and the evolution of the existing telecommunications network to an integrated services digital network(ISDN) is based on two technological developments: I.e. digital transmission and digital switching. However, existing narrowband ISDN will eventually evolve to broadband ISDN, and current switching network configurations are not adequate to handle the broadband ISDN bit rates Consequently, new switching networks will be required. of course, the new switching networks may be constructed by the new switching systems, and the new switching technology concerns deeply the new-generation computer technology. therefore, the next generation switching and networks among the future telecommunications systems will be emphasized in the topic of this paper. perhaps, we need to previse the following situation, I.e. the prototype of the intelligence-computer will soon come out, and it may be practicable gradually After that, whether the design of SPC-switching systems, operation and maintenance (O & M)of  the   SPC-exchange    offices, telecommunication network planning and administration,......,etc, will or not be reformed by the next generation computer technology. probably, it wiil be a new challenge: choice and strategy.

2. A new idea

As an example a new idea to improve the Man-Machine Communications(MMC) of SPC switching systems will be discussed, in other words, it is a prospect to the application of the artificial intelligence (AI) in the field of the switching systems under the existing computer technology, such that, it may be restricted by historical conditions. However, if the MMC problems may be resolved by the next-generation computers then maybe it will be quite easy The Man-Machine Language (MML) of the SPC switching systems have been defined by CCITT, the MML is similar to the general programming language MML may be defined by MML-Syntax, MML-semantic, and procedures of massage interactions The new idea to improve the existing MML may be as follows:

(1) the MML may be replaced by the Al-languages

(2) the Chinese-MML with Al-languages may be generated

3. Prototype and Experiment

The Chinese-MMC—language (CMMCL) may be implemented by a SPC switching of special purpose and the CMMCL of the “prototype and Experiment" type may be constructed by prolog("PROgramming in Logic") language after that, the Chinese-MMCL will be obtained

Design of Non-Fuzzy Prediction Based on K-Medoids Clustering

D. Y. Lee*, K. J. Park**

*Department of Electrical Electronic Engineering, Joongbu University, 305, Dongheon-ro, Deogyang-gu, Goyang-si,

Gyeonggi-do, Republic of Korea

E-mail: dylee@joongbu.ac.kr

**Department of Information and Communication Engineering, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk,

Republic of Korea

E-mail: bird75@wku.ac.kr

Abstract

This paper introduces a non-fuzzy model that has hard partition of input space. In rule-based modeling, the generation of the rules of target system has the problem that the number of the rules exponentially increases in high dimension. To solve this, the rules of non-fuzzy model are generated by using k-medoids clustering algorithm to partition the input space. Each of subspaces is expressed by polynomial functions. The inference engine is used for prediction. The data set from nonlinear system is used for analysis and evaluation of the proposed model.

Keywords: Non-Fuzzy Systems, Hard Partition, Rule Generation, K-Medoids Clustering, Prediction

1. Introduction

A design of fuzzy model is one of the representative models representing the nonlinear system. There are the varieties of approaches to design of fuzzy model [1,2]. One of these methods is to design a fuzzy model using the clustering. In particular, a fuzzy model using fuzzy clustering algorithm divides the input space by clustering the data using the fuzzy membership functions [3]. Other approach is to design a fuzzy model by dividing the input space by using a subtractive algorithm that uses the density of the data [4]. Alternatively, a fuzzy model is designed to divide the input space by using the hard partition method, namely, k-means clustering algorithm [5]. These methods are taking into consideration the nature of the data. This paper introduce a design of non-fuzzy prediction based on k-medoids clustering algorithm that has a representative objects as prototypes in clusters. The premise part of the model is represented by the scatter partition generated by k-medoids clustering algorithms. The partitioned subspaces describe the rules of predictor. The consequence part of the rules is expressed by polynomials. The proposed model is evaluated for prediction.

2. K-Medoids-based Non-Fuzzy Inference System

The premise part of the proposed model is developed by using the k-medoids clustering algorithm that has the representative objects in clusters. This clustering algorithm divides the input space by the clusters and each partitioned local space represents the rules. The proposed k-medoids clustering algorithm is based on k-means clustering algorithm [5]. The process of the proposed method to generate the rule is as follows.

[Step 1] Set the number of clusters.

[Step 2] Update the partition matrix.

[Step 3] Calculate the center vectors.

[Step 4] Find the nearest data points in each cluster.

[Step 5] Check a termination criterion.

The identification of the consequence parts of the rules deals with rule structure followed by the determination of the parameters. The fuzzy rules are expressed as follows.

	(1)

There are four types of consequent structure

Type 1: , Type 2:

Type 3:

Type 4:

Where is the j-th rule, X represents the input variables, Hj is a partition matrix obtained using k-medoids clustering algorithm, a’s are coefficient of polynomials.

The final output of the model is calculated in the way of weighted average.

3. Experiment result

In this section, nonlinear process is considered in order to evaluate the advantages and the effectiveness of the proposed approach. The time series data coming from the gas furnace nonlinear process has been studied in the previous literature [6]. The methane gas flow rate u(t) and carbon dioxide density y(t) are used as input variables organized in a vector format as [u(t-3), y(t-1)]. The output variable is y(t). The first part of the data set (consisting of 148 pairs) is used for training data set. The remaining part of the data set is used for testing data set. We consider the MSE as a performance index. From table 1, In general, the proposed predictor shows a good performance. The best model has 9 rules with quadratic inference that exhibits PI = 0.013 and E_PI = 0.282.

Table 1. Evaluation of performance

Type	No. of rule	PI	E_PI	Type	No. of rule	PI	E_PI
1	2	3.512	4.271	3	2	0.022	0.334
	3	1.708	2.323		3	0.021	0.340
	4	0.990	1.878		4	0.019	0.352
	5	0.830	1.450		5	0.015	0.299
	6	0.791	1.439		6	0.015	0.301
	7	0.817	1.448		7	0.015	0.299
	8	0.780	1.442		8	0.014	0.299
	9	0.699	1.673		9	0.013	0.282
	10	0.661	1.364		10	0.012	0.297
2	2	0.023	0.334	4	2	0.022	0.347
	3	0.022	0.348		3	0.021	0.355
	4	0.021	0.344		4	0.021	0.341
	5	0.019	0.340		5	0.017	0.297
	6	0.019	0.341		6	0.016	0.297
	7	0.019	0.338		7	0.016	0.295
	8	0.019	0.338		8	0.016	0.295
	9	0.017	0.313		9	0.015	0.311
	10	0.017	0.331		10	0.014	0.294

4. Conclusions

In this paper, we have introduced a non-fuzzy predictor based on k-medoids clustering algorithm to partition the input space. By this method, we are able to be easy to solve the problem of the curse of dimensionality. From the results, we were able to achieve a balance between the approximation and generalization abilities. Finally, the proposed approach would find potential application to utilize the characteristics of data generated from the real systems.

References

[1] J.S.R, Jang, E., Mizutani, C.T. Sun, Neuro-Fuzzy and Soft Computing, A Computational Approach to Learning and Machine Intelligence. Prentice Hall, NJ, (1997)

[2] A.K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, 31 (8) (2010) 651–666.

[3] J. C., Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, (1981).

[4] S.L. Chiu, “Fuzzy model identification based on cluster estimation,” J. Intell. Fuzzy Systems, 2, (1994) 267-278.

[5] P. N. Tan, M. Steinbach, V. Kumar, Introduction to data mining, Addison-Wesley, (2005).

[6] G.E.P., Box, G.M. Jenkins,  Time Series Analysis: Forecasting and Control, 2nd ed., Holden-Day, San Francisco, CA (1976)

Reversible data hiding algorithm using surface characteristics of image

S. M. Jung*

*Division of Computer Science & EngineeringSahmyook UniversitySeoul, Korea

E-mail: jungsm@syu.ac.kr

Abstract

In this paper, we propose a very efficient reversible data hiding algorithm using surface characteristics of image. It is possible to precisely predict the pixel value using the locality and surface characteristics of image. Therefore, the frequency is increased greatly at the peak point of the difference histogram using the precisely predicted pixel values. Thus, it is possible to increase the amount of data to be embedded in image using the spatial locality and surface characteristics of image. By using the proposed reversible data hiding algorithm, the embedded data are much greater than that of the previous algorithm. The experimental results show the superiority of the proposed algorithm.

Keywords: data hiding; reversible data hiding; cover image; stego-image

1. Introduction

Data hiding is an important scheme to embedding secrete data into cover image. The quality of stego-image must be excellent to satisfy the imperceptibility of stego-image. In reversible data hiding algorithm, the recovered cover image from stego-image is equal to the original cover image.  Reversible data hiding scheme is important for medical applications, military applications etc.

NSAS algorithm was proposed to embed secrete data into cover image using histogram shifting. The maximum number of bits to be embed into cover image are limited to the number of pixels at peak point in the histogram of the cover image. To overcome the limit of NSAS, APD(Adjacent Pixel Difference) algorithm was proposed. The maximum number of bits to be embedded of APD is greater than that of NSAS.

To overcome the disadvantage of APD, we proposed a new reversible data hiding algorithm in this paper. The proposed algorithm increases the frequency greatly at the peak point of histogram. So, the embedded data are much greater than that of APD.

2. Proposed algorithm

In general, spatial locality exists in natural image. So, pixel value can be predicted using neighbor pixels. The difference sequence can be constructed after precisely predict the pixel values in cover image. The difference sequence is consist of difference values between neighboring pixels value. The frequency at the peak point of the histogram is increased greatly. Therefore, the number of data bits to be embedded into cover image is increased greatly. Pixel value prediction at a position is performed using neighbor 7 pixel values scanned in inverse s-order when the surface at the position is simple slope or flat as shown in Fig. 1 and equation (1).

A	B	C	D	E
F	G	val

Fig. 1. Pixel value prediction

val=int( (B+C+D+G+(A+E+F)*0.1) / (4+3*0.1) + 0.5)    (1)

In the same manner as described above, all the pixel values from the left the second pixel to the second pixel of the far right and from the top the second pixel to the last can be predicted according to inverse s-order. After prediction, prediction image can be generated. Prediction image is composed of prediction values and original values of pixels at the other position. The difference sequence is composed of difference values between adjacent pixel values. The difference sequence is generated according to inverse s-order in prediction image. The elements of the difference sequence are smaller than the elements of the sequence of cover image. So, the frequency of the maximum peak point and the second largest peak point are much greater than those of difference sequence of cover image. Therefore the total amount of embedding data into cover image increase greatly.

The data embedding process is as follows:

Step 1. Prediction image generation. Predict the pixel values from the left the second pixel to the second pixel of the far right and from the top the second pixel to the last. The pixel values of left-right 2 columns and top 2 rows and in the prediction image are equal to the pixel values of cover image.

Step 2. Scan the pixel values of cover image and prediction image in inverse s-order. Generate sequence C and CP that consist of scanned pixel values.

Step 3. Generate sequence Q of difference values as shown in the formula (2)

Step 4. Generate the histogram of sequence Q. Determine the maximum peak point, the closest zero point 1, the second largest peak point, closest zero point 2.

Step 5. The rest of the procedure is the same as the APD scheme.

The data extraction process is as follows:

Step 1. Generate sequence FG by scanning the pixel values of stego-image in inverse s-order.

Step 2. Determine prediction was applied or not at each position from 0 to n-1 where n is equal to (image with) x (image height).

If the current and previous pixel value are not predicted value or current position is within the second row, the cover image and the embedded data can be extracted as the APD scheme.

If the current pixel value is predicted value, generate sequence FE as shown in formula (3) and generate sequence element Ci using CPi, Ci-1, FGi, FEi as shown in formula (4)  where sdj value is the same as formula (5) .

If the current pixel value is not predicted value but the previous pixel value is predicted value, Ci is calculated as shown in formula (6).

Step 3. Extract embedded data from FE, generate cover image from C as the APD scheme.

3. Experimental results

In order to evaluate the performance of the proposed algorithm, 512x512 gray scale image of Lenna, sail-boat were used as cover images. The abstract of this paper was inserted in each of the cover image. The experimental results of APD and proposed algorithm is shown in Table 1. The original cover image and embedded data are extracted from the stego-image without loss. The cover image and stego-image are shown in Fig. 1. When applying the proposed algorithm, the number of bits to be embedded into cover image was increased up to 5.03 times compared to the APD algorithm. Although the PSNR values decrease somewhat, stego-images are visually almost equal to cover image. So, the proposed algorithm is a very useful reversible data hiding algorithm for medical applications and military applications.

Table 1. Experimental results

Image	Algorithm	Embedded bits	PSNR (dB)	Embedding bits increase (times)
Lenna	APD	48,424	48.60	-
	Proposed	166,416	25.24	2.44
Sail-boat	APD	30,880	48.42	-
	Proposed	186,315	22.60	5.03

(a-1) Lenna       (b-1) APD      (c-1) Proposed

(a-2) Sail-boat       (b-2) APD      (c-2) Proposed

Fig. 2. Cover images & stego-images

4. Conclusions

In this paper, we propose an efficient reversible data hiding algorithm. The number of embedded bits was increased up to 5.03 times compared to the APD algorithm. Therefore, the proposed algorithm is a very useful reversible data hiding algorithm for medical applications and military applications.

References

[1] Z. Ni, Y. Q. Shi, N. Ansari, and W. Su, “Reversible data hiding,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 16, no. 3, pp. 734-749, March 2006.

[2] Y. C. Li, C. M. Yeh, and C. C. Chang, “Data hiding based on the similarity between neighboring pixels with reversibility,” Digital Signal Processing, vol. 20, no. 4, pp. 1116-1128, July 2010

[3] X. Li, B. Li, B. Yang, and T. Zeng, “General Framework to Histogram shifting based Reversible Data Hiding,” IEEE Trans. on Image Processing, vol. 22, no. 6, pp. 2181-2191, Feb. 2013.

[4] C. C. Lo, Y. C. Hu, W. L. Chen, and C. M. Wu, “Reversible Data Hiding Scheme for BTC-compressed Images based on Histogram Shifting,” International Journal of Security and Its Applications, vol. 8, no. 2, pp. 301-314, March 2014.

[5] S. M. Jung, “An advanced reversible data hiding algorithm based on the similarity between neighboring pixels,” Journal of The Korea Society of Computer and Information, vol. 21, no. 2, pp. 33-42, Feb. 2016.

Correcting Forward Head Posture using Computer Vision Methodologies

Dong-young Kim*, Chung-Hee Lee**

*Division of IoT Robotics Convergence Research, DGIST, Daegu, South Korea

E-mail: dckyoung@dgist.ac.kr

**Division of IoT Robotics Convergence Research, DGIST, Daegu, South Korea

E-mail: chlee@dgist.ac.kr

Abstract

Many computer-related injuries today has kept increasing due to living and working in a technologically driven society. Forward Head Posture (FHP) is one of the injuries caused by various reasons such as extended use of computers and smart devices, lack of developed back muscle strength and malnutrition. Fortunately, most of modern PCs or smart devices are equipped with a good performance camera on the devices. In this paper, we propose a computer vision based notification system to protect the FHP. Preliminary evaluation of our prototype system leads to the conclusion that computer-vision based notification system will be helpful to correct the users’ bad posture.

Keywords: forward head posture; computer vision; healcare; machine learning

1. Introduction

In 2020, above 70% of world populations will use internet connected devices as following: PC, smartphone, tablets, and so on [1]. In addition, it is natural way to increase various concerns and issues not only physically but also mentally by using them. Forward Head Posture (FHP), also called turtle neck syndrome in Korea, is one of the several health problems of computer-related injuries. Current people cannot live without a computer and a smartphone. Some of them may use the smart devices all day long except time to go to bed. Furthermore, it is difficult to assure that they are in a good posture while using them. The FHP is caused by extended usage of the devices in a bad posture. In this paper, we present a computer vision based notification system which allows users to know their current posture information.

The paper is organized as follows. Overview of the method used in this paper is described in section 2. In section 3, we introduce our proposed system and survey results. Finally, discussion and conclusion are presented in the last section.

2. Overview of the method

A. Haar-like Feature

Haar-like feature is famous for face detection applications due to the simplicity. It is very easy to construct simple structure to represent a textual of the given image. Moreover, using the Haar-like feature is very fast to compute compared to pixel based system [2].

Figure 1. Haar-like Feature

The feature value is a difference value between black and white regions.

B. Integral Image

Rectangular feature, such as the Haar-like feature above, can be computed very rapidly using the integral image regardless of the size of the rectangle since each pixel contains previous sum of pixel values. Calculating four array references brought you a size of the specific region shown in figure 2 [2].

Figure 2. Integral Image

C. Cascade classifier using AdaBoost

A cascade structure allows early rejection of samples. Thus, it achieves increased detection performance with reduced computational burdens. The cascade structure is trained by AdaBoost algorithm and consists of some stages from sparse to dense features per each stage [2].

Figure 3. Cascade Structure

3. Proposed Computer Vision based System

There are many ways to capture depth information of users’ current head position. A stereo camera provides left and right images with a small difference in x-axis, so that it is able to calculate z-index which is a depth information. However, cost of such device is still very expensive and it requires high computational power due to complexity of stereo matching algorithms [5]. Another good resource of capture depth information is a Microsoft Kinect which provides high quality depth map within some limited condition [4]. It is perfect to use indoor condition, but needs extra device to purchase. Therefore, we are going to use a single image captured from ordinary camera installed in a computer or a smartphone.

D. Development environment

OpenCV is the best open source computer vision library with lots of resources [3]. A base IDE is eclipse while using C++ for the development language. All works are conducted under Ubuntu 14.04 64bit

Figure 4. System prototype

E. Process

⚫ Using a mono camera, it is unable to collect a depth information of the users’ head position, so the system asks the users to sit a correct posture before running it. By this step, the system acquires users’ initial face size.

⚫ Now, the system capture current user’s face as figure 4. The system keep compares the size of face between current size and the initial size.

⚫ Assume that the initial posture is just correct posture, if the size of face increases, we expect that the user is closer to the screen of the device which means the user is in a bad posture with high possibility. At this moment, the system can generate notification to inform current posture to the user.

F. Survey

System Usability Scale (SUS – from strongly disagree to strongly agree) is used for quantitative data and some interviews are conducted for qualitative data. Participants are gathered from various areas in order to analyze needs from the user groups and collect diverse feedback. Followings are a few of sample survey questions.

- What is your occupation?

- Specify your gender.

- How many hours do you use a smart device per a day?

- Do you have any health concerns while using them?

- The notifications are helpful?

- I think that I would like to use the system frequently

- Any concerns while using the system?

G. Results

The age range is between late 20s and 40s. 12 participants, 8 males and 4 females, answered the survey questions.  Except a housewife, the rest of them uses the smart devices more than 7 hours per day. They all recognize a potential health problem by overusing and the system may be helpful to correct the posture in a real time. One common big issue is that the system capture users’ face images, so privacy issue may be occurred at some point.

4. Discussion & Conclusion

A relationship between human beings and smart devices is inevitable and it will be closer and closer by time goes by. For now, most of the people use the devices to improve productivity or entertainment. Some early adapters are using Internet of things (IoT) devices applying directly to their life, but it is still passive approaches.

In the near future, the interaction between human and machine will be more active. In terms of this paper, for example, the system keeps generate notification to the users whenever they are in a bad posture. Furthermore, there exist a great demands based on the survey result, so we could extend another features like preventing dry-eyes to inform users eye blinking counts or so forth.

Finally, we could apply Deep learning machine learning algorithms to such applications in order to improve the detection performance.

Acknowledgment

This work was supported by a grant from the Ministry of Science, ICT and Future Planning and Cognitive platform on the basis of various image resources project in DGIST(16-IT-01).

References

[1] “Ericsson Mobility Report,” Nov-2015. [Online]. Available at: https://www.ericsson.com/res/docs/2015/

ericsson-mobility-report-june-2015.pdf. [Accessed: 20-May-2016].

[2] Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2, 2004: 137-154

[3] “OpenCV Library.” [Online]. Available at: http://www.opencv.org/. [Accessed: 20-May-2016].

[4] Andersen, Michael Riis, et al. "Kinect depth sensor evaluation for computer vision applications." Technical Report Electronics and Computer Engineering 1.6, 2015.

[5] Sun, Jian, Nan-Ning Zheng, and Heung-Yeung Shum. "Stereo matching using belief propagation." Pattern Analysis and Machine Intelligence, IEEE Transactions on 25.7, 2003: 787-800.

Development of Kinetic interactive art using dynamixel control        -Shadow Mobile-

Sung-Dae Hong*, Ok-Hue Cho**

* Department of Film and Digital Media, Seokyeong University, Seoul, South Korea

E-mail: sungdaehong@gmail.com

** Correspond author, Digital contents, Sungkonghoe University, Seoul, South Korea

E-mail: pluszzang@hotmail.com

Abstract

We developed a Kinetic interactive art using dynamixel control.  Our work is inspired by the baby mobile. In this work, the rotation of the motor is controlled by the breath of the audience using sensor. Kinetic sensors mounted on structure, recognizes the position of the spectator, is able to control that depends on eight motors installed in parallel with the vertical motion. MaxMSP, Jitter are used to programming. Dynamic cell motor, planted a unique ID number, was developed to allow control of the rotation and the angle of direct communication to each of the motor. Kinetic image the Sensor, making the rotation angle with a value of 0 to 1023 of the eight motors deeply recognized through the audience’s location and video. It was designed to be able to control the length of the line. Further, the audience of the deposit sensor is connected to the Arduino board, the analog data value of 0-254 entered, ligated to the rotational speed of the motor, and implemented as a system can be controlled in real time.

Key Words: Kinetic, interactive art, dynamixel

1. Introduction

We developed a ‘Shadow Mobile’. It works by dynamixel control. Our work is inspired by the baby mobile. Kinetic sensors mounted on structure, recognizes the position of the spectator, is able to control that depends on eight motors installed in parallel with the vertical motion. MaxMSP, Jitter are used to programming. Kinetic image the Sensor, making the rotation angle with a value of 0 to 1023 of the eight motors deeply recognized through the audience’s location and video. It was designed to be able to control the length of the line. Further, the audience of the deposit sensor is connected to the Arduino board, the analog data value of 0-254 entered, ligated to the rotational speed of the motor, and implemented as a system can be controlled in real time.

2. Implement of Art work

2.1 System Design

Fig. 1. System design of ‘Shadow Mobile’

System design of our art work ‘Shadow Mobile’ is as Fig 1. MaxMSP, Jitter are used to programming. MAX is a visual programming language for music and multimedia developed and maintained by San Francisco-based Software Company Cycling '74. During its history, it has been used by composers, performers, software designers, researchers, and artists to create recordings, performances, and installations.[1] Dynamic cell motor, planted a unique ID number, was developed to allow control of the rotation and the angle of direct communication to each of the motor. We used parallel motor communication.

2.2 Floor Plan

Fig. 2. Floor Plan of ‘Shadow Mobile’

Floor Plane of ‘Shadow Mobile’ is as Fig 2. Our interactive art work using dynamixel control. DYNAMIXEL (DXL) is a line-up high performance networked actuators for robots developed by a Korean manufacturer ROBOTIS. It can be used for multi-joint robot systems such as robotic arms, robotic hand, bi-pedal robot, [2] hexapod robot, snake robot,[3] scorpions, pan tilts, kinematic art, animatronics and automation, etc.

2.3 Software Programming

Fig. 3. Programming of ‘Shadow Mobile’

Programming of ‘Shadow Mobile’ is as Fig 3, Fig 4. Fig 4 present the programming of parallel control

Fig. 4. Parallel control programming

3. Result and Conclusion

Fig. 5. Shadow Mobile

Fig 5 is image of displayed ‘Shadow Mobile’. The audience appreciate our art work using touch, sound via Kinetic sensor. Synesthesia is important concept in our work. We implemented the interactive shadow mobile using dynamixel control and Kinetic sensor. These task will expand the representation area of interactive art.

References

[1] “Max (software)” Wikipedia : The Free Encyclopedia.

[2] http://flowers.inria.fr/AcrobanPressRelease.pdf

[3] NTU bime - snake robot project – YouTube

*Corresponding author: Ok-Hue Cho

Digital contents, Sungkonghoe University, Seoul, South Korea

E-mail: pluszzang@hotmail.com

A Study on Disease Pattern with Lifestyle Risk Factors Using CP of EMR for Healthcare Promotion Plan

Young-Sung Cho*, Song-Chul Moon**

*Database and Bioinformatics Laboratory in Department of Computer Science, Chungbuk National University, Cheongju, Korea

E-mail: youngscho@empal.com

**Department of Computer Science, Namseoul University, Cheonan-city, Korea

E-mail: moon@nsu.ac.kr

Abstract

Nowadays, the Critical Pathway(CP) of EMR is used to the guideline for a treatment in the public hospital. We propose a healthcare recommending service using disease pattern with lifestyle risk factors for healthcare promotion plan. We segment a medical historical data to join patient’s data with disease code with lifestyle risk factors (high blood pressure, diabetes, smoking, overweight, excessive alcohol intake, and low physical activity) to make the weight of lifestyle risk factors through the classification. We finally make the clusters of disease code with lifestyle risk factors using the medical historical data based on CP of EMR. As the result of that, we do a healthcare recommending service based on the disease pattern with lifestyle risk factors using CP of EMR for healthcare promotion plan. We can build a medical help desk of a public hospital to support people as we check into the public hospital; how to get the procedure of curing, the preferred curing clinical method for the healthcare promotion plan by each disease code, and how to improve our healthcare. We evaluate the proposing system on the data set collected in a medical center to measure its performance. We report some of the experimental results.

Keywords: EMR; CP; SVM; Classification; Clustering

1. Introduction

It seems that the development of bioscience and of changes in the mode of living today bring about the extension of the span of life and lead the aspects of the disease to represent a chronic and metabolic tendency. The death rate by gastric cancer disease  increasing year by year, which raises a serious issue about the health of the people in Korea. Especially, diabetes, if attacked once, is hard to be completely recovered but with treatment and management can alleviate the state of the disease and so be able to prevent complications for the maintenance of a healthy life. So, in order for a diabetic to successfully carry out efficient self-care of the disease, it is needed to receive such education of diabetes as can be adapted to practical life with improving lifestyle habits so that the patient can incessantly cope with it with correct knowledge according to the CP. Lifestyle changes directly address the root causes for much of our disease. The objective of the study was to evaluate a proposing system as the healthcare recommending service to reflect the degree of weight of common lifestyle risk factors in the patient and to make the cluster deteriorated with lifestyle habits. The CP, also known as the clinical pathway, is a set of guidelines for patient care that outlines the objectives of the patient care plan along with the corresponding ideal sequence and timing of physician and staff action for achieving these goals with optimal efficiency [1,2]. The use of the CP is intended to allow for the standardization of patient management, as well as improve the quality and efficiency of patient care. The CP is used for the treatment of many medical conditions across the continuum of care [3,4]. The healthcare recommending service is required in the medical center for the healthcare promotion plan, the healthcare treatment service and the plan of clinical treatment. It is necessary for them to obtain the service of helping information to keep their health care. The healthcare recommending service is the purpose of the function of help desk in the healthcare center. It suggests how to cure the disease or how to make the plan to recover their health for patient's healthcare treatment based on successful the healthcare treatment history records using electronic discharge summaries data of EMR. Active computerized treatment information is increasing interest in some predictive methods for the healthcare diagnosis and the healthcare recommending service. Generally, there are three methods, that have attracted particular attention, logistic regression, classification trees, and the neural network. There is an important problem of classification extensively which is studied in several research areas, such as statistical pattern recognition, machine learning and data mining[5][6][7]. We propose the healthcare recommending service using disease pattern with lifestyle risk factors for healthcare promotion plan. We segment a medical historical data to join patient’s data with disease code with lifestyle risk factors (high blood pressure, diabetes, smoking, overweight, excessive alcohol intake, and low physical activity) to make the weight of lifestyle risk factors through the classification using SVM. Finally, we make the clusters of the healthcare historical data based on the electronic discharge summaries data according to the CP of EMR, with different features, disease code and input vectors to take the healthcare promotion plan of disease patients in Korea. This proposing method helps patient to find easily how to get the healthcare recommending service and helps their target patient in the healthcare center easily according to the CP. Therefore, patients and the healthcare centers take some benefit from the service. A healthcare recommending service, the technique for clustering analysis of disease pattern with to meet the needs of the patient to recover their health condition, it is important to remain as studied as always in the research[8]. We make the solution of disease pattern with the risk rate based on lifestyle risk factors for healthcare promotion plan. The tendency for risk factors to cumulate has important implications for health promotion. Information on high-risk groups will help in planning future preventive strategies. We carry out experiments with data set of the healthcare center to measure its performance of the healthcare recommending service using disease pattern with lifestyle risk factors for healthcare promotion plan based on the electronic discharge summaries data according to the CP of EMR. We report some of the experimental results.  The rest of this paper is organized as follows. Section 2 describes briefly reviews the literature related to studies. Section 3 illustrates the proposed personalized healthcare treatment recommending service in detail.  In Section 4, the experimental results are presented the conclusions are given in Section 5.

2. RELATIVE WORKS

2.1. Electronic Medical Record (EMR)

The electronic medical record is used to the hospital data how to operate the effective controlling program for hospital management with nurses and patients. Recently, most hospitals have adopted some form of electronic medical record system instead of writing a paper without any loss of process structure, scope and content of information. Compliance with the documentation of institutional, professional or governmental regulation must be ensured to the purpose of increasing the electronic medical record. Much public organization for the health care industry can use patients' medical information as sharing the medical data. The electronic medical record includes electronic health record (EHR) and a clinical data repository (CDR). It also has the medical information in common with various healthcare institutions. Recently almost hospitals have adopted artificial neural network form of electric medical record system that computerizes existing medical records instead of writing on a paper without any loss of process structure, scope and content of information[7].

2.2. Clustering

Clustering divides objects in a given group into a number of clusters, in order that the objects in a particular cluster. The objects are similar to the objects of the other clusters [9]. It is grouping physical or abstract objects into similar objects in classes. It classifies or segments the data into groups as the natural data structure. It falls into a group of undirected data mining tools[10][11]. It maximizes the similarity inside an object group and minimizes the similarity between the object groups. This partition is the simplest clustering algorithm.

2.3. SVM

The SVMs are designed for binary classification, which has classifiers, even if those are considered easier to use than neural networks. Those are a useful technique to classify the data. The task usually takes training set and testing sets. It is neccesary for us to have training data with relevance features of causations and irrelevance features of causations to do learning marked by users. It retrieves more images relevant to the query in the database. It has two kinds of task. One is interactive learning and the other is retrieval process to find correct features of causations increasingly

3. OUR PROPOSAL FOR HEALTHCARE PROMOTION PLAN USING DISEASE PATTERN WITH LIFESTYLE RISK FACTORS

3.1. Application for Clustering Disease Code With Lifestyle Risk Factors

We generate cluster with the risk rate of the lifestyle risk factors. The system analyzes the medical historical data, which is recorded by EMR data according to the CP. The prototyping application is run and the prototyping shows the result to classify disease pattern with lifestyle risk factors. From the result of the patient's membership data, we find 4 levels based on the rate of the patient’s risk to recommend the services. We show the result with statistics of percent output for the possession of patients based on the medical membership data for the weight based on the lifestyle risk rate. We can use the social variable code such as age, gender, blood, region and patient's lifestyle risk factors (high blood pressure, diabetes, smoking, overweight, excessive alcohol intake, and low physical activity) as input vectors for pre-processing to make the rate of lifestyle risk factors through the classification for healthcare recommending service.

It is depicted in the result, that level 1 is the risk rate(1.0%) without risk factor, level 2 is the range of the risk rate(1.1%) with 1 time of risk factor, level 3 is the range of the risk rate(1.2%) with 2 times of risk factors and level 3 is the range of the risk rate(1.3%) with 3 times more of the risk factors.

Figure 1. The result of the statistics of risk factors for possession of patients

The system makes clusters with neighborhood patient group using a new clustering reflected the weight based on lifestyle risk factors, that is, it classified by the code of classification, which is generated through the algorithm of classification and patient’s disease code based on medical historical data, the whole clinical data sets. Then, the system provides how to get the method improved the performance for a healthcare recommending service using the disease pattern with the lifestyle risk factors for the healthcare promotion plan. The learning algorithm for clustering of medical history record. The representative pattern of bits for patient's  data factors as input vectors including (age, gender, blood type, high blood pressure, diabetes, smoking, overweight, excessive alcohol intake, and low physical activity). The learning agent system scans pattern of bits using user information, using the algorithm inputs and learns input patterns. It also shows the procedural steps for learning algorithm as follows.

TABLE 1. THE PROCEDURAL STEPS FOR LEARNING ALGORITHM

Step 1: Calculate the distance between the input pattern and connection weight vector of each neuron.

Step 2: Provide the neurons of which the calculated distance are shortest and their neighboring neurons with the privilege.

Step 3: Modify connection weight vector so that the neurons with short distance may be closer to input patterns.

Step 4: Recognition starts at the time when learning finishes.

Step 5: Search the neuron from which input pattern is closest to among neurons in the learned competition layer.

Step 6: Recognize the searched neuron by comparing the input pattern with its output value.

The learning algorithm is based on the statistical model, and it works well on large networks to increase accuracy [13]. We apply to make clustering of disease code with the risk rate based on the lifestyle risk factors based on the medical data sets to join the electronic discharge summaries data according to CP of EMR to classify disease pattern with the lifestyle risk factors for the healthcare promotion plan of patients in Korea. We make the prototyping the application and the results of the application are divided to create clusters to be classified disease pattern with the lifestyle risk factors. The system uses the input vectors as patient's  clinical factors (including age, gender, symptoms, signals, system review, allergies, surgical history and family history, etc.) to provide how to have the healthcare promotion plan of patients in Korea. The system creates clusters with the neighborhood patient group using a new clustering of disease code with the risk rate based on the lifestyle risk factors, that is classified by the code of classification and patient’s disease code with the risk rate using electronic discharge summary according to the CP of EMR. The system takes the preprocessing task that is able to use the whole medical data sets by the healthcare recommending service of the disease code with the risk rate and then makes the cluster of the medical data sets sorted by category of disease code, joined the cluster of patient data called by patient DB, the neighborhood patient group. As a matter of course, the system uses the whole medical data sets. After all, the system provides how to get the healthcare recommending service by the preferred curing clinical rate of the disease code with the risk rate.

3.2. The healthcare recommending service for healthcare promotion plan

The healthcare recommending service using disease pattern with the risk rate for healthcare promotion plan is the purpose of the function of help desk in the medical center. It suggests how to cure the disease or how to make the plan to recover their health to consider the patient’s the risk rate for patient's medical treatment based on the best practice, successful the medical treatment history records according to CP of EMR. The system searches patient's disease code with the risk rate in patients' information. It scans the preference of curing disease code with the risk rate; the highest the preferred curing clinical rate of the disease code in the cluster. This system takes the task of clustering method using disease code with the risk rate based on the risk rate based on the medical treatment history records. It provides the associated recommending service for the healthcare promotion plan to the best recommending service with the medical treatment list.

4. EXPERIMENTAL RESULT

A.  Experimental data for  evaluation

We have the experimental data of 250 patients who have the experience to have had the clinic for cancer treatment in the medical center, the data of 7 medical doctors, the data of 29 categories of disease codes  about cancer category used in the medical center. The results of 497 medical records for the clinic is used to do the experiment of the proposal system. It could be evaluated in precision, recall, F-measure in clusters by the preferred curing clinical rate of  the disease code with the risk rate based on successful the medical history records for the healthcare recommending service. We report some of the experimental results through the experiment with learning data set for 9 months and testing data set for 3 months in the medical center. We carry out experiments with data set of the medical center to measure its performance for healthcare recommending service.

B.  Experiment and evaluation

For doing clustering, the experimental data sets are transformed into an equivalent set of the simple sentence in temporal order by the formation. We make the evaluation, which is precision, recall and F-measure for proposing the system. We carry out experiments with data set of the medical center to measure its performance. We report some of the experimental results as follows.

TABLE 2. THE RESULT OF THE HEALTHCARE RECOMMENDING SERVICE

Cluster	Proposing(SVM)			Previous(SOM)			Existing
	Precision1	Recall1	F-measure1	Precision2	Recall2	F-measure2	Precision3	Recall3	F-measure3
C1	35.56	30.94	32.25	36.25	27.98	30.79	36.25	10.19	15.63
C2	49.45	49.14	44.88	49.42	45.76	42.80	49.44	23.81	28.87
C3	38.34	51.86	42.51	39.20	46.75	41.28	39.20	18.33	24.32
C4	38.82	81.48	50.79	38.90	80.69	50.56	38.90	38.89	36.71
C5	43.04	70.34	50.23	45.31	61.37	49.45	45.31	28.70	33.08
C6	40.00	52.63	45.45	40.00	52.63	45.45	37.11	51.11	38.89

Figure 2.  Precision, the result of recommending service

.

Figure 3. Recall, the result of recommending service

Figure 4. F-measure,  the result of recommending service

The proposing system's overall performance evaluation presents the result of evaluation of recommending service for the healthcare promotion plan of patients in Korea with  the clinical rate on Table 2. Table 2 presents the result of evaluation metrics (precision, recall and F-measure) for healthcare recommending service. The clusters (C1~C6) are created based on the medical historical data based on disease code.(C1: gastric, C2: lung, C3: liver, C4: pancreas, C5: large intestine, C6: breast). The proposed is improved better than the existing systems. We use SVM with the risk rate to segment the medical historical data with the risk rate based on the lifestyle risk factors. We report some of the experimental results as follows. The recall of proposing system is 27.56 %, which is higher than the existing system, the recall of proposing system is 3.54 %, which is higher than the previous system.  The F-measure of proposing system is 14.77% , which is better than the existing system even if the precision of proposing system is lower 0.17 % than existing system, the F-measure of proposing system is 0.96 % , which is higher, tiny difference than the previous system even if the precision is lower 0.64 %,  than existing system. As a result, we obtained recommender system with the preferred curing clinical rate the for the healthcare promotion plan of patients in Korea. The result of the proposal using disease code with the risk rate based on the lifestyle risk factors is improved, which is better than the existing system in the performance, which is 3.54 better than the previous system in F-measure.

5. CONCLUSION

Nowadays, the hospital almost uses the EMR system without any loss of process structure, scope and content of information[11]. We used disease pattern analysis based on the medical historical data to consider the patient’s the lifestyle risk factors. We finally made cluster based on the medical historical data. For doing the analysis of disease pattern based on the medical historical data, we segmented the patient’s membership data to make 4 each level by the risk factors of the patient for the healthcare recommending service. We could install a medical help desk of hospital information to help people as they check into the hospital; how to get the method improved the performance for a healthcare recommending service for healthcare promotion plan by each disease code, and how to recover their health for the diagnosis. We evaluated the proposing system on the data set collected in a medical center to measure its performance. We reported some of the experimental results. The tendency for risk factors to cumulate has critical implications for healthcare promotion. Information on high-risk groups will help in planning future preventive strategies. Finally, it is meaningful to present a method of healthcare promotion plan using disease pattern with lifestyle risk factors for the healthcare recommending service. We expect to give help in preventing cancer and estimating prognosis by discovering useful knowledge that is provided by this service.

ACKNOWLEDGMENT

This work was supported by Namseoul University.

References

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Sermeus W, Depreitere R, De Waele K, Vanhaecht K, Vlayen J. “An intruduction to clinical pathways,” Brussel: Federaal Kenniscentrum voor de Gezondheidszorg (KCE), 2005.

[4] Hand DJ. “Construction and assessment of classification rules,” Chicester: Wiley. 1997.

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[7] Cho YS, Ryu KH. “Predictive pattern analysis using SOM in medical data sets for medical treatment service,” IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology. 2014, pp. 1-5.

[8] Vivekanandan, K., and P. Krishnakumari. “Discrete wavelet transformation of an image based on genetic-algorithm clustering,” Indian Journal of Science and Technology. 2008, vol. 1, No.3, pp. 1-5.

[9] Hand D, Mannila H, Smyth P. “Principles of Data Mining,” The MIT Press. 2001.

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[12] Chang, Chih-Chung, and Chih-Jen Lin. "LIBSVM: a library for support vector machines." ACM Transactions on Intelligent Systems and Technology (TIST). 2011, vol 2.No.3 pp. 27.

[13] Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. “An algorithmic framework for performing collaborative filtering,” In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. 1999, pp. 230-237.

A Study on the Development of Change Blade using Curved Precision Process Technology

Jeong-Beom Kim*, Soon-Gohn Kim(Corresponding Author)**

*Dept. of  Industrial Academic Cooperation, Namseoul  University, 91Daehakro, Seonghwan-eup, Cheonan-shi,

Choongnam-do, Korea

E-mail :jbkim@nsu.ac.kr

**Dept. of Computer and Game Science,  Joongbu University, 101 Daehakro, Chubu-Meon, GumsanGun, Chungnam,, Korea

E-mail: sgkim@joongbu.ac.kr

Abstract

Most of the blades used in laser printers are made of rubber. The main problem of rubber material is lacking in sensor quality. This paper is to study about the development of SUS blade to replace the rubber blade to increase the printing quality and toner usage as well. The main technology for this development is curved precision process to have “R” data. During printing using laser printers, toners tend to be wasted by the low sensor quality of rubber blade. Using SUS blade in laser printers, we can decrease the waste of toner from 16% to 3%. This can contribute the environmental protection a lot. Also, this SUS blade can upgrade the printing quality. From the view points of economy, this development can impact to printing materials markets with cost saving effect.

Keywords : Doctor Blade, Blade, Laser Printer, Rubber Blade, Part Cartridge

1. INTRODUCTION

Toners are being used in printer cartridge to print out papers usually. When toners are mostly used up at the printer, the cartridge is being throw away without consuming the toner totally. This problem comes from the sensing ability of blade of cartridge. The cartridge is made up of plastic, chemical rubber and toners. According to the report from Green Purchase Committee 2010, it takes around 500 years to be nature decomposition totally. If we develop new type of blade which is more sensible than the existing one, it will help protect environment tremendously as well as save cost from the perspective view point of usage  term of cartridge. The main purpose of this paper is to study on the development of new blade to change existing ones using curved precision process technology.

2. RELATED WORKS

Accord to recent IDC report, Worldwide printer market has grown 141B$ and cartridge market is also growing accordingly each year. To protect the environment caused by disposal of cartridge toner, many developments are being conducted toward saving toners using several technologies in terms of cost saving and environment protection [1].

Mainly, the background of this study has started to increase the usage of toner amount at cartridge using more upgraded sensing technology and hence to decrease the disposal amount of used cartridge. Fig 1 shows yearly trend of laser printer market in Korea which is linked tightly to the consumption of cartridge.

Fig. 1. Trend of Laser Printer Market in Korea.

Various functions and types of laser printers are being used related to blade which toners are attached synchronous ways. One of the main point of this study is to focus on blade type to save toner amount through type of blade as well as increase of more sensing capability attached to it. In this paper, we propose a  study of new development which can  ensure this capability of cartridge system which is mostly important in terms of cost saving as well as environment protection eventually.

3. The Design of Processes Separation Model

A. Concepts of the new development

New concept of the cartridge system is to change the type of blade using the technology of curved precision process. This new design can increase to upgrade the sensing capability of cartridge and increase the usage of toner compared to existing types[5]. Through the study related to many functional elements such as cutting, bending, curve degree and precision, we have found out the new blade type can impact the toner usage mostly. Also we have added the design procedure in considerations of the importance of the printing quality as well. It includes the step of designing reliable structure. Fig 2 shows the new type of blade at cartridge.

Fig. 2. The new type of blade attached to cartridge

Also additional process of precision has been required    based on those that are related to printing quality, through many module tests. We have identified the problems of existing type with a view to fix the problems through an upgrade idea[2].

B. The Design of Processes Separation Model

In this paper, we have designed the new type of blade which is SUS blade which can replace rubber blade. This new model using the type of architecture can be another platform for the cartridge toner operation[4]. The new type blade is located in the lift of cartridge body. This new functioning architecture will support to provide these characteristics we need to efficiently manage the problem that occurs in a current cartridge device. The different shapes of rubber blade and SUS blade are shown in Figure 3.

Fig. 3. Rubber Blade type and SUS Blade Type

4. IMPLEMENTATION MODEL

Implementation model for the new blade platform configuration for the device  is shown in Figure 5.

This new type is provided by the technology of curved precision process at blade area converted to sensing  methods.  This will be applicable to serviceable structure without interruption through the process upgrade model[3].

Fig. 4. New Type Blade Platform

5. CONCLUSIONS

Through this new type of toner system, we can not  only save a lot of toner usage but also contribute to environment protection thru applicable development test at production site. The new technology is required to the various needs for building a stable and efficient cartridge system. In this paper, we proposed the study of design architecture for blade type for the stability service environment. For this, we suggest the new technology models will upgrade to printer mechanism. Further study will be done to do the survey of efficient rate compared to existing models from the perspective views of cost saving and environment protection.

References

[1] IDC report  (2013).

[2] Jeongbeom Kim, Korea Information Science Journal, Vol.3, No.5, pp.163-275(2010)

[3] Autodesk FUSION 360 and 3D real application book, (2016)

[4]  Software, Networks, Advertising, and Displays, a Primer for Understanding the Business, NAB Broadcasters (2014).

[5] VR/AR technology based market and future forecasting lead future IT, Cho Alliance, (2015).

Process separation model for reliability and efficient memory management and sustainable software architecture

Woon-Yong Kim*, Soon-Gohn Kim(Corresponding Author)**

*Dept. of Computer & Internet Technique, Gangwon State University, 115 Gyohang-ro, Jumunjin-eup, Gangneung-shi

Gangwon-do, Korea

E-mail: wykim@gw.ac.kr

**Dept. of Computer and Game Science,  Joongbu University, 101 Daehakro, Chubu-Meon, GumsanGun,

Chungnam,, Korea

E-mail: sgkim@joongbu.ac.kr

Abstract

The various needs for building a stable and efficient system environment with the variety service platforms for the Internet of Things technology exist. In this paper, we propose the software design architecture needed in the environmental services that continuity and stability is required. So we suggest the processes divide models using stability service model, memory management and synchronous methods. Based on this, we will be able to build a model that can quickly response to and stably operate the system when it may occur the unexpected situations.

Keywords : Internet of Things, Inter Process Communication, Processes divide, Sustainable software architecture

1. INTRODUCTION

structures that human interaction is minimized in accordance with the increase of the service platform based on IoT technology [1]. In this case, the most important factor is the implementation of reliable service. In other words, it is necessary to cope with problem situations in the most stable platform configuration for a variety of situations and responding to them effectively. In general, a package is composed of a single process. This structure has a weakness for the process will die when a crash occurs in any part in the process. In particular, the combined structure of the various services may be generated a lot of exceptions situation. So we need to control these problems efficiently. Specifically, this problem in the system where human intervention is minimized exerts serious harm to stability [2].

After all, during the software design, through the separation of the processes in consideration of the importance aspect, the management techniques that manage the functions to be constantly maintained at a stable is needed for coping with the problem. When the system occur a crash from an exception of a specific part of the software, the system will be able to make a more reliable system to process management, while maintaining the process that need to stay alive. The remaining parts of the paper are organized as follows, In Section 2, we discuss the related technology for the processes separation. In Section 3, we propose the design model of the processes separation. Section 4 presents the implementation model of the Digital Signage Platform for the lift system based processes separation. And last, Section 5 concludes this paper.

2. RELATED WORKS

A. Processes Separation Technique

In general, if the process is separated, and this may provide an important function in a more stable and has a feature that can more effectively manage the available memory in the platform position. Android system provides a variety of service environment for these processes management [3].

Mainly, the background service that operate in a long time or the service that is operated separately from the main process can be separated from main process. This approach reduces the load on the system and also may provide a model that can be stably operated with the exceptions that occur in other services. Typically, when the process is separated, the processes will take each assigned a separated heap space, it does not share the memory space to each other. In order to solve this problem, we need to modify an existing business logic with IPC(Inter Process Communication) mechanism. The IPC in the Android system is implemented as a high-performance based RPC(Remote Procedure Call) mechanism that called Binder IPC . Fig 1 shows the operation architecture for the Inter Process Communication mechanism.

Fig. 1. Inter Process Communication Operation Architecture in Android System.

An IPC mechanism is basically the implementation of the RPC mechanism based binder (Binder) and sends and receives data using Kernel space [4]. At this point, we cannot send or receive an object instance directly. We need to serialize the instance to be transmitted by a separate format to pass the object instance across the process. In the Android system, it defines the interface using AIDL (Android interface definition language), and the Activity that received the binder implements RPC (Remote Procedure Call) formant API directly. It can provide functions of various types with synchronous or asynchronous ways. We define API specification using AIDL, and translate to the binder interface and Stub abstract class. And then we implement the Stub class according to the service bind and unbind cycle. Activity has a structure that can be called a function based on AIDL on the inter process as if using a system service. In this paper, we propose a structure that ensures stability of important logic based on the IPC environment and to cope with exceptional situations.

3. The Design of Processes Separation Model

B. Concepts of the processes separation

The The concept of the software design to separate process divides the software functional elements such as main process and service process, and then adds the design procedure in considerations of the importance of the each service. It includes the step of designing reliable system architecture. In general, the process should identity the service structure that made in the requirements analysis process and then we assign processes for their structure and set the relationship between each processes. Fig 2 shows the relationship among the processes and service module.

Fig. 2. The relation model of the processes for processes allocation and control flows.

Each process is allocated based on those that are related to services, through the Process Control Module, they handle the life cycle and exceptions. When the problem situation occurs, the module is to maintain the normal flow by the process was killed and then restart that process with recording log information at the same time so that we can identify the problems without shutdown of the system and then fix the problems through an upgrade service

.

The Design of Processes Separation Model

In this paper, we propose the process separation model using the software architecture of the digital signage platform for the lift system that is operated by Kiosk mode. The digital signage platform for the lift is located in the lift inside, lobby and each floor for the display of the lift information and advertisement. And this system allows to efficiently servicing the elevator status information and the advertisement information by itself without user intervention. This system has a feature that runs continuously in the year. Also has a feature that must support a minimum set of information services in a problem situation. To provide these characteristics, we need to efficiently manage the problem that occurs in a specific service, and create a non-stop service structure. The general requirements of the digital signage system of the lift are shown in Figure 3.

Fig. 3. The requirement specification of the digital signage platform for the lift system based on Kiosk mode

Kiosk-based digital signage software for a lift has various components, such as Kiosk, Update, Info, Contents, Sync, Media Service. These components may be operated independently or in association with each other. When designing the architecture, we can define process separation structure in consideration of the importance and independence of the required service configuration. When designing the process separation structure, we identify the non-stop service elements, the relationship of the services and memory utilization structures. Figure 4 shows the Digital Signage software process separation model for a lift based on Kiosk service.

Fig. 4. The processes separation model of the digital signage platform for the lift system based on Kiosk mode

The process separation model has four processes. First process includes Kiosk, Info, Sync Services and Second process includes Content and Update Service. And Third process has Media Service. The processes are control by Main Activity and Process Control Module.

4. IMPLEMENTATION MODEL

Implementation model for digital signage platform configuration for the lift is shown in Figure 5.

Lift Information provided by elevator control panel is converted to TCP/IP communication methods from the system communication method such as RS-485 and then is passed to the Main Set top Box and the monitoring server. And also the content and schedule information provided from content server is passed to the Main Set Top Box. In addition, Main Set top Box share the content and information through the synchronization process on the devices displayed on the elevator floor. The Set top box applied in this process has a serviceable structure without interruption through the process separation model.

Fig. 5. The implement model of the digital signage platform for the lift system based on processes separation

5. CONCLUSIONS

The embedded system such as Kiosk and IoT services is required of the various needs for building a stable and efficient system environment. In this paper, we proposed the processes separation software design architecture for continuity and stability service environment. For this, we suggest the processes separation models based on IPC mechanism for memory management and stability services. Based on this, we will be able to build a model that can quickly response to and stably operate the system when it may occur the unexpected situations.

References

[1] J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami, Internet of Things(IoT): A vision, architectural elements,and future directions, Future Generation Computer Systems, Vol.29, No.7 (2013).

[2] J. Schaeffler, Digital Signage - Software, Networks, Advertising, and Displays, a Primer for Understanding the Business, NAB Broadcasters (2014).

[3] C. Min. A platform-independent smartphone application development Framework. Computer Science and Convergence. Springer Netherlands((2012).

[4] Huang, Jim. Android IPC Mechanism. (2012)

An Implementation of the effective service operation model for a Lift Digital Signage Platform based on Web Socket

Soon-Gohn Kim*, Seok-Gyu Park**, Woon-Yong Kim(Corresponding Author)**

*Dept. of Computer and Game Science,  Joongbu University, 101 Daehakro, Chubu-Meon, GumsanGun,

Chungnam,, Korea

E-mail: sgkim@joongbu.ac.kr

**Dept. of Computer & Internet Technique, Gangwon State University, 115 Gyohang-ro, Jumunjin-eup, Gangneung-shi

Gangwon-do, Korea

E-mail: skpark@gw.ac.kr, wykim@gw.ac.kr

Abstract

Digital Signage market is evolving and operated in a variety of ways. Therefore, the efficient management and operating models that support the needs of the environment is required in the Digital Signage market. In this paper, we propose a Web-based service model that can operate efficiently manage the digital signage system specifically for the lift environment. Through real-time monitoring and operation using a web sockets, the system management display devices constantly and it could provide the reliable and effective approach for a four function such as schedule, user, device and content management including the streaming service

Keywords: Lift, Digital Signage, Web Socket, Operating Model, Web Service

1. INTRODUCTION

A digital signage market has continued to grow with the development of IT technology. And it is also operated in a variety of approaches in a variety of fields such as way finding, place making, exhibitions, public installations, marking and outdoor[1]. In addition, in the process of the advanced lift, the display of lift information using display device and advertising service have constantly expanded in the lift industry[2]. However, such digital signage has mainly been developed by focusing on how to express the information in the display device; it is a lack of measures of the effective management for the devices and contents. In this paper, we propose a web-based service management models capable of operating the service in the digital signage specialized in the elevator. In particular, the display device is contained inside the lift without human intervention and has to service a variety of contents continuously. So it need to the effective model to control distribution and operation to the devices. To do this, we build the environment of connection-oriented real time monitoring system based on web socket, based on this; we propose an efficient operation model to manage contents and lift information. In addition, by including a variety of services structure considering the lift properties, we will be able to define a specific service model for the digital signage of the lift.

The remaining parts of the paper are organized as follows, In Section 2, we discuss the related technology for the digital signage service. In Section 3, we propose the operation model of the digital signage for the lift. Section 4 presents the implementation model of the system. And last, Section 5 concludes this paper.

2. RELATED WORKS

A. The service technique of the digital signage

Digital signage technique is being used in various industries. It is utilized mainly in the areas for advertising with the existing information system for end users and also has been extended to a model in combination with a user interaction [3]. And also, the methods of information representation have a various forms such as text, images, animation, video and streaming services. And it has been utilized in various fields [4]. And a lift also has been a change in a structure that utilized the display devices for a call service and information representation. To cope with these changes, the system is required more effective information transfer, and management techniques.

B. The protocol of web socket

Web Sockets is a bidirectional communication technology for web applications which operates over a single socket. And the Web Socket protocol works in two phases named handshake and data transfer. First, the opening handshake phase is a unique HTTP request/response between the one who initiate the connection client and the server. This HTTP exchange is specific because it uses the concept of Upgrade, defined in the HTTP specification. Once the handshake is approved, the use of Web Socket protocol is established. There is an open connection on the server side as well on the client side; callback handlers are called to initiate the communication. And last, closing handshake phase can be initiated by both peers. A peer that wants to close the communication need to send a close control frame and it will receive a close control frame too as a response. Fig. 1 shows the work flow of the Web socket protocol [5].

Fig. 1. The work flows of the Web Socket protocol.

3. Web-based Digital Signage Operation Model for the Lift

C. Architecture of the Web-based Digital Signage Service for the Lift

The lift system operated with a unique identifier to manage the lift state. So it needs to manage the groups of devices effectively. In addition, with this group management, the system has to be provided the effective monitoring and content services for the various devices in the lift. Figure 2 shows the web-based digital signage operation architecture for these characteristics in the lift.

Fig. 2. The web-based digital signage architecture for the Lifts.

Lift system is represented by the elements in the Group, each system may have some devices for providing digital information. Web-based management server for the digital signage monitors and provides services based on web socket communication environment. It may also provide a variety of service environment through the smart phone or web client.

.

D. The Design Model of the Digital Signage Operation Server in the Lifts

To design a digital signage server operating model for the lift, we configure the operating model as shown in Figure 3.

Fig. 3. The structure of the digital signage operation server model in the lifts.

Web-based digital signage operation server provide a various management modules such as company management, device management, schedule management, content management, user management, streaming service management using a database system and web socket. Each management module is associated with the web socket management class to handle the real-time status information monitoring and content management. Contents provided in the system include videos, images, web pages, news, emergency messages and streaming services so on. The main classes’ structure for web socket-based real-time operation is shown in Figure 4.

Fig. 4. The structure diagram of main classes for web socket-based real-time operation module.

The classes in the structure are composed web socket classes related with contents, user and device management connected with DsDBControl class. Also it includes a DsEndpoint web socket class responsible for the real-time control between the display devices and the server. The system manages the group included in the company, the lift in the group and the devices in the lift based on a lift unique identifier.

4. Web-based Digital Signage Operation Server Implementation Model for the Lift

We implemented a real-time monitoring operation server model that is the lift-specific digital signage based on Tomcat server defined in Section 3. And also we defined the schema as shown in Figure 5 to manage various contents and lift information using MySQL.

Fig. 5. The schema of the lift operation database system

Based on the above information, we implement such a system in Figure 9 to establish the operating environment for real-time monitoring and provide efficient management from the user's point of view. This system provides monitoring for a company, group, lift, and devices and at the same time can manage contents and schedule data for the devices so that the system could provide the various services to operate remote device effectively.

Fig. 6. The user interface of the digital signage operation server model in the lifts

5. CONCLUSIONS

Lift industry is making progress in advanced courses in combination with the Information technology. In this growth process, the digital signage for the lift has the purpose of providing the lift information and a variety of contents to the end users using display devices. To provide these requirements effectively, the system requires the efficient management of the operating device and a variety of contents and schedules. In this paper, we presented a web-based digital signage operation server model for the lift to reflect this demand. The system provide a web socket-based real-time monitoring environment for the device monitoring and contents service and will be able to provide a more efficient resource management and services through efficient scheduling and control of various contents.

References

[1] J. V. Harrison and A. Andrusiewicz, The digital signage exchange: a virtual marketplace for out-of-home digital advertising, Proceedings 4th ACM Conference on Electronic Commerce, (2003) June.

[2] J. Jang, Elevators of the latest technology trends, The korea society of elevator engineering, (2004) December

[3] J. Schaeffler, Digital Signage - Software, Networks, Advertising, and Displays, a Primer for Understanding the Business, NAB Broadcasters (2014)

[4] Q. Chen, F. Malric, Y. Zhang, M. Abid, A. Cordeiro, E. M. Petriu and N. D. Georganas, “Interacting with Digital Signage Using Hand Gestures, Image Analysis and Recognition Lecture Notes in Computer Science”, vol. 5627, (2009), pp 347-358

[5] Pimentel, Victoria, and Bradford G. Nickerson. "Communicating and displaying real-time data with WebSocket." Internet Computing,IEEE 16.4 (2012) C. A. Grimes, E. C. Dickey, C. Mungle, K. G. Ong, and D. Qian, J. Appl. Phys. 90, 4134 (2001).

Implementation of the Moving Objects Detection System of Spherical Panorama

Soon-Gohn Kim*, Kwang-Man Ko(Corresponding Author)**

*Dept. of Computer and Game Science,  Joongbu University, 101 Daehakro, Chubu-Meon, GumsanGun,Chungnam,, Korea

E-mail: sgkim@joongbu.ac.kr

**School  of Computer and Information Engineering, Sangji University, 83 Sangjidaegil Wonju0si Gangwon-do, Korea

E-mail: kkman@sangji.ac.kr

Abstract

As a security camera system, there is an image security system that uses CCTV (Closed Circuit Television). For supervisory imaging device for CCTV, cameras are used. It is possible to classify cameras for security camera system. However, it was not possible to get valid level of result and not possible to detect minor changes since it is based on histogram. Therefore, this research suggests spherical panorama image supervisory system with DOF (difference of frame) technique.

Keywords : CCTT, Spherical Panorama, Moving Object Detection, Image Processing

1. Introduction

Security camera system is a system that prevents non-expected situation and conduct visual security actions through behaviours such as detecting non-expected movement and behaviour, as well as non-wanted items or tracking certain target. As a security camera system, there is an image security system that uses CCTV(Closed Circuit Television). For supervisory imaging device for CCTV, cameras are used. It is possible to classify cameras for security camera system. First is a general type of camera that observe certain location by fixed into a position as fixed camera. Second is a dome type camera which is called as PTZ(Pan, Tilt and Zoom) camera. The position of PTZ camera is fixed, but it can change recording direction. Also, it can zoom in and out freely. For the second classification of camera is usually called as ‘rotary type camera’. The last classification is tracking robot camera. Cameras are attached on small robot or portable drone that has been spotlighted recently and observe certain target by tracking them. However, those three classified observation cameras have the common problem which is having blind spot of detection. This is critical problem for the system.

To solve this, there is a method to install number of cameras to minimize blind spot. However, installation cost, using power, maintenance time and cost are dramatically increased due to increasing number of cameras. Therefore, it is not possible to be called as the best solution. There is number software such as VidoeCad that are developed to install number of supervisory cameras efficiently. However, this is the only a tool to find an arrangement of cameras that can minimize blind spot. It does not decrease the number of cameras.

The remaining parts of the paper are organized as follows, In Section 2, we discuss the related technology for the moving objects detection system f spherical panorama. In Section 3, we propose the detection system model for our research works And last, Section45 concludes this paper.

2. RELATED WORKS

This problem can face problem[1] that are caused by hiding from walls or different objects like Fig. 1 which was suggested from articles by Guler and others.

Fig. 1. Ares of Hidden and Non-Areas

To overcome this problem, Onoe[2] and others composed Hemispherical Panorama lens that is called as Hyper Omni vision and suggested all direction supervisory by using hemispherical panorama image. However, this method has limitation of directional property and arrangement of lens shows a problem such as having 90 degree  for top-bottom viewing angle (hereinafter VFOV) even though left-right viewing angle (hereinafter HFoV) goes up to 360 degree. hemispherical panorama image like Fig. 2 is hard to understand by normal people. It is hard to find and detect minor change by eyes. For alternatives, spherical cameras can detect all directions effectively compare with existing methods since it can supervisor in all directions.

Fig. 2. Hemispherical Panorama

3. Moving Objects System of Spherical panorama

Detecting change is the most basic for image supervisory system. However, small change of light source changes resolution difference for constant space. Stauffer and other [3] used Gaussian Mixture Model to be success on corresponding against small change of light source by training background image model, but it was not possible to correspond on rapid light source change. Noriega and others [4] used histogram of sectional kernel to solve above problem and it was possible to improve performance by suggesting background training methods against rapid change of light source. However, it was not possible to get valid level of result and not possible to detect minor changes since it is based on histogram. Therefore, this paper suggests spherical panorama image supervisory system with DOF (difference of frame) technique.

As the suggested method, spherical panorama image of width and length ratio 2:1 is inputted at the first. Then, it gets 1 frame of the same screen from repetition. When a frame is achieved, diffence image is achieved by getting absolute value after conduction difference calculationfn from frame image that are achieved from repetition. For each brightness value of difference image, binary images are achieved by conducting thresholding based on user inputted value. Then, all closed changed images are combined into one by conducting open morphology calculation. After that, several Bounding Rect that surrounds each area are achieved. The biggest Bounding Rect from several achieved Bounding Rect can count occurred behaviour in the closest distance. Therefore, the center point of the Bouncing Rect should be achieved. Covert the center point of the Bounding Rect into geographical coordinate and get new view by reflecting images of corresponding bounding Rect from spherical contact.  When new achieved plane view and spherical panorama image, hemispherical panorama view are displayed at the same time, it is possible to build a movement supervisory system that can be understood by people who are not familiar with spherical panorama image. Also, it can be the basement of using high level detection technology by applying existing image process technology through achieved plane view. Fig. 3  shows of the results of difference  operation . And then, we got a thresholding results as following Fig. 4.

Fig. 3. The Results of Difference of Frame

Fig. 4. The Results of Thresholding related to Fig. 3.

Fig. 5. shows the Bounding Rect operation results and due to the suggestion method, it is appropriate to set plane view which displaces movement detection and 360 over view as half size of the spherical panorama image and put those two images under the spherical image. Also, displaying converted panorama screen for the case of 360 over view, it is possible to display panorama image that can be easily understood by users.

Fig. 5. Result of Bounding Rect operation

4. CONCLUSIONS

As a security camera system, there is an image security system that uses CCTV(Closed Circuit Television). For supervisory imaging device for CCTV, cameras are used. there is a method to install number of cameras to minimize blind spot. However, installation cost, using power, maintenance time and cost are dramatically increased due to increasing number of cameras. This paper suggests spherical panorama image supervisory system with DOF (difference of frame) technique and shows the experimental results. Now, we are plan to research related to motion detection and recognition of walker on the spherical panorama.

References

[1] Guler, S., Griffith, J. M., &Pushee, I. A. (2003, October). Tracking and handoff between multiple perspective camera views. In Applied Imagery Pattern Recognition Workshop, 2003. Proceedings. 32nd (pp. 275-281). IEEE.

[2] Onoe, Y., Yokoya, N., Yamazawa, K., &Takemur, H. (1998, August). Visual surveillance and monitoring system using an omnidirectional video camera. InPattern Recognition, 1998. Proceedings. Fourteenth International Conference on (Vol. 1, pp. 588-592). IEEE.

[3] C. Stauffer and W.E.L. Grimson, “Adaptive ackground mixture models for real-time tracking,” Proc. IEEE CVPR 1999, pp. 24&252, June 1999.

[4] Noriega, P., &Bernier, O. (2006, September). Real Time Illumination Invariant Background Subtraction Using Local Kernel Histograms. In BMVC (Vol. 6, pp. 979-988).

A study on music video analysis by photographer

H. K. Kang*

* Broadcasting & visual art ,Joongbu University, 101 daehak-ro, Geumsan-gun, Chungnam, 312-702, Republic of Korea

E-mail: hong7 @joongbu.ac.kr

Abstract

The music video was produced by the multimedia type of a video clip by adding the image to match it with the release of audio recordings.  Although originally it started as a means of promoting music as mere receive attention as the main content of the music cable broadcast M-TV began mass production. The music video is now an everyday cultural forms that you can access from anywhere. The music video is currently ahead of the visual culture has influenced many film directors. Among them are representatives and photographer to create a music video in the eye of the photographer and music video director Anton Corbijin and Stephen Sednaoui.

Key Words:  Music video, Visual Culture , Film Director

1. Introduction

Meet new media and visual arts are reshaping the entire process of the production of art, consumption and distribution to non-material system. In this process, as well as the artistic imagination power users as possible to the computer program, computational expression has emerged as a new principal.[1]

One of the things that have renewed interest in digital culture, the music video. Music videos represent a variety of artistic images, so many artists initially started as a music video is now being reborn as a new kind of artist. Many young artists to express their own thoughts using the medium of computer music and video.[2] It can be divided into cinematic music video and photographic images of the music video. Cinematic music video is a widely used imaging technique in film and music video image is composed of a non-narrative style without the use of film technology. Now it is used in the various fields of film making techniques, including animation, live-action films, documentaries, etc. are grafted. It analyzes the work of the famous photographer and music video director Anton Corbijin trends and Stephane Sednaoui and want to study their unique visual expression techniques.

2. Extension

Anton Corbijin images of music videos tend to emphasize only the media that is generally listed as the flow of the narrative, rather than a more consistent image message. Specifically, the music video will mobilize a lot of extreme close-ups and unrealistic quick editing techniques such as avant-garde art techniques  shown in the image.[3]

[In the Figure 1] he will respect the atmosphere of the place taking pictures. So the personal characteristics of the person and to reveal their individual characteristics are reflected in its place, rather than directly to surface.

Figure 1. Kylle Minogue 1999

Shooting face bandeutyi Anton Corbin (Anton Corbijin) that instead of taking an image of a stylized star in a unique style photographers who want to contain the souls in monochrome photographs. One of his biggest feature is that they prefer natural light. He especially hates to use artificial indoor setting or hire a stylist.

Figure 2. Metallica - Hero of the Day

Figure 2 shows the natural atmosphere using, grainy black and white photo toned music video of a Hero of the day Metallica. The general look of the stars at all the artificial elements that can go into creating an image with photographic image.

Unlike the picture is taken and when the music video will look seems to reflect a vision of the world at stake.

Stephane Sednaoui has been active for nearly thirty years, while exploring a wide range of video and photo areas. He is the uptake of the genre of popular culture, such as photos and music videos, photojournalism, portrait.

[Figure 3] shows an image before it goes in like.[4]

Figure 3. Vogue Italia May 2010

Which city landscape Stranger is ambiguous space, I do not know whether the dream a reality, as shown in the figurative space.

Figure 4.  Madonna – Fever

Figure 4 is a music video of Madonna fever too. It turns out a lot of his music video representation of the image in the space of virtual reality and imagination intertwined behind. It may be said to create a musical representation of the surrealistic world of photographic images in the video image. It will manifest the true state of consciousness hidden in a false representation of reality in the unconscious music video.[5]

3. Conclusions

Historically, the music video is a departure from the long tradition of combining music and images. There are two clear trends can be divided into first cinematic music videos and photos of music videos. The work represented by the first two authors, was able to see the photographic image is a black and white image of a person through the Anton Corbin, Stephane Sednaoui through the surreal, yet you can see the photographic image to present the past and the future simultaneously.

They will say that it is a surreal representation of creating a music video for the photographic images of your photos a new world based on fantasy and imagination through the tool and the tool of the video.

References

[1] Ann Kaplan, how to read music videos, hannarae, 2009.

[2] Nammyeonghui, "Music of the narrative," Contemporary Film Studies, Vol.5, pp.51-82, 2008.

[3] http://blog.gaseum.co.kr/article/entry/Anton- Corbijin

[4] https://en.wikipedia.org/wiki/St%C3%A9phane_ Sednaoui

[5] Kim, Hyung - Gon, "a new visual medium," music video critique of the post-modern erajournalism, HANWOOL, 1992.

*Corresponding author:

Broadcasting & visual art ,

Joongbu University

101 daehak-ro, Geumsan-gun, Chungnam, 312-702, Republic of Korea

E-mail:hong7@joongbu.ac.kr

Comparison of Sensor Programming Schemes in MSRDS

Jong-In Chung*

* Department of Computer Education, Kongju National University, Kongju, Korea

E-mail: jichung@kongju.ac.kr

Abstract

SPL in MSRDS helps you start programming with easy and fun by simplifying complicated coding pattern into simple script. A novice user with little to no coding experience can start creative IT, embedded, mobile and robotics programming right away without any preliminary preparation. SPL provides many functions for sensor programming. The sensor programming can be made in 2 types of patterns: the procedure pattern and while–loop pattern. This paper studies characteristics of the programming schemes and compares between them.

Keywords: component; MSRDS, Robot sensor programming

1. Introduction

MSRDS(Microsoft Robotics Developer Studio) enables hobbyists and professional or non-professional developers to create robotics applications targeting a wide range of scenarios.

MSRDS can support a broad set of robotics platforms by either running directly on the platform if it has an embedded PC running Windows or controlling the robot from a Windows PC through a communication channel such as Wi-Fi or Bluetooth. MSRDS provides the simulation robots and environments enabling to the basic robot programming without hardware robots.

MSRDS provides a Visual Programming Language (VPL) which allows developers to create applications simply by dragging and dropping components onto a canvas and wiring them together. The powerful Visual Simulation Environment (VSE) provides a high-fidelity simulation environment powered by NVIDIA PhysX engine for running game-quality 3D simulations with real-world physics interactions [1,2].

You can use SPL (Simple Script Language) for easy, fun and simplified programming for the creative IT, robotics, embedded and mobile. SPL helps you start programming with easy and fun by simplifying complicated coding pattern into simple script. A novice user with little to no coding experience can start creative IT, embedded, mobile and robotics programming right away without any preliminary preparation [3,4].

2. Sensor Programming Schemes

A. Operation of Robot with Sensors

MSRDS provides the several sensors for robotics simulation programming and you can use the sensors attached on many robot platforms. Programmers can use the Kinect, Bumper (Touch), LRF, IR, Sonar, bright, color, compass, GPS and RFID sensor on VPL and SPL to control robots. Each sensor is used for its different areas, the LRF sensor has the highest performance but the bumper sensor has the lowest performance on maze explorer [5].

SPL in especially provides the several functionalities for easy sensor programming with robot. SPL script editor for MDRDS provides many simulated sensor entities. You can add the Differential Drive entity to use robot with motors in SPL.

To control the action of robot (go, power setup, rotation, driving distance setup) in SPL, the methods and attributes of AddDifferentialDriveEntity are used. For example, if robot calling “robot1” is turned to 30 degrees after setting its power to 0.5 and moving to 5m, you can make scripts as the following.

AddDifferentialDriveEntity robot1

/Position:0 0 4

robot1. GoTo(5, 0.5)

robot1.Turn(30, 0.2)

With getting information from sensors, the robot attached sensors can run autonomically. Robot can be controlled by information obtaining through the methods and values in the simulated sensor entity. For example, bumper sensor has several options and attributes, and Get() method. Get() method means that sensor lets information to get at present state

For example, if robot “robot1” with bumper sensor “bumper1” is bumpered against an obstacle, it must go back to 0.5m, turn to 180 degrees, go to 3m, and then stop. You can write the following script for the above situation.

if(bumper1.Pressed)

{

robot1.DriveDistance(0.5, -0.3)

robot1.RotateDegrees(180, 0.3)

robot1.DriveDistance(3.0, 0.3)

robot1.SetDrivePower(0.0, 0.0)

}

B. procedure pattern

Robot has to trace a particular situation continuously through values measured from sensors. The most easy way to check values measured from sensors is the procedure pattern in SPL. The procedure pattern can make robot sensing program using values measured from sensors. To make robot sensing program into procedure pattern, you have to use the “/Procedure_SensorNotify” option in sensor entities of SPL editor.

The “/Procedure_SensorNotify” option means that program jumps to and executes the specified procedure whenever a sensor measures any value.

The following is the procedure pattern.

sensor_entity sensor_name

/Procedure_SensorNotify: procedure_name

Procedure procedure_name

…

…

…

End

Consider the situation for a robot to avoid an obstacle. [Fig. 1] shows the situation that goes back, turns 90 degrees to the left, goes straight, and turns 90 degrees to the right when a robot is conflicted with obstacle.

The following is the example program written in procedure pattern for [Fig. 1].

Fig. 1. Running path of a robot with bumper sensor

AddDifferentialDriveEntity base1

/Position:0 0 0

AddBumperEntity bumper1

/Procedure_SensorNotify:BumperEvent

FlushScript

WaitForServiceCeation bumper1

Wait 1000

base1.SetDrivePower(0.2, 0.2)

//procedure for bumper1 notifying

Procedure BumperEvent

if(value.Pressed)

{

base1.DriveDistance(0.5, -0.2)

base1.RotateDegrees(90, 0.2)

base1.DriveDistance(1.0, 0.2)

base1.RotateDegrees(-90, 0.2)

base1.DriveDistance(2.0, 0.2)

base1.RotateDegrees(-90, 0.2)

base1.DriveDistance(1.0, 0.2)

base1.RotateDegrees(90, 0.2)

base1.SetDrivePower(0.2, 0.2)

}

End

Procedure BumperEvent is executed whenever the sensor “bumper1” senses with any value.

if the value.Pressed is True, then the following block is executed but otherwise the block is not bypassed. The execution frequency of the specified procedure is depend on how often sensor measures. Procedure pattern can have some trouble in the case with complicated routine for robot control. System cannot execute the specified next procedure again when the sensor measures so frequently and notifies new sensing data during the specified procedure is executed. Therefore, this pattern can have the synchronization problem between the procedure execution and sensor notification.

C. While-loop pattern

While-Loop pattern is another pattern for robot control. The pattern with While statement gets sensing data from the sensor and executes the routine for robot control in While block in SPL. This pattern controls robot according to sensed data using Get() attribute of the sensor entity in While block.

The following is While-loop pattern.

sensor_entity sensor_name

…

while(true)

{

sensor_name.Get()

if(…)

{

…

…

…

}

}

The following is the program written in while-loop pattern for [Fig. 1].

AddDifferentialDriveEntity base1

/Position:0 0 0

AddBumperEntity bumper1

FlushScript

WaitForServiceCeation bumper1

Wait 1000

base1.Go(0.2, 0.2)

while (true)

{

bumper1_pressed=bumper1.Get()

if(bumper1_pressed==true)

{base1.DriveDistance(0.5, -0.2)

base1.RotateDegrees(90, 0.2)

base1.DriveDistance(1.0, 0.2)

base1.RotateDegrees(-90, 0.2)

base1.DriveDistance(2.0, 0.2)

base1.RotateDegrees(-90, 0.2)

base1.DriveDistance(1.0, 0.2)

base1.RotateDegrees(90, 0.2)

base1.SetDrivePower(0.2, 0.2)

}

}

The drawback of this pattern is making system overload excessively because the specified procedure is executed whenever while statement executes.

3. Comparison of 2 types of patterns

There are 2 types of patterns for robot sensing programming: procedure pattern and the While-loop pattern.

It is usually known that the While-loop pattern has better performance than the procedure pattern. The While-loop pattern is good for making sensing program into user’s intent. Users can get the measured data and execute the robot control routine in the While block. There is no synchronization problem as the procedure pattern. But the While-loop pattern makes system overload excessively because the robot controlling routine is executed whenever While statement executes.

In procedure pattern, system cannot execute the specified procedure again when the sensor measures so frequently and notifies new sensing data during the specified procedure is executed. This pattern can have the synchronization problem between the procedure execution and sensor notification. Therefore the procedure pattern can have the abnormality of robot control. t is usually known that programmers prefer the While-loop pattern to the procedure pattern.

We can infer the following characteristics for 2 types of patterns.

4. Conclusion

MSRDS is the appropriate tool for making robot simulation program. Robot simulation program can control robot’s action through several sensor entities in SPL and VPL. We have studied the characteristics about robot sensing program schemes(procedure pattern and while loop pattern) of SPL. And we knew that the While-loop pattern has better performance than the procedure pattern.

TABLE1. Comparison of 2 types of patterns

type	sensing method	abnormality	detailed control	preference	over-load
procedure	/procedure_sensorNotify	much	No	less	less
while	Get()	less	Yes	much	much

In addition we will compare the simulation performances (the number of robot’s turn, the number of robot’s conflicting, total exploring time) from 2 sensor program patterns point when robots attached different sensor(bumper, LRF, IR sensor) explore the maze in SPL.

References

[1] http://www.microsoft.com/robotics/

[2] http://www.helloapps.com

[3] Y. J. Kim, J. I. Chung, “Robot Programming for Logical Thinking Improvement, ”HongRung Pub. 2012.

[4] S. Y. Hong, “Intelligent Robot Programming for SMART Creative Engineering, IT Holic, 2012.

[5] J. W. Lee, J. I. Chung, “Comparative Analysis of the Performance of Robot Sensors in the MSRDS Platform, ”Journal of the Korea Industrial Information Systems Research Vol. 19 No.5, Oct. 2014.

FADECT: A Middleware Platform for Automatic Fault Detection of Building Energy Consumption

Jong-Wook Kim*

*Department of Media Software, Sangmyung University, Seoul, Korea

E-mail: jkim@smu.ac.kr

Abstract

Commercial and residential building sectors are consuming more energy than other sectors in developed countries. Moreover, it is predicted that the total energy consumption in the building sectors will be continuously increased in future, in both developed and developing countries. Concerns over energy security and environmental issues, around the world, have led to the worldwide focus on energy use reduction. Field studies have shown that malfunctioning controls and operations are a primary cause of inefficiencies in energy usage. By automatically detecting abnormal energy consumption and timely diagnosing the root cause of faults, a significant energy saving can be archived. Thus, in this paper, we introduce our ongoing work which aims to develop a middleware platform for automatically detecting abnormal energy consumption by leveraging BEMS (Building Energy Management System) big data analytics.

Keywords: component; fault detection; BEMS; big data analytics

1. Introduction

In developed countries, commercial and residential building sectors are consuming more energy than other sectors, such as the transportation, industry, agriculture, or service sectors. For example, in the United States, the building sectors currently consume nearly half of all primary energies. Moreover, it is predicted that the total energy consumption in the building sectors will be continuously increased in future, in both developed and developing countries.

Over the past few decades, global concerns over energy security and environmental issues, around the world, have led to the worldwide focus on energy use reduction. Considering that the building sectors consume more primary energy than other sectors in most countries, it is important to reduce energy use from these regions. In the building sectors, it is well known that malfunctioning controls and operations of building equipment are a primary cause of energy leakage, which leads to inefficiencies in energy usage. Thus, by automatically detecting abnormal energy consumption and timely diagnosing the root cause of faults, a significant energy use reduction in the building sectors can be archived. Although substantial research works have been done on automatic fault detection on building energy usage [1,2,3,4,5,6,7,8], there has been little attempt to investigate this issue by analyzing high volumes of data collections produced by BEMS (Building Energy Management System), which is a computer-based system that automatically manages and controls diverse building facilities, including electricity, lighting, ventilation and air-conditioning, and water supply and drainage, while realizing energy saving [9]. Therefore, in this paper, we introduce our ongoing work which aims to develop a middleware platform for automatically detecting abnormal energy consumption by leveraging BEMS big data analytics.

Figure 1: An overview of the proposed middleware platform, FADECT, for detecting abnormal energy consumption

2. FADECT: A Proposed Middleware Platform

Figure 1 provides an overview of the proposed middleware platform, FADECT (Fault Detection of Building Energy Consumption), for detecting abnormal energy consumption by leveraging BEMS big data analytics. Note that the proposed FADECT is built on the top of BEMS. As shown in the figure, the proposed FADECT is mainly composed of BEMS Data Modeler, Similar Historical Data Extractor, Feature Extractor and Fault Detector.  We now explain and describe each of these components in detail.

A. BEMS Data Modeler

Generally, BEMS is collecting high volumes of data, such as building operational data (i.e., temperature, electricity, lighting and ventilation and air-conditioning), energy usage pattern data, and weather data. These data sets have temporal properties. Thus, BEMS Data Modeler models the data sets, which are collected by BEMS, by using a temporal data model.

B. Similar Historical Data Extractor

Once current BEMS data is modeled by using a temporal data model, next step is to extract BEMS historical data (which is stored in the database of BEMS) that were collected under very similar current conditions, such as weather and building operational modes. We note that the extracted BEMS historical data will be compared with the current BEMS data in order to detect abnormality of building energy usage pattern.

C. Feature Extractor

Given BEMS data represented by using a temporal data model, FADECT extracts key features that can help discover the underlying latent structure among temporal data. It is known that temporal data, such as time series data, often contains localized salient features which can be exploited for search and analysis. Thus, Feature Extractor of FADECT extracts localized temporal features that can be used for big data analysis.

D. Fault Detector

Fault Detector is the key component of the proposed FADECT. This component takes two input data sets, the current BEMS data and the similar historical BEMS data extracted in the previous step, and computes the similarity between them. The reason for comparing these data sets is based on the assumption that building systems, including energy usage pattern, behave very similarly under similar weather and operational modes. Thus, if the similar score between these two data sets is less than the predefined threshold, we can conclude that there is abnormality in current building energy usage pattern.

3. Conclusion and Future Work

With the growing concern about the energy crisis, the need for improving energy efficiency has become very important topic all over the world. Considering that the building sectors consume more primary energy than other sectors in both developed and developing countries, it is important to save energy consumption at these regions by detecting wasted energy. Thus, in this paper, we presented a middleware platform, FADECT, for automatically detecting abnormal energy consumption by leveraging BEMS big data analytics. Future work includes developing effective mechanisms that are able to diagnose the root cause of faults by exploiting big data analytics, when abnormal energy usage is detected.

Acknowledgment

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1C1A1A01054462).

References

[1] R. Fontugne, J. Ortiz, N. Tremblay, P. Borgnat, P. Flandrin, K. Fukuda, D. Culler and H. Esaki, “Strip, bind, and search: a method for identifying abnormal energy consumption in buildings,” Proceedings of the 12th international conference on Information processing in sensor networks, April 8-11, 2013.

[2] Z. O’Neill, M. Shashanka, X. Pang, P. Bhattacharya, T. Bailey and P. Haves, “Real Time Model-Based Energy Diagnostics in Buildings,” Proceedings of the 12th Conference of International Building Performance Simulation Association, November 14-16, 2011.

[3] H. Zhao and F. Magoules, “Feature Selection for Predicting Building Energy Consumption Based on Statistical Learning Method,” Journal of Algorithms & Computational Technology, Volume  6, Issue 1, Pages 59-77, March 12.

[4] S.R. Iyer, V. Sarangan, A. Vasan and A. Sivasubramaniam, “Watts in the basket?: Energy Analysis of a Retail Chain,” Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, November 14-15, 2013.

[5] D. Jacob, S. Dietz, S. Komhard, C. Neumann and S. Herkel, “Black-box models for fault detection and performance monitoring of buildings,” Journal of building performance simulation Volume 3, No.1, Pages 53-62, 2010.

[6] S. Katipamula and M.R. Brambleya, “Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems? A Review, Part I,” HVAC&R RESEARCH, Volume 11, Issue 1, Pages 169-187, 2005.

[7] J. Schein and S.T. Bushby, “A Hierarchical Rule-Based Fault Detection and Diagnostic Method for HVAC Systems,” HVAC&R RESEARCH, Volume 12, Issue 1, Pages 111-125, 2006.

[8] J.E. Seem, “Using intelligent data analysis to detect abnormal energy consumption in buildings,” Energy and Buildings, Volume 39, Issue 1, Pages 52-58, January 2007.

[9]Buildings Energy Data Book, http://buildingsdatabook.eren.do

A Scheme for Privacy Preservation in Ubiquitous Computing

Arijit Ukil*

*Innovation Lab, Tata Consultancy Services, Kolkata

BIPL, Sector-5, Saltlake, Kolkata-91, India

E-mail: arijit@tcs.com

Abstract

Ubiquitous computing consists of many new devices, sensors, and protocols. With the emergence of ubiquitous computing, user’s data are being captured, processed and used for various purposes. These along with new means of individual identification constitute individual as well as organizational privacy concern. So, privacy preservation becomes an important issue in today’s context. In this paper, we make an attempt lo find a solution for this problem. We develop a scheme for secure multiparty data aggregation with the help of modular arithmetic concept. Specifically, we consider a scenario in which two or more parties owning confidential data need to share for only aggregation purpose to a third party, without revealing any unnecessary information. Through simulation results, we show the efficacy of our proposal mainly from ease of computation perspective which is an issue for resource- limited sensor nodes.

1. Introduction

Ubiquitous computing enhances computer use by making many computers available throughout the physical environment, while making them effectively invisible to (he user. This requires functioning of multitude of devices in the environment to be oblivious to the users. Mark Weiser in his paper “The Computer for the Twenty-First Century” defines ubiquitous computing as a technology that “weave itself into the fabric of everyday life until it is indistinguishable from the it” [1]

Privacy in our context can be defined as the control over access to information about oneself. In his famous paper [2], Yao has introduces the millionaire problem. This problem is analogous to a more general problem where there are two numbers a and  the goal is to solve the inequality without revealing the actual values of a and In this paper, we consider the case of privacy preservation data aggregation, where the data from individual sources will be aggregated, but the content of individual data is not revealed. The most interesting application is E-voting. The aggregation methods of privacy-preservation are dealt well in [3 - 4]. In [3], the authors reviewed privacy-preserving techniques for protecting two types of private information: data-oriented and context-oriented. In [5], Wenbo He et.al. propose schemes to achieve data aggregation while preserving privacy. The scheme they proposed, CPDA (Cluster-based Private Data Aggregation) performs privacy-preserving data aggregation in low communication overhead with high computational overhead. In this work, our scheme performs . privacy-preserving data aggregation using Securer Multiparty Communication (SMC) [6]. Through simulation results we show that under the ubiquitous computing scenario, our scheme has less computational complexity than in [6]

The paper is organized as follows. In Section 2, we depict the system model. In Section 3, proposed scheme is described. Simulation results are shown in section 4. Lastly we conclude the paper in Section 5

2. System model

We consider the following system model, where N numbers of nodes are present and one central server monitors the activity. This we can visualize in a shopping mall, where each of the items (RFID enabled) are nodes and one main server controls the billing and other activities. When a person purchases some items, the total (aggregated) bill is generated at the shopping mail’s side, but the shopping mall cannot know the items the consumer purchased. This preserves the privacy of user’s consumption trend, which if revealed can be used for customized advertisement targeted to each consumer. We have assumed the every node can be directly or indirectly reachable through bi-directional wireless link with the central server and at least another node. This is shown in Fig. 1

Figure 1 : System model

3. Privacy Preservation Scheme

In this section we describe our privacy preservation scheme. Firstly, we secure the data transmission by secure key distribution policy. For secure key distribution, we follow the method proposed by Eschenauer and Gligor, which is called random key pre-distribution [7]. Let K be the number of distinct cryptographic keys that can be stored on a client node. At the pre-deployment phase, a random pool of keys k out of the total possible key space is chosen. For each node, K keys are randomly selected from the key pool k and stored into the node’s memory. This set of K keys is called the node’s key ring. The number of keys in the key pool, is chosen such that two random subsets of size K in k shares at least one key with some probability p. After the client nodes are deployed, a key-setup phase is performed. The nodes first perform key-discovery to find out with which of their neighbors they should share a key

4. Simulation Results

In Fig. 2, we compare the computational time requirement of our scheme to that of the CPDA scheme proposed in[5].CPDA scheme and the corresponding algorithm leverage the algebraic properties of polynomials to compute the aggregated result, where as our scheme uses the simple algebraic property of modular arithmetic. So, in our scheme the computational time for employing the privacy preservation scheme is much lesser.

Figure 2: Computational time comparison

5. Conclusion

In this paper we have presented a scheme for secure privacy preservation in ubiquitous computing. We have considered the case of consumer’s purchasing trend protection in a shop. Our algorithm is simple and effective particularly for resource-limited sensor nodes

Reference

[1]Mark Weiser, "The Computer for the Twenty First Century," Scientific American, pp. 94-104, September, 2012

[2]A. Yao, "Protocols for secure computations," 23rd Annual Symposium on Foundations of Computer Science, pp. 160-164, 2013

[3]N. Li, N. Zhang, S.K. Das and B. Thuraisingham, ^Privacy- preserving in wireless sensor networks: A state-of-the-art survey，” Ad Hoc Networks vol. 7, pp. 15이一 1514, 2014

[4]M. Conti, et al.，“Privacy-preserving robust data aggregation in wireless sensor networks,” Secur. Commun. Netw. vol. 2, pp. 195-213, 2013

[5]W. He. et al, “PDA: Privacy-preserving Data Aggregation in Wireless Sensor Networks” IEEE INFOCOM, pp. 2045 - 2053, 2012

Integrated services switching system

J.H.M.Abu Baker*, M-Abbas*

*Department of Communication Engineering University Tecknology Malasia

Kuala Lumpur, Malaysia

Anstract

The ISSS ia a Bmall digital exchange (PABX) which is designed to integrate both analog subscribers and digital multi sevice subcriber in a accordance with the CCITTISDN Reeomendation. The ISSS architecture has been designed to have a high degree of modulanly, with distributed control and intelligence the syatem intended to provide with flodbflily and allow easy modification and enhancement. Hie development of the ISSS aim to create the intereat and awareness of ISDN to students in Faculty of Electrical Engineering. This will provide a flatiorm and foundation for student to taste project to devdop and experience the switching architecture, interface Btroctuie, protocol handling nd signalling. Readily available LSI and VLSI components in the market are extensily being used in designing the hardware. The first stage of ISSS development involves the design interface card for analog and digital subscriber including an interface card for PC interface module for integration of analog and RS-232 data and also the 出 gital switching network. Theae modules provide a basic circuit switching function for 64 kb/a channels Thia paper intended to describe the architectural concepts, design of the system laboraUny and implementation of selected ISDN Gicflities specified by the CCITT Emphasis has been placed on subscriber-oriented services whidi include digital voice and circuit switched data

1. INTRODUCTION

For the past nine years, the vision of designing and implementing a truly Integrated Services Digital Network (ISDN) have been a sweet dream for some people but also a nightmare for others. The end- users are expected to be very happy with all the services such as voice, data, image or even video that they can use at their reach. The complexity of the ISDN protocols and hardware architecture are hidden (transparent) from the users. What seems a simple telephone call and transfer of data to the user, on the other hand, in reality it involves a series of complex and detail procedures of communications which passes through many dardware and fortware level.ISDN hardware, software and protocol researchers had been working on this area for almost a decade and had produced ISDN standards throughout two study periods (1980 - 19公4 and 1984 -1988) which we obtained today in the Red Book and Blue Book. Even as the work in 1980 - 1984 period is progressing, country like United Kingdom had already started in their ISDN Pilot project leaving others behind. However, today countries such as West Germany, Japan and France had experiencing their ISDN their ISDN commercial trials. Some even dare to take the risk and be a pioneer in broadband ISDN implemention

This paper will contain initially a discussion on tii issues related to the integration of ISDN services in| the new generation PABXs and it will provide th basis from where the proposal of research on ISS can start. Next, the principle and its moduli concept will be presented. The main element in th design is tibe choice of ISDN VLSI componets will be the base for future reference.

2. ISDN EVOLUTION

The telephone network existed before the telex and any private data networks. Its early use for dab applications was for airline bookings an{ timesharing of computers. To overcome the speel and point-to-point limitation, packet switclid network was set up. However, services like videotq require continuous open path and packet switching inadequate.

The telex network largely replaced the telegrq services. The advantages of a telex network being that it is unattended use cheap rate. It can be that how each of the above network evolved to sat펴 a need. These incompatible networks will continue to ecolve. In most cases, incompatible equipments and acces method forbid both inter-network and inter-netm communication. The problem amplifies as publl networks attempt to accommodate fast-rising volt and data traffic demands by corporat government and private users.

To avoid the crisis of network overloads, total leading industry participants propose  networking alternatives. Among them, the moi hopeful to the interests of local, nation international communications, is the ISDNm'S technique unify separate networks through stands interfaces

The ISDN is conceptually, a network based evolving from, the digital telephony network. multi-service network, serving both voice and da{ In order to maximize compatibility, the IS; supports a limited range of standard, multi-purpose interfaces betwwen the network the user.

ISENS – INTEGRATED SERVICES

in the ISEN environment and to get the benefit from it, the PABX has to change. a PABX which primary objective to servevices, it has to change to an Interated Private Branch Exchange (ISPBX), which vate based ISDN node.

port term, the ISDN will run prallel with the PSTN; so ISPBXs will have to able to support cerminals, To begin with, at least, the role will e a mixed one offering the versatility mainly voice using the existing telephone also with a varity of data/text/image ications involving a wide range of terminals.

will provide economical solutions for the g of text and data as well as voice. In » this, a number of objectives must be met. There must be no need for special wiring or  e-writing betwwen the swith and the user luipment. The transmission mechanisms sleeted should allow 2-wire connections, instances of at least 2 km apart must be ceptable.

tandard interfaces with a wide range of ’ansmission modes must be provided, iterfacing shall be possible in conformity to .24 X21 and x25

connections via the public switched telephone (PSTN) should be possible.

The switch must have the capability to rovide gateways to special/dedicated networks such as Telex, LANs, X.21 and 1.25 Public Data networks

The design shall permit the introduction of  ISDN-type interfaces and services.

The switch should be able to provide  internetworking to the existing ISDN.

The call set-up and call clearing facilities hall cover a wide range of applications economically and in a uesr-friendly manner.The system shall offer security faclities. ISDN standards were published in 1984. It he Red Book which consists of I-Series lendations which covers from the general v of ISDN to the user-network interfaces. the Red Book still did not answe rall the is andre main open for the1984-1988study The standards are quite suitable after the publishing of the CCITT Blue Book in July 1989 and made powible the long awaiting of ISDN implementation to the public. Some countries are brave enough to venture early into supplying commercial ISDNs to the public during the 1984 -1988  period. However, it is yet to be seen whether their ISDNs are in accordance to the Blue Book or not as they claimed to be.

3. INTERGRATED SERVICES SWITHING SYSTEM(ISSS)

The design and implementation of ISSS beean in  architecture is modular, a number of different functional modules are built by the Undergraduates students such as Analog Line Card Digital Line Card, Data Switching Card and Digital Telephone. Most of these projects are still online by diffrernt student. Smce the initial design does not consider efficiency, reliability, flexibility and high-performance of the system, therefore, it is needed to re-define the overall architecture so as to suit the characteristics mentioned above.

A. block diagram of the proposed system is shown Figure 1. It shows all the features to be supported by a simple ISDN PABX type network. Other than the voice services, there a number of data services; e.g. host computer, VDU and facsimile. Access to the other networks such as Public Switched Telephone Network (PSTN), Packed Switched Public Data Network (PSPDN), Circuit Switched fublic Data Network (CSPDN) and Local Area Network (LAN) can also be provided in the future. Ot!ier sophisticated 明rvices such as Electronic Mail, y〇ice Mail and Slow scan Video could also be added in the later strages.

4. Modular architecture concept of ISSS

In principle, the hardware and software design of ISSS must be modular. This provides much greater flexibility for the user and allowing him to grow and alter their system as their needs grows or alters. In software terms, it is adaptable to meet the circumstances of each application. ISSS is basically divided into 3 functional module as shown in Figure 2-the switching nodule, central control unit and system interface unit. However, these functional modules can be divided into sub-functional modules.

The switching module is the heart of the exchange to which the central control and system units are connected. It contains the digital non-blocking switching matrix and system clock for synchronization.

The central module takes care of the overall control and management functions which co-ordinates the communications process between all the system interface units. For this purpose it is equipped with

REFERENCES

[1] E.BRYAN Came, "New Dimensions In Telecommunications". IEEE Communi- cations  Magazine Vol. 20 No.1, January 2012, p. 17

[2] G.w.a. Dummer, Electronic Inventions and Discovreries, 2013, p.10

[3] OMAR Ismail，"ISDN - Overview", STM Journal, Volune 1, no.12003 p 49 – 52

[4] MAURIZIO Decina, ^l^Maging ISDN Through International Standards Activities , IEEE Communication Mag^ine Vol. 20 No. 5 setenver 2013, p.19

[5]A Short Course on ISDN, June 27 – 29 2004, paper work by pernas NEC Telecommumicatiom Sdb. Bhd

[6]SCOTT W. Augerson, "ISDN - Evolution not revolution", COM, Siemens Magazine of Computer & Communications, 3/87, Vol.XV, Jun 2012 p 7-9

[7]International Telegraph and Telephone Consultative Committee (CCITT), Data Communication OSI and ISDN Recommendation, I-series, approved by the Eighth Plenary Assembly October 2013 p. 102

A design of an Optimized Indoor Positioning System for tracing a patient’s current location in a general hospital

Yusrina Tifani*, Byung-Kwan Lee**, Eun-Hee Jeong***

*Dept. of Computer Engineering Catholic Kwandong University Gangneung-si, Gangwon-do, Korea

E-mail:　Yusrina.tifani@gmail.com

**Dept. of Computer Engineering Catholic Kwandong University Gangneung-si, Gangwon-do, Korea

E-mail:　bklee@cku.ac.kr

(Corresponding author)

***Dept. of Regional Economics Kangwon National University Samcheok-si, Gangwon-do, Korea

E-mail:　jeongeh@kangwon.ac.kr

Abstract

This paper proposes an Optimized Indoor Positioning System (OIPS) for tracing a patient’s current location in a general hospital. The OIPS consists of a Wi-Fi Fingerprinting Module (WFM), an Improved Reinforcement Neural Network (IRNN). The WFM is a module which collects the fingerprinting data. The data is made up of Received Signal Strength (RSS), Wireless Access Point ID (WAP ID), and user ID. The IRNN processes the fingerprinting data and obtains the user estimated location. The simulation of the IRNN shows a higher R-Values and a lower Mean Squared Error (MSE) than the Artificial Neural Network (ANN). The result for PosData by ANN shows a MSE 4.7 and a R-value 0.97, while The IRNN shows a MSE 1.89 and a R-value 0.99.

Keywords：optimized indoor positioning system, improved reinforcement neural networ, Wi-Fi fingerprinting, received signal strength

1. Introduction

A Global Positioning System (GPS) and a Global Navigation Satellite System (GNSS) have helped the OPS development. With the rising of OPS, an Indoor Positioning System (IPS) started to be developed by researchers. However, GPS or GNSS is difficult to use for IPS with the environment boundaries. The signal strength is decreased because of the wall material of buildings, so GPS and GNSS are unlikely to be accurate inside buildings. There are several ways to overcome these limitations, such as RFID sensor, Wireless Sensor Networks, and Wi-Fi fingerprinting. In the current environment, Wi-Fi is becoming common everywhere. One of the techniques is using Wi-Fi fingerprinting data. The Received Signal Strength (RSS) of each Access Point (AP) will be sent to each user or Reference Point (RP)[1].

This paper proposes an Optimized Indoor Positioning System (OIPS) for tracing a patient’s current location in a general hospital. The OIPS consists of a Wi-Fi Fingerprinting Module (WFM), an IRNN Module. The WFM is a module which collects the fingerprinting data. The IRNN utilizes one of Reinforcement Learning (RL) which is Q-Learning. Therefore, OIPS provides a good result of estimation.

2. A Design of OPIS

The overall architecture of the OIPS shows in Fig.1. The user mobile phone sends the RSS of each detected WAPs to the server with the WAPs information. The RSS and WAPs information data are stored into the database and processed using Improved Reinforcement Neural Network (IRNN).

Figure 1. The structure of the OIPS

A. A Wi-Fi Fingerprinting Module(WFM)

This paper proposes OIPS with WFM and the IRNN. The WFM is Wi-Fi fingerprinting technique to collect the data. Wi-Fi fingerprinting is a technique for positioning system using RSS of Wi-Fi on the user phone. The user phone searches for existing WAP. Then the user phone retrieves RSS, time, and WAP ID. The retrieved data is stored into a database. This paper uses two datasets from different sources to simulate the IRNN.

The used data set is Gints Jekabsons’s dataset [2]. The data is collected using Wi-Fi fingerprinting technique. The data contains RSS and the position of the reference points (x, y) from GNSS. The reference point (RP) is the user location when the Wi-Fi fingerprinting is processed.

B. An Improved Reinforcement Netural Network(IRNN)

RL has several methods, and Q-Learning is one of RL methods. Q-Learning usually consists of S (states) and A (actions per state)[3]. The IRNN uses the RL that is Q Learning method. The IRNN is combined with modified Multi-Layers Perceptron for regression. It also used a back-propagation rule.

Figure 2. The structure of the IRNN

Fig. 2 shows the structure of the IRNN. The RSS from each WAP is the input of the IRNN. The number of hidden layers where the input data is processed is obtained from (1).

Number of hidden layers = (Number of inputs + Number of outputs) * 2      (1)

Each of the input is calculated based on est,  (delta weight output),  (delta weight output prime), (delta weight input), and  (delta weight input prime). The estimated X and Y value of user position are the output of IRNN.  The value is obtained by multiplying est, , RSS, the sigmoid of RSS and . For the first layer nodes, the  value is randomly set. For the next layer nodes, the  value is the  value of the previous nodes. T is epoch value. However, after the second node, the   value and the  value are updated, the weight input and output only are re-calculated if the new node or iteration estimated error is lower than the estimated error from previous node or iteration. The algorithm of IRNN shows below.

Improved Reinforcement Neural Network Algorithm

Begin

Randomly initialize Weight (, )  in range [-1,1]

For i=1 to N

Calculate est[i]

Calculate error[i]

If i ≠ 1 && error[i] < error[i-1]

Calculate

Calculate

Calculate

Calculate

End If

End For

End

The first step of the algorithm is randomly initialized weight input and output. Then the algorithm estimates the location and error. If the node or iteration is not the first node or iteration and the new estimation error is less than the previous node or iteration estimation error, it re-calculates or updates the Q-value of weight input and output. It is recursive until N and the estimated X and Y output are calculated. N is the number of hidden layer.

3. Simulation

The IRNN simulation is held in Matlab. The simulation uses Gints Jekabsons’s Data Set calibration data [2]. The simulation conducts in ASUS laptop with 2 GB RAM and Intel Core i7 processor. The PosData calibration data contains 65 user’s points from 326 WAPs. First, PosData is  run by training data, validation, and test data. For PosData, the calibration data file is split into 70% for training data, 15% validation data, and 15% test data.

Table1 shows the result of MSE and the R-value. The MSE of ANN is higher than the IRNN, and The R-value of IRNN is higher than the ANN. That is, The MSE of ANN is 4.7 while the MSE of IRNN is 1.8, and the R-value of ANN is 0.97 while the R-value of IRNN is 0.99.

Table 1. The MSE and R-value

PosData	ANN	IRNN
MSE	4.7	1.8
R-value	0.97	0.99

4. Conclusion

This paper proposes an OIPS with WFM and IRNN module. The IRNN uses the basic concept of RL and the ANN with modified parts. The used RL method is Q-learning.

The input data for the system is the RSS of each user from each available Wireless Access Point (WAP). Then the data is processed using the IRNN, which is the proposed algorithm in this paper. The IRNN uses Q-value to calculate weight input and weight output. The weight input and weight output is computed in each node of the neural network. Using Q-learning it learns how to handle the following data from previously calculated weight input and weight output. The simulation of the IRNN shows a higher R-value and a lower MSE than the ANN. The result of ANN shows a MSE 4.7 and a R-value 0.97, while the IRNN shows a MSE 1.89 and a R-value 0.99.

Acknowledgment

This work was supported by research fund of Catholic Kwandong University (CKURF-201407130001).

References

[1] Abdulhai, B., Pringle, R., and Karakoulas, G. J. “Reinforcement learning for true adaptive traffic signal control,” Journal of Transportation Engineering, vol.129, no.3, 2003, pp.278-285.

[2] Jekabsons, G., and Zuravlyov, V. Gints Jekabsons’s Data Set, 2010, http://www.cs.rtu.lv/jekabsons/datasets.html

[3] Goldberg, Y., & Kosorok, M. R. “Q-learning with censored data,” Annals of statistics, vol.40, no.1, 2012, pp.529.

Performance of Pragmatic 16-state 8-PSK TCM Codes in Non-Linear

Satellite Channel

VK Dubey*, NK Lim*, E Gunawan*

* School of Electrical & Electronic Engineering

Nanyang Technological Institute, Singapore

ABSTRACT

Recent paper [3], Viterbi has proposed Pragmatic codes 3h are punctured versions of the basic 1/2 revolutional code mapped into MPSK signal phase tellation. These codes have single coder and decoder for )us bandwidth efficiencies and achieve comparable performance to that of Ungerboeck codes on a linear ssian channel. In this paper, the performance of pragmatic 16-state 8-PSK codes is estimated on a band-non-linear channel using computer simulation. The is indicate that the Pragmatic codes are able to achieve a table coding gain of 2.8dB over uncoded system in# linear band-limited channel

1. INTRODUCTION

Erboeck has showm a spectrally bandwidth efficient lation scheme ( Trellis-Coded Modulation ) which oves error performance without reducing data rate or iring more bandwidth than conventional uncoded nes [1]. This is achieved by channel coding with tided sets of multilevel / phase signals such that the free .dean distance is maximised, followed by a maximum- hood soft decoding scheme Algorithm decoding ecode the received signals [2]. iractical digital communication systems, there exists accepted standard whereby convolutional code of raint length K=7 and punctured versions of rate 1/2 code imployed for power-limited and moderately band-limited ids. Punctured code rates of 2/3 and 7/8 combined with K signal phase constellation and M-QASK quadrature itude constellation are able to achieve 2 to 6 b/s/Hz width efficiencies and still retaining the power encies comparable to that of Ungerboeck codes [3]. theses pragmatic codes enable the ues a single basic coder and decoder, thus reducing the complexity of the communication system but achieving the necessary coding gains. When applied to satellite channels where band-limiting and non-linearity prevail, these pragmatic codes are still able to achieve respectable coding gains as compared to that of the Ungerboeck codes and displaying its robustness to band- limiting and non-linear distortion, In this paper, we demonstrate through computer simulation, the performance of punctured half-rate ( code rate of \H ) pragmatic code encoder in band-limited and non-linear satellite channel. The simulation program in [4] is suitably modified to include pragmatic codes and can be simulated with different values of time-bandwidth factor ( BT ) and input backoff for the Travelling Wave Tube Amplifier ( TWTA ). With encoder of 16 states and the mapping of 2/3 code rate (punctured version of basic 1/2 code rate) into 8- PSK signals as shown in Figure 2, the performance of the pragmatic 8-PSK codes is simulated with the simulation work kept between 1.0E-1 TO 1.0E-7 and the performance comparison made at 1.0E-5

2. PRAGMATIC TCM CODES

In digital communication systems with prevailing power- limited and bandwidth-limited channels, use of simple coder and decoder scheme to achieve respectable coding gains is well sorted. The pragmatic approach whereby punctured versions of the basic 1/2 rate convolutional code is mapped into MPSK signal phase constellation has shown that comparable coding gains to that of the best Ungerboeck codes can be achieved [3] With the availability of VLSI single integrated circuit K=7, rate 1/2 convolutional code Werbi Decoder Device[8] acceptance of this pragmatic approach and its performance in practical application is an indication of.

A Grey-Scale refinement coding scheme for progressive transmission of images

Da-yong zhao*

*Institute of Electronics, Academia Sinica

Beijing, China

ABSTRACT

Simple image coding scheme for progressive smission of imagery is transmitting main and gross rmation portion of a image first, with detail wing this method enable us to realize imagert rmation interchange through a narrow band nel efficiently.

1. Introduction

transmission of high resolution images by a low dw으th channel requires a great amount of time, interaction in such a communication environment . efrustrating. The problem could beeasedtoaamdegree by transmitting a series of approximate 3es which converge to the final image. Several hods including Pyramid, quadtree and vth-geometry and so on of computing such a 3s of images were presented Most of these hods concentrated their discussion on dividing a le image into many smaller area by a certain data cture. They relay on the ability of local VDU to rpaint an existed image, and generally require le transmission and computation overhead. Two of ;e method, slow-scanning television mode and dtree mode are shown in Fig. 1 and Fig. 2 actively where t represent the transmission time portion.   his paper, a grey-scale refinement scheme is sented. The efficient and distortionless scheme 3h is based on a explicit and peculiar hierarchy cture requires no transmissiom overhead and little o computation.

is of the scheme primarily concern low bandwidth nnels, for example, telephone lines used for conferencing or video telephone where one wants ransmit visual material by first presenting a rough tch, with detail following. Another use would be 선sing interactively through remotely stored images are the recipient wants quickly to abort ismission of the unwanted ones as soon as they recognized

2. CODING SCHEME

When we imagine the progressive refinement of grey-scale image, we naturally think of a decayed picture of less grey-scale, progressively replaced by more grey-scale. We consider a digital image of p pels quantized into k bits each. For the nature binary number base, k bits a pef are configurated as follows. To achieve that an image is refined progressively, according to the bit numbers in a pel, we divide the transmission of the whole image into k stages. In the first stage, we transmit the MSB, [eqn: b _{k-1}]of- every pel one bu one These data that are totally p bits represent an image of only two grey-scale. The successive data to be transmitted are bk.2ofeverypel.Asaresultof the second stage, with the information transmitted previously an image of four grey-scale is obtained. After every stage, the grey-scale numbers of the image are doubled, and the image quality is getting finer. The LSB, bo, of all are transmitted in the last stage. Finally, wq obtain the image with the maximum possible grey-scale of L(where L=2)

With the same transmission Baud rate, obviously, the time to transmit the first approximate image is 1/k of that to transmit the whole image, and each stage would take the same time period. In comparison with Fig.1 and Fig.2, the result shown in Fig.3 were the transmission process of a 256 X 256 X 8 image using grey-scale refinement coding SCHEME.

Further discussion

we can improve the realizable results by following means.

(1) Compressing the data of every stage by run-length coding or other methods else. However, it is noteworthy that the spatial correlativity of

corresponding bits between pels is becoming weaker as the stage changes from MSB to LSB

(2)Changing the frame memory address or program addressing mode to produce simply a interlace display effects.

(3)Using other progressive transmission schemes as mentioned above to reprocess the first approximate image.

All these means would reduce the time that transmitting each approximate image requires by a factor of 2  more. Combining the greg—scale refinement scheme with the quadtree, we obtain image series of 1 bit grey-scale (see fig.4) and 2 bit greyscale (see Fig.5)

There are two normal formats to configurate the greyscale value in a pel, one of which is illustrated as follows:

Dark (Black) <	Brightness	> Light (White)

0 <----- grey-scale Value Coding -----> L – 1

The other is opposite to the former. For both the formats, we have to need of changing the transmission procedure. The two different coding result in the difference of absolute brightness between the two corresponding images during the transmission process. However, the two final images are exactly the same.

refinement scheme is somewhat sensitive about the average grey-scale of the image On rare occasions, pernaps, nothing meaningful could be found on receiver’s screen after the first transmission stage, if the maximum

grey-scale of the image is less than L/2. Therefore, histogram equalization or histogram specification is necessary and effective to modify some images with singular statistical property.

3. Conclusion

As the simply coding scheme, all computation are only "AND” & “OR" operation. It is very easy for us to implement the function with hardware. We have finished the implementation of such a scheme. Under the Baud rate of 4800 BPS, a picture of 256X256X8 bits has been transmitted and received via a common telephone line. It takes about 2 minutes to transmit the whole image including little auxiliary bit for communication protocol. As to image Fig. 3. (a), Fig. 5(a) and Fig. 4(a》the corresponding transmission time is about 17s, 1/2 s & 1/4s respectively

References

[1]. Michele Covell and Jae Lim, “Low data-rate video conferencing," SPIE vol. 707, Visual Communications and Image Processing, PP. 75-82, 2012.

[2]. Ken Knowlton, “Progressive transmission of grey-scale and binary pictures by simple, efficient, and lossless encoding schemes, ” Proceedings of the IEEE, Vol. 68, No. 7, PP. 885-896, July 2013

[3] Amalie J. Frank, J. D. Daniels, and Diane R

Unangst, “Progressive image transmission using a growth-geometry coding,” Proceedings of the IEEE, Vol. 68, No. 7, July 2014

[4] K. R. Sloan, Jr., and S. L. Tanimoto

"Progressive refinement of raster scan images," IEEE Trans. Compute, Vol. C-28, PP. 871-874, Nov. 2012

[5] K. R. Sloan, Jr. and S. L. Tanimoto, "Progressive

refinement of raster images, ” TR 39, Dep. Compute. Sci., Univ. Rochester, Rochester, NY., TR39, Nov. 2012

[6]S. L. Tanimoto, “A pyramid model for

picture complexity,” in Proc. IEEE compute. Soc. Conf. on Pattern Recognition and Image Processing, Troy, NY, June 2014, PP. 25-28

[7] S. L. Tanimoto, “Image transmission with gross

information first, ” Dep. Compute. Sci., Univ. Washington, Seattle, Tech. Rep. 77-10-06, Oct. 2014

[8] S. L. Tanimoto and T. Pavlidis, “A hierarchical

data structure for picture processing," Compute. Graphics and Image Processing, Vol. 4, No. 2, PP. 104-119, 2013

[9] A. J. Frank, "Progressive transmission and

display of still images by means of growth geometry," Tech. Rep., Bell Lab., June 2,2013

[10] Petros Maragos, “Tutorial on advances in morpho morphological image processing and analysis," SPIE Vol. 707, Visual Communication and Image Processing, PP 64-74, 2013

[11]. M. Onoe, Y. Yasuda, and Y. Inamoto, “time’ sequential coding of dithered binary pictures, ” in Proc. Picture Coding Symp. (Ipswich, England, July 2012), P. 3.4.

[12] A. J. Frank and R. H. Groff, “On statistical coding of two-tone image ensembles," Proc. Soc. Information Display, Vol. 17, No. 2, PP. t〇2-110, 2013

[13] . Y. Yasuda, M. Takagi, and T. Awano “Hierarchical coding of still images, " in Abstr. 1979 Picture Coding Symp., (Ipswich, England), paper 3.2, July 2013

[14] A. Klinger and C. R. Dyer, "Experiments on picture representations using regular decomposition, ” Compute. Graphics and Image Processing, Vol. 5 No 1 PP 68-105. 2013

Design and Measurement of Logic eFuse OTP Memory

Ri-jun Jin*, Heon Park*, Yong-hee Kim*, Pan-bong Ha*, Yongxu Ren**

*Department of Electronic Engineering, Changwon National University, Changwon, Gyeongsangnam-do, Korea

E-mail: pha@changwon.ac.kr

**IP Development Team, Design Service division, Shanghai Huali Microelectronics Corporation

Shanghai, China

Abstract

In this paper, an eFuse OTP memory IP is designed to apply a high voltage to the eFuse OTP memory IP by an external program voltage, and thus no to affect the reliability of other devices than the eFuse OTP memory. Program power is increased by being supplied to the two-dimensional array of 128 rows × 8 columns through FSOURCE and eFuse OTP memory cells of which the decoding logic is implemented with low power are used. The layout size of the designed 1Kb NMOS-diode eFuse OTP memory IP based on Dongbu HiTek’s 110nm CIS process is 5.951µm × 2.52µm (=14.99µm2). It is confirmed by testing wafers that a high DOUT datum is outputted when reading is done after programming a ‘1’ datum into an eFuse OTP memory cell.

Keywords : Logic, eFuse OTP, PMIC, DDI, CIS

1. Introduction

An eFuse OTP memory is required to do analog trimming and calibration and to store a chip ID, an encryption key, SRAM repair addresses, bad pixel addresses. in such system semiconductor applications as PMICs (Power Management ICs), DDIs (Display Driver ICs), CISs (CMOS Image Sensors), MCUs (Micro-Controller Units) etc.[1]-[3].

There are two cases in designing an eFuse OTP memory IP: one case of using logic and MV transistors[4] and the other case of using just logic transistors[5][6]. High power is supplied between two terminals of an eFuse link to a high post-program resistance of the eFuse link. Thus, a high voltage should be supplied as a method of supplying a high program power[5].

A logic eFuse OTP memory IP with logic eFuse OTP memory cells using just logic transistors is designed to reduce the development cost and period in this paper. But it is difficult to apply a high voltage to the terminals of an eFuse since a logic eFuse OTP memory IP uses  a low-VDD. In this paper, an eFuse OTP memory IP is designed to apply a high voltage to the eFuse OTP memory IP by an external program voltage, and thus no to affect the reliability of other devices than the eFuse OTP memory since the external high voltage (=FSOURCE) of 2.4V higher than VDD (=1.5V) is applied to the FSOURCE pad from the testing equipment in testing wafers. Program power is increased by being supplied to the two-dimensional array of 128 rows × 8 columns through FSOURCE and eFuse OTP memory cells of which the decoding logic is implemented with low power[7] are used. It is confirmed by testing wafers that a high DOUT datum is outputted when reading is done after programming a ‘1’ datum into an eFuse OTP memory cell.

2. Circuit Design

As a method of applying a high program voltage to the terminals of an eFuse, FSOURCE is applied directly instead of applying to the anode of the eFuse link through a PD (Program Data) driver in the IP.

As a method of applying FSOURCE directly to all the eFuse cells FSOURCE, we apply FSOURCE to the anode of each eFuse cell as shown in Figure 1. NMOS transistors MN11 and MN12 turn on by a NOR operation of the WL select signal WL_S and the BL select signal BL_S in the program or read mode. An eFuse link is blown out by a big current if a program voltage is applied to the FSOURCE node through a test switch in the program mode. On the other hand, a datum is transferred to the BL node according to whether the eFuse is programmed or not if the FSOURCE is left in the floating state in the read mode. WL_S and BL_S are both active-low signals. The program voltage is applied directly to each eFuse in the two-dimensional array through the FSOURCE node by using the eFuse cell in Figure 1.

Figure 1. eFuse OTP memory cell including decoding logic[8]..

A logic eFuse OTP memory IP with logic eFuse OTP memory cells using just logic transistors is designed to reduce the development cost and period in this paper. But it is difficult to apply a high voltage to the terminals of an eFuse since a logic eFuse OTP memory IP uses a low-VDD. In this paper, an eFuse OTP memory IP is designed to apply a high voltage to the eFuse OTP memory IP by an external program voltage, and thus no to affect the reliability of other devices than the eFuse OTP memory since the external high voltage (=FSOURCE) of 2.4V higher than VDD (=1.5V) is applied to the FSOURCE pad from the testing equipment in testing wafers. In programming, just one bit must be selected to program one bit of a cell array of 128rows × 8 columns by the row and column decoding. A decoding logic as shown in Figure 2 is added to connect all the eFuse OTP memory cells directly to FSOURCE (=2.4V) and also to select one eFuse OTP memory cell. An eFuse OTP memory cell using just logic transistors consists an eFuse, a 1.5V NMOS transistor of big channel width flowing a big program current (MN21), a 1.5V NMOS transistor (MN22) for the read mode, and a decoding logic to control the gate NG of a program transistor and the pre-program resistance of the eFuse is about 50~100Ω[7].

Figure 2. Designed eFuse OTP memory cell[7].

The input signals of the decoding logic in Fig. 2 are WWLb (Write Word-Line bar) and PD (Program Data); and the output signal is NG. Since the WWLb of the non-selected row is in the logic-‘1’ state and NG in the logic-‘0’ state either in the read mode or in the program mode, MN21 turns off. On the other hand, MN21 turns on since NG is the logic-‘1’ state in case that the WWLb of the selected row is in the logic-‘0’ state and the PD in the logic-‘1’ state in the program mode, MN21 turns on and thus the eFuse is blown out. MN21 holds in the OFF state and the eFuse in the non-blown state since the NG falls negative a little by the coupling of gate oxide capacitance from an overlap between the gate and drain of MP21 as the WWLb switches from VDD to 0V although the NG is in the floating state in case that the WWLb and the PD are in the logic-‘0’ state. Thus, the two transistors function as a two-input nor gate.

Major specifications of the designed 1Kb eFuse OTP memory IP are as follows. The cell array consists of 128 rows × 8 columns. The used VDD (Supply Voltage) is a logic voltage of 1.5V in the read mode, and 2.4V through the FSOURCE node in the program mode. There are program, normal read, and program-verify-read mode as operating modes. Reading and writing are done in one byte and in one bit, respectively. Only logic transistors of 1.5V are used in this design.

As shown in Figure 3, the block diagram of the designed 1Kb eFuse OTP memory IP consists of a cell array of 128 rows × 8 columns, a WL driver, a PD selecting circuit, a BL S/A (Bit-Line Sense Amplifier) to read out a datum, and a control logic to supply internal control signals appropriate for the program or read mode according to the control signals (RD, PGM, and TM_EN). The address bus consists of the row address A[9:3] and the column address A[2:0]. The output DOUT[7:0] and the input signal DIN are separated In addition, one bit of 1K bits is selected by ten address lines. Reading and writing are done in one byte and in one bit, respectively.

Figure 3. Block diagram of 1Kb logic eFuse OTP memory IP [7].

3. Layout and Measurement Results

The layout plot of the designed 1Kb eFuse OTP memory IP with Dongbu HiTek’s 0.11µm CIS process is shown in Figure 4, and the layout size is 295.595µm × 455.873µm (=0.134mm2).

Figure 4. Layout plot of the designed 1Kb eFuse OTP memory IP[7].

Measurements on test chips of eFuse OTP memory IPs in the read mode before and after program are shown in Figure 5. These show that they are functioning normally.

(a)

(b)

Figure 5. Simulation results in the read mode: (a) before program and (b) after program.

4. Conclusion

In this paper, an eFuse OTP memory IP is designed to apply a high voltage to the eFuse OTP memory IP by an external program voltage, and thus no to affect the reliability of other devices than the eFuse OTP memory since the external high voltage (=FSOURCE) of 2.4V higher than VDD (=1.5V) is applied to the FSOURCE pad from the testing equipment in testing wafers. Program power is increased by being supplied to the two-dimensional array of 128 rows × 8 columns through FSOURCE and eFuse OTP memory cells of which the decoding logic is implemented with low power are used.  The cell size of the designed eFuse OTP memory IP based on Dongbu HiTek’s 0.11µm CIS process is 5.951µm × 2.52µm  (=14.99µm2). Also, the layout size of the designed 4Kb NMOS-diode eFuse OTP memory IP based on Dongbu HiTek’s 110nm CIS process is 295.595µm × 455.873µm (=0.134mm2). It is confirmed by testing wafers that a high DOUT datum is outputted when reading is done after programming a ‘1’ datum into an eFuse OTP memory cell.

References

[1] http://www.kilopass.com/wp-content/uploads/2010/04/comparison_of_ embedded_nvm.pdf.

[2] http://www.umc.com/english/pdf/eNVM_DM.pdf.

[3] http://www.sidense.com/applications.

[4] G. S. Cho, M. Y. Jin, M. C. Kang, J. H. Jang, P. B. Ha, Y. H. Kim, “Design of an 8-Bit eFuse One-Time Programmable Memory IP Using an External Voltage,” KIMICS, pp. 183–190, January 2010.

[5] J. Safran, A. Leslie, G. Fredeman, C. Kothandaraman, A. Cestero, X. Chen, “A Compact eFuse Programmable Array Memory for SOI CMOS,” Digest of Technical Papers, Symposium on VLSI Circuits,  pp. 72–73, June 2007.

[6] J. H. Kim, J. H. Jang, L. Y. Jin, P. B. Ha, Y. H. Kim, “Design of Low-Power OTP Memory IP and its Measurement,” KIMICS, pp. 2541–2547, November 2010.

[7] Y. X. Ren, P. B. Ha, Y. H. Kim, “Design of a Logic eFuse OTP Memory IP,” JKIICE, vol. 20, no. 2, pp. 317–326, February 2016.

[8] T. Kirihata, Phil C. Paone, Vimal R. Patel, Gregory J. Uhlmann, “Electronic Fuse Cell and array,” U.S. Patent 0253220, September 2014.

Design and Measurement of NMOS-Diode eFuse OTP Memory

Heon Park*, Ri-jun Jin*, Pan-bong Ha*, Yong-hee Kim*, Seung-hoon Lee**

*Department of Electronic Engineering, Changwon National University, Changwon, Gyeongsangnam-do, Korea

E-mail: youngkim@changwon.ac.kr

**FSG-Platform Technology-NVM Team, MagnaChip Semiconductor Inc, Cheongju, Chungcheongbuk-do, Korea

Abstract

In this paper, we design a 4Kb eFuse OTP (One-Time Programmable) memory IP using an NMOS-diode eFuse OTP memory cell using a parasitic junction diode formed between PW (P-Well), that is the body of isolated NMOS (N-channel MOSFET) transistors with big channel width in the DNW (Deep N-Well), and n+ diffusion region, that is the source node, instead of an NMOS transistor with big channel width as a program selector device. The layout size of the designed 4Kb NMOS-diode eFuse OTP memory IP based on Dongbu HiTek’s 110nm CIS process is 206.414㎛ × 788.6㎛ (=0.163㎟). It is confirmed by testing wafers that a high DOUT datum is outputted when reading is done after programming a ‘1’ datum into an eFuse OTP memory cell.

Keywords : NMOS-Diode; eFuse OTP; CMOS Image Sensor

1. Introduction

A CMOS image sensor is an optoelectronic device which converts a photon flux entering a lens into a digital signal[1]. CMOS image sensors are widening application areas to new markets of unmanned cars, drones, and IoT (Internet of Things) necessary for smart homes and wearable devices including the conventional application areas of smart phones and digital cameras[2].

CMOS image sensors require OTP (One-Time Programmable) memory IP (Intellectual Property) in order to do analog trimming and calibration and to store a chip ID, an encryption key, SRAM repair addresses, bad pixel addresses. eFuse OTP memories are popular in application areas of small-density OTP memories since they are easy to design[3].

An eFuse OTP memory cell used in this paper consists of an NMOS transistor with small channel width and an eFuse link as a storage[4]. In this paper, we use an NMOS-diode eFuse OTP memory cell using a parasitic junction diode formed between PW (P-Well), that is the body of isolated NMOS (N-channel MOSFET) transistors with big channel width in the DNW (Deep N-Well), and n+ diffusion region, that is the source node, instead of an NMOS transistor with big channel width as a program selector device[4]. The eFuse is blown by using the parasitic junction diode formed in the NMOS transistor in programming. Also, sensing failures of ‘0’ data can be removed as the contact voltage drop of a diode can be removed by using an NMOS transistor instead of a PN junction diode in the read mode. The cell size of the designed 4Kb eFuse OTP memory IP based on Dongbu HiTek’s 110nm CIS process is 5.951µm × 2.52µm (=14.99µm2). It is confirmed by testing wafers that a high DOUT datum is outputted when reading is done after programming a ‘1’ datum into an eFuse OTP memory cell.

2. Circuit Design

The used eFuse OTP memory cell is shown in Figure 1, and consists of an NMOS transistor (MN1) with small channel width and an eFuse link as a storage. we use an NMOS-diode eFuse OTP memory cell using a parasitic junction diode formed between PW (P-Well), that is the body of isolated NMOS (N-channel MOSFET) transistors with big channel width in the DNW (Deep N-Well), and N1 node, that is n+ diffusion region, instead of an NMOS transistor with big channel width as a program selector device[4]. The eFuse is blown by using the parasitic junction diode formed in the NMOS transistor in programming. In addition, sensing failures of ‘0’ data can be removed as the contact voltage drop of a diode can be removed by using an NMOS transistor instead of a PN junction diode in the read mode. The cell size of the designed eFuse OTP memory cell based on Dongbu HiTek’s 110nm CIS process is 5.951µm × 2.52µm (=14.99µm2).

Figure 1. Used OTP memory cell[4].

Major specifications of the designed 4Kb eFuse OTP memory are as follows. The cell array consists of (128 rows × 8 columns) × 4 blocks. The used VDD (Supply Voltage) is 3.3V ± 0.3V in the read mode, and 5.5V in the program mode. eFuse is cobalt silicide n+ polysilicon fuse. There are program, normal read, and program-verify-read mode as operating modes. Programming is done bitwise and reading in eight bits. Only MV (Medium Voltage) transistors of 3.3V are used in the design.

The block diagram of the designed 4Kb eFuse OTP memory IP is shown in Figure 2 and consists of a cell array of 128 rows × 8 columns, a WL (Word-Line) driver, a SL (Source Line) driver, a BL (Bit-Line) S/A (Sense Amplifier) to read out a BL datum, and a control logic to supply appropriate internal control signals for program, normal read, or program-verify-read mode according to control signals (RD, PGM, and TM_EN). A[10:3] are the row address, A[2:0] the column address, and A[11] the block address selecting the left and the right block. The output DOUT[7:0] and the input signal DIN are separated In addition, one bit of 4K bits is selected by twelve address lines. Reading and writing are done in one byte and in one bit, respectively.

Figure 2. Block diagram of 4kb eFuse OTP memory[4].

As shown in Figure 3, the floor plan of the core circuit is partitioned into four 1Kb cell arrays. In addition, the WL driver is positioned in the center, the SL driver in the middle, and the BL S/A at the bottom. One of 256 rows is selected by decoding of A[10:3]. One of 16 SLs is driven by decoding the block address A[11] and the column address A[2:0] in the program mode, and eight of 16 BLs are selected by decoding A[11] in the read mode.

Figure 3.Floor plan of 4Kb core circuit[4]

3. Layout and Measurement Results

The layout size of the designed 4Kb eFuse OTP memory IP based on Dongbu HiTek’s 110nm CIS process is 206.414㎛ × 788.6㎛ (=0.163㎟), which is 14.7 % smaller than that of the dual-port eFuse OTP memory IP: 456.322㎛ × 419.383㎛ (=0.191㎟). Figure 4 shows a layout plot of 4Kb eFuse OTP memory.

Figure 4.Layout plot of the designed 4Kb eFuse OTP memory IP[4].

Figure 5 shows measurement results of eFuse OTP memory test chips before and after programming in the read mode. It is confirmed that all the chips can be read out normally.

(a)

(b)

Figure 5. Simulation results in the read mode: (a) before programming and (b) after programming.

4. Conclusion

In this paper, an eFuse OTP memory cell which consists of an NMOS transistor with small channel width and an eFuse link as a storage is used.. A program selecting device is a parasitic junction diode formed between PW, that is the body of an isolated NMOS transistor with big channel width in the DNW, and n+ diffusion region, that is the source node, instead of an NMOS transistor with big channel width. This type of eFuse cell is called as an NMOS-diode eFuse OTP memory cell. The cell size of the designed eFuse OTP memory IP based on Dongbu HiTek’s 110nm CIS process is 5.951µm × 2.52µm (=14.99µm2). Also, the layout size of the designed 4Kb NMOS-diode eFuse OTP memory IP based on Dongbu HiTek’s 110nm CIS process is 206.414㎛ × 788.6㎛ (=0.163㎟). It is confirmed by testing wafers that a high DOUT datum is outputted when reading is done after programming a ‘1’ datum into an eFuse OTP memory cell.

References

[1] J. H. Jang, “Design of Intra Oral X-Ray CMOS Image Sensing ,” Ph. D. dissertation. Changwon National University, 2013.

[2] http://webzine.iitp.kr/down/vol02/brief/ICT_Brief_2015_31.pdf.

[3] J. H. Kim, J. H. Jang, L. Y. Jin, P. B. Ha, Y. H. Kim, “Design of Low-Power OTP Memory IP and its Measurement,” KIMICS, pp. 2541–2547, November 2010.

[4] S. H. Lee, P. B. Ha, Y. H. Kim, “Design of an NMOS–Diode eFuse OTP Memory IP for CMOS Image Sensors,” JKIICE, vol. 20, no. 2, pp. 306–316, February 2016.

Analysis of Biofeedback Signals Algorithm for Face Muscle Motion

Sang-sik Lee*, Ki-young Lee*

*Department of Biomedical Engineering, Catholic Kwandong University, Gangneung, Korea

E-mail: lsskyj@cku.ac.kr

Abstract.

In this study, the bio-feedback to prevent the overuse of the 60 Hz frequency stimulation was studied in the method of using the parameters of the EMG. EMG and the parameters measured in the muscles of the face before and after applying a stimulation of the facial motion is due to an active potential for contracting the facial muscles that can occur during overuse of the stimulation of the facial muscle motion. The active potential is not measured because there is no shrinkage. Thus, the RMS of the amount of muscle contraction or muscle tone reflected in the parameters of the parameter for EMG biofeedback was examined in real time even for a parameter reflecting the degree of muscle fatigue.

Keywords: Biofeedback, Face, Motion, Fatigue, EMG

1. Introduction

Our society is aging and , while we expect afford the increased product demand for low frequency treatment to increase steadily. Expansion of personal medical devices using low frequency is increasing because the market is expanding by giving them a significant impact on the market cosmetic products are released suddenly much.

Low frequency of pain was improved , as well as the clinical validation of the anti-wrinkle effect, it is a trend that is being released this product .

In domestic anti-wrinkle equipment using  cosmetic devices almost rely on expensive imported products , there is no consideration of such products systematically safety.

2. Experimental Principles and Methods

And building a signal processing algorithm for the bio-signal analysis , look at the key signal processing theory in the bio-signal analysis absolutely bunchi (Integral Absolute Value) is the following equation(1)  to a value obtained by integrating the absolute value of the electromyogram signal obtained for a predetermined time  and is expressed as .

In this equation, j is the channel number , Xj is the EMG signal measured in channel j, i is the order of sample, N is the number of samples. Through the buffer the incoming physiological signal from the sensor and to the amplifier through the amplifier after adjusting impedance , go through a 60Hz filter for removing power supply noise ,
the bandwidth filtering is can pass  from 0.1Hz to 500Hz to remove low frequency noise and high frequency noise.

The effective value of the bio-signal is defined by the formula (2) of Root mean - squared value .

3. Results and Discussion

low-frequency therapy is through a weak electric to massaging muscles, helps to calm the nerves and promote blood circuration the recovery and cyclization of muscles.

Induced by the physiological activities, promotion of blood circulation, lipolytic effect, reduction of muscle spasms, there is an effect of enhancing the function of the cells and alleviate the muscle pain.

In this study, subjects with low frequencies intended for 10 people attached to the four areas of facial muscle movements were measured.
It was compared by converting it to the RMS value.
Attaching an electrode to the face of the patient and connected to the EMG sensor, and then out the waveforms for each channel by moving the face for 5 seconds, and calculated the RMS value of the waveform amplitude .

Acknowledgment

It will support by the National Research Foundation of Korea Regional Innovation creative personnel training project (2015035925).

References

[1] Arge, J. C., Sliwa, J.A., 2000. “Neuromuscular rehabilitation and electrodiagnosis.4. Specialized neuropathy,” Arch Phys Med Rehabil., Vol. 81, pp.27-31

[2] Allison GT, Fujiwara T, 2002, “The relationship between EMG median frequency and low frequency band amplitude changes at different levels of muscle capacity.” Clin Biomech(Bristol, Avon), Vol. 17(6), pp. 464-469

[3] Ament W, Bonga GJ, Hof AL, Verkerke GJ., 1996, “Electromyogram median power frequency in dynamic exercise at medium exercise intensities,” Eun J Appl Physiol, Vol. 74, pp. 180-186.

[4] Arabadzhiev T. I., Dimitrov, G. V., Dimitrova, N. A., 2005, “Simulation analysis of the performance of a novel high sensituve index for quantifying M-wave spectral changes during fatigue,” J. Electromyography and Kinesiology Vol. 15, pp. 149-158.

[5] Bigland-Richie B, Donovan EF, Roussos CS., 1981, “Conduction velocity and EMG changes during fatigue of sustained maximal efforts,” J Appl Physiol., Vol. 51, No. 5, pp. 1300-1305.

[6] Boashash B. 1992, “Time frequency signal analysis: methods and applications. Longman-Cheshire”.

[7] Bonato P, Gagliati G, Knaflitz M., 1996,. “Analysis of myoelectric signals recorded during dynamic contractions,” IEEE Eng Med Biol Mag Vol. 15(6), pp. 102-111.

[8] Bonato P, Cheng MS, Gonzalez-Cueto J, Leardini A, O’Connor J, Roy SH., 2001a, “EMG-based measures of fatigue during a repetitive squat exercise,” IEEE Eng Med Biol Mag., Vol. 20(6), pp. 133-143.

[9] Cheng AJ, Rice CL., 2005, “Fatigue and recovery of power and isometric torque following isotonic knee extensions,” J Appl Physiol., Vol. 99(4), pp. 1446-1452.

[10] Chesler, N. C., Durfee, W. K., 1997, “Surface EMG as a fatigue indicator during FES-induced isometric muscle contractions,” J. Electomyography and Kinesiology, Vol. 7, pp. 27-37.

Dental colorimetry system studies using smartphone

Sang-sik Lee*, Jin-hyoung Jeong*, Chang-dong Lee**

* Department of Biomedical Engineering, Catholic Kwandong University, Gangneung, Korea

E-mail: lsskyj@cku.ac.kr

** FTS, Wonju, Korea

E-mail: Cd1225@hanmail.net

Abstract

In the case of colorimetry using a smartphone essential to control of the measurement environment, such as other sensors and the lighting. In this study by applying a variety of optical systems to the selection any standard color propose a structure of an optical system that can minimize the color difference by comparing the color difference from the standard color.

Keywords: Smartphone, Dental colorimetry, Lenz, Optical system

. Introduction

Prosthodontics is the dental procedure involved the most performed in dentistry. In recent years,  procedure of aesthetic treatment including cosmetic parts with improved quality of life has increased dramatically. Therefore measure shade and prosthetic dentistry is artificial tooth make to similar way as natural tooth almost everyday In the dental clinic and the laboratory. However, in existing dentist procedure vita shade guide of 16 colors compare with tooth one by one found similar color in existing dentist   to   take a long time to procedure and expensive remake artificial tooth by color difference was common case. Because human sense is really subjectively when  use shade guide for measure it, being influenced by various environmental factors  so same colors also can be recognized different colors. But even if the color shade perception is wrong because the gums, lips or lipstick, also by the indoor illumination or sunlight ambient conditions it may be difficult to accurately measure the shade. Therefore, in order to obtain the correct data by measuring the shade in quantitative levels you need to compare with the shade guide. In this study, it removed a number of factors that affect the shade measurement and study a higher reliability and operation time and cost savings are possible in the dental colorimetry apparatus system by automating measurement .

2. Research Methods

Techniques to be developed in this study  using the smart phone camera to read the color of the subject , this is a device to determine the exact color codes through a calibration procedure using a standard color .

Determine the absolute color code by using this , of course, it can be quantitatively confirmed by a color difference between the two test body

1) Development colorimetric algorithm through the distortion correction of the standard color marker

Standard color (R, G, B, Black, White) and disposed within the optical system, and correcting the color value of the subject by using the color value difference of the recorded standard color chip in order to compensate for the subject color in the original color It was.

2) Measurement and Color code conversion App. making

After measuring the subject and the colors extracted using a smartphone camera and macro lens was manufactured android App to convert it to a color code for the color matching.

3. conclusion

In this study, when applied with or cross the light if cross- polarization applying the ring light source through the polarization was confirmed that the color difference of primary color and the least measured value appears.

After the cross- direction polarization is planning to offer inexpensive and universal colorimetric method through the development of algorithms that can be generalized features of the smart phone camera through the correction of the distortion at the same time under controlled conditions using a ring light source.

Acknowledgement

It will support by Small Business Administration academic-industrial collaborative technology development projects(C03329850100436866).

References

[1] Sproull RC. Color matching in dentistry. Part Ⅱ: Practical applications of the organization of color. J Prosthet Dent 29:556-566, 1973.

[2] Miller LL. Organizing color in dentistry. J Am Dent Assoc (Special Issue) December:26-40, 1987.

[3] McPhee ER. Light and color in dentistry. Part I--Nature and perception. J Mich Dent Assoc 1978;60:565-72.

[4] Goodkind RJ, Keenan KM, Schwabacher WB. A comparison of Chromascan and spectrophotometric color measurements of 100 natural teeth. J Prosthet Dent 1985;53:105-9.

[5] Jorgenson MW, Goodkind RJ. Spectrophotometric study of five porcelain shades relative to the dimensions of color, porcelain thickness, and repeated firings. J Prosthet Dent 1979;42:96-105.

[6] O’Neal SJ, Powell WD. Color discrimination and shade matching ability of third year dental student. J Prosthet Dent 63:174, 1984.

[7] Paravina RD, Powers JM, Fay RM. Color comparison of two shade guides. Int J Prosthodont 2002;15:73-8.

Maximum Strength and Isokinetic Exercise of Biceps

Sang-sik Lee*, Jin-hyoung Jeong*, Jee-hun Jang**, Ki-young Lee*, Yoon-jeong Kim**

* Department of Biomedical Engineering, Catholic Kwandong University, Gangneung, Korea

E-mail: lsskyj@cku.ac.kr

** Department of Sport and Leisure Studies, Catholic Kwandong University, Gangneung, Korea

E-mail: jjh@cku.ac.kr

Abstract

In this study, I examined the maximum strength and isokinetic exercise of biceps. After measuring the isokinetic muscle strength, was subjected to reliability analysis between each other. It was performed regression analysis to calculate the equation for estimating the correlation isokinetic muscle force in accordance with the angle of the biceps through the exercise. The maximum muscular strength of the specific behavior as independent variables, and a multiple regression analysis was performed by the weight as dependent variables. Isokinetic muscle strength has a significance of each exercise, can be seen by the difference angle. By utilizing the weight and maximum strength of each operation, when obtaining the isokinetic temple maximum strength, it can be seen that the higher reliability when moving. Also, when obtaining the isokinetic temple maximum strength, it can be seen that the 75° orientation has a higher reliability.

Keywords: Maximum strength, Isokinetic muscle force, Biceps, Adult woman, Regression

1.Introduction

Strength measurements are made usually by maximum strength measurement.

When you set the appropriate intensity for your fitness goals MVC is the basis for the exercise selection. Thus, according to the study area it is a situation that is medley. Particularly in the course of sports rehabilitation, many cases muscle strength is reduced, If the case of the upper limb muscle endurance than the muscle strength is important in many cases. The purpose of this study was to estimate the correlation between the angle of the elbow maximum strength and other operations by targeting women.
Through rehabilitation sports leaders estimate the maximum strength of the various operations on the front lines in the field will be able to set up and evaluate the strength of sports rehabilitation .

2. materials and methods

The subject in this study were enrolled in 19~23 year old women in  Sports Leisure undergraduate in Catholic Kwandong Univ. The experiment was to target non- trainees , subjects were healthy women without the disease.
In addition, we then proceed to the procedure described for the purpose of the experiment well , specific body information are as follows: Table 1 .

Table 1. Body information of the subject

-	age	weight	height
Women	20.9	61.6kg	162.5cm

2.1. Strength Measurement
The maximum isometric muscle strength measurements (Maximal Voluntary Contraction; MVC) was conducted of the subjects personal height, weight, upper arm after arm's length measurement.
Measurement of the isometric muscular strength was performed using the MMT ( Manual Muscle Test ) equipment , measurement posture was used Biodex.

Isometric exercise (Isometric) was measured in 30degrees and 75 degrees to  new power (Extension) and flexural strength (Flexion).

2.2. Data processing methods
Data processing method for this study was used SPSS 18.0 for Window Statistical Package. First analyzed with Pearson product-moment correlation analysis to the reliability with each other. In addition, the maximum isokinetic strength , new maximum muscle strength, isokinetic muscle force of the in 30-degree angle, in 75 degree angle, the unit is Nm that change to kg, isokinetic flexor power, new power. ( kg=Nm/9.8/ arms length m)

3.conclusion
In this study, using the electromyography detected a significant difference between the maximum muscular strength from a particular position of an adult woman , and calculate the maximum strength of the respective positions were compared by using a regression analysis .

When obtained in this results biceps brachii  isokinetic strength of woman was used maximum isometric strength, maximum of isotonicity exercise has correlation to measured,

also, the new power of maximum isokinetic strength from isometric exercise get different reliability between 30degree and 75degree in position angle.

In the future, you can get a different result depending on the angle and position is determined , considering these effects and would be able to show a higher correlation in the case of male group.

References

[1] Arabadzhiev T. I., Dimitrov, G. V., Dimitrova, N. A., 2005, “Simulation analysis of the performance of a novel high sensituve index for quantifying M-wave spectral changes during fatigue,” J. Electromyography and Kinesiology Vol. 15, pp. 149-158.

[2] Bigland-Richie B, Donovan EF, Roussos CS., 1981, “Conduction velocity and EMG changes during fatigue of sustained maximal efforts,” J Appl Physiol., Vol. 51, No. 5, pp. 1300-1305.

[3] Boashash B. 1992, “Time frequency signal analysis: methods and applications. Longman-Cheshire”.

[4] Bonato P, Gagliati G, Knaflitz M., 1996,. “Analysis of myoelectric signals recorded during dynamic contractions,” IEEE Eng Med Biol Mag Vol. 15(6), pp. 102-111.

[5] Bonato P, Cheng MS, Gonzalez-Cueto J, Leardini A, O’Connor J, Roy SH., 2001a, “EMG-based measures of fatigue during a repetitive squat exercise,” IEEE Eng Med Biol Mag., Vol. 20(6), pp. 133-143.

[6] Cheng AJ, Rice CL., 2005, “Fatigue and recovery of power and isometric torque following isotonic knee extensions,” J Appl Physiol., Vol. 99(4), pp. 1446-1452.

[7] Chesler, N. C., Durfee, W. K., 1997, “Surface EMG as a fatigue indicator during FES-induced isometric muscle contractions,” J. Electomyography and Kinesiology, Vol. 7, pp. 27-37.

[8] Cioni R, Giannini F, Paradiso C, Battistini N, Navona C, Starita A., 1994, “Sex differences in surface EMG power spectrum from dynamic and static contractions,” J. Electromyography and Kinesiology, Vol. 5(1), pp. 27-36.

[9] Cobb S., A. Frobes, 1923, “Electromyographic studies of muscular fatigue in man,” American journal of physiology – Am Physiological Soc, Vol. 65, pp. 234-251.

[10]Coorevits, P., Danneels, L., cambier, D., Ramon, H., Druyts, H., Karlsson, J. S., De Moor, G. and Vanderstraeten, G., 2008, “Test-retest Reliability of Wavelet – and Fourier based EMG (instantneous) Median Frequencies in the Evaluation of Back and Hip Muscle Fatigue during Isometric Back Extensions,” J. Electromyography and Kinesionlogy, Vol. 18(5), pp. 798-806.

[11] Creshaw AG, Karlsson S, Gerdle B, Fiden J., 1997, “Differential responses in intramuscular pressure and EMG fatigue indicators during low- vs. high-level isometric contractions to fatigue,” Acta Physiol Scand, Vol. 160, pp. 353-361.

Research on Influence of Job Characteristics of Social Workers at Welfare Institution for the Disabled on their Emotional Labor

Jong-Pil Kim*, Sung-je Cho**

*Department of Education at Dongbang Graduate University, 1-34, Isa-dong, Dong-gu, Daejeon, 300-270, KOREA

**Department of Education at Dongbang Graduate University, Seoul, Korea, 60, Seongbuk-ro 28gil, Seongbuk-gu, Seoul 136-823, KOREA

Abstract

The purpose of this research is to analyze influence of job characteristics of social workers at welfare institution for the disabled on their emotional labor. The study targets are the social workers at the welfare institutions for disabled in Daejeon Metropolitan City and a survey was conducted from the 5th of January to the 29thof February in 2016. For analysis, SPSS WIN 18.0 and Amos 21.0 programs were used to conduct t-test, one-way analysis of variance, correlation analysis and multiple regression analysis at a significance level of 5%. Firstly, according to the research result, the level of emotional labor of social workers at welfare institutions for the disabled varied significantly upon their gender and age group, while it is found to be independent of marital status, religions and education levels. Secondly, at a significance level of 5%, it was found that the working period, average working hours per week, conditions on working contract and annual income have no significant influence on the level of emotional labor. The implication of this study is that it serves the fundamental information for policies on enhancement of the emotional labor of social workers at the welfare institutions for the disabled in the future.

Key Words: Welfare institution for the disabled, social worker, emotional labor, job characteristics

1. Introduction

Recently, due to the aging society and industrial disasters, the number of disabled population has been increasing. According to the National Statistics Portal, the number of the disabled registered in 2015 are 2,494,460. The number has increased by 153.6% from 958,000 in 2011 [1].

The disabled people at the institutions experience various inconveniences in their daily life. In fact, it is especially difficult for the people with intellectual disabilities to enter a company and start economic activities than those with physical disabilities. For such reason, the disabled require care of family members or social workers at institutions [3]. Moreover, there are researched being conducted on the relevant problems and current status in the field, as well as on the social workers who serve at institutions for the disabled. Unlike the general welfare institutions, those for the disabled require a lot more professional knowledge and administrative abilities in order to provide appropriate services for the patients’complicated situations and life-cycle stages [4]. Accordingly, this research aims to focus on the on importance of the emotional labor of social workers at the welfare institutions for the disabled to investigate general influences thereon and to analyze the influence of job characteristics of social workers on their level of emotional labor.

1.1 Issues Raised

Research question 1. How does the level of emotional labor of social workers at the welfare institutions for the disabled vary upon the general characteristics?

Research question 2.How does the level of emotional labor of social workers at the welfare institutions for the disabled vary upon their job characteristics?

2. Theoretical Background

Unlike the profit organizations, social welfare institutions pursue the service-centered altruism. Specifically, the emotional labor of assistants for the disabled has statistically significant and direct influences on the exhaustion. Accordingly, in this study, the partial mediating effect of the emotional labor of assistants for the disabled on the exhaustion is verified to be having a direct and significant influence [5]. Therefore, the role of social workers, who are different from the assistants for the disabled, is extremely critical in providing services to the disabled. Thus, this research aims to study the job characteristics and emotional labor of the social workers at the welfare institution for the disabled.

3. Study Methods

3.1 Research Targets and Methods

For this study, 157 social workers at the welfare institutions for the disabled in Daejeon were surveyed from the 5th of January to the 29th of February in 2016. The sample size of the research target is deduced using G*Power 3.1 program, a statistical power analysis program, based on the Cohen’s sampling formula. The minimum sample size is calculated to be 119, with a significance level of 5%, test power of 95% and effect size of 0.15 mid-exponential index. Consequently, ofthe total number of 200 survey sheets, unanswered or inappropriate results were eliminated and 157 sets of results were used for the analysis. Additionally, one-to-one interview with the surveyor was conducted.

3.2 Research Tools

3.2.1 Social Demographical Characteristics

The terms developed by investigation on community health [6] were revised and improved to be used

as a research tool. Regarding the demographical characteristics, survey questions were designed on the personal status. The questions include information on gender, age group, marital status, religion, education level, career experience, average working hours, conditions on labor contract, annual income and possession of licenses.

3.2.2 Scale of Emotional Labor

For the measurement of level of emotional labor, the survey questions used in a study of Minjoo Kim (1998) [7], which was based on the research of Morris & Feldman (1996), were rearranged for the research on the exhaustion of workers at institutions for the disabled and the organizational characteristics thereof. This tool is scaled from 1 to 5, with higher number indicating the more emotional labor. The reliability of this scale (Cronbach’s a) is .707.

4. Research Result

4.1 General and Job Characteristics of Social Workers at Welfare Institutions for the Disabled

As a result of investigation on the general characteristics of social workers at welfare institutions for the disabled, 37.6% ‘males’ and 62.4% of ‘females’ have responded. The ‘20s’ was the largest age group (35.7%), followed by ‘30s’ (26.8%), ‘40s’ (18.5%) and ‘50s or over’ (19.1%). 51.0% responded to be single, while 45.2% responded to be married. 55.4% of the respondents were ‘Protestants’, 20.4% has ‘no religion’ and 9.6% has ‘other’ religions.. The majority were ‘graduated from university’ (52.9%), followed by ‘graduated from junior college’ (35.7%) and ‘graduated from graduate school or higher’ (11.5%).

As a result of investigation on the job characteristics of social workers at welfare institutions for the disabled, the majority worked for ‘less than 3 years’ (42.7%), followed by ‘5-10 years’ (27.4%), ‘3-5 years’ (24.2%) and ’10 years of more’ (5.7%). 36.3% responded to be working average of ‘41-45 hours’ per week, 33.8% answered ’46-50 hours’ and 26.1% answered ’51 hours or more.’ The majority has condition of the job contract as ‘regular worker’ (75.8%) and the rest as ‘contract worker’(24.2%). Regarding the annual income, 35.0% answered ’20-25 million won,’ 26.1% answered ‘25-30 million won,’ 21.7% answered ‘15-20 million won’ and 10.8% answered ‘less than 15 million won.’

4.2 Emotional Labor

As a result of investigation on the emotional labor of social workers at welfare institutions for the disabled, it is found that the average level their emotional labor is 3.09. The result shows that the level of emotional labor of social workers at welfare institutions for the disabled is higher than the average.

5. Hypothesis Verification

5.1 Influence of the general characteristics of social workers at welfare institutions for the disabled on their emotional labor

As a result of investigation on influence of the general characteristics of social workers at welfare institutions for the disabledon their emotional labor, it was found that the level of emotional labor significantly depends on the genders and age groups of the workers, whereas it is independent of their marital status, religion and education level. In terms of the gender, the average level of emotional labor of ‘male’ social workers is found to be 2.96, whereas that of ‘female’ social workers is found to be 3.17, indicating that the female social workers experience higher emotional labor. In terms of the age groups, the level of emotional labor was found to be the lowest for those in 50s or over, followed by those in 30s, 20s and 40s.

5.2 Influence of the job characteristics of social workers at welfare institutions for the disabled on their emotional labor

As a result of investigation on influence of the general characteristics of social workers at welfare institutions for the disabled on their emotional labor, it was found that, at a significant level of 5%, the level of emotional labor is independent of the working period, average working hours per week, conditions of job contract and annual income of the social worker at welfare institutions for the disabled.

6. Discussion and Conclusion

In this study, influence of job characteristics of social workers at the welfare institution for the disabled on their emotional labor was analyzed. The discussions on the research results are as follows.

First, the influence of general characteristics of social workers at the welfare institutions for the disabled was investigated. As a result, it was found that the level of emotional labor of social workers at the welfare institutions is significantly influenced by their gender and age group, whereas it is independent of their marital status, religion and education level. In terms of the gender, the average level of emotional labor of ‘male’ social workers is found to be 2.96, whereas that of ‘female’ social workers is found to be 3.17, indicating that the female social workers experience higher emotional labor. In terms of the age groups, the level of emotional labor was found to be the lowest for those in 50s or over, followed by those in 30s, 20s and 40s. Such result semantically corresponds to a research result that showed the emotional labor of community nurses is significantly influenced by their career experience, while it is independent of their marital status [8].

Second, the influence of job characteristics of social workers at the welfare institutions for the disabled was investigated. As a result, it was found that the level of emotional labor of social workers at welfare institutions for the disabled is not influenced by their working period, conditions of job contract and annual income. Such result semantically corresponds to a research result that shows the gender, age group, guard class, education level, position, wage and welfare of community nurse is independent of their emotional labor [9].

Based on the above research result, the following suggestions can be drawn.

Firstly, the level of emotional labor of social workers at the welfare institutions for the disabled is found to be significant dependent on the gender and age group, while it is independent of the marital status, religion and education level. This result is considered to serve as the fundamental information on enhancement of the emotional labor of social workers at the welfare institutions for the disabled.

Secondly, the level of emotional labor of social workers at the welfare institutions for the disabled is found to be independent on the conditions of job contract and annual income. This result is considered to serve as the fundamental information on enhancement of the emotional labor of social workers at the welfare institutions for the disabled.

Consequently, there is a necessity for further researches on the other relevant factors on the social workers at the welfare institutions for the disabled.

References

[1] KOSIS, http://kosis.kr/, (2015).

[2] Ko, G. h., Jang, Y. S., Kang, J. W., Kim, J. W., Choi, S. Y., Jung, Y. E.: "2009 Year of the Disabled in Korea Proceedings of welfare spending and income security system compared to OECD countries", Ministry of Health, Social Affairs, (2011).

[3] Kim, S. W.: "The Impact of Job Stress on the Life Satisfaction of the Disabled Worker", Journal of Community Welfare, Vol. 44,   pp. 223-246. (2013).

[4] Choi, S. K.: A Study on Job Satisfaction of Workers at welfare Center for the Disabled, The Research Institute of Social Science, 22(4), 371-384, (2015).

[5] Lee, W., Kim, D. K.: A study on emotional labor affecting a burnout level of personal assistants - Focused on the mediating effects of job stress and job satisfaction, Social Science Research Review, 27(2), 115-140, (2011).

[6] Community Health Survey,(2011).

[7] Kim, M. J.: Effects of Emotional Labor of Hotel Workers on their Working Attitude. Tourism Research, 21(2), 129-141,(1998).

[8] Han, S. J., Kwon, M. S., Yoon, O. S., Moon, M. Y.: "Emotional Labor and Job Stress of Public Health Nurses", Korean Society of Public Health Nursing, 26(2), 314-327, (2012).

[9] Lee, E. D., You M. J.: A Study on the Effect of Emotional Hardship on Job Satisfaction and Turnover of Nurses in the correctional field, Corrction Review , (55), 129-155, (2012).

Research on the Job Stress and Job Satisfaction of Social Workers at Welfare Institution for the Disabled

Jong-Pil Kim*, Sung-je Cho**

*Department of Education at Dongbang Graduate University

1-34, Isa-dong, Dong-gu, Daejeon 300-270, KOREA

**Department of Education at Dongbang Graduate University, Seoul, Korea

60, Seongbuk-ro 28gil, Seongbuk-gu, Seoul 136-823, KOREA

Abstract

The purpose of this research is to investigate the job stress and job satisfaction of social workers at welfare institution for the disabled. The study targets are the social workers at the welfare institutions for disabled in Daejeon Metropolitan City and a survey was conducted from the 11th of February to the 10th of March in 2016. For analysis, SPSS WIN 18.0 and Amos 21.0 programs were used to conduct t-test, one-way analysis of variance, correlation analysis and multiple regression analysis at a significance level of 5%. Firstly, according to the result, the level of job stress of social workers at welfare institutions for the disabled varied significantly upon their marital status, while it was found to be independent of gender, age group, religion, education level and annual income. Specifically, social workers, who are single, were found to be experiencing higher level of job stress. Secondly, the level of job satisfaction of social workers at welfare institutions for the disabled varied significantly upon their education level and annual income, while it is found tobe independent of gender, age group, marital status and religions. The implication of this study is that it serves the fundamental information for policies on enhancement of the job stress and job satisfaction of social workers at the welfare institutions for the disabled in the future.

Key Words: Welfare institution for the disabled, social worker, job satisfaction, job stress

1. Introduction

The recent development of medical technology and enhancement of life quality, the society has started aging. Accordingly, the number of welfare institutions for the aged is increasing every year to meet the increasing demand for welfare for the aged. As of 2015, the statistics on the welfare institutions for the aged shows that the number of welfare institution for the aged has increased annually from 69,238 in 2010 to 73,774 in 2014 [1]. On the side of welfare for the aged, welfare for the disabled is being issued as well. According to the National Statistical Office in 2015, the number of the disabled in institution has increased annually from 47,627 in 2005 to 80,846 in 2014 [2]. As the number of such social welfare institutions has increased, the number of social workers therein has been increasing as well.Nevertheless, the workers of social welfare institutions are working in a poor environment with lower wage and harder physical labor than workers in other fields. Consequently, the problem of efficient management of social welfare institutions due to decreasing job satisfaction of social workers therein is an important issue on the rise [3]. Thus, this study aims to focus on the job stress and job satisfaction of social workers at the welfare institutions for the disabled and to investigate general influences thereon.

1.1 Issues Raised

Research question 1. How does the level of job stress of social workers at the welfare institutions for the disabled vary upon their general characteristic?

Research question 2. How does the level of job satisfaction of social workers at the welfare institutions for the disabled vary upon their general characteristics?

2. Theoretical Background

Currently, there are researches being conducted on the jobsatisfaction of social workers at the welfare institutions for the disabled and the various problems and situations that are facing [3-6]. Regarding the job satisfaction, it was found that the workers at welfare institutions for the disabled have more awareness on the distributive justice than those at welfare institutions for the aged. Therefore, it is essential to understand that the level of job satisfaction differs for workers at institutions for the aged and that for the disabled as their satisfaction on compensations for their work or past experiences, fairness in complaint process and etc. varies [4]. Accordingly, this study aims to focus on an investigation on the job stress and job satisfaction of social workers at welfare institutions for the disabled.

3. Study Methods

3.1 Research Targets and Methods

For this study, 157 social workers at the welfare institutions for the disabled in Daejeon were surveyed from the 11th of February to the 10th of March in 2016. The sample size of the research target is deduced using G*Power 3.1 program, a statistical power analysis program, based on the Cohen’s sampling formula. The minimum sample size is calculated to be 119, with a significance level of 5%, test power of 95% and effect size of 0.15 mid-exponential index. Consequently, of the total number of 200 survey sheets, unanswered or inappropriate results were eliminated and 157 sets of results were used for the analysis. Additionally, one-to-one interview with the surveyor was conducted.

3.2 Research Tools

3.2.1 Social Demographical Characteristics

The terms developed by investigation on community health [6] were revised and improved to be used

as a research tool. Regarding the demographical characteristics, survey questions were designed on the personal status. The questions include information on gender, age group, marital status, religion, education level, career experience, average working hours, conditions on labor contract, annual income and possession of licenses.

3.2.2 Scale of Job Stress

The terms developed by Seungtaek Park [7] were revised and improved to be used as a research tool. For measuring the job stress, 9 terms on role-conflict, 8 terms on role-ambiguity and 7 terms on role-overload were used. The survey questions were arranged with 5-point Likert scale.

3.2.3 Scale of Job Satisfaction

For the measurement of level of job satisfaction, the survey questions used in a study of Aebyeol Kim [8], which was based on the research of Smith, Kendall and Hulin (1969), were used. The tool consists of 5 point Likert scale from ‘strongly agree’ to ‘strongly disagree,’ with the higher sum of all points indicating the higher the level of job satisfaction. The reliability of this scale (Cronbach’s a) is .683.

4. Research Result

4.1 General characteristics of social workers at welfare institutions for the disabled

As a result of investigation on the general characteristics of social workers at welfare institutions

for the disabled, 36.5% ‘males’ and 63.5% of ‘females’ have responded. The ‘20s’ was the largest age group (35.6%), followed by ‘30s’ (31.0%), ‘40s’ (17.8%) and ‘50s or over’ (15.5%). 51.4% responded to be married, while 43.7% responded to be single. 38.5% of the respondents were ‘Protestants’, 32.5% has ‘no religion,’ 12.4% has ‘other’ religions, 8.3% was ‘Catholic’ and 8.3% was ‘Buddhists’. The majority were ‘graduated from junior college’ (49.1%), followed by ‘graduated from university’ (43.1) and ‘graduated from graduate school or higher’ (7.8%). The majority has annual income of ‘20-25 million won’ (37.1%), followed by ’25-30 million won’ (28.2%), ‘15-20 million won’ (15.2%), ‘30 million won or more’ (14.4%) and ’15 million won or less’(5.2%).

4.2 Descriptive Statistics on Job Stress

As a result of descriptive statistics investigation on the job stress of social workers at welfare institutions for the disabled, it was found that the overall average level of job stress is 2.73. The subdivisions of job stress of social workers at welfare institutions for the disabled were found to be high in the order of ‘role-overload’, ‘role-conflict’ and ‘role-ambiguity’.

4.3 Descriptive Statistics on Job Satisfaction

As a result of descriptive statistics investigation on the job satisfaction of social workers at welfare institutions for the disabled, it was found that the average level of job satisfaction is 3.00. The result shows that the level of job satisfaction of social workers at welfare institutions for the disabled is at an average level.

5. Hypothesis Verification

5.1Influence of the general characteristics of social workers at welfare institutions for the disabled on their job stress

As a result of investigation on influence of the general characteristics of social workers at welfare institutions for the disabled on their job stress, it was found that the level of job stress significantly depends on their marital status, while it was found to be independent of their gender, age group, religion, education level and annual income. Specifically, social workers who are single are found to be experiencing higher level of job stress than those who are married.

5.2 Influence of the general characteristics of social workers at welfare institutions for the disabled on their job satisfaction

As a result of investigation on influence of the general characteristics of social workers at welfare institutions for the disabled on their job satisfaction, it was found that the level of job satisfaction significantly depends on the education level and annual income of the workers, whereas it is independent of their gender, age group, marital statusand religion. In terms of education level, the level of job satisfaction was the highest for social workers who graduated from graduate school or higher, followed by the university graduates and junior college graduates. In terms of the annual income, workers who earn 25-30 million won as their annual income were found to be having the highest level of job satisfaction, followed by those with annual income of 30 million won or over, 20-25 million won and 20 million won or less, indicating the workers who earn 20 million won or less annually have the lowest level of job satisfaction.

6. Discussion and Conclusion

In this study, the job satisfaction of social workers at the welfare institutions for the disabled was analyzed. The discussions on the research results are as follows.

Firstly, it was found that the level of job stress of social workers at the welfare institutions is significantly influencedby their marital status, while it was found to be independent of their gender, age group, religion, education level and annual income. Specifically, social workers who are single were found to be experiencing higher level of job stress than those who are married. Such result semantically corresponds with a research result that shows the level of role stress of nursing teachers has a significant negative influence on their job satisfaction [9].

Secondly, it was found that the level of job satisfaction of social workers at the welfare institutions is significantly influenced by their educational level and annual income, whereas it is independent of their gender, age group, marital status and religion. The result shows that the level of job satisfaction of a social worker tends to be higher if the education level is higher and that the level of job stress and job satisfaction of a social worker tends to be lower if the annual income is lower.

Consequently, there is a necessity for further researches on the influences of the job satisfaction of social workers at welfare institutions on their life quality.

References

[1] Ministry of Health and Welfare, http://www.mv.go.kr/, (2015).

[2] Statistics Korea, http://www.kostat.go.kr/, (2015).

[3] Cha, I. S., Kim, H. M.: A Study on the Influential Factors and Policy Implications for Employees Turnover Intention in Social Welfare Centers, GRI REVIEW, Vol. 14, No. 2(2012).

[4] Lee, D. S.: The Effects on the Work Attitude to the Perception of Justice in Employee of the Social Welfare Facilities: Focused on the Aged and the Disabled Social Welfare Facilities, Local Government Studies, Vol.12(2). pp.133-152(2008).

[5] Kim, J. P.: Research on the Job Satisfaction of Social Workers in Welfare Facilities for the Disabled, Advanced Science and Technology Letters 101: 102-105(2015).

[6] Community Health Survey (2011).

[7] Park, S. T.: A Study on the Factors Affecting on Turnover Intention and Burnout of Social Workers : Focusing on Job Stress and Organizational Commitment, Graduate School of Won kwang University, Ph.d( 2012).

[8] Kim, A. B.: A Study on the welfare leisure facilities for the elderly of Social workers' Emotion Labor on burnout,  Department of Social Welfare College of Social Development Chung-Ang University, (2014).

[9] Kim, K. H., Cha, E. J.: Effects of Role Stresses on Turnover Intention Among Child Care Teachers: Focused on the Mediating, Effect of Job Satisfaction, Journal of Korean social welfare administration, Vol. 14(4), pp. 231-262(2012).

Human Bio-Sensor Monitoring for Autonomous Navigation System of Power Wheelchair

Moon-ShuJung*, Kuk-Se Kim*, Seong-Soo Ahn**,Joon Lee*

* Dept. of Computer Engineering, University of Chosun, Korea

**University of Dongshin, Korea

E-mail: moonshu@nate.com*

ABSTRACT

The power wheelchair is an important and convenient mobility device. The demand of power wheelchair is increasing for assistance in mobility. In this paper we proposed a robotic wheelchair for mobility aid to reduce the burden from the disabled 김d can monitors human bio-sensors. The main issue in an autonomous wheelchair is the automatic detection and avoidance of obstacles and going to the pre-designated place. The proposed algorithm detects the obstacles and avoids them to drive the wheelchair to the desired place safely with panning scan from sensors of distance measurement and monitors human bio-sensor for users. By this way, the disabled will not always have to worry about paying deep attention to the surroundings and his path.

Key Words : Power Wheelchair, Bio-Sensor Monitoring, Autonomous Navigation

1. Introduction

Nowadays with advancement in technology and aging society, the number of disabled citizens is increasing. The disabled citizens always need a caretaker for daily life routines especially for mobility. In future, the need is considered to increase more. To reduce the burden from the disabled, various devices for healthcare are introduced using computer technology. The power wheelchair is an important and convenient mobility device. The demand of power wheelchair is increasing for assistance in mobility. Generally, a joystick is used at the arm of the unaffected side to mobilize the wheelchair, however, if both arms are affected, the leg of unaffected side is used. The use of power wheelchair is convenient. However, the driving of conventional wheelchair may cause a burden to the disabled due to continuous use of joystick and always paying deep attention to the surroundings and to the obstacles on the drive-way. Then the capacity to recognize path condition and to avoid obstacles is required. Moreover, the user has to be proficient in driving the wheelchair.

In this paper we proposed a robotic wheelchair for mobility aid to reduce the burden from the disabled. The main issue in an autonomous wheelchair is the automatic detection and avoidance of obstacles and going to the pre-designated place. The proposed algorithm detects the obstacles and avoids them to drive the wheelchair to the desired place safely. By this way, the disabled will not always have to worry about paying deep attention to the surroundings and his path. For the obstacle detection, we have used two infrared sensors mounted on step motors. The infrared sensors scan the area for obstacles, detect the obstacles and inform the control system. The infrared sensors are connected to the control system. The step motors and sensors are controlled by ATMEGA 128 Processor. The joystick is connected to the system. The proposed algorithm is for office like environment where the user can mobilize easily avoiding hazardous areas. The sensors will scan the area while driving the wheelchair, and signal the control system. The control system, on finding an obstacle, will consider it as hazardous and will stop the motion by controlling the joystick. After stopping the motion, it decides which direction to move. The purpose of the system is to avoid the obstacle and keep on moving in the same direction to go to the desired place. The key issue in this system is to keep the motion of the wheelchair in the same direction after avoiding the obstacle as it was before stopping. And this system can monitor human bio-sensors which get mobile bio-sensor. This system has 3 bio-sensors which are ECG, PPG, Skin Temperature. This paper show how can detect bio-sensors monitoring for users.

2. Panning scan of distance measurement sensors and autonomous navigation system of fuzzy control

In this paper, we have shown the setting up of the model theory of the autonomous navigation mechanism of power wheelchair for controller design. Figure 1 shows the model theory of mechanism for designing the best suited controller [1].

Figure 1. Kinematic model of an ARV

Figure 1 shows the model theory of mechanism expression of autonomous navigation power wheelchair in two wheel drive for modeling.

(1)

(2)

(3)

This expression is restricted to equation 2, in other words it satisfies the complete condition of the surface of the path, i.e., it does not slide on a slide surface [2].

(4)

This drive method is steering method based on the difference between the speeds of both wheels. Equation 1 is not for control; however, it is the best generation theory of mechanism of the two wheel drive of power wheelchair. Because Equation 2 is not a completed integral calculus; is has the non-holonomic feature [3]. In this paper we have proposed to follow the two wheel drive method basically, using two assistance drive-wheels and two drive wheels to control the non-holonomic feature [4].

2.1 Sensor panning scan algorithm for course driving:

This makes our algorithm to decide for position and posture of autonomous navigation power wheelchair by defining the course form. Figure 2 shows the relation between coordinates of sensors and obstacle or Wall.

Figure 2. Coordinates of sensors and Obstacle or Wall

In case of movement towards the obstacle, the new coordinates can show 4x4 determinate of standard coordinates. That is, to express the direction of obstacle coordinate axis, and to express the vector translation of moving obstacle coordinates origin, the composition of three vectors can be used to create a matrix. The point p1 = [x, y] T is used to create a 2x2 matrix, that the random point p shows [x, y] T on 2 dimension system.

(5)

Obstacle recognition is done by panning scan of x coordinates of course form. Next shows geometrical relation.

(6)

(7)

Following is the distance measurement process by panning scan.

① Distance sensor detects obstacle in a range of 50cm, and stops. ② Then starts panning scan on right hand side in a range of 50cm. ③ At the first time step motor scans 5 rounds of 45° with a span of 9° panning. ④ After completing steps, ①②③, the step motor comes back to the center position. ⑤ Starting panning scan at the left side, with a distance of 50 cm from the obstacle. ⑥ At the first time step motor scans 10 rounds of 90° with a span of 9° panning. ⑦ After completing steps, ⑤⑥, the step motor comes back to the center position.

This algorithm detects obstacles on the path of autonomous wheelchair. It is done by panning scan in front of the wheelchair. Figure 3 shows the coordinates of the sensors and the obstacle.

Figure 3. Coordinates of sensors and obstacle

The following equation (8) is used to find the size of the obstacle.

(8)

This equation is used for the detection of the obstacle size.

2.2 Autonomous navigation system of fuzzy control

We propose fuzzy control algorithm for autonomous navigation power wheelchair system that has learning ability. This algorithm is used to avoid the obstacle after recognizing it while the autonomous navigation power wheelchair system is driving. Furthermore, this system learns the position and shape of obstacles by getting data from sensors every time. Figure 4 and 5 is navigation of drive path and the obstacle avoidance algorithm.

Figure 4. Both Left and Right Autonomous Navigation and Obstacle avoidance of iPw1000 algorithm.

Figure 5. Either Left or Right Autonomous Navigation and Obstacle avoidance of iPw1000 algorithm

This paper has eight assumptions for autonomous navigation power wheelchair.

Assumption1. Detect left, right of front obstacle and left obstacle

Assumption2. Detect left, right of front obstacle and right obstacle

Assumption3. Assumption1. Detect left, right of front obstacle and left obstacle isn't

Assumption4. Only detect left of front obstacle.

Assumption5. Only detect right of front obstacle.

Assumption6. Right sensor don't detect obstacle while left, forward movement

Assumption7. Left sensor don't detect obstacle while right, forward movement

Assumption8. Detect back obstacle while back drive.

The Power wheelchair decides navigation course by these eight assumptions. Assumption 1 is moving after turn right. Assumption 2 is moving as opposition direction after turn left. Assumption 3 is moving with standard position is left when don't have any learning data. Assumption 4 is void obstacle after turn right and turn left and forward movement. Assumption 5 is opposite to assumption 4. Assumption 6 is forward movement. Assumption 7 is forward movement after turn left. Finally, Assumption 8 is stop.

2.2.1  Autonomous navigation learning algorithm

In this paper, the algorithm needs some learning for autonomous navigation power wheelchair using distance sensors. That is, learning to move obstacle area anywhere.

Figure 6 shows the learning algorithm.

Figure 6. Learning algorithm of autonomous navigation

This algorithm learns navigation course to change start point for the best navigation course when initially wheelchair starts. Power Wheelchair can detect obstacle, total driving distance, and learns the best navigation course.

2.2.2 Fuzzy control for avoid obstacle.

The Power Wheelchair when sometimes meet unexpected obstacles, it sets the best navigation course for driving. It should be control speed of power wheelchair for can avoid obstacle according as distances. The speed of the Power Wheelchair should be controlled so that it can avoid obstacles according to appropriate distance. In this paper we used fuzzy controller to control the speed of the Power wheelchair system.

3. Human Bio-Sensors Monitoring Using ZigBee

We have a 3 bio-sensors ECG(Electrocardiogram), PPG(Photo-plethysmography), Skin Temperature and using TinyOS and NesC for this paper. Figure 7 shows sensor mote protocol structure.

Figure 7. Sensor-Mote Protocol Structure

This system 7 byte is base protocol and 6 byte is data for protocol structure and CRC is 3 bytes in Figure 7.

3.1 Bio-Sensor Monitoring using ZigBee

This system send bio-sensors signal to main system anytime and anywhere for users [10]. This system sends alarm signal to family or doctors if user’s bio-sensors detect abnormal signals using Web or Mobile Phone. Figure 8 shows to collect the structure of Bio-Sensor information. Sensor signal sends base mote from each node ID independently.

Figure 8. The Structure of Bio-Sensor information

Figure 8 show how to get sensor data and Figure 9, 10, 11 shows each protocol structure and transmission system.

①  ECG Sensor Measurement

Figure 9. ECG Sensor Protocol

Figure 10. ECG Measurement Method

ECG is one of the basic life signals and is a conduction signal indicating cardiac action potential, and it is used to monitor condition and operation of heart. ECG is a cardiac action potential graph measured by leading myocardial stimulation potential created from venous sinus and advancing into the atrium and the cardiac ventricle at two points. Like at Figure 10, methods of getting ECG are divided into unipolar lead, precordial lead, and standard limb lead including both hands, right hand and left foot, left hand and left foot but this paper check precordial lead.

②    PPG Sensor Measurement

Figure 11. PPG Sensor Protocol

Pulse Oximeter sensor module measures pulse using index finger. Oxygen saturation means a percentage of concentration of hemoglobin oxide combined with oxygen over the whole hemoglobin. If oxygen is reduced, energy metabolism inside cell of tissue is limited. In this case, oxygen is not supplied for logn, which might stop activity of the human body. Oxygen saturation is measured by using pulse oximeter. The measurement principle is to transmit each different-waved red light and infrared light on fingers, reflect if on body surface, and seek for absorbance by wave for artery pulse element attenuated according to Beer-Lambert rules and measure through the absorbance.

Figure 12. The principle of PPG Sensor measurement

Hmote240 is RF communication which sends main system from base mote after get ECG, PPG, Skin Temperature sensor data.

4. The Result of Test and Implementation

We have set up ARM System for iPw1000 system (Intelligent Power Wheelchair System) test and implementation, sensor platforms, and Control systems. This is Auto Control and driving using joystick signal without change in the basic system of P110 power wheelchair.

Figure 13. Power Wheelchair

Following Figure 14 shows Intel Pxa270 ARM System for test.

Figure 14. IPw1000 Main Control Embedded System

Figure 15 is the architecture of  iPw1000 System.

Figure 15. IPw1000 System

4.1 Bio-Sensor Monitoring system

Figure 16. Bio-Sensors and H-MOTE

Figure 16 show h-mote and bio-sensors using ZigBee. We use embedded linux system for bio-sensor monitoring. Intel Pxa270 system and Qplus system figure 14. Figure 17 show monitoring system screen.

Figure 17. Bio-Sensor monitoring system using ZigBee

4.2 Panning scan System of Sensors

We produce a sensor system for test, using sensor panning scanning. Distance measurement sensor is Sharp GP2Y0A2YKOF and distance measurement is from 10 to 150cm. Step motor is NK243-01AT 1.8 degree. Data measurement gets distance data to move step motor at steps of 1.8 degrees from distance measurement sensor.

Figure 18. Panning Scan of Sensor

Sensor system scans left and right by panning scan using step motor. The obtained data is transmitted to ATmega128 system of sensor system by ADC Convertor to calculate 10bit sampling signal data. This data use learning data for autonomous navigation system. Figure 18 and 19 shows system architecture of Obstacle detection using distance measurement sensor panning.

Figure 19. Obstacle detection system by distance measurement sensors and panning scan

4.3 Implementation of Autonomous Navigation System using fuzzy control

The map environment has 255cm width and 700cm height for test. This test sets up Obstacles, 200cm on left of y-axis, 100cm width and 30cm height near 600cm, 400cm on right of y-axis. Driving course goal figure out the best driving courses from 0 to 650 coordinate of y-axis. The power wheelchair size is 50cm in width and 100cm in height. Figure 20 is the environment map for test.

Figure 20. Testing map for autonomous navigation

While testing of the power wheelchair, it drives on course after setting up the start point to A(75, 0), B(128, 0) and C(175, 0) of center-axis of the wheelchair. Figure 21, 22, and 23 show result of the simulation test of autonomous navigation driving.

Figure 21. Testing result of starting left at A(75, 0)

Figure 22. Testing result of starting right at C(155,0)

This  increases  the  moving  distance. The Power Wheelchair detects obstacle in a range of 50cm ahead, assumes that power wheelchair speed is regular. Power wheelchair keeps up 30cm of each side to get the point values of Obstacle while driving on a side of obstacle. At first start of Power wheelchair, it detected obstacles three times at A(75,0)point. Total distance moved by the power wheelchair was 740cm. At the second start, while driving straight line at B(128,0)point, Power Wheelchair didn't detect obstacle. Total distance moved by the power wheelchair was 650cm. At the third start, Power wheelchair detected obstacle two times and turned 4times at C(175,0)point. Total distance moved by the power wheelchair was 680cm. Finally, the Power wheelchair decides that the best course is start point of B because the moving distance was the shortest.

5. Conclusion

The important point of autonomous power wheelchair system is to go to goal, and do it very safely and human bio-sensor monitoring. The purpose of system developed is to make a very safe Power Wheelchair System without any discomfort for the user. In this paper, we have proposed the autonomous navigation driving algorithms for autonomous driving and that can avoid obstacle using fuzzy control for the instant situations and human bio-sensor monitoring system. This system can figure out the best navigation course and can set up start point by learning data. This system is not yet completed, and can get better data from ultra sensor and laser sensor for measurement of distance from obstacles and to be human interface is more than small. This system will be further developed to Smart Power Wheelchair having camera system which is going to make 3-D shape matching sensor data to avoid obstacles and measure the distance from obstacles.

References

[1] Rory A. Cooper, Lana M. Widman et al. “Force Sensing Control for Electric Power Wheelchairs”, IEEE Transaction on control system technology, Vol.8:112-17, 2000, Jan

[2] Takanori Fukao, Hiroshi Nakagawa and Norihicko Adchi, "Adaptive Tracking Control of a Nonholonomic Mobile Robot", IEEE Transaction on Robotics and Automation, Vol. 16, No. 5, Oct. 2000.

[3] Gisbert Lawitzky, "A Navigation System for Cleaning Robots", Autonomous Robots, Vol. 9, No.3, pp 255-260, 2000.

[4]  X. Yun and Nilanjan Sarkar, "Dynamic Feedback Control of Vehicle with Two Steerable Wheels", IEEE International Conference on Robotics and Automation, pp.3105-3110, April 1996.

[5] Kim, S. C., "A Study on Collision Avoidance system of Vehicle using Fuzzy Control Algorithms," Dong-a University, pp. 28-47, 2002.

[6] Zigbee Alliance, Zigbee Architecture Overview, Sep 2005.

[7] Design of Location based Continuous Vital sign Monitoring System using Zigbee Sensor Network and Wireless LAN, kim, Soo jung, 2006.

[8] Zigbee Alliance, Zigbee Document 053474r06, Version 1.0, June 27, 2005.

[9] J. Newbury, W. Miller, "Potential Communication Services Using Power Line Carriers and Broadband Integrated Services Digital Network," IEEE Transactions on Power

[10] I. F. Akyildiz, W, Su, Y. Sankarasubramaniam, and E. Cayirci, A Survey on Sensor Networks, IEEE Communications Magazine, August 2002.

Bridge Resistance Deviation-to-Period Converter for Resistive Biosensors

Chang-su Won*, Hoon Kim*, Won-Sup Chung*, Sang-Hee Son*

*Cheongju University

E-mail : wcs79@cju.ac.kr

ABSTRACT

A bridge resistance deviation-to-period (BRD-to-P) converter is presented for interfacing resistive biosensors. It consists of a linear operational transconductance amplifier (OTA) and a current-controlled oscillator (CCO) formed by a current-tunable Schmitt trigger and an integrator. The free running period of the converter is 1.824 ms when the bridge offset resistance is 1 kΩ. The conversion sensitivity of the converter amounts to 3.814 ms/Ω over the resistance deviation range of 0-1.2 Ω. The linearity error of the conversion characteristic is less than ±0.004 %.

Key Words : resistive biosensor, resistive bridge circuit, bridge resistance deviation-to-period converter, interface

1. Introduction

Due to their high sensitivity, resistive biosensor bridges with four sensors can be widely used in many areas, especially biomedical measurement such as blood pressure, pulse and pulse transit time (PTT), and body temperature. In order to interface these sensors with digital systems, an accurate interface circuit converting a small change of resistance into a digital readout is required. A simplest approach of converting the bridge resistance deviation into a digital form is to convert the unknown resistance to frequency or time interval. Resistance-to-frequency conversion is mainly based on a relaxation oscillator [1], [2], which consists of a resistive bridge followed by an analog voltage differentiator (i.e., differential amplifier or differential integrator). On the other hand, resistance-to -time interval conversion is based on pulse-width modulators (PWMs) [3] or current- tunable Schmitt triggers [4]. The former consists of a resistive bridge followed by PWMs and a digital time differentiator, while the latter by voltage-to-current converters, current-tunable Schmitt triggers, a ramp voltage generator, and a digital time differentiator. These converters allow linear conversion of resistance deviation over time with high sensitivity. The main disadvantages of the converters are their complex circuit configurations and relatively low conversion linearity.

In this paper a new simple bridge resistance deviation-to-period (BRD-to-P) converter with high linearity is presented. The converter consists of a linear operational transconductance amplifier (OTA) and a current-controlled oscillator (CCO) formed by a current-tunable Schmitt trigger and an integrator.

2. Circuit Description and Operation

Fig. 1(a) shows the circuit diagram of the proposed BRD-to-P converter. The resistive biosensor bridge provides the voltage outputs given by

[eqn: V _{OB} = {2 DELTA R+R _{F}} over {2R+R _{F}} V _{CC}]            (1)

where VCC is the source exciting the bridge and ΔR represents the change in resistance of the biosensors and RFis fixed resistance for free running oscillation when ΔR is zero. The bio- sensor bridge output voltage VOB is converted into its corresponding dc current IB1 by the linear OTA marked LOTA

[eqn: I _{B1} =G _{m} V _{OB} =G _{m} V _{CC} {2 DELTA R+R _{F}} over {2R+R _{F}}]    (2)

where Gmis the transconductance gain of the LOTA. This dc current IB1 controls the oscillation period of the CCO so that the oscillation period is proportional to ΔR of the resistive biosensor. Two voltage amplifiers (one is composed of the comparator and R1 and the other OTA1 and R2) connected in a positive- feedback manner form a Schmitt trigger whose threshold voltage is directly proportional to the dc current IB1. OTA2 and the timing capacitor C form an integrator whose integration time constant is proportional to IB2. The waveforms associated with the oscillator are shown in Fig. 1(b). From these waveforms, one can find that the oscillation period T is given by

[eqn: T=4CR _{2} {I _{B1}} over {I _{B2}}]                (3)

Substituting (2) into (3), we have

[eqn: T=4CR _{2} {G _{m} V _{CC}} over {I _{B2}} LEFT ( {2 DELTA R} over {2R+R _{F}} + {R _{F}} over {2R+R _{F}} RIGHT )] (4)

(a)

(b)

Figure 1. (a) Circuit diagram of the proposed bridge BRD-to-P converter. (b) Output waveforms of the BRD-to-P converter.

Equation (4) indicates that the oscillation period is linearly proportional to ΔR of the resistive biosensor. The digital information can be obtained by counting the period T with an external clock. The conversion sensitivity of the converter is given by

[eqn: {Partial T} over {Partial DELTA R} = {8CR _{2}} over {I _{B2}} {G _{m} V _{CC}} over {2R+R _{F}}]          (5)

It is noticeable that the conversion sensitivity can be controlled by the dc bias current IB2.

3. Linear OTA

A circuit diagram of a LOTA designed for the BRD-to-P converter is shown in Fig. 2 [5], [6]. It consists of a linear transconductor formed by transistors Q1~Q8 and an emitter- degeneration resistor RE, a translinear current gain cell Q9~Q12, and three Wilson current mirrors Q13~Q21. The LOTA converts the differential input voltage Vin into the single- ended output current Iout expressed as follows:

[eqn: I _{out} = {I _{Y}} over {I _{X}} {V _{IN }} over {R _{E}} =G _{m} V _{i`n}]          (6)

The transconductance gain Gm is given by (IY/IX)(1/RE). Equation (6) indicates that the transconductance gain of the LOTA can be adjusted by varying the ratio of the dc bias currents IY and IX. The input linear range of the LOTA is [eqn: LEFT | V _{i`n} RIGHT | LEQ R _{E} I _{X}].

Figure 2. Circuit diagram of a LOTA designed for the BRD-to-P converter.

4. Experiment Results

The LOTA circuit shown in Fig. 2 was breadboarded using the commercially available transistor arrays. The transistor arrays used were 2N2222 (npn) and 2N2907 (pnp). The resistors were RC = 150 kΩ and RE = 1 kΩ. The current sources were implemented with a Wilson current-mirror circuit. The bias currents IX and IY was set to 50 μA and 10 mA, respectively. The supply voltage VCC was 5 V and VEE was 5 V. The measured linearity error in its dc transfer characteristic of the LOTA was less than ±0.2 % in the differential input voltage range of ±20 mV.

The BRD-to-P converter circuit shown in Fig. 1(a) was built using the following discrete components: LM13600 for the OTA1 and the OTA2, LM318 for the comparator. The passive component values were adopted as follow:  C = 5 nF, R1 = 10 kΩ, R2 = 2 kΩ, and RF = 1 ΩThe bias current IB2 was set to 10.4 μA. All measurements were performed at the supply voltages of VCC = 5 V and VEE = 5 V.

One arm of the bridge was constructed with a resistor of 1 kΩin series with a potentiometer of 10 Ω. All resistors are 0.5 % tolerance. The free running period of the converter is 1.824 ms when the bridge offset resistance R is 1 kΩ. Fig. 3 shows the measured period changes when ΔR was changed in 0.1 Ω steps from its fixed offset value of 1 kΩ. Resistance was measured using the Agilent digital multimeter type 3458A, and the HP frequency counter 53131A with a resolution of 0.01 µs in "period mode" was used to measure the period of the output rectangular waveform. Fig. 3 indicates that the conversion sensitivity of the converter amounts to 3.814 ms/Ω over the resistance deviation range of 0-1.2 Ω.

Figure 3. Measured period versus resistance deviation and its linearity error.

This sensitivity is comparable to that of the previous work [4]. The linearity error of the conversion characteristic is less than ±0.004 %, which is about three times lower than that of the previous work.

5. Conclusions

A new circuit has been described which converts a resistance change in the bridge into its equivalent period change. The design principle and circuit configuration of the converter is simple. Besides these, the converter features high sensitivity and linearity for small change of the resistive biosensors. Because of these advantages, the converter is expected to find wide applications in the signal processing of resistive bridge sensors.

References

[1] K. Mochizuki and K. Watanabe, "A high-resolution, linear resistance -to-frequency converter", IEEE Trans. Instrum. Meas., vol. 45, no. 3, pp. 761-764, June 1996.

[2] S. Kaliyugavaradan, "A linear resistance-to-time converter with high resolu- tion", IEEE Trans. Instrum. Meas., vol. 49, no. 1, pp. 151-153, Feb. 2000.

[3] C. B. Morlan, B. O. Buafull, G. M. Morales, and A. Regueior-Gomez, "A low-cost circuit with direct digital output for pressure measurement", IEEE Trans. Instrum. Meas., vol. 48, no. 4, Aug. 1999.

[4] H. Kim, W.-S. Chung, S.-H. Son, and H.-J. Kim, "A bridge resistance deviation-to-time interval converter for resistive sensor bridges", IEICE Electronics Express, vol. 4, no. 10, pp. 326-331, May 2007.

[5] W.-S. Chung, K.-H. Kim, and H.-W. Cha, "A linear operational transconductance amplifier for instrumentation applications", IEEE Trans. Instrum. Meas., vol. 41, no. 3, June 1992.

[6] W.-S. Chung, H.-W. Cha, and S.-H. Son, "A low-voltage low-power bipolar transconductor with high linearity", IEICE Trans. Fundamentals, vol. E88-A, no. 1, pp. 353-356, Jan. 2005.

Vocal Tract Modeling Based Unfixed Sectionlength Acoustic Tubes

Dong-jun Kim*

*Choungju University

E-mail : djkim@cju.ac.kr

ABSTRACT

This paper proposes a new vocal tract model with unfixed sectionlengths, which uses the reduced lattice filter for modeling the vocal tract. This model transforms the lattice filter to reduced structure and the Burg algorithm to modified version.

When the conventional and the proposed models are implemented with the same order of linear prediction analysis, the proposed model can produce more accurate results than the conventional one. To implement a system within similar accuracy level, it may be possible to reduce the stages of the lattice filter structure. The proposed model produces the more similar vocal tract shape than the conventional one.

Key WordS : Vocal tract modeling, Unfixed sectionlength acoustic tube, Lattice filter, Area function

1. Introduction

Speech production can be viewed as a filtering operation in which a sound source excites a vocal tract filter. The vocal tract is the most important component in the speech production process[1].

For the determination of the vocal tract shapes, X-ray techniques have been used as direct methods. Several indirect methods for computing the vocal tract area function from measured acoustic data that avoid the drawbacks of X-ray techniques have been published by many researchers[2-4]. There are two methods for acoustically estimating the vocal tract area function ; one is the linear prediction acoustic tube(LPAT) method and the other is the lip impulse resonance method. In LPAT method, which has many advantages, the vocal tract is modeled as a chain of cylinders of varying cross-sectional area. The most common implementation assumes equal length of tube sections. Therefore, to model complex vocal tract shapes, a large number of tube sections are needed[5].

This paper proposes a new vocal tract model which is a kind of acoustic tube model with unfixed sectionlengths. The method uses the reduced lattice filter for modeling the vocal tract. This model transforms the lattice filter to reduced structure and the Burg algorithm to modified version. The USLAT vocal tract model is designed considering actual vocal tract shapes. Using this model, vocal tract area estimation is executed.

The new vocal tract model is called unfixed sectionlength acoustic tube model, and the abbreviation is USLAT. The advantages of the USLAT model are as follows. First, redundancy of conventional vocal tract model which use equal tube sections can be eliminated in this proposed model. Second, when the conventional and the USLAT models are implemented with the same order, the USLAT model can produce more accurate results than the conventional one. Third, to implement a system within similar accuracy level, it may be possible to reduce the stages of the lattice filter structure. And finally, the USLAT vocal tract model can coincide well with the speech production process.

2. Vocal Tract Modeling

2.1. Unfixed Sectionlength Acoustic Tube Model

Figure 1 shows the conventional acoustic tube model[2].

Fig. 1. Conventional acoustic tube model

In this figure, the left side corresponds to glottis and this is the excitation source of vocal tract filter. The right side is lips. [eqn: A] represent the vocal tract area of the relevant section and [eqn: TRIANGLE l] is sectionlength of this conventional acoustic tube model.

The proposed new vocal tract model is shown in figure 2.

Fig. 2. Proposed acoustic tube model

This model is based on the actual vocal tract shapes. If any adjacent sections, [eqn: i] and [eqn: i+1] have little difference in real vocal tract area, these two sections can be combined into a section with twice [eqn: TRIANGLE l]. In this study, the USLAT is composed of eight sections with equal sectionlength and one section with twice sectionlength. So, the model order of this structure is nine.

2.2. Estimation of Vocal Tract Area Function

The sampling frequency and the number of sections is constrained by equation (1).

[eqn: F _{s} = {Mc} over {2l}]	(1)

Where, [eqn: F _{s}], [eqn: M], [eqn: c] and [eqn: l] means sampling frequency, linear prediction order, sound velocity(340m/sec) and vocal tract length, respectively. As long as the sampling frequency is constant, the vocal tract length is assumed to be fixed[6].

Some amount of pre-emphasis is necessary to eliminate the source characteristic and the lip radiation characteristic.

[eqn: y[n]=s[n]- mu s[n-1]]                     (2)

Where, [eqn: s[n]], [eqn: y[n]] represents raw speech signal, preemphasized signal and [eqn: mu] is preemphasis factor. The source characteristic is modeled by about -12dB/oct and the lip radiation characteristic is +6dB/oct. For steady-state vowels, the reasonable choice of [eqn: mu] is 1.

In the case of vowel-type speech generation in which the source is located at the glottis with no side branch, there exist two possibilities for boundary conditions at the lips and glottis for sound-wave propagation through the vocal tract. One was used by Nakajima and Wakita and the other was by Atal[2,3]. The Wakita's condition seems to give reasonable results in the form of area function.

The vocal tract area of i-th section is computed by equation (3) and (4).

[eqn: gamma _{i} = {A _{i+1} -A _{i}} over {A _{i+1} +A _{i}}]                           (3)

[eqn: A _{i+1} =A _{i} {1+ gamma _{i}} over {1- gamma _{i}}]                          (4)

In this equation, [eqn: gamma _{i}] is reflection coefficients, and these coefficients are extracted by LP analysis[6].

2.3. Modified Burg Algorithm

In the lattice section for delay 1, the reflection coefficients and the forward/backward errors can be computed by the equation (5) and (6)[6].

[eqn: gamma _{p} = {2 sum _{n=p} ^{N-1} e _{p-1} ^{+} (n)e _{p-1} ^{-} (n-1)} over {sum _{n=p} ^{N-1} [e _{p-1} ^{+} (n) ^{2} +e _{p-1} ^{-} (n-1) ^{2} ]}]          (5)

[eqn: e _{p} ^{+} (n)=e _{p-1} ^{+} (n)- gamma _{p} e _{p-1} ^{-} (n-1)# e _{p} ^{-} (n)=e _{p-1} ^{-} (n-1)- gamma _{p} e _{p-1} ^{+} (n)]           (6)

But, in the combined section, the corresponding lattice section must have delay 2. So, conventional equations (5) and (6) are modified to equations (7) and (8).

[eqn: gamma _{p} = {2 sum _{n=p} ^{N-1} e _{p-1} ^{+} (n)e _{p-1} ^{-} (n-2)} over {sum _{n=p} ^{N-1} [e _{p-1} ^{+} (n) ^{2} +e _{p-1} ^{-} (n-2) ^{2} ]}]          (7)

[eqn: e _{p} ^{+} (n)=e _{p-1} ^{+} (n)- gamma _{p} e _{p-1} ^{-} (n-2)# e _{p} ^{-} (n)=e _{p-1} ^{-} (n-2)- gamma _{p} e _{p-1} ^{+} (n)]           (8)

3. Experiments and Results

Figure 3 shows the procedure of speech analysis of this study.

Fig. 3. Procedure of speech analysis

Five Korean vowels /a/, /e/, /i/, /o/ and /u/ are used. These speech data are filtered using low pass filter. The cutoff frequency of this low pass filter is about 4.7kHz, and the slope is -34dB/oct. These filtered speech is digitized in 10kHz sampling frequency.

Figure 4 represents the estimated vocal tract area function for vowel /a/.

(a) Conventional(order:10) (b) Modified(order:10)

Fig. 4. Vocal tract area function for vowel /a/

In this figure, the bold line is real X-ray data of Fant's experiments[1] and the fine line is the estimated data.

The figure (a) is the result of conventional acoustic tube model of order 10, and the figure (b) is the result of proposed model of order 9. In each figure, the left side corresponds to lips and the right is glottis.

In figure four, the modified model show the slightly better result in the back portion. But, in figure 5, the modified method shows more similar shapes than the conventional one.

(a) Conventional(order:10) (b) Modified(order:10)

Fig. 5. Vocal tract area function for vowel /e/

Figure 6 also produces good results. In figure 7 and 8, though the order of the modified model is lower than the conventional one, the modified method shows nearly equal results with the conventional one.

(a) Conventional(order:10) (b) Modified(order:10)

Fig. 6. Vocal tract area function for vowel /i/

(a) Conventional(order:10) (b) Modified(order:10)

Fig. 7. Vocal tract area function for vowel /o/

(a) Conventional(order:10) (b) Modified(order:10)

Fig. 8. Vocal tract area function for vowel /u/

AR spectra with same order and with different order are estimated. The spectra for five vowels with same order are shown in figure 9.

(a) Vowel /a/          (b) Vowel /e/

(c) Vowel /i/          (d) Vowel /o/

(e) Vowel /u/

Fig. 9. Spectra for five vowels with same order

In this figure, the first and the second formants of the conventional model are sometimes lumped together, but the USLAT model can divide them accurately.

The spectra with different order are shown in figure 10.

(a) Vowel /a/         (b) Vowel /e/

(c) Vowel /i/          (d) Vowel /o/

(e) Vowel /u/

Fig. 10. Spectra for five vowels with different orders (USLAT : 9, conventional : 10)

4. Conlusion

In this study, a new vocal tract model with unfixed sectionlengths is proposed. And, using this model, speech analyses, such as, area function estimation and spectrum estimation are performed.

The obtained results are as follows.

First, the vocal tract area functions of the USLAT model of order 9 are more similar to real vocal tract areas than those of the conventional vocal tract model of order 10. Specially, in back vowels, outstanding similarity of the vocal tract area functions was found using the USLAT model. Second, in the AR spectra of each model order 9, the first and the second formants of the conventional model are sometimes lumped together, but the USLAT model can divide them accurately. Third, in the AR spectra of the USLAT model of order 9 and the conventional model of order 10, the first and the second formants of the USLAT model are as accurate as those of the conventional one, but the third and the forth are somewhat biarsed or lumped together. And finally, redundancy of conventional vocal tract model which use equal tube sections can be eliminated in this proposed model.

Referece

[1] G. Fant : Acoustic Theory of Speech Production, Mouton, 1970.

[2] H. Wakita, “Direct Estimation of the Vocal Tract Shape  by Inverse Filtering of Acoustic Speech Waveforms,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. AU-21, No. 5, Oct. 1973.

[3] B. S. Atal, “Speech analysis and synthesis by linear prediction of speech wave,” J. Acoust. Soc. Am, Vol. 41, pp. 65(A), 1970.

[4] J. Schroter, J. N. Larar, and M. M. Sondhi, “Speech Parameter Estimation using a Vocal Tract/Cord Model,” IEEE Int. Conf. on Acoustics. Speech. and Signal Processing, pp. 308-311, 1987.

[5] J. D. Markel, A. H. Gray : Linear Prediction of Speech, Springer-Verlag‧Berlin‧Heidelberg‧New York, 1976.

[6] T. F. Quatieri : Discrete-Time Speech Signal Processing, Principles and Practice, Prentice Hall, 2002.

[7] E.P. Neuburg, W.R. Bauer, “On the Source-Filter Model of the Vocal Tract,” IEEE Int. Conf. on Acoustics. Speech. and Signal Processing, pp. 1609-1612, 1986.

[8] H. Fusisaki, M. Ljungqvist, “Estimation of Voice Source and Vocal Tract Parameters Based on ARMA Analysis and a Model for the Glottal Source Waveform,” IEEE Int. Conf. on Acoustics. Speech. and Signal Processing, pp. 637-640, 1987.

[9] A. M. de L. Araújo, F. Violaro, “Formant Frequency Estimation Using a MEL Scale LPC Algorithm,” IEEE Int. Conf. on Acoustics. Speech. and Signal Processing, pp. 207-212, 1998.

RGB Motion Segmentation using Background Subtraction based on AMF

Alla Kim* · Yoon-Ho Kim*

*Mokwon University

E-mail : KimAlyusha@yahoo.com

ABSTRACT

Motion segmentation is a fundamental technique for analysing image sequences of real scenes. A process of identifying moving objects from data is a typical task in many computer vision applications. In this paper, we propose motion segmentation that generally consists from background subtraction and foreground pixel segmentation. The Approximated Median Filter (AMF) was chosen to perform background modeling. Motion segmentation in this paper covers RGB video data.

Key Words : background subtraction, background modeling, approximated median filter (AFM), gray-scale and color motion segmentation

1. Introduction

In order to identify moving objects from video sequence we used motion segmentation procedure in static scenes that includes background subtraction and foreground pixel segmentation.

The process of separating background and object is a common problem in vision system applications like video surveillance, object tracking, object detection and so on.

In the paper a technique of background modelling based on the Approximated Median Filter AFM) was used. Following motion segmentation step, moving objects are detected.

The organization of the paper is as follows: in section Ⅱ a description of background research is given. In section Ⅲ detection of moving objects in a video sequence is proposed. In section Ⅳ the result of experiment is presented and in section V test performance is done. Finally section VI is devoted to conclusion and pointing on some further issues.

2. Introduction

Background subtraction algorithms can be divided into 4 steps: preprocessing, background modeling, background detection and data validation[1][2].

Fig 1. Subtraction algorithm

In a pre-processing stage raw input video is changed in a form that can be used and conformed to an algorithm's goal and capability of acquisition device. Image processing tasks as frame, color, noise reduction and spatial operations are commonly used in most visual applications on the first stage. To make a real-time system some size and resolution processed can be done in order to suppress an amount of input information, also conditions of storing data should be considered properly. frame, color, noise reduction and spatial operations are commonly used in most visual applications on the first stage. To make a real-time system some size and resolution processed can be done in order to suppress an amount of input information, also conditions of storing data should be considered properly.

Background modeling is a description of the current background scene. The simplest way to model the background is to acquire a background image which doesn't include any moving objects. In some environments, the background isn't available and can always be changed under critical situations like illumination changes, objects being introduced or removed from the scene. To take into account these problems of robustness and adaptation, many background modeling methods have been developed recently. They can be categorized as follows: Basic Background Modeling, Statistical, Fuzzy and Background Estimation. Also classification can be made in term of recursion, prediction, adaptation, modality and so on[4].

Foreground detection is supposed to check whether the input pixels are foreground or background. The most commonly approach to find all pixels that match next equitation:

[eqn: LEFT | I _{t} (x,y)-B _{t(x,y)} RIGHT | >T]    (1)

Another popular foreground detection scheme is to threshold based on the normalized statistics:

[eqn: LEFT | {I _{t} (x,y)-B _{t} (x,y)- mu _{d}} over {delta _{d}} RIGHT | >T _{d}] (2)

where  and  are the mean and the standard deviation of [eqn: I _{t} (x,y)-B _{t} (x,y)]for all spatial locations (x,y). Most schemes determine the foreground threshold T or experimentally. Of course an ideal threshold should be a function of the spatial location, that it could be scaled for different types regions. For example the threshold must be higher for regions with high contrast and smaller for low contrast regions.

Another approach to introduce spatial variability is to use two thresholds with hysteresis. The basic idea is that first threshold will identify pixels whose absolute differences with the background estimates exceeded a large threshold. Then, foreground regions are grown from strong foreground pixels by including neighboring pixels with absolute differences larger then a smaller threshold. So algorithm will perform foreground detection by using a two-pass foreground detection scheme[3].

Data validation process aims to improve output. Commonly a lot of different techniques can be used to preserve false-positive and false-negative result.

3. Proposed method of moving objects detection in a video sequence

Background modelling (also referred too as background subtraction) was based on Approximated Median filter (AMF). AMF had been previously used for classification system and urban traffic monitoring [5][6]. In this paper we focus on the problem of motion segmentation and though many algorithms have been proposed in the literature we will try to extend the AFM scheme for RGB color space.

The main disadvantage of Median filtering is that the previous frames of video are buffered and background modeled as the median of buffered frames. And though even if Median filtering is robust enough it'll require a lot of memory space. Due to the success of non-recursive median filtering, AMF was devised as efficient recursive approximation of the median filter by McFarlane and Schofield[7].

In order to implement AMF we use next classification. If a pixel value in the current frame has a value larger than the corresponding background pixel, the background pixel is incremented by 1. Otherwise the background is decremented by one. In this way input data comes to a state when half of pixels are greater then the background and half are less than the background. The AMF does good job in separating moving object across video.   The pseudocode written below gives a brief introduction to AMF.

Algorithm for AMF

1) B and F are sets  of RGB describing background and foreground pixels

Initialization

2) X is a test sequence

3) F is output foreground sequence

4) Foreground detection

FOR x = 1 to w

FOR y = 1 to h

IF all pixels of test sequence for all color spaces (R,G,B respectively) are within the distance [eqn: tau] of corresponding background    value B(x,y) THEN

F(x, y) = 0

ELSE

F(x, y) = X(x,y)

ENDIF

5) The background model B={}

IF a pixel value in the current frame has a value larger than the corresponding background pixel	THEN

B(x, y, z) = B(x,y,z) + 1 //z is color space index

ELSE

B(x,y,z) = B(x,y,z) - 1;

ENDIF

ENDFOR

Foreground pixel are detected by calculating the Euclidean norm at time t:

[eqn: LEFT ∥ I _{t} (x,y)-B _{t} (x,y) RIGHT ∥ >T _{e}]       (3)

where  is the pixel intensity value,  is the background intensity value at time t and  is the foreground threshold.

[eqn: I _{t} = LEFT [ I _{1,t} ...I _{c,t} RIGHT ] ^{T}]

[eqn: B _{t} = LEFT [ B _{1,t} ...B _{c,t} RIGHT ] ^{T}]	(4)

where c is the number of image channels[5].

The foreground threshold [eqn: T _{e}] determined experimentally.

4. Experimental Result

Motion segmentation was conducted by using Matlab Image Acquisition toolbox. Single static 1.3 mega pixel PC "Wezel" webcam was used to acquire video sequence, test was performed on Intel Processor 2.83 HGz, 1.98 Gb of RAM.

We acquired ideo sequence with RGB human   in-doors motion. The result is presented in figure 3.

Fig 2. Original RGB frames.

Fig 3. Result image with shadows

As shown above result image (Figure 3) contains some noise and in some frames even trails behind the object or shadows.

Shadows are caused by illumination effects so common remedial is converting an RGB color space to normalized RGB that should remove the effect of any intensity variations. We normalized RGB as follows:

1) Every frame from video sequence was splitted into three separated red, green and blue component images. For every pixel (5) in component images eq. 6 was calculated.

Z = [eqn: LEFT | pile{pile{Z _{R}#Z _{G}}#Z _{B}} RIGHT |]              (5)

where Z is a color pixel, ZR, ZG, ZB are all color components respectively.

z= [eqn: LEFT | pile{pile{Z _{r}#Z _{g}}#Z _{b}} RIGHT |]=>	[eqn: LEFT [ eqalign{r= {R} over {sqrt {R ^{2} +G ^{2} +B ^{2}}}# pile{eqalign{g= {G} over {sqrt {R ^{2} +G ^{2} +B ^{2}}}# b= {B} over {sqrt {R ^{2} +G ^{2} +B ^{2}}}}#}} RIGHT ]]	(6)

where z is normalized color pixel, color components defined as follows. Normalized planes were restacked again to form normalized RGB image as presented in Figure 4.

Fig 4. Normalized frames

Figure 5 shows motion segmentation performed on video sequence that was preprocessed as described above, but because some particularly important pixels were removed too and preprocessing step (RGB normalization) distorted color information (black parts of his shorts became blue),

Fig 5. Output image (normalized)

I decided to keep shadows but safe color information as though my application is color based.

Commonly for background subtraction algorithm and color classifier as well, binary mask is used in order to mark all matched components. However I wanted to keep color information for some future work. So I focused on RGB space and implemented RGB normalization on first video sequence to check whether output is better then not normalized RGB motion segmentation. The test didn't satisfy my color criteria (almost all colors were distorted as you can see in Figure 5) and I preferred not normalized output though it is more corrupted with noise (Figure 3) compare with normalized one.

Though the AMF has a good performance it adapts very slowly to a large change in background and sensitives to environmental noise. So low frame rate can also improve the result.

All others motion segmentation details are described in a section Ⅱ and III.

5. Test performance

In order to have a quantitative evaluation of the performance, we have selected sixteen frames with moving objects in test sequence, and manually highlighted all the moving objects in them. Manually highlighted pixels are regarded as "ground-truth" information and include only object's movements. The shadows weren't been included since we do not use any shadow suppression scheme in our comparison, all shadows would be cast as noise and false positives.

We used ROC (receiver operating characteristics) curve to quantify algorithm[14].

The true positive rate (also called as hit rate and recall) is estimated as

[eqn: TPR= {TP} over {TP+PN}]             (7)

where  TPR - true positive rate, TP - true positives, TN- true negatives.

The false positive rate (also called false alarm rate) is

[eqn: FPR= {FP} over {FP+TN}]        (8)

where FPR - false positives rate, FP - false positives, TN - true negatives.

Tab. 1. The operating characteristics

cutoff	true positives	false positives
4	0.62	0.4
8	0.65	0.5
12	0.66	0.5
16	0.7	0.5

The measures in Table 1. can be represented graphically as shown below.

Fig 6. ROC curve of test performance

In Figure 9. TPR is plotted on the Y axis and FPR is plotted on the X axis. Test accuracy is characterized by the plot: the closer curve comes to the 45-degree diagonal of the ROC space, the less accurate the test. Also area under the curve can be a measure of test accuracy.

Test accuracy and other performance characteristics can be improved by using special shadows suppression, applying to some data validation procedures and some pre-processing that not necessary for my future purpose so I was satisfied with test’s result.

6. Conclusion and Future Work

In this paper, we have developed a motion segmentation technique using background subtraction and tested it both for gray-scale and RGB color space. Background subtraction procedure was based on the AMF. Future work will include next aims:

1) Elaborating data validation procedures, especially RGB shadow removal techniques

2) Also I want to implement some ideas about color detection system Similar that mentioned in reference list

Acknowledgements

The authors would like to thank to the PoDIT in Mokwon University. Support for this work was provided in part by Prevention of Disaster with Information Technology RIC.

References

[1] Gary Brodski and Adrian Kaehler, "Learning OpenCV", O'Reilly, 2008.

[2] Thanarat Horprasert, David Harwood, Larry S. Davis, “A statistical approach for real-time robust background subtraction and shadow detection”, 1999.

[3] Sen-Ching S. Cheung, Chandrika Kamath, “Robust techniques for background subtraction in urban traffic video”, proceedings of the SPIE, Vol. 5308, pp. 881-892, 2004.

[4] T.Bouwmans, F. E Baf, B.Vachon, “Background Modeling using Mixture of Gaussians for Foreground Detection - A Survay”, Recent Patents on Computer Science, Vol. 1, no. 3, 219-237, 2008.

[5] Swantje Johnsen and Ashkey Tews, “Real-Time Object Tracking and Classification Using a Static Camera”, Proceedings of the IEEE ICRA 2009,

Workshop on People Detection and Tracking, Kobe,Japan,May,2009.

[6] P.Remagnino et al., "An integrated traffic and pedestrian model-based vision system”, Proceedings of BMVC97, Vol. 2, Colchester, 8-11 th September, UniversityofEssex, UK, pp380-389, 1997.

[7] N.McFarlane and C. Shofield, “Segmentation and tracking of piglets in images”, Machine Vision and Applications, Springer, Vol. 8, no.3, 1995.

[8] Seth Benton, “Background subtraction, Matlab Models”, 2008.

[10] Rastislav Lukac, Konstantinos N. Plataniotis, “Color image processing: methods and applications”, Published by CRC Press, ISBN 084939774X, 9780849397745, 2006.

[11] Turgay Celik, Hasan Demirel, Huseyin Ozkaramanli, Mustafa Uyguroglu, “Fire detection using statistical color model in video sequences”, Journal of Visual Communication and Image Representation,Vol. 18, Issue 2, 2007.

Osteonal Inclination Alters The Bone Fluid Pore Pressure

Young-June Yoon*

*kwandong University

E-mail : yoon.youngjune@gmail.com

ABSTRACT

Osteon is a subunit of the cortical bone. As the osteonal axis being inclined to the long axis of the cortical bone, the pore pressure in the osteon is varied: (a) The osteocytes face larger bone fluid pressure as those are close to the cement line, and (b) the osteonal pressure is not uniform around the Haversian canal. This result would be expanded in developing the computational model understanding how new bone is formed at the periosteum by using the neural network model.

Key Words : Bone fluid pressure, osteon, new bone formation, cortical bone

1. INTRODUCTION

The repetitiveloading induces bone fluid flow [1], which is believed to play an important role in bone adaptation [2,3]. Since bone is a porous material, proelasticitic models are developed to elucidate the bone fluid flow phenomena as well as bone adaptation [2,3,4]. Mostly the bone fluid flow in canalicular network is the interest because osteocytes reside in the lacunar-canalicular space.  A model developed for the cellular level [5] shows that bone fluid flow deforms the cell membrane and amplifies the hoop strain of the cell membrane. A model for the tissue level [4] differs from that at the cellular level by that tissue level models look at the environment around cells rather than cells themselves. The Haversian canal, the central canal, in the osteon, is the source of small amplitude of blood as well as the small amount pressure alteration due to the repetive loading. However pressure alteration due to the repetive loading was not examined especially for the case when the osteon is inclined 5 to 15 degrees [6]. Thus the pressure distribution around the Haversian canal in the osteon is examined by employing the borehole problem, which is widely used in the old and gas industry [7].

2. METHOD

Cui et al. [7] provides an analytical solution for a circular, infinite long borehole, embedded in a linear and homogenous  isotropic poroelastic medium. The problem is decomposed into three fundamental problems, [i] a poroelastic plane-strain, [ii]an elastic uni-axial, and [iii] an elastic antiplane shear problem.

Before establishing the boundary conditions, three governing equations, [a] equilibrium equations, [b] compatibility equations, and [c] diffusion equation, are required:

[a] Equilibrium equations

.

[b] Compatibility equations

.

[c] Diffusion equations

.

The Cartesian coordinate system x'y'z' is chosen to coincide with the principal axes of the compressive stresses, Sx', Sy', and Sz'. The inclined borehole is defined by a local coordinate system xyz with the borehole axis is coincident with the z-axis. The local coordinate is formed by a rotation of an angle jz' about the z'-axis, and then by an inclination of a angle jytoward the x-axis.

The boundary conditions at the far field (or r  ), where r is the radial distance from the borehole axis, are

where po is the original pore pressure.  Those at the borehole wall, r = R, where R is the borehole radius, surface tractions and pore pressure are assumed to vanish instantly,

where H is the Heaviside unit step function and  such that  for , and

for .  These boundary conditions, Eqs (4) and (5) are decomposed for the boundary conditions for three fundamental problems, [i] a poroelastic plane-strain, [ii] an elastic uni-axial, and [iii] an elastic antiplane shear problem.

[i] The boundary conditions for a poroelastic plane-strain

In the far field,

.

At the borehole,

[ii] The boundary conditions for an elastic uni-axial

In the far field,

.

At the borehole,

.

[iii] The boundary conditions for an elastic antiplane shear problem

In the far field,

.

At the borehole ,

.

Note that ain Eqs (6) and (8) are Biot effective stress coefficient. After employing three boundary conditions, the solution of inclined borehole problem is given by

,where

,

..	.

For numerical calculation, we use the 1000 microstrain for the bone whose elastic modulus is 20 GPa because the normal activity deforms the bone less than 1000 microstrain. The angle between the borehole axis and the repetive loading is 15 degrees. 19% of applied stress is the pressure estimate in the Haversian canals [8]. The poroelastic constants for bone are given in the previous literature [3] n=0.32, nu=0.33, B=0.40, a=0.14. Fig1 is a figure of an osteon inclined 15 degrees from the long axis of bone.

Figure 1. The defined angle and location (r/R) are shown in the schematic diagram of an inclined osteon.

3. RESULTS AND DISCUSSION

The local pore pressure in the osteon is not uniform because osteons are inclined up to 15 degrees from the long bone axis (Fig2). Since the repetitive loading is a sinusoidal type, the pore pressure patterns change sinusoidally as increases the loading time.  Similarly Figure 3 shows the pore pressure changes sinusoidally as increases the repetitive loading time, but not as increases the radius of the osteon (r/R). For example, where the location is twice the radius of

Haversian canal (r/R=2), the pressure varies from -200 KPa to 200 KPa. The magnitude of these values is lessthan the previous estimates [8,9] because this calculation assumes that the pore pressure vanishes instantly at the wall of the Haversian canal. But this results help to develop a new bone adaptation model combined with the neural network models because the bone tends to adapt to the mechanical stimulus, which is modeled as a symmetric or uniform, but it's response is not symmetric and uniform.

Figure2.  Pressure variation against the defined angle (radian) and the loading time (sec)

Figure3. Pressure distribution against the defined location (r/R) and the loading time (sec). Pi/4

REFERENCES

[1] Fritton SP, Rubin CT (2001) In: Bone Mechanics Handbook (ed., Cowin SC), CRC Press

[2] Cowin SC, Weinbaum S, Zeng Y (1995) J. Biomech. 28:1281-1297

[3] Cowin SC (1999) J. Biomech. 32: 217-238

[4] Wang L, Fritton SP, Weinbaum S, Cowin SC (1999) J. Biomech. 32:663-672

[5] Han et al. (2004) PNAS 101: 16689-16694

[6]Hert et al. (1994) Bone. 15:269-277

[7] Cui et al. (1994) J. Appl Mech 64:32-38

[8] Zhang et al. (1998) J. Biomech. Eng. 120:697-703

[9] Yoon et al. (2008)Biomech Model Mechnobiol 7(1) 13-26

Autonomous Navigation System of Power Wheelchair using Distance Measurement Sensors

Kuk-Se Kim* ․Moon-Shu Jung* ․ Seong-Soo Ahn** ․ Joon Lee*

*Dept. of Computer Engineering, Chosun University

**Dongshin University

E-mail : kimkukse@hanmail.net

ABSTRACT

The power wheelchair is an important and convenient mobility device. The demand of power wheelchair is increasing for assistance in mobility. In this paper we proposed a robotic wheelchair for mobility aid to reduce the burden from the disabled. The main issue in an autonomous wheelchair is the automatic detection and avoidance of obstacles and going to the pre-designated place. The proposed algorithm detects the obstacles and avoids them to drive the wheelchair to the desired place safely with panning scan from sensors of distance measurement and fuzzy control. By this way, the disabled will not always have to worry about paying deep attention to the surroundings and his path.

Key words : Power Wheelchair, distance measurement sensors

1. Introduction

Nowadays with advancement in technology and aging society, the number of disabled citizens is increasing. The disabled citizens always need a caretaker for daily life routines especially for mobility. In future, the need is considered to increase more. To reduce the burden from the disabled, various devices for healthcare are introduced using computer technology. The power wheelchair is an important and convenient mobility device. The demand of power wheelchair is increasing for assistance in mobility. Generally, a joystick is used at the arm of the unaffected side to mobilize the wheelchair, however, if both arms are affected, the leg of unaffected side is used. The use of power wheelchair is convenient. However, the driving of conventional wheelchair may cause a burden to the disabled due to continuous use of joystick and always paying deep attention to the surroundings and to the obstacles on the drive-way. Then the capacity to recognize path condition and to avoid obstacles is required. Moreover, the user has to be proficient in driving the wheelchair.

In this paper we proposed a robotic wheelchair for mobility aid to reduce the burden from the disabled. The main issue in an autonomous wheelchair is the automatic detection and avoidance of obstacles and going to the pre-designated place. The proposed algorithm detects the obstacles and avoids them to drive the wheelchair to the desired place safely. By this way, the disabled will not always have to worry about paying deep attention to the surroundings and his path. For the obstacle detection, we have used two infrared sensors mounted on step motors. The infrared sensors scan the area for obstacles, detect the obstacles and inform the control system. The infrared sensors are connected to the control system. The step motors and sensors are controlled by ATMEGA 128 Processor. The joystick is connected to the system. The proposed algorithm is for office like environment where the user can mobilize easily avoiding hazardous areas. The sensors will scan the area while driving the wheelchair, and signal the control system. The control system, on finding an obstacle, will consider it as hazardous and will stop the motion by controlling the joystick. After stopping the motion, it decides which direction to move. The purpose of the system is to avoid the obstacle and keep on moving in the same direction to go to the desired place. The key issue in this system is to keep the motion of the wheelchair in the same direction after avoiding the obstacle as it was before stopping.

2. Panning scan of Distance measurement sensors

In this paper, we have shown the setting up of the model theory of the autonomous navigation mechanism of power wheelchair for controller design. Figure 1 shows the model theory of mechanism for designing the best suited controller [1].

Figure 1. Kinematic model of an ARV

Figure 1 shows the model theory of mechanism expression of autonomous navigation power wheelchair in two wheel drive for modeling.

(1)

(2)

(3)

This expression is restricted to equation 2, in other words it satisfies the complete condition of the surface of the path, i.e., it does not slide on a slide surface [2].

(4)

This drive method is steering method based on the difference between the speeds of both wheels. Equation 1 is not for control; however, it is the best generation theory of mechanism of the two wheel drive of power wheelchair. Because Equation 2 is not a completed integral calculus; is has the non-holonomic feature [3]. In this paper we have proposed to follow the two wheel drive method basically, using two assistance drive-wheels and two drive wheels to control the non-holonomic feature [4].

2.1. Sensor panning scan algorithm for course driving:

This makes our algorithm to decide for position and posture of autonomous navigation power wheelchair by defining the course form. Figure 2 shows the relation between coordinates of sensors and obstacle or Wall.

In case of movement towards the obstacle, the new coordinates can show 4x4 determinate of standard coordinates. That is, to express the direction of obstacle coordinate axis, and to

Figure 2. Coordinates of sensors and Obstacle or Wall

express the vector translation of moving obstacle coordinates origin, the composition of three vectors can be used to create a matrix. The point p1 = [x, y] T is used to create a 2x2 matrix, that the random point p shows [x, y] T on 2 dimension system.

(5)

Obstacle recognition is done by panning scan of x coordinates of course form. Next shows geometrical relation.

(6)

(7)

Following is the distance measurement process by panning scan.

① Distance sensor detects obstacle in a range of 50cm, and stops.

② Then starts panning scan on right hand side in a range of 50cm

③ At the first time step motor scans 5 rounds of 45° with a span of 9° panning.

④ After completing steps, ①②③, the step motor comes back to the center position.

⑤ Starting panning scan at the left side, with a distance of 50 cm from the obstacle.

⑥ At the first time step motor scans 10 rounds of 90° with a span of 9° panning.

⑦ After completing steps, ⑤⑥, the step motor comes back to the center position.

This algorithm detects obstacles on the path of autonomous wheelchair. It is done by panning scan in front of the wheelchair. Figure 3 shows the coordinates of the sensors and the obstacle.

Figure 3. Coordinates of sensors and obstacle

The following equation (8) is used to find the size of the obstacle.

(8)

This equation is used for the detection of the obstacle size.

3. Autonomous navigation system of fuzzy control

We propose fuzzy control algorithm for autonomous navigation power wheelchair system that has learning ability. This algorithm is used to avoid the obstacle after recognizing it while the autonomous navigation power wheelchair system is driving. Furthermore, this system learns the position and shape of obstacles by getting data from sensors every time. Figure 4 and 5 is navigation of drive path and the obstacle avoidance algorithm.

This paper has eight assumptions for autonomous navigation power wheelchair.

Assumption1. Detect left, right of front obstacle and left obstacle

Figure 4. Both Left and Right Autonomous Navigation and Obstacle avoidance of iPw1000 algorithm.

Assumption2. Detect left, right of front obstacle and right obstacle

Assumption3. Assumption1. Detect left, right of front obstacle and left obstacle isn't

Assumption4. Only detect left of front obstacle.

Assumption5. Only detect right of front obstacle.

Assumption6. Right sensor don't detect obstacle while left, forward movement

Assumption7. Left sensor don't detect obstacle while right, forward movement

Assumption8. Detect back obstacle while back drive.

Figure 5. Either Left or Right Autonomous Navigation and Obstacle avoidance of iPw1000 algorithm

The Power wheelchair decides navigation course by these eight assumptions. Assumption 1 is moving after turn right. Assumption 2 is moving as opposition direction after turn left. Assumption 3 is moving with standard position is left when don't have any learning data. Assumption 4 is void obstacle after turn right and turn left and forward movement. Assumption 5 is opposite to assumption 4. Assumption 6 is forward movement. Assumption 7 is forward movement after turn left. Finally, Assumption 8 is stop.

3.1. Autonomous navigation learning algorithm

In this paper, the algorithm needs some learning for autonomous navigation power wheelchair using distance sensors. That is, learning to move obstacle area anywhere.

Figure 6 shows the learning algorithm.

Figure 6. learning algorithm of autonomous navigation

This algorithm learns navigation course to change start point for the best navigation course when initially wheelchair starts. Power Wheelchair can detect obstacle, total driving distance, and learns the best navigation course.

3.2. Fuzzy control for avoid obstacle.

The Power Wheelchair when sometimes meet unexpected obstacles, it sets the best navigation course for driving. It should be control speed of power wheelchair for can avoid obstacle according as distances. The speed of the Power Wheelchair should be controlled so that it can avoid obstacles according to appropriate distance. In this paper we used fuzzy controller to control the speed of the Power wheelchair system.

3.2.1 Definition of Input and Output

There are Input, Output variable data, and functions in this paper.

- Input Variable: Distance(d), axis(Θ)

- Output Variable: Left, Right wheel speed (vl, vr)

Distance of Input data is the detected distance between power wheelchair and obstacle, angle is the angle between setting navigation course data of power wheelchair and driving navigation course data of power wheelchair. Table 1 is fuzzy function.

Table 1: Fuzzy Function

Distance	Error Angle
NE : Near	NB : Negative Big
	NS : Negative Short
	ZZ : Zero
FA : Far	PS : Positive Short
	PB : Positive Big

3.2.2 The Control rule and Fuzzy function

Table 2 shows the left and right control of wheel using input and output variable data. There are 20 control rules to apply on the left and right speed of wheel when select each 2 and 5 fuzzy control variables as table 2 input variable dconditionally.

Figure 7. Function by distance

Table 2: Fuzzy Control Rule of Left Wheel

Fuzzy Input		Fuzzy Output
Distance	Delta	Left motor Velocity	Right motor velocity
NE	NB	PB	NB
NE	NS	PS	NS
NE	ZZ	PS	NS
NE	PS	PS	PS
NE	PB	PS	PS
FA	NB	PS	NS
FA	NS	PS	NS
FA	ZZ	PB	PB
FA	PS	PB	PB
FA	PB	PB	PB

3.2.3 Non-fuzzy

Fuzzy inference result of Fuzzy control is output fuzzy set to define total set of control input. Inference output value should be changed to deterministic value for input value of processor. This process of changing needs fuzzy value to change into non-fuzzy. In this paper we used a good method, i.e., using center of gravity which is the best method and intuition [5].

4. The Result of Test and Implementation

We have set up ARM System for iPw1000 system (Intelligent Power Wheelchair System) test and implementation, sensor platforms, and Control systems. This is Auto Control and driving using joystick signal without change in the basic system of P110 power wheelchair.

Following Figure 9 shows Intel Pxa270 ARM System for test.

Figure 8. Power Wheelchair

Figure 9. iPw1000 Main Control Embedded System

Figure 10 is the architecture of  iPw1000 System.

Figure 10. iPw1000 system

4.1. Panning scan System of Sensors

We produce a sensor system for test, using sensor panning scanning. Distance measurement sensor is Sharp GP2Y0A2YKOF and distance measurement is from 10 to 150cm. Step motor is NK243-01AT 1.8 degree. Data measurement gets distance data to move step motor at steps of 1.8 degrees from distance measurement sensor.

Sensor system scans left and right by panning scan using step motor. The obtained data is transmitted to ATmega128 system of sensor system by ADC Convertor to calculate 10bit sampling signal data. This data use learning data for autonomous navigation system. Figure 11and 12 shows system architecture of Obstacle detection using distance measurement sensor panning.

Figure 11. Panning scan of sensor

Figure 12. Obstacle detection system by distance measurement sensors and panning scan

4.2. Implementation of Autonomous Navigation System using fuzzy control

The map environment has 255cm width and 700cm height for test. This test sets up Obstacles, 200cm on left of y-axis, 100cm width and 30cm height near 600cm, 400cm on right of y-axis. Driving course goal figure out the best driving courses from 0 to 650 coordinate of y-axis. The power wheelchair size is 50cm in width and 100cm in height. Figure 13 is the environment map for test.

While testing of the power wheelchair, it drives on course after setting up the start point to A(75, 0), B(128, 0) and C(175, 0) of center-axis of the wheelchair. Figure 14, 15, and 16 show result of the simulation test of autonomous navigation driving.

Figure 13. Testing map for autonomous navigation

Figure 14. Testing result of starting left at A(75, 0)

Figure 15. Testing result of Starting center at B(128, 0)

Figure 16. Testing result of starting right at C(155, 0)

This increases the moving distance. The Power Wheelchair detects obstacle in arange of 50cm ahead, assumes that power wheelchair speed is regular. Power wheelchair keeps up 30cm of each side to get the point values of Obstacle while driving on a side of obstacle. At first start of Power wheelchair, it detected obstacles three times at A(75,0) point. Total distance moved by the power wheelchair was 740cm. At the second start, while driving straight line at B(128,0) point, Power Wheelchair didn't detect obstacle. Total distance moved by the power wheelchair was 650cm. At the third start, Power wheelchair detected obstacle two times and turned 4 times at C(175,0) point. Total distance moved by the power wheelchair was 680cm. Finally, the Power wheelchair decides that the best course is start point of B because the moving distance was the shortest.

5. Conclusions

The important point of autonomous power wheelchair system is to go to goal, and do it very safely. The purpose of system developed is to make a very safe Power Wheelchair System without any discomfort for the user. In this paper, we have proposed the autonomous navigation driving algorithms for autonomous driving and that can avoid obstacle using fuzzy control for the instant situations. This system can figure out the best navigation course and can set up start point by learning data. This system is not yet completed, and can get better data from ultra sensor and laser sensor for measurement of distance from obstacles. This system will be further developed to Smart Power Wheelchair having camera system which is going to make 3-D shape matching sensor data to avoid obstacles and measure the distance from obstacles.

Acknowledgement

This research was supported by the Program for the Training of Graduate Students in Regional Innovation which was conducted by the Ministry of Commerce Industry and Energy of the Korean Government

References

[1] Rory A. Cooper, Lana M. Widman et al. “Force Sensing Control for Electric Power Wheelchairs”, IEEE Transaction on control system technology, Vol.8:112-17, 2000, Jan

[2] Takanori Fukao, Hiroshi Nakagawa and Norihicko Adchi, "Adaptive Tracking Control of a Nonholonomic Mobile Robot", IEEE Transaction on Robotics and Automation, Vol. 16, No. 5, Oct. 2000.

[3] Gisbert Lawitzky, "A Navigation System for Cleaning Robots", Autonomous Robots, Vol. 9, No.3, pp 255-260, 2000.

[4]  X. Yun and Nilanjan Sarkar, "Dynamic Feedback Control of Vehicle with Two Steerable Wheels", IEEE International Conference on Robotics and Automation, pp.3105-3110, April 1996.

[5] Kim, S. C., "A Study on Collision Avoidance system of Vehicle using Fuzzy Control Algorithms," Dong-a University, pp. 28-47, 2002.

Hybrid Type Wireless Power Transfer System for Structural Health Monitoring System

Ji-min Kim*, Joon-seon Ahn**, Ho-sik Park***, Min-seok Han***

* Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea

E-mail: jimin.kim@kaist.ac.kr

**Department of Electrical Engineering, Osan University, Gyeonggi-do, Korea

***Department of Electronics, Osan University, Gyeonggi-do, Korea

E-mail: mshan1024@osan.ac.kr (corresponding author)

Abstract

Power management is critical for operating wireless sensors. In particular, the wireless sensor used for continuous monitoring of civil infrastructure is often installed on the location that is difficult to access, and the expected lifespan of these sensors exceeds that of conventional batteries. In this study, the use of electromagnetic wave energy is investigated as an option for powering wireless sensors. The proposed hybrid type wireless power transfer system is composed of four main modules, namely a magnetic resonance (MR) based wireless power transfer (WPT) module, a radio frequency (RF) based wireless power transmission (WPT) module, an energy harvesting module and a structural health monitoring (SHM) sensor module with a rechargeable battery. MR based WPT module consists of a resonator, a primary coil, and a secondary coil, while RF based WPT module consists of transmitter (Tx) and receiver (Rx) modules; the Tx-module contains oscillator, regulator, class-E amplifier, and directional patch antenna, and the rectenna in Rx module is composed of a patch antenna, a high order harmonic rejection band pass filter (BPF), a voltage doubler type rectifier, and LPF for the DC path. The effectiveness of the proposed HWPT system is examined in a laboratory setting and in a field experiment on a steel-concrete girder bridge. This paper describes design aspects to implement the energy transmission system, experimental procedures and results, methods of increasing the efficiency, optimization of circuits, and target applications. The results show that the HWPT system is a feasible solution to transmitting energy to sensor nodes on an as-needed basis.

Keywords: wireless power transfer (WPT) system; structural health monitoring (SHM) system; wireless sensor network (WSN).

1. Introduction

Wireless sensor networks (WSNs) for structural health monitoring (SHM) applications can allow for a rapid assessment of structural integrity after an event such as a natural disaster puts the reliability of civil infrastructure. Recently, the rapid evolution of WSN technologies has enabled the development of sensors that can be embedded in given structure to monitor the structural health of infrastructure. Wireless embedded sensors monitoring system is also a promising solution for decreasing the high installation and maintenance cost of the conventional wire based monitoring systems [1-4]. However, sensor batteries have finite life-time. Therefore, in order to enable long operational life of wireless sensors, novel wireless powering methods, which can charge the sensors’ rechargeable batteries wirelessly, need to be developed.

Wireless power transmission (WPT) can be considered as another effective way to power the wireless sensor. WPT system using a conventional inductive coupling for mobile devices has been already commercialized and used in daily life [5]. However, power transmission distance (PTD) is more or less a few centimeters to achieve a sufficient power transmission efficiency (PTE). Meanwhile, as another popular WPT technique, radio‐frequency technique is advantageous at the aspects of long PTD [6, 7]. Nevertheless, this benefit of radio‐frequency is no longer valid under concrete obstacles inducing serious power attenuation during passing through concrete. In recent, Kurs develops magnetic resonance based WPT (MRWPT) [8].

In this paper, hybrid type WPT system is proposed and its performance is validated. The uniqueness of the developed “HWPT” system lies in (1) Development of a HWPT system combining magnetic-resonance (MR) and Radio-frequency (RF) power transmission techniques: (2) Application to SHM of civil infrastructure: (3) Design and fabrication of Transmitter (Tx) and Receiver (Rx) modules.

2. Development of Hybrid type Wireless Power Transfer module

A. Overview of a HWPT system

Figure 1. Basic block diagram of HWPT System

The proposed HWPT system comprises four main modules, namely a magnetic resonance (MR) based wireless power transmission module, radio frequency (RF) based wireless power transmission module, energy harvesting module, and SHM sensor module. The concept of HWPT system is explained with the functional block diagram shown in figure. 1. Further configuration of the system is described in the following sections.

B. Magnetic resonance (MR) based WPT module

The former part of HWPT system is MR-WPT module to transfer power through a steel-reinforced concrete. It consists of a primary circuit, a secondary circuit and a Tx resonator and an Rx resonator. The primary circuit produces alternating voltage signal, and intensifies power level of Tx resonator. Then, alternating magnetic fields induce strong coupling be-tween Tx and Rx resonators, which is a power transfer link. Transferred power to the secondary circuit is rectified to DC power using full-bridge diodes. In sequence, output voltage is controlled through a DC-DC converter to offer an operating voltage of RF based wireless power transfer in the next step.

C. Radio frequency (RF) based WPT module

We propose a wireless power transfer module based on RF. A Power Transmitter (Tx) consists of four main functional units: a RF oscillator, power amplifier (PA), DC bias and transmitting antenna. The Power Receiver (Rx) comprises a receiving antenna, band pass filter, RF/DC rectifier and resistor load. In the transmitter (Tx), the voltage controlled oscillator (VCO) converts DC power into an RF signal so that it can be transferred through the PA, in which RF signal is amplified. The amplified RF signal radiates through the transmitting antenna and is captured in the receiving antenna of Rx. The received power is then transferred to a matching circuit, and AC signal is converted into DC voltage by a rectifier. Here a rectifier circuit with high efficiency is essential for extending the charging distance and reducing the power requirement of the transmitter.

D. Energy harvesting module

The energy stored after the rectification is then transferred to a 3.3V battery using an energy harvesting module, which acts basically as a DC-DC converter. Conventional DC-DC converters are not favorable in this system, because the DC-DC converters requires a stable DC input voltage to operate properly, while the resulting voltage after the Rx module varies widely with the environmental condition. In contrast, the energy harvesting circuits generally have an ability to adapt to a varying input condition with a maximum power point tracking (MPPT) algorithm. The energy harvesting module used in this system has an energy harvesting IC chip developed in Seoul National University which utilizes a perturb and observation (P&O) MPPT algorithm.

E. SHM sensor module

An active “stick-and-detect” wireless sensor node is developed for real field applications in the future. By sticking this sensor node to a target structure, it can locally excite the structure and catch the response. In addition, the damage diagnosis is conducted inside the sensor node autonomously and independently. Thus, we define this sensor node as a “stick-and-detect” sensor node. This sensor consumes 398.22J in 3 weeks.

3. Components of HWPT Systems

F. Magnetic resonance (MR) based wireless power transfer module

Figure 2. MR-WPT module fabricated in this study:

(a) Resonator, (b) Primary circuit and (c) Secondary circuit

Figure. 2 (a), (b) and (c) show a resonator, a primary circuit and a secondary circuit, respectively, consisting of MR-WPT module. In this study, we used a resonator and a primary circuit developed by author’s group. Resonant frequency of the resonators is finely tuned to 1.7 MHz, and the primary circuit is composed of a DC-AC inverter and Class-E amplifier with maximum power of 120 W. Then, a secondary circuit in Figure. 2 (c) consists of a full-bridge rectifier and a DC-DC converter to produce an output voltage of 12 VDC which is an operating voltage of RF transmitter, and its maximum output power is   30 W.

G. Radio Frequency (RF) based wireless power transfer module

Figure 3. RF-WPT module fabricated in this study:

(a) Patch antenna, (b) Oscillator and CI circuit, (c) 2 stage class-e amplifier and (d) Receiver

The RF transmission frequency of the Transmitter (Tx) is 915MHz. As shown in Figure. 3, Transmitter (Tx) is composed of a 915MHz signal generator, power amplifiers made of GaN HEMT, a DC Bias circuit, and a 915 MHz antenna. To drive the voltage controlled oscillator (VCO) and the 2-stage power amplifier (PA), four different voltage values (28V, 12V, -3.1V, 2~3V) are required. The proposed PA uses 28V drain voltage and -3.1V negative gate voltage. The selected VCO uses 12V supply volt-age and 2~3V tuning voltage. Since, the input voltage from MR based WPT receiver module is 8~12V, we placed converter and inverter circuits in one board using ‘WEBENCH’ tool to connect the MR based Rx module and RF based Tx module.

At first, we set DC voltage boost converters to convert the input DC to 28V and 12V DC voltage using TPS43060 and LM2731 chipset. As the PA uses a lot of drain current, we set the limiting current value up to 2A using TPS43060 chipset. Then, we design a step-down voltage regulator to convert the input DC to 2~3V DC voltage using LM2674 chipset. This range of voltage is suitable for tuning the voltage of VCO. Here, the circuit is designed to enable the voltage tuning with a variable resistor. Lastly, we design an inverting circuit using LM5088 chipset. This chipset is an inverting DC/DC buck converter generating a negative output voltage down to -3.1V.

Knowing that the transistors of the proposed amplifier will get damaged if 28V drain voltage is applied prior to gate voltage, we add a switch to control the speed of impressed voltage. The proposed DC/DC converter and inverter module are depicted in figure. 3.

H. Energy harvesting module

Energy harvesting module is composed of the energy harvesting IC and few passive components. The IC is designed with a 4-input buck-boost converter, an analog driver and a digital controller block. The digital block implements a P&O MPPT algorithm, which is the most common MPPT algorithm used in photovoltaic energy harvesting. Sleep and wake-up algorithm is also implemented in digital, which sets the chip in sleep mode to reduce the power consumption when there is no energy coming in. With its low-power mixed-signal design, the module has peak energy conversion efficiency of 80% and the power consumption during the sleep period is kept below 500nW. This module has an in-put power range of 100uW-30mW and a voltage range of 100mV-5V, which is currently a bottleneck of the system.

I. SHM sensor module

The prototype of the “stick-and-detect” wireless sensor node detects fatigue crack in structures with the nonlinear wave modulation technique. This self-contained sensor node is consists of an excitation/sensing module, a high-speed processor module and a processing core/communication module. Packaged PZT transducers attached to a target structure pro-duce ultrasonic waves at two different frequencies, and another PZT transducer detects the modulation resulting from fatigue crack formation. In field applications in general, the target structure is tested once a week or with a longer time interval. Therefore, reducing the power requirement during the idle states is essential in the design and is well accomplished in the node developed in this study, consuming only 400J with a 3-weeks test interval.

4. results and discussion

To verify the performance of the developed HWPT system, the lab-scale test and the field test are conducted. The first experiment used the MR and RF based power delivery set-up in the laboratory environment. The second test involved the use of the HWPT system to charge up an S&D sensor node in the bridge. The performance is then compared with the simulation and field test results. A summary of the results from each test is as follows.

J. MR based WPT test results

Figure. 4. Wireless power transmission using MR-WPT module through a reinforced concrete:  (a) experimental setup and (b) the transmitted power according to input power

The performance of the developed MR-WPT module is tested using a lab-scale rein-forced concrete structure as shown in Figure. 4 (a). The magnitude of concrete structure is 600 mm × 600 mm × 300 mm (W×H×T), and contains a single layer of Φ-15 mm steel rebar with 150 mm spacing. Tx and Rx resonators are placed at top and bottom surface of the concrete structure, respectively, and Rx resonator is surrounded by small concrete blocks to make itself separated from Tx resonator. The total distance between them is 450 mm. Power source supplies power to the primary circuit. Input power from the power source is 56 W, and final output power at the end of MR-WPT module is 14.1 W with overall system efficiency of 25.16 %.

K. RF based WPT test results

Figure. 5. Wireless power transmission using MR-WPT module through a reinforced concrete:

A class-E 2-stage PA was implemented with Cree’s CGH40006P and CGH 40010F GaN HEMT, having a P1 dB of 42.7dBm with a gain of 29.7 dB and power-added efficiency (PAE) of 39.6% at VDD=28 V, when the Pin is 13dBm(VCO’s Pout). The size of amplifier is 4.4×3.5 cm2. Figure. 5 shows the measured results for output power level, PAE, and power gain according to input power levels with comparison to the simulation results. For the measured results, the maximum PAE of 40.1% with a gain of 28.7 dB is achieved at a Pout of 42.8dBm.

For receiver test, the rectifier was designed and printed on a FR4 substrate with 0.8mm thickness. The circuit occupies a total area of 2 cm2; this is still very small compared to the area of rectifier structures, which used micro-strip stub lines either in an output or an input filter. The rectifier is slightly shifted but is well matched with -15 dB S11 at 915 MHz for 850 Ω resistor load. An 83% maximum power conversion efficiency is simulated at 915 MHz for 20 dBm input power. In the lab test, measured power conversion efficiency is reduced by 33% when compared to the simulation due to ohmic losses of the diodes.

L. Field test results

Figure 6. The WPT test results in chamber and Yeondae Bridge

RF power was transmitted to 2.3m, which is the scalable length of a section in the steel box girder. It was confirmed that power above 20 dBm was received from the transmission module to 50 cm distance, which is higher than the result in laboratory tests. Figure. 6 shows the received power by distance. From the plots, we can see that the nonlinear characteristics of receiving power in the field test results. The reason that the received power is not linear in the experiment using steel box-girder comes from reinforcing and cancelling effects of radiated RF signals, which is due to the fact that the material of the box girder is steel. After the conversion of DC by the energy harvesting module, average power of 60mW is attained at 50 cm. It reveals that the average time for the sensor node to charge 3.3V was 106 minutes.

M. HWPT test results

Figure 7. Experimental set-up for HWPT system

The proposed each systems successfully delivered sufficient energy to charge the battery of wireless sensor node. However, HWPT test was not conducted in field by main power supply problem. And this system need long wire to connect MR based WPT module and RF based WPT module, due to characteristic of field. These problems will be improved soon and updated.

5. conclusions

In this paper, we proposed the hybrid type wireless power transfer system to develop an efficient system which can supply power to SHM sensor nodes. We utilized the MR based WPT system with bandwidth of 1.7 MHz and RF based WPT system, as it operates at 915 MHz. In the RF based WPT system, we implemented the several modules, including the C&I module for supply DC bias, the 2-stage PA module having 10W output, and the voltage doubler type Rx module. The proposed HWPT system successfully delivered sufficient energy to charge the battery of wireless sensor nodes. Due to the reflection inside the steel girder box, the transmitted efficiency in the field test turned out to be higher than the efficiency in the chamber experiment. The result also showed that the HWPT system is feasible enough to transmit energy to sensor nodes on an as-needed basis. Its uniqueness in academic value for implementing the hybrid type wireless power transfer system for sensors within an infrastructure will be improved and updated.

Acknowledgment

This research was supported by a grant (13SCIPA01) from Smart Civil Infrastructure Research Program funded by Ministry of Land, Infrastructure and Transport (MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement (KAIA) and the Center for Integrated Smart Sensors funded by the Ministry of Science, ICT & Future Planning as Global Frontier Project" (CISS-2015M3A6A6066117).

References

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[2] A. Sophian, G. Y. Tian, and S. Zairi, "Pulsed magnetic flux leakage techniques for crack detection and characterization,"Sensors and Actuators A: Physical, vol. 125, no.2, pp. 186-191, 2006.

[3] S. Wagle and H. Kato, "Ultrasonic detection of fretting fatigue damage at bolt joints of aluminum alloy plates," International Journal of Fatigue, vol. 31, no. 8, pp.1378-1385, 2009.

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[7] H. Kulah and K. Najafi, "Energy scavenging from low frequency vibrations by using frequency up-conversion for wireless sensor applications," IEEE Sensors Journal, vol. 8, no. 3, pp. 261-268, 2008.

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High Accuracy Gradient Magnitude Caculator Using Angle Correlation of Neighborhood Pixels for Energy Efficient Edge Detector

Han-bin Kim*, Ju-seong Lee**, Seok-Geum Son***, Ho-Myoung An****

*Dept. of Electrical Electronics and Control Engineering,Hankyuong University, Gyeoggi-do, Korea.

**Center of Human-centered Interaction for Coexistence, Seoul, Korea.

***Department of Electrical Engineering,Osan University, Gyeonggi-do, Korea.

****Department of Electronics,Osan University, Gyeonggi-do, Korea.

E-mail:callname@osan.ac.kr

Abstract

In this paper, we present a high accuracy gradient magnitude calculator for edge detector. The key techniques for increasing detection accuracy of the edge detector is presented. For the gradient magnitude computation, only five directions of neighborhood pixels are considered to reduce computation with remaining high accuracy. The experimental result shows that our gradient magnitude calculator achieves 98% accuracy with low computational complexity.

Keywords: edge detection, low-complexity image pre-processing, high-throughput digital signal processing.

1. Introduction

Recently, due to the requirement of low power image sensor node for internet of things (IoT) applications, the demand of low complexity image pre-processing algorithm has been increasing. However, the efficient feature detection algorithm is needed since feature detection is the most computationally intensive part of computer vision applications. Among various algorithms, derivative based edge detection is the most used for image pre-processing algorithm of various   applications such as object tracking [1], image enhancement [2], segmentation [3], compression [4], and video dimension conversion [5], etc.

The block diagram of derivative based edge detector is shown in the Fig. 1 [6]: firstly, image noise filtering and vertical/horizontal derivative generating with Gaussian blur filter and gradient filter, second, calculating gradient magnitude for finding the intensity gradients of the image, then, generating edge with magnitude thresholding. Many of the previous efforts for the low complexity edge detector implementation have focused on the gradient magnitude calculator due to the computational complexity of gradient magnitude algorithm. For the gradient magnitude calculator complexity reduction, low complexity methods such as absolute value summation of vertical and horizontal derivative values [7], monolithic derivative filter using Sobel operator [8], and piecewise planar approximation [9] are proposed. One of difficulties encountered with those methods is that considerable output accuracy degradation is incurred as a result of computation complexity reduction.

Figure 1.  Block diagram of derivative based edge detector

In this paper, we present a high accuracy gradient magnitude computation approximation based on angle correlation of neighborhood pixels with minimum overhead. The proposed approach can efficiently reduce the computational complexity of the

gradient magnitude calculator of edge detector without seriously sacrificing the data quality. In the proposed method, through the projection on to the five direction lines and finding maximum values, gradient magnitude can be efficiently approximated.

(a)                             (b)

Figure 2.  High accuracy gradient magnitude calculation method. (a) Considering directions. (b) Gradient magnitude approximation.

2. Previous Works for Gradient Magnitude Caculator

Gradient magnitude of image is used for boundary classification between background and object as a clue. The gradient magnitude (M) calculation is as expressed as follows:

(1)

MX and MY are the horizontal and vertical gradient of image, respectively. The absolute value summation of two gradients [7] have been proposed for reducing the computational complexity of the gradient magnitude. Since the approach does not consider diagonal directions, a detection error can occur in 45˚ and 135˚. In order to improve the detection accuracy, two additional gradient filters for diagonal directions [8] and piecewise planar approximation with two multiplications [9] are also proposed. However, the detection quality degradation is still significantly large.

3. Proposed Gradient Magnitude Computatation

Since direct calculation of (1) needs large computational complexity and/or look-up tables, approximation techniques [7] – [9] are needed while guaranteeing the detection accuracy.

In [10], the gradient magnitude of a pixel and eight neighborhood pixels. This property can also be applied to the proposed gradient magnitude computation. In our approach, 16 pixel directions that have angle intervals of 22.5˚ degrees are considered to compute the gradient magnitude. With the rotation invariant characteristic of gradient magnitude, (1) can be simplified considerably. As shown in the Fig. 2, when a point (|MX|, |MY|) is projected onto the unit vectors, and considering only 0˚, 22.5˚, 45˚, 67.5˚, 90˚ directions, respectively, the maximum value among the five vectors can be the approximate gradient magnitude of (|MX|, |MY|).

4. Experimental Result

The proposed gradient magnitude and direction calculation approaches are simulated, and Fig 3. Shows the data quality performance comparisons with previous works. The proposed gradient magnitude computation has a maximum error of 2% compared to the original Euclidean norm [6] using absolute value comparison of gradient magnitude. The computational cost and conformance performance comparisons for the various gradient magnitude calculators [6], [7], and [9] are also presented in Table I. As shown in the table, the proposed scheme shows 98.72% accuracy compared to the original edge detection algorithm [6]. The proposed edge detector has the lowest data quality degradation among the approximation schemes shown in the table.

5. Conclusion

(a)                   (b)

(c)                  (d)

Figure 3. Data quality comparison. (a) Euclidean Norm[6]. (b) Proposed.

(c) Absolute value summation[7]. (d) Piecewise planar approximation[9].

TABLE I. COMPUTATIONAL COST AND DATA ACCURACY PERFORMANCE COMPARISON FOR THE GRADIENT MAGNITUDE CALCULATOR.

In this paper, a high accuracy gradient magnitude calculator has been proposed for energy efficient edge detector. We present the high accuracy image processing algorithm inside edge detector. Numerical result from implementation show that the proposed algorithm achieves number of arithmetic saving without seriously sacrificing the edge detection accuracy in terms of conformance performance (over 98%). The proposed solution presented in this paper can assist in the design of a low area and energy efficient edge detector.

References

[1]P. Zhao, H. Zhu, H. Li, and T. Shibata, ”A Directional-Edge-Based RealTime Object Tracking System Employing Multiple Candidate-Location Generation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 3, pp. 503-517, Mar. 2013

[2]S.-L. Chen and E.-D. Ma, ”VLSI Implementation of an Adaptive EdgeEnhanced Color Interpolation Processor for Real-Time Video Applications,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 24, no. 11, pp. 1982-1991, Nov. 2014.

[3]K. Somkantha, N. Theera-Umpon, and S. Auephanwiriyakul, ”Boundary Detection in Medical Images Using Edge Following Algorithm Based on Intensity Gradient and Texture Gradient Features,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 567-573, Mar. 2011.

[4]S. Na, W. Lee, and K. Yoo, ”Edge-based fast mode decision algorithm for intra prediction in HEVC,” in IEEE International Conference on Consumer Electronics (ICCE), pp. 11-14, Jan. 2014.

[5]S.-F. Tsai, C.-C. Cheng, C.-T. Li, and L.-G. Chen, ”A real-time 1080p 2D-to-3D video conversion system,” IEEE Transactions on Consumer Electronics, vol. 57, no. 2, pp. 915-922, May 2011.

[6]Nixon and A. Aguado, “Low-level feature extraction,” in Feature Extraction & Image Processing for Computer Vision, 3rd ed., Academic Press, pp. 153-161, Sep.2012.

[7]R. Possa, S. A. Mahamoudi, N. Harb, C. Valderrama, and P. Manneback, “A Multi-Resolution FPGA-Based Architecture for Real-Time Edge and Corner detection,” IEEE Transactions on Conputers,  vol. 63, no. 10, pp. 2376-2388, Oct. 2014.

[8]Kanopoulos, N. Vasanthavada, and R. L. Baker, “Design of an image edge detection filter using the Sobel operator,” IEEE Journal of Solid-State Circuits, vol. 23, no. 2, pp. 358-367, Apr. 1988.

[9]B. Geelen, F. Deboeverie, and P. Veelaert, “Implementation of Canny edge detection on the WiCa SmartCam architecture,” in Proc. ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), pp. 1-8, Aug. 2008.

[10]J. Scharcanski and A. N. Venetsanopoulos ”Edge detection of color images using directional operators,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 7, no. 2, pp. 397-401, Apr. 1997.

Type-2 Fuzzy Model for Emotion Reasoning

Keon-Jun Park*, Ji-Seong Kim*, Won-Kyu Lee*, Yong-kab Kim*, Ho-Jun Kim**, Geun-Chang Hoang***

*Department of Information and Communication Engineering, Wonkwang University, Iksan-si, South Korea

{bird75, kjs1659, ykim}@wku.ac.kr

**Department of Information and Communication Engineering, Jeonju University, Jeonju-si, South Korea

junekim@jeonju.ac.kr

***Division of Microelectronics and Display Technology, Wonkwang University, Iksan-si, South Korea

gchoang@wku.ac.kr

Abstract

Today, the development of technologies utilizing the emotion of human is being actively carried out. Because human emotion and emotional language are subjective and ambiguous, it is hard to handle directly. To alleviate these difficulties, in this paper, we design a type-2 fuzzy model which can deal with the subjective and ambiguous characteristics to deduce the emotion of human. The proposed fuzzy model is constituted by the premise, which is represented by a type-2 fuzzy sets, and the consequence part with fuzzy properties. Through experiments we evaluate the proposed fuzzy model.

Key words: human emotion; emotional language; emotional reasoning; subjective and ambiguous type-2 fuzzy model

1. Introduction

Highlighted as environmentally-friendly energy sources, LED has been rapidly developed and is being popularly used in diverse fields of human society. LED emotional lighting relates to the lighting technology which can change the space atmosphere. Especially, the color and brightness suitable for person’s psychological state or biorhythms are applied to emotional control. The space representation by LED lighting is an important element affecting the human psychology. The emotional lighting has been used to increase the sales revenue by stimulating human emotions as it targets for special goals such as sales facilities, exhibition, and so on [1, 2]. Many studies are also carrying out to identify external environmental factors so that they allow the emotional lighting control in diverse situations rather than the simple conventional emotional lighting [3].

Type-2 fuzzy inference models were systematically established by Mendel [4], it has been actively studied until now. Fuzzy sets to deal with uncertain information are key elements of the fuzzy model and the premise part as well as the consequence part can be applied to implement a wider range of fuzzy model. To establish emotional lighting control, it should handle the uncertainties inherent in emotional language.

In this paper, we design a type-2 fuzzy model for emotional reasoning which change a color according to a situation. The emotional languages according to the situation with the temperature and concentration are selected through I.R.I adjective image scale. And emotional colors are chosen by contrasting to the I.R.I solid color image scale. The fuzzy rule of type-2 fuzzy model is set and adjusted through the color table with the emotional languages.

2. Emotional Lighting

The emotional lighting is to provide a lighting environment similar to natural light according to the human emotion and refers to a lighting which can change the atmosphere by using the temperature and intensity of the color. The emotional language is to express linguistically the emotions that human feels, such as love, joy, hatred, and so on. Human can feel different emotions depending on the color and the linguistic adjectives.

The emotional colors are classified into I.R.I solid color image scale and I.R.I adjective image scale in which Lee Bok-sin classified the emotional elements through the color emotional study taking into consideration of the emotional structure of Korean in 1997 [5, 6]. LED emotional lighting is displayed by extracting the emotional color according to the situation and comparing the extracted color with CIE color coordinate.

3. Type-2 Fuzzy Neural Network for Emotional Lighting Control

Type-2 fuzzy set can be useful to handle the uncertainty inherent in the given problem [4]. Uncertainty presents in the membership values of the type-2 fuzzy set, consist of a bounded region - footprint of uncertainty (FOU). When the secondary membership functions are intervals and type-2 membership function is referred to as an interval type-2 membership function. Type-2 fuzzy set have the following expression.

	(1)

Fuzzy neural networks [7] come from the hybrid techniques between fuzzy sets and neural networks. Fuzzy neural networks are represented by fuzzy “If–Then” rules, while the parameters of the network are adjusted by back propagation (BP). Fuzzy neural networks have the ability of learning from the data sets from some environments.

The structure of a type-2 fuzzy neural network consists of fuzzy inference system represented by the mutual relationship between input variables in the premise part. In this sense, the fuzzy rule of fuzzy neural network can be expressed as the following format.

	(2)

To be more specific, Rj is the j-th fuzzy rule, while Akj denotes k, j-th membership function.  is consequent parameters as the connecting weights. and are the center and the spread of, respectively.

Mapping from input to inference output is determined by the fuzzy inferences. The output has the interval value due to the interval set. This leads to need a type-reduction scheme to obtain the final output. And final output is obtained by using Karnik-Mendel (KM) algorithm [4].

The parametric learning is realized by adjusting connections of the network and carrying out a back-propagation algorithm. The change amount of the connection weight can be obtained using the gradient descent method for minimizing the error.

	(3)
	(4)
	(5)
	(6)

4. Experimental Studies

We first design the fuzzy rules of the fuzzy neural network according to the temperature and concentration for emotional lighting. And then the connections of the network are adjusted by BP algorithm. Regarding the membership function for temperature, the range of temperature is set to 17~25 ℃ and the temperature interval is set to 4℃. The linguistic definition of concentration situation is divided into art/music, language section, mathematics section and computer section. The IF-THEN rules of twenty are set through the membership function of temperature (Tem) and concentration (Con) as follows.

R1 : If Temp is Very Cold and Con is art/music
Then Color is Orange

R2 : If Temp is Very Cold and Con is language section
Then Color is Yellow

R20 : If Temp is Very Hot and Con is computer section  Then Color is Cyan

Table 1 shows the fuzzy lookup table of the fuzzy neural network is generated through the IF-THEN rules for temperature and concentration. The distinction of colors by hard regions is distributed by four colors such as orange, yellow, white, cyan color through color-matching according to the temperature and concentration situations.

Figure 1. Fuzzy lookup table.

In order to estimate the colors from the situation, we used a total of 100 data set from temperature and concentration situations. The Type-2 fuzzy neural network is trained and adjusted using the data. Table 2 shows the results of the colors after learning process for situation. Form the Table 2, we note that the colors obtained are changed and adjusted more smoothly according to the situation.

Figure 2. Fuzzy lookup table after learning.

5. Conclusion

In this paper we have designed type-2 fuzzy model for emotional reasoning on temperature and concentration situations. The colors for the emotional languages were selected and coordinated with the fuzzy rules of the fuzzy neural network using I.R.I adjective image scale and I.R.I solid color image scale. The fuzzy lookup table were designed and adjusted by learning the fuzzy rules of fuzzy neural network.

The experiment result showed that emotional lighting system through the fuzzy model could be controlled and adjusted in some situations. Therefore the emotional reasoning using fuzzy neural network could have a significant emotional effect on human behavior.

Acknowledgment

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2011835) and LED center for R&D Manpower Education.

References

[1] J. Y. Shin, S. Y. Chun and C. S. Lee, “Analysis of the effect on Attention and Relaxation Level by Correlated Color Temperature and Illuminance of LED Lighting using EEG Signal,” Jourmal of korean Institute of Illumainating and Electrical Installation Engineers, Vol 27, No. 5, pp. 9~17, 2013.

[2] E. S. Lee, and H. J. Suk, “The Emotional Response to Lighting Hue Focusing on Relaxation and Attention,” Journal of Korean Society of Design Science, Vol. 25, No. 2, 2012.

[3] S. C. No, “Emotional Lighting System. Korean Society of Design Science,” Vol. 3, pp. 86-87, 2005.

[4] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, Prentice Hall PTR, 2001.

[5] B.S. Lee, "The Development of Color Image Scale by Using Adjectives", Journal of Korean Society of design science, No.19, pp. 17-18, 1997.

[6] C. H. Baek, S. O. Park and H. S. Kim, “The Analysis of Emotion Adjective for LED Light Colors by using Kobayashi scale and I.R.I scale,” Journal of the Korean Institute of IIIuminating and Electrical Installation Engineers, Vol. 25, No. 10, pp.1∼13, 2011.

[7] K. J. Park, Y. K. Kim, B. G. Kim and G. C. Hoang, “Design of Optimized Pattern Recognizer by Means of Fuzzy Neural Networks Based on Individual Input Space,” The Journal of The Institute of Internet, Broadcasting and Communication , Vol. 13, No. 2, pp.181-189, 2013.

Design of Emotional Lighting Suited for relaxation and Meditation by EEG

Jong-Hyeok Jang*, Ji-Seong Kim*, Won-Kyu Lee*, Keon-Jun Park*, Yong-kab Kim*, Geun-Chang Hoang**

*Department of Information and Communication Engineering, Wonkwang University, Iksan-si, South Korea

{cine, kjs1659, jyjduddls, bird75, ykim}@wku.ac.kr

**Department of Division of Microelectronics and Display Technology, Wonkwang University, Iksan-si, South Korea

gchoang@wku.ac.kr

Abstract

Emotional lighting is a lighting that gives beneficial effect to human emotions. By identifying the characteristics of the emotional lighting and human emotions, the color temperature and intensity of illumination that harmonize with meditation and relaxation were found to be applied to the LED  lighting. Through EEG, alpha and theta waves that clearly indicate the relaxation and meditation state were analyzed, and for the lighting color, IRI adjective scale and IRI white scale were used to extract the emotional language and color to be applied on the ATmega128 based LED lighting. In this study, the evaluation of the emotional lighting that gives beneficial effect to relaxation and meditation was verified through EEG and analysis.

Keywords: LED Emotional Lighting, EEG, Color Temperature, Image Scale, EEG Analysis

1. Introduction

There are neurons in human brain. Small potential variations occur when neuron bundles are simultaneously excited and synchronized with one another or gather together, which are referred to as brain wave. The brain wave can be measured by attaching electrodes to the scalp, and the technology for amplifying these signals and displaying them as waves on the monitor is called electroencephalogram (EEG). Brain waves are largely classified into five types: delta, theta, alpha, beta, and gamma waves. The delta wave which is the lowest (below 4 Hz) appears during a comma or deep sleep when the cerebrum rests and the body recovery function is activated. The theta wave (4~8 Hz) mainly appears when the body and mind are relaxed or when we dream during a shallow sleep. The alpha wave (8~13 Hz) mainly appears during medication when the mind is comfortable, resting, and concentrating. The beta wave (14~30 Hz) is relatively fast and mainly appears during daily life. The gamma wave (over 30 Hz) generally appears when the brain is immersed in some work or is alert and active.

LEDs are continuously researched as interest in them as next-generation lighting source is rising, and one of the research fields is emotional lighting. Light has great effect on human life, and it can be a means to make a space more comfortable by applying the psychologic effects of color temperature to lighting. Emotional lighting is a technology that responds to such needs of the times and is essential for modern people who desire well-being. In this study, emotional language and emotional colors were extracted and applied to lighting, and its beneficial effects on resting and medication are verified through brain wave measurements and analysis.

2. Emotional Colors and Language

Emotional colors and language must be extracted to determine the color temperature of emotional lighting. For the extraction of emotional colors and language, IRI color image scale and IRI adjective image scale were used. The IRI adjective image scale is a matrix that provides the criteria for distinguishing the correlations of colors and words. The IRI color image scale provides the criteria for analyzing and distinguishing emotions that are felt from watching colors that match words expressing feelings. The emotional language was extracted between static and soft of the X-axis and Y-axis on the adjective image scale. Fresh, Clear, Shallow, Clean, Light, and Transparent were selected for medication, whereas Quiet, Calm, and Static were selected for resting. The colors that are most overlapped with each word were selected on the color image scale based on these selected words.

Figure 1. Adjective Image Scale

The emotional words associated with resting and meditation from the adjective image scale are listed in Table 1. The emotional colors were extracted through the color image scale based on the listed emotional language.

Figure 2. Color Image Scale

Table 1. Emotional word relaxation and meditation

Relaxation	Meditation
Fresh	Quiet
Clear	Calm
Shallow	Modest
Clean	Static
Light
Transparent

3. Implementation and Experiment of Emotional

ATmega128-based MCU and full color LED bar were used to implement lighting. The AVR Codevision was used to program in C++ in such a way to allow light on/off and dimming control. A yellowish resting light which matches resting was implemented by applying emotional colors to lighting, and a greyish and bluish light which matches meditation was implemented. R:150, G:150 and B:200 were used for resting, and R:255, G:255 and B:40 were used for medication. For the experiment method, general and emotional lighting conditions were set up for the resting and meditation activities of six subjects. One set consisting of measurement for four minutes and waiting for one minute was repeated twice. For resting, they sat in a comfortable position while closing their eyes; for medication, they concentrated while listening to calm music. Their brain waves during emotional lighting and general lighting were obtained and analyzed. The results of this experiment showed that the concentration level was higher during meditation lighting than during general lighting, and alpha brain waves could be verified. When the resting situation was experimented in the same way, waveforms between alpha and beta waves could be obtained during resting. This result shows that the emotional lighting for resting and meditation was more beneficial than the general lighting.

The emotional lighting for resting was set to R: 150, G: 150 and B: 200, implementing a yellowish light.

Figure 3. Relaxation Emotional Lighting

The emotional lighting for meditation was set to R: 255, G: 255 and B: 40, implementing a greyish and bluish light. The general lighting was set to R:115, G:100, and B: 40, which was measured to compare with meditation and resting lights.

Figure 4. Meditation Emotional Lighting

Figure 5. EEG Analysis

4. conclusions

In this study, emotional language and colors were applied to lighting through image scale, and their beneficial effects compared to general lighting were verified through brain wave measurements and analysis. The emotional language and colors were extracted through IRI adjective image scale and color image scale, and the extracted color temperatures were applied to LED lights to implement emotional lights. When the implemented emotional lights were compared to a general light, the waveforms that stand out during meditation and resting compared to general lighting were found. Therefore, this study arrived at the conclusion that emotional lighting is more effective in resting and meditation than general lighting. Low alpha waves that appear during a comfortable state such as relaxation were acquired through a brain wave measuring instrument during the lighting for resting, and high alpha and beta waves were acquired during the lighting for meditation. In order to achieve reliability about the brain wave measurement results in the future, we will conduct a study that arithmetically represent vague emotions and apply them to lighting by using the neuro-fuzzy theory

Acknowledgment (Heading 5)

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2011835) and LED center for R&D Manpower Education.

References

[1] Seung-Ho Lee, “Meditation and EEG”, Korea Institute of Brain Science 2015.1, 32~39.

[2] Keum-yeon Choi1* and Ik-soo, “LED Sensitive Light System Development by Brain-wave”, Korea Academia-Industrial cooperation Society, Vol. 11, No. 1, pp. 61-66, 2010.

[3] Lee, Eunsol, Suk, Hyeon-Jeong, “The Emotional Response to Lighting Hue Focusing on Relaxation and Attention”, Korean Society of Design Science, Archives of Design Research 25(2), 2012.5, 27-3.

[4] Ji-Yea Shin, Sung-Yong Chun, Chan-Su Lee, “Analysis of the Effect on Attention and Relaxation Level by Correlated Color Temperature and Illuminance of LED Lighting using EEG Signal”, Journal of the Korean Institute of Illuminating and Electrical Installation Engineers 27(5), 2013.5, 9-1.

[5] Su-Jeong Yun, Chi-Ho Lin, “Design and Implementation of Optimal LED Emotional-Lighting Control System”, The Journal of The Korean Institute of Communication Sciences 40(8), 2015.8, 1637-164.

Analysis of EEG of Left Brain and Right Brain to Identify the Effects of Emotional Lighting in Meditation

Jong-Hyeok Jang*, Young-In Jo*, Ji-Seong Kim*, Keon-Jun Park*, Yong-Kab Kim* Geun-Chang Hoang**, Byun-Gon Kim***

*Department of Information and Communication Engineering, Wonkwang University, Iksan-si, South Korea

{cine, kjs1659, bird75, ykim}@wku.ac.kr

**Department of Division of Microelectronics and Display Technology, Wonkwang University, Iksan-si, South Korea

gchoang@wku.ac.kr

***Department of Electronic & Information Engineering, Kunsan National University, Kunsan-si, South Korea

bgkim@kunsan.ac.kr

Abstract

The numerous studies and developments on LED emotional lighting resulted in the increase of cases in obtaining beneficial effects on indoors. Due to the appearance of various emotional lightings, there are many studies conducted on the effect of emotional lighting. The emotions that humans feel visually are mostly effected from the right side of the brain. The left brain feels the cognitive response, and the right brain feels the emotional response. While using the LED emotional lighting and general lighting in meditation activities, the EEG through a brain wave monitoring device and a EEG program to identify the change in the right side and left side of the brain, and to verify the effect of the emotional lighting

Keywords: EEG, Left Brain, Right Brain, LED Emotional Lighting, Meditation

1. Introduction

A human brain has about 1,000 neurons which are interconnected through synapses. The interconnected neurons exchange information via electric signals. Potential variations occur when neuron are simultaneously excited and synchronized with one another, which are referred to as brain waves [1]. The technology for measuring these potential variations with electrodes and displaying them is called electroencephalogram (EEG). EEG is used in a wide variety of areas such as meditation, concentration, treatment, and resting to analyze brain waveforms [1]. Recently, users of lighting put emphasis on the uses of space and the comfort of users in a space, and they began to search for lighting that raises work efficiency. Psychological aspects centering on subjective assessment has taken priority in the evaluation of comfort of lighting, and many studies are being conducted to objectively measure the changes over time by physiological indices as a measure to objectively assess the psychological aspects of people [3]. In this study, the beneficial effects of emotional LED lights during meditation were analyzed through the waveforms of brain waves through a brain wave measuring instrument and verified their applicability to lights.

2. Brain Waves and Meditation

According to the hypothesis of lateral specification, the left brain and the right brain play different roles.  A previous study by Simons et al. demonstrated that the parietal lobe is in charge of emotional reaction [3]. Based on the lateral specification hypothesis and the results of previous studies, the right brain is in charge of emotional reaction, whereas the left brain is in charge of cognitive reaction [1-5]. It is generally acknowledged that the right brain is responsible for spatial thinking, integration, and intuition, and the left brain is responsible for logical and analytical thinking, details, language, and mathematics. The left brain and the light brain can be compared in terms of load level, recognition, and perception by analyzing the active spectrums of the brain waves. Brain waves are also influenced by drinking alcohol, eating food, and fatigue [1]. There are different types of brain waves, which are largely classified into five types: delta (slowest), theta, alpha, beta, and gamma (fastest) waves. In terms of the balance between the left and right brains, the balance is better if it is closer to 5:5. However, brain waves that are inclined more to one side can be better to obtain greater effects in meditation or other activities. When our mind becomes more comfortable through meditation, our individual potential and healing power can be exhibited better, and we can understand ourselves better and improve our mental health. Furthermore, medication can correct or reduce our distorted thoughts, helping us cultivate our positive emotions [4]. Theta and alpha waves tend to be dominant when we pay attention to our inner side through meditation, but beta and gamma waves tend to be dominant when we focus on the external world [3].

3. Brain Wave Measurement and Analysis According to Lighting

he effects of emotional lighting were checked by measuring the emotional changes felt by the brain as indicated by the changes in brain waves. In this experiment, the brain waves of eight subjects were measured for 3 minutes in general lighting and for 3 minutes in emotional lighting for meditation and resting, respectively. Two minutes were given to each subject for adaptation to the lighting before the measurement. During the meditation, subjects sat in a comfortable position and listened to calm music that contained natural sounds. The emotional light for meditation was selected through IRI adjective scale and IRI color scale. For the adjective scale, four words, that is, Quiet, Plain, Calm and Static were selected. The color that was most overlapped with the words when the adjective scale and the color scale were overlaid was selected as the emotional color. The selected color was blue tinged with gray. For application to the light, the RGB values were extracted using a color pallets. Figure 1 shows the picture of the LED light to which the emotional color was applied.

Figure 1. Meditation Emotional Lighting

Brain waves were measured from general and emotional lights, and the results were compared. Figure 2 shows the left and right brain balance diagrams measured in the general light in resting and meditation. The balance between the left and right brains are represented as percentages through a brain wave measuring instrument.

Figure 2. Lighting

The left and right brains are relatively balanced when the brain waves were measured in the general light. Figure 3 shows the brain wave measurement results for meditation in the emotional light.

Figure 3. Meditation Emotional Lighting

The activity and use of the right brain increased while the subjects adapted to and meditated in the emotional light. The theory that emotional stimulation activates the right brain could be verified partially through the brain wave measuring instrument. When the same experiment was conducted additionally for other seven subjects, the greater use of the right brain than the left brain in the emotional light compared to the general light was obtained in five of the eight subjects. Not all subjects showed a higher percentage of the right brain in the emotional light. Table 1 outlines the left and right brain wave measurements of the subjects.

Table 1. EEG  Result

Lighting	Emotional Lighting
51:49	31:69
43:57	28:72
55:45	21:79
44:56	50:50
40:60	40:60
51:49	31:69
41:59	42:58

4. Conclusions

In this study, the effects of emotional lighting during meditation were verified through the balance between the left and right brains as shown by brain wave measurements. In general, the right brain is responsible for visual sense and emotional reactions. The experiment results showed that when the brain waves were measured by applying general and emotional lights in meditation, the balance between left and right brains indicated a higher use of the right brain during the emotional light than during the general light in some of the subjects. The objective of this experiment was to clearly verify the emotional reaction of the right brain, but the measurement results indicated that not all subjects showed the same brain waves. Additional experiments and studies are required because the result values are not satisfactory. In the future, further research will be conducted on the cognitive reactions of the left brain by analyzing the power spectrums of brain waves separately for the left and right brains.

Acknowledgment (Heading 5)

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2011835) and LED center for R&D Manpower Education.

References

[1] Seung-Ho Lee, “Meditation and EEG”, Korea Institute of Brain Science 2015.1, 32~39

[2] Jacobs, G. D., Benson, H., Friedman, R.. “Spectral analyses of the central nervous system effects of the relaxation response elicited by autogenic training” Behavioral Medicine, 15(3), 126~132.

[3] In-Soo Lee, Jin-Hwan Oh, In-Sook Kwon, “Effects of Maum Meditation Training on Mental Health according to the Length of the Training Period”, JOURNAL OF THE KOREA CONTENTS ASSOCIATION 13(1), 2013.1, 342-353.

[4] Lee, Eunsol, Suk, Hyeon-Jeong, “The Emotional Response to Lighting Hue Focusing on Relaxation and Attention”, Korean Society of Design Science, Archives of Design Research 25(2), 2012.5, 27-3.

[5] James Gu, Hyung gun Yoon, “Artistic Expressions of LED(Light-Emitting Diode) as an Interactive Media”, The HCI Society of Korea, 2004.2, 1733-173

Study on the Distance Calculation Revision Algorithm Using RSSI Measurement Result Filtering

Ji-Seong Kim*, Young-In Jo*, Jong-Hyeok Jang*, Keon-Jun Park*, Yong-Kab Kim* Geun-Chang Hoang**, Kwan-Woong Kim***

*Department of Information and Communication Engineering, Wonkwang University, Iksan-si, South Korea

{kjs1659, cine, bird75, ykim}@wku.ac.kr

**Department of Division of Microelectronics and Display Technology, Wonkwang University, Iksan-si, South Korea

gchoang@wku.ac.kr

***Digital Signal Processing Team, Thunder Technology, Jeonju-si, South Korea

kwkim@thunderspeaker.com

Abstract

The indoor location based service proposed in the study is subjected to moving users. The RSSI value, the delivery signal of the Bluetooth beacon, is used to measure the RSSI value of the beacon according to the distance, and to database this. Because the RSSI value is unstable in long distance, the RSSI measurement result filtering is used to calibrate the unstable signal, and the algorithm to calibrate the value on the long distance is designed and realized. Also, to reduce the error range, the distance calculation calibration algorithm according to the measured distance is proposed.

Keywords: Bluetooth4.0, RSSI, Distance measurement correction algorithm, Measurement result filtering, Location-based service

1. Introduction

The emergence of user location-based services using the GPS has brought about many changes across the society, and such services have maximized user convenience in real life. The user location-based services are becoming more active with the appearance of smart phones with a GPS sensor [1]. However, unlike the outdoor space where user and terminal locations can be ascertained through the GPS, there is no location determination technology that is being used widely for the indoor space. Many technologies are being developed to activate location determination in indoor space [1]. Currently known technologies include Bluetooth 4.0, infrared, Wi-Fi, Zigbee and UWB [2].

The indoor location-based service proposed in this paper is targeted at moving users. The RSSI values, which are the transmission signals of Bluetooth beacons, are measured according to the distance and converted into a database. A distance calculation correction algorithm to filter the RSSI measurement results and reduce the error range is proposed. The RSSI feedback method filtering and distance calculation correction algorithm proposed to remotely correct unstable RSSI signals and reduce the error range is compared with the case where the RSSI feedback method filtering is not used.

2. Location Determination Using Beacons

2.1 Characteristics of RSSI

RSSI stands for received signal strength indicator, which can be interpreted as an indicator of the strength of received signals. The transmission strength of the signals typically ranges from -99dBm to 35dBm, and a higher value indicates a stronger signal. Beacons are designed to continuously send out only RSSI data using a low-power system, and the distance is calculated using RSSI values in the received device [2].

2.2 RSSI Measurement result filtering

RSSI measurements have irregular and discontinuous attenuations due to the environmental factors of the indoor space. The measurements must be filtered in order to apply the measurements to the signal strength attenuation model according to distance. There are two filtering methods for RSSI values : average type filter and feedback type filter.

In this paper, the average type filtering is proposed, which uses the average value obtained by dividing the total of all the RSSI values received for a unit time by the number of RSSI values [3]. The feedback type filtering is proposed, where the received RSSI values are changed by the RSSI values received later [4].

2.3 Distance Calculation Correction Algorithm

Theoretically, the strength of radio signals is inversely proportional to the square of distance, and the RSSI value is an expression of this strength in log proportion to a random benchmark. A closer distance indicates a stronger signal strength, and a longer distance indicates a weaker strength [2].

The distance can be calculated using a difference in RSSI value between two points. Path loss means the strength of signals that decrease as the transmission signals propagate. As the decrease of signal strength is inversely proportional to the square of distance, the propagation distance of the signal can be calculated through the path loss. The path loss can be expressed as follows [4].

□□□□

□□

□□□□□□□□□□□

Equation (3) can be obtained by simultaneously solving equations (1) and (2). The distance to the sending point can be calculated by substituting the RSSI value of Tx power which is the sending signal and the RSSI value of the Rx power which is the receiving signal in equation [3].

In this paper, therefore, the RSSI measurement result filtering and distance calculation correction algorithm is proposed to correct the RSSI values which unstable as the distance increases.

3. Experiment and Result

3.1 RSSI Average Type Filtering Measurement Results

To evaluate the proposed RSSI average type filtering method, the received RSSI values were recorded with constant signal strength and transmission distance through beacon setting while maintaining the distance at 1m, 2m and 3m. Comparisons of the recorded RSSI values and the average RSSI values are converted into a database. Figure 1 shows the measurement results of the average type filtering which uses the average value obtained by dividing the total of all the RSSI values received for a unit time by the number of RSSI values. The RSSI values of the conventional measurement method become higher and more irregular as the distance increases, whereas the RSSI values using the average type filtering become less high and irregular at 3m even though the differences at 1m and 2m are small.

Figure 1. Measurement Result of RSSI Average Filtering

3.2 RSSI Feedback Type Filtering Measurement Results

To evaluate the RSSI feedback type filtering measurement experiment, constant signal strength and transmission interval were maintained at the distance of 1m, 2m and 3m. The recorded RSSI values and the changing RSSI values were recorded. Figure 2 shows the measurement results using the RSSI feedback type filtering where the received RSSI value is changed by the RSSI value received later. The RSSI values of the conventional measurement method at 3m distance were irregular, but the RSSI values using the feedback type filtering were somewhat regular.

Figure 2. Measurement Result of Feedback Filtering

3.3 Distance Calculation Correction Algorithm Measurement Results

In this paper, the path loss formula was used to measure the distance calculation correction algorithm. Figure 3 shows the distance measurement results when RSSI values were compared using equation (3) by applying the average type filtering due to the irregular distribution of the received RSSI values. The measurement result values of the distance calculation correction algorithm using RSSI values through general measurement generated a large error at 3m. On the other hand, the difference between the measured distance and the actual distance decreased as the distance increased in the measurement result values of the distance calculation correction algorithm using RSSI values of the average type filtering.

Figure 4 shows a total of 30 RSSI values received at 1m, 2m and 3m, and the distance measurement values calculated using the path loss formula for the RSSI values received through feedback filtering. The measurement values of the distance calculation correction algorithm using the RSSI values through general measurement resulted in a large difference between the actual distance of 3m and the measured distance. On the other hand, the difference between the actual distance of 3m and the measured distance decreased in the measurement values of the distance calculation correction algorithm using the RSSI unstable RSSI values are reduced by the distance calculation correction algorithm using RSSI feedback filtering compared to the distance calculation correction algorithm using RSSI values of the general measurement method.

Figure 3. Measurement Results of Distance Calculation Revision Algorithm

Figure 4. Measurement Results of Distance Calculation Revision Algorithm

4. Conclusion

The development of location-based services for indoor spaces is essential considering that conventional location-based services such as GPS are inappropriate for many consumers who need accurate location tracking inside buildings which are cut off from satellite signals.

In this paper, the RSSI values according to distance were measured using the RSSI values which are the transmission signals of beacons and converted into a database. The RSSI measurement result filtering was researched to reduce the error range. When the RSSI measurement result filtering and distance calculation correction algorithm was used to remotely correct unstable RSSI values, the irregular and high RSSI values decreased compared to the conventional measurement values. Future studies will address the distance calculation correction algorithm to deduce remote unstable data and error range using the RSSI measurement result filtering method.

Acknowledgment

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2011835) and LED center for R&D Manpower Education.

References

[1] Jae-jun Yu, “Trends in Technical Development and Standardization of Indoor Location Based Services”, ETRI, National IT Industry Promotion Agency, 2014.10

[2] Mi-Kyung Kim, “A wireless LAN based Indoor Positioning System Using Environment Information Surrounding Access Points”, Hanbat National University Information&Communication Engineering Graduate Schhol of Information&Communications, 2011.02

[3] Young-Jun Song, “The Tracking Algorithm of Zigbee Locations Using Received Power Intensity”, Journal of the Research Institute for Computer and Information Communication, Vol.18 No.1, 2010

[4] Aamodt, K. CC2431 Location Engine. Applications Note A N042. Texas Instrument Incorporated, 2006.

A FAST ALGORITHM FOR REGION-ORIENTED TEXTURE CODING

Young-Gyu Choi*, Cheol-Soo Bae**,Panlinhan***

*Department of computer Information Technology,Korea National Univ of Transportation Korea

**Department of Information and communication  Engineering Catholic Kwandong Univ Korea

*** Departmeant of Communications, Computer and Sysrems Science University of Genova USA

Abstract

This paper addresses the framework of object-oriented image coding, describing a new algorithm, based on monodimensional Legendre polynomials, for texture approximation. Through the use of 1D orthogonal basis functions, the computational complexity which usually makes prohibitive most of 2D region-oriented approaches is significantly reduced, while only a slight increment of distortion is introduced. In the aim of preserving the bidimensional intersample correlation of the texture information as much as possible, suitable pseudo-bidimensional basis functions have been used, yielding significant improvements with respect to the straightforward 1D approach. The algorithm has been experimented for coding still images as well as motion compensated sequences, showing interesting possibilities of application for very low bitrate video coding.

1. INTRODUCTION

The idea of using object-oriented approaches for efficient coding of images and sequences has inspired almost all the so-called “second generation” techniques[1-6], based on the shared opinion that any effective video compression necessarily relies on a preliminary good image segmentation.

The standardization process, both in real-time[7] and interactive [9] visual communication, has strongly recommended the universality of the proposed techniques the intrinsic characteristics of the scene (real edges, 3D objects structure, texture- homogeneous regions, etc.) are substantially ignored.

On the other hand, application-oriented approaches are drawing more and more attention to very low bitrate communications where the specific visual information can be efficiently formalized, modeled and predicted. Videophone sequences, in particular, identify an easy-to-model class of images, suitable to object-oriented processing for high compression coding. Different methodologies have been proposed to exploit the a priori knowledge and to design the appropriate scene model, depending on the aimed compression, complexity and quality. These methodologies, however, usually address the problem from a too specific point of view without the necessary requirement of generality.

The on-goin activities fo the MPEG group are presently facing the prdblm of driving the short-term research towards new algorithms and techniques for very low bitrate coding of moving pictures, and a new standard MPEG-4 is expected in 1998 [10].

2. 2D ORTHOGONAL BASIS FUNCTIONS

The Gram-schmidt procedure, or the Householder procedure, have been recently used by Gilge et al. [9] to  obtain a set of orthogonal bidimensional functions starting from set of independent polynomials. Since these orthogonal functions depend exclusively on the geometry of the specific region, they can be implicitely computed at the decoder just after having reconstructed the region contour. Differently from polynomial approximation techniques, where at each variation of the polynomial order all the coefficients must be recomputed, the use of orthogonal functions keeps them unchanged. This allows an easy control of the approximation error, which can be reduced to a whatever extent by adding new basis functions without altering the previously computed coefficients.

These significant advantages are however paid in therms of the computational overhead due to the orthogonalization procedures: for each region many high-precision operations, involving high-dimensional vectors,are executed. This complexity is actually affordable only in case of small regions, thus becoming prohibitive for real-time applications.

3. THE DICRETE LEGENDRE POLYNOMIALS

Applying the “Gram-Schmidt” procedure to the set of independednt polynomials S= 1, t, t,t, ··· in the interval [-1,1], the so-called “Legender” polynomials are obtained whose fast computation can be implemented by means of well-known recursive equations.

Through a novel mathematical formalism, the “Legendre” recursive equations have been extended from the continuous to the discrete domain and a fast orthogonalization algorithm has been defined whose basic results are summarized in the following. Let us consider a region with M pixel area, whose texture has been converted into an M pixel sequence of samples.

let pn (t)be the generic n-th order polynomial defined in the interval [0, ···, M-1] and let || pn ||2 be the norm of pn (t) computed as:

The discrete recursive equations result:

and the coefficients are consequently computed as:

where (t) represents the 1D texture waveform to be approximated.

4. A FAST ALGORITHM FOR TEXTURE APPROXIMATION

Our approach employs a new algorithm for texture coding, based on orthogonal basis functions, capable to provide appreciable rate-distortion performances with very low complexity

As opposed to the 2D approach, the algorithm we have developed decimates the encoding complexity by means of two operations: I) the region is scanned along low-entropy paths to convert its texture into monodi-mensional waveforms; ii) the so obtained waveforms are approximated by means of 1D orthogonal functions.

To odtain a smooth representation of the texture information, the scanning path must maximize the in tersample correlation. Based on previous studies [11], different solutions have been invioustigated for the geometry of the scanning path like spiral or zig-zag paths.

Experimental results have shown that the approximation quality is generally very low and most of times independent of the particular scanning path. This means that the monodimensional representation of the texture information evidently reduces data correlation to an unacceptable degree, unsuited to low order polynomial approximation.

To partially regain the lost bidimensional inter-sample correlation, a trade-off solution between the 2D and the 1D approach has been devised, thying to prserve as much as possidle the good characteristics of both of them, i.e. the good approximation capability of the former and the low complexity of the latter. A satisfactory answer to this erquirement has been found out by means of what we call “pseudo-bidimensional” orthogonal basis functions. As shown in Figure 1, each row of the region is considered separately as an independent subregion. The 0-th order Legendre polynomials pni (t) are computed for each i-th row of the region and are combined together to form the 0-th order 2D function f0. the same procedure is further applied to compute the higher order Legendre polynomials of each row and to dbtain the corresponding functions F1,F2, ··· Fn, where n is the maximum approximation order.

Functions F1 and F1,[0,], being orthogonal row by row, are consequently orthogonal also in the 2D domain where they represent a valid set of orthogonal basis functions. Although it is arbitrary to scan the region along the rows rather than along the columns, it is preferable to choose the direction of the region maximum extension: the longer is the 1D sequence the higher is number of orthogonal polynomials which can be employed for its approximation. In case of a predominant horizontal extension of the region, the following operations are performed:

1. the region is scanned along the direction of the reow;

2. a set  of pseudo-bidimensional functions is obtained;

3. the region texture is approximated and a set  of coefficients is obtained;

4. the region texture is replaced with the approximation error;

5. the region is scanned along the direction of the columns;

6. a set  of pseudo-bidimensional functions is obtained;

7. the error is approximated and a set  of coefficients is obtained;

8. coefficients  and  are coded.

The isformation on the first scanning direction, as well as the parameters of the pseudo-bidimensional basis functions in  and , can be derived directly from the region contour and do not require to be coded.

5. EXPERIMENTAL RESULTS

The algorithm complexity, as shown in Table 1, is significantly reduced with respect to the 2D orthogonalization procedure proposed by Gilge et al. [8], passing from a quadratic dependence on the number  of coefficients per region, to a linear dependence. The number

Figure 1: Pseudo-bidimensional basis functions are obtained by combining, row by row, 1D orthogonal Legendre poltnomials.

of operations per poxel does not depend on the region area, but exclusively on the number  of coefficients per region. In the example of Figure 2 the gray-level image “Pepper” has been coded and reconstructed employing both the Gilge 2D approach and the novel 1D algorithm. From the experimental results it comes out that only a slight loss in the rate-distortion performances id paid in exchange of the significant complexity reduction. The rate-distortion performances, obtained by varying the coding parameters, have been computed to test the algorithm sensitivity with respect to the number of regions, number of coefficients per region and quantization bits per coefficient. the corresponding curbes are plotted in Figures 3, 4 and 5, respectively. the novel algorithm has been successfully applied for encoding the prediction error within a conventional motion compensation scheme for video coding. The preliminary experimental results show the concrete possibility of its application for very low speed video communication. The preliminary experimental results show the concrete possibility of its application for very low speed video communication. In Table 2 the results obtained by applying the algorithm to the videophone sequence “Salesman” are presented.

6. CONCLUSIONS

A new method for high compression image coding has been described, based on discrete Legendre polynomials, capable to approximate natural textures with acceptable quality and fast computation. The algorithm sensitivity to variations in the number of regions, in the order of the polynomials and in the quantization precision has been further investigated and the achieved results are reported and discussed. The method has been compared to recently proposed similar 2D approaches showing ednsible improvements, as complexity is concerned, for coding both still and moving pictures.

Figure 2: Image “pepper”, 200regions, 6 coefficients/region, 8bits/coefficients. (Left) Reconstruction through 1D orthogonal functions at 0.51 bpp with PSNR=25.75. (Right) Reconstruction by means of the 2D algorithm (Gilge) with 2D orthogonal functions, at 0.49 bpp with PSNR=25.98.

Figure 3: PSNR curve as a function of the bitrate, with increasing number of regions. Evaluated on “pepper” with 6 coefficients/region, 8 bits/coefficient.

Figure 4:  PSNR curve as a function of the bitrate, with increasing number of coefficients/region. Evaluated on “pepper” with 200 region, 8 bits/coefficient.

Table 1. Comparison between the complexoty of the 2D algorithm proposed by Gilge, employing the Gram-Schmidt orthogonalization procedure, and the complexity of the pseudo-bidimensional algorithm. The evaluation is in therms of number of multiplications/additions per pixel, where  is the number of coefficinets per region.

Number of operations		Coding
Multiplications per pel	Additions per pel	Algorithm
(2+3)	2(+1)	2D orthogonalization
5-6	3+1	1D orthogonalization

Figure 5: Psnr curve as a function of the bitrate, with increading number of bits/coefficient. Evaluated on “pepper” with 200 region and 6 coefficients/region.

The overall characteristics of the method make it suitable for real-time video coding at very low bitrate.

Table 2. Performances evaluated on the sequence Salesman, Luminance only, format 256x256, 15 frames/second. Images have been motion compensated and the prediction error has been coded through the region-baded algorithm. The frame in column 2 while the bitrate and PSNR are reported in columns 3 and 6, respectively. The bitrate percentage due to texture encoding, contour encoding and motion field encoding are reported in columns 3,4 and 5, respectivelt.

frame	regions	bpp	%	%	%	PSNR
22	43	0.115	22.91	37.77	39.31	30.68
24	46	0.104	26.23	42.68	31.09	30.71
26	43	0.106	23.84	41.31	34.84	30.77
28	41	0.108	21.59	36.58	41.82	30.46
30	39	0.106	21.16	30.31	48.52	30.55
32	43	0.118	21.32	37.24	41.43	30.49
34	44	0.120	19.05	41.46	39.49	30.65
36	43	0.106	24.35	37.81	37.83	30.66
38	36	0.092	23.86	34.44	41.69	30.79
40	37	0.093	22.02	36.56	41.41	30.69

References

[1]Kunt, M., Ikonomopoulos, A. and Kocher, M.(1985) Second-Generation Image Coding Techniques, IEEE Proceedings 73:549-574.

[2]Musmann, H.G., Pirsh, P. and Grallert, H.J.(1985) Advances in Picture Coding, IEEE Procedings, 73:523-548.

[3]Kunt, M., Benard, M. and Leonardi, R. (1987) Recent Results in High-Compression Image Coding, IEEE trans. on Circuits and Systems, 34:1306-1336.

[4]Hoetter, M. and Thoma, R. (1988) Image Segmentation Based on Object Oriented Mappong Parameter Estimation, Signal Processing, 15:315-334.

[5]Musmann, H.H., Hoetter, M. and Ostermann, J.(1989) Object-Oriented Analysis-Synthesis Coding of Moving Images, Signal Processing: Image Communication, 1:117-138.

[6]Braccini, C., Grattarola, A., Lavagetto, F. and Zappatore, S. (1992) “VQ Coding for Videophone Applications Adopting Knowledge-Based Techniques: Implementation on Pa allel Architectures:, European Trans. on Telecommunications, 3:137-144.

[7]CCITT, (1990) Video codec for audiovisual services at p*64 kbits/s”, Recommendation H.261, CDM XV_R 37_E.

[8]MPEG ISO-IEC JTC 1/SC 29/WG11, (1992) Coding of moving pictures and associated audio for digital storage media up to about 1,5 Mbit/s.

[9]Gilge, m., Engelhardt, T. and Melhan, R. (1989) Coding of Arbitrarily Shaped Image Segments Based on a Generalized Orthogonal Transform, Signal Processing: Image Communication, 1:103-116.

[10]Mussman, H.G. (1993) A Unified Point of View on Video Compression: A Model-based Perspective, Workshop on Very Low Bitrate Video Compression, Urbana IL.

[11]Leou, F. and Chen, C. (1991) A C contour-based Image Coding Technique with its Texture Ingormation Reconstructed by Polyline Reconstructed by Polyline Representation, Signal Processing, 25:81-89

ADDRESS PREDICTIVE COLOR QUANTIZATION IMAGE COMPRESSION FOR MULTIMEDIA APPLICATIONS

Chong-Hwan Choi*, Kwang-Chae Park**, Young-Geil Kim***, Cheol-Soo Bae****

*Department of  Informationand Communication Technology,DaeDuck Univ Korea

**Department of Electronic Engineering Chosun Univ Korea

***Department of computer Information Technology,Korea National Univ of Transportation Korea

****Department of Information and communication Engineering Catholic Kwandong Univ Korea

Abstract

Most of the modern workstations and personal computers are using 8-bit color mapped graphics architecture for displaying color images. To display a decompressed color image of the conventional DCT-based compression algorithms on these palette-based display systems, the decoded image must first be color quantized. To avoid the computational intensive color quantization process and provide a fast decoding process, new address predictive color quantization image compression schemes for multimedia application are proposed in this paper. A closet pairs color palette ordering technique is also proposed for effectively exploited the redundancy of the palettized image. Computer simulation results are given to show the performance of the new compression algorithms in terms of bit rate and signal to noise ratio.

1. Introduction

Multimedia [1] is the merging of various forms of communication, namely: text, graphics, audio, still and motion video, onto a common platform e.g. desktop PC.

Many of the elements of multimedia already exist, indeed the handling of text, graphic and, most recently, audio output are now common place in PCs. The challenge in providing real multimedia systems is to embrace video compression, which have become the biggest single stumbling block, in terms of both cost and interoperability. The industry could not settle on one image compression algorithm for its standard. The MPEG algorithm, the JPEG algorithm and other contenders have attracted attention. Due to these compression schemes don't interoperate with each other, the multimedia platform is faced with supporting many standards. The algorithms tend to be complex. Implementing any one of them makes for a relatively expensive solution. Programmable chips that run many of th e algorithm-specific chips and that doesn't solve the cost problems. On the other hand, newer multimedia applications tend to be based on digital video stored on CD-ROM and most of them are still of the re-play only class. These include information kiosks of the type found at exhibition, airports and certain retail locations. In conclusion, the best way to tackle cost and interoperability problems of image compression algorithms for multimedia application is software-only decompression image coding method. Software decompression makes video playback virtually free, as any multimedia platforms can playback the video without special hardware. In this paper, we propose new color image compression algorithms with extremely low decoding complexity, that is very suitable for software-only image decompression applications of multimedia.

Fig. 1. Encoding scheme of address predictive color quantization image coding system.

2. Address Predictive Color Quantization Coding

True color digital image obtained from image capturing devices are usually represented by 24 bits/pixel with 8 bits each assigned to the three primary colors of red, green and blue. Moreover, these color images are usually compressed in a luminance-chrominance

coordinate space [2], with the compression performed independently for each coordinate. Color quantization [3-6] is, therefore, required for displaying these images on a palette-based display system, which can only allow a limited number of colors called color pallets, from the higher color resolution image. During the last decade, a number of color palette design algorithms have been developed, for examples mean-split [3], variance-based [4], and principal-axis split [5] algorithms. Basically, color quantization is an instance of vector quantization [7]. The color pixels(3-dimensional vectors) of the digital color image are considered as the training set for generation the color palette (codebook), thus the pixel values of the color palettized(or quantized) image are lookup table indices of the palette instead of physical intensity values. In general, the palettized image with the corresponding color palette can be considered as compressed data of the original color image. However, the compression ratio is just about 3 times and the bit rate is about 8bits/pixel for the palette with 256 colors(or 256 color-level)

Experimental results shown that the palettized image's indices are highly correlated with the adjacent indiced if the color palette is appropriately arranged. It is because similar color pixel are allocated very close to each other for effect the graphic or nature senses pictures. Thus, if the color palette is arranged so that similar colors are assigned close position, predictive coding techniques such as differential PCM can be effectively exploited the redundancy of the palettized image. In Figure 1 the encoding scheme of this address predicative color quantization coding (APCQC) system is shown. The idea of the APCQC is that a palette is first generated based on the encoding image and then the palette is ordered to exploit the redundancy of the palettized image. After that each index of the palettized image is substracted from a prediction performed on the basis of some previous indices. Finally, the resulting prediction error is encoded using Huffman coding technique. The compression ratio of the APCQC is mainly affected by color palette ordering. In order to exploit the redundancy among the palettized Image's indices for Huffman encoding, a very simple palette ordering technique called closet pairs method for the APCQC is proposed in this paper.

Table 1: The performance of APCQC using principal-axis split color quantization algorithm, closest pairs colour palette ordering and 1-dimensional predication for DPCM.

3. Closet Pairs Color Palette Ordering

The purpose of the color palette ordering is to arrange the color indices such that similar colors are assigned close position. This way, in fact, the topological relationships are preserved from the vector space to the address space through the quantization, and hence highly correlated input colors are mapped into highly correlated address. As the complexity for finding the optimal ordered color paletted is extremely high, we developed a closet pairs ordering method with very low computational requirement. This ordering method is based on the minimization of the vector distance between two colors with indices assignment of  and . Moreover, the color's components are usually encoded in form of 8-bit positive integer, thus we can assign the color which closet to the origin (0,0,0) as the index 0 for starting the palette ordering algorithm. The closest pairs color palette ordering method is summarized as:

1) Assign the color of the palette which located closet to the origin in the 3-dimensional color space to index 0 and set ;

2) Assign the color of the palette's colors, which is closet to the color with index , to  and set ;

3) Repeat step 2 until  is equal the color-level of the palette.

This technique is optimal in 1-dimensional case. The entropy and the reproduced image's Peak Signal to Noise Ratio(PSNR) of the APCQC using 1-dimensional prediction and the closet pairs color palette ordering for three standard test images are shown in Table 1. In this experiment, the color-principal-axis split algorithm [5]. Table 1 shows that the average bit rate and PSNR of the encoded images are about 4.7bits/pixel and 37dB, respectively.

4. Block Address Predictive Color Quantization Coding.

The compression ratio of the APCQC is less than 6, which is relatively low for multimedia applications. Fortunately, when the APCQC scheme is applied on image subblocks with proper block size and color-level, the bit rate is significantly reduced while visual reconstructed image fidelity is also maintained. The main idea of the block address predictive color quantization coding(BAPCQC) is applying the APCQC technique on an image subblock with a proper block size and adaptively select color-level for the color quantization of the image subblock. The encoding scheme of the BAPCQC os shown in figure 2 and 32x32 block size is found to be the best. The input image is first partitioned into 32x32 image subblock and each of the subblock is color quantized with palette ordering and adaptive color-level selection which is according to the colors contents of the image subblock, The palettized image subblock is then encoded as APCQC while the prediction error is encoded using run-length and Huffman coding techniques. It is because the color-level required to reproduce the image subblock is much fewer than for the whole picture, the prediction error producing a lot of zero and continuous zero codewords. Then the run-length coding is the most effective encoding technique for reducing the bit rate. The total number of colors of all the subblock's color palette are always greater tnan 256, thus we have to combined these small color palettes into a global color palette with maximum color-level of 256. The color palette combination is just color quantized all the small subblock's palettes using the same color quantization algorithm in APCQC with a predefined color-level. Finally the combined color palette and subblock's palettes are transmitted to the receiver, while the subblock's palettes are indices of the combined palette.

The bit rate reduction of the BAPCQC is mainly selecting the color-level of each image subblock. However, the criterion for determining the color-level suffice to reproduce an image subblock without a noticeable distotion is not well defined. As color quantization are usually used for displaying true color image on a display device with limited and fixed number of colors, the color-level is predefined. Basically, the popular used color palette generation algorithms are hyperplane approach which partitions the 3-dimensional color space into K-disjoint subregions. For simplicity, the partition hyperpanes are aessumed to be perpendicular to one of the R-G-B axes (i.e.orthogonal) and the subregions are actually hyperboxes, such as the median-cut, mean-split and variance-based algorithms. Better performance can be obtained by partitioning the regions perpendicular to the principal axis of the color distribution, like the principal-axis split algorithm[5]. Finally, the centroids of the data in the K-resulting subregions are chosen to be the cluster centers (colors of the palette). There are teo fundamental issues in the deign of the algorithms. At each step of the partition, one must decide which subregions should be further partitioned (partition selection criterion) and choose an appropriate hyperplane subdivide the region(hyperplane selection criterion). We can find that the partition selection criterion is used to find a partition which should be further split. One a subregion is further partitioned, the color-level is increased by one. For adaptive color-level selection, therefore, we can introduce a threshold value on the partition selection criterion of the conventional color quantization algorithm.

The proposed BAPCQC scheme is employed the principal-axis split algorithm for the color quantization process, as the performance of this algorithm in term of reconstructed image quality is relatively high and the computational complexity is affordable for most of the practical applications. The partition selection criterion of the principal-axis split algorithm is the largest eigenvalue. A threshold value for the largest eigenvalue is, therefore, introduced in the principal-axis split algorithm find the largest eigenvalue for further node splitting is less than the predefined threshold, the color quantization algorithm will stop and output the current color-level and color palette. The performance of the BAPCQC algorithm in terms of bit rate and PSNR using different threshold values for three standard test images(lenna, peppers and F-16) are shown in Table 2. The computer simulation results show that if the eigenvalue threshold is equal to 15000, the visual quality of the reconstructed images are good for most of the observes. With this threshold value, the bit rates are less than 2bits/pixel and the PSNR is higher than 31dB for three test images. To show the visual quality of the BAPCQC, the luminance component of the reproduced color image for the test image lenna with 15000 threshold is shown in Figure  3. These results are significant improvement on the compression ratio of the APCQC shown in Table 1. In addition, experiments on cartoons and non-realistic images show that very low bit rate(about 0.3 bits/pixel)can be obtained by the proposed BAPCQC algorithm with nearly no visible change(PSNR is above 37dB).

Fig. 2. Encoding scheme of Block Address predictive Color Quantization Image Coding System.

5. Conclusions

New address predictive color quantization coding and block address predictive color quantization coding scheme are proposed in this paper. With the use of 32x32 image subblock, about 2 bits/pixel and 32dB (PSNR) are obtained for natures senses images from USC database. The main feature of the mew schemes are that color quantization is not required for displaying the decoded image on a palette-based display system. Moreover, the decoding process is mainly table lookup operation, which so far the simplest and hence fastest decoding process for color image compression algorithms. Thus, the BAPCQC algorithm and the further development of such color quantization based image compression algorithms are very suitable for developing software-only decodable image/video compression algorithms in multimedia applications. Software-only decompression makes video playback virtually free, as any multimedia platforms can payback the video without special hardware. Thus, software-only decordable compression algorithm is one of the best way to tackle cost and interoperability problems of image/video compression algorithms for multimedia applications. We hope that the results of this paper can provide a new insight for software-only decordable image/video compression algorithms development in multimedia applications.

Fig. 3. The luminance component of the reproduced color image lenna encoded by BAPCQC with 15,000 eigenvalue threshold and 256 color-level for the combined color palette. (Bit rate= 2.087 bits/pixel and PSNR = 32.96dB)

Acknowledgment

This work was supported by Research Grant Comittee, City University of Hong Kong.

References

[1] "Special Report: Interactive Multimedia", IEEE Spectrum, pp. 22-39. March 1993.

[2] M. Kunt, A. Ikonomopoulos and M.Kocher, "Advances in picture image coding techniques," Proc. IEEE 73 (4), pp.549-574, April 1985.

[3] P.Heckbert, "Color image quantization for frame buffer display," Proceeding of SIGGRAPH'82, ACM, New Yourk, pp.297-307, 1982.

[4] S.J Wan, P. Prusinkiewicz, and S.K.M. Wong, "Variance-based color image quantization for frame buffer display," COLOR research and application, vol. 15, no .1, pp.52-58, Feb.1990

[5] M.T. Orchard and C.A.Bouman, "Color quantization of images," IEEE Trans. Sig. Pro., vol.39, pp.2677-2690, Dec. 1991.

[6] X. Wu, "Color quantization by dynamic programming and principal analysis," ACM Transactions on Graphics, vol.11, No.4, pp. 348-372, October 1992.

[7] A. Gersho and R.M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, Boton, 1991.

Fire Detection System Using Arduino Sensor

Joong-Chang Chun*, You-sik Hong**, Cheon-shik Kim***, Cheol-Soo Bae****

*Department of Electronic Engineering, GNTECH, Jinju, Gyeongnam, Korea

**Department of Computer Science, Sangji University, Wonju, Kangwon, Korea

***Department of Computer Science, Sejong University, Seoul, Korea

****Department of Information and communication Engineering Catholic Kwandong Univ Korea

Abstract

Recently various types of disaster monitoring system using smart-phones are under active studying. In this paper, we propose a system that automatically performs the disaster and fire detection. Additionally we implement the Arduino-based smart image sensor system in the web platform. When a fire is detected, an SMS is sent to the Fire and Disaster Management Agency. In order to improve fire detection probability, we proposed a smart Arduino fire detection sensor simulation which searches the smart sensor inference algorithm using fuzzy rules

1. Introduction

These days, many researchers are actively working to detect fires and disasters based on smart systems. In addition, there are some other researches to detect the flood and forest fire using drones to provide urgent messages in real-time to the disaster emergency center. Services using drones were developed by the military drone for the first time. Drone was derived from that of a bee buzzing in nature. Recently, some researchers are working to detect and analyze the fire combining advantages of smart phones and drones. We call it a smart video fire alarm system. However, the conventional fire detection system is determined by using a differential image. In this case, the error from the noise and illumination changes occurs and thereby the wrong judgment can be introduced. In this paper, we applied the fuzzy rule in order to solve these problems. In other words, the agent detection imaging experiments were applied to smart features.

This paper is organized as follows. In section 2, we explain fire detection theory using smartphone-based drones. In section 3, the computer simulation to improve fire detection probability is performed combining the fire detection sensor with the Arduino processor. Finally a conclusion is discussed in section 4.

2. Fire Detection Theory

Recently, many researchers have conducted studies to detect and analyze the fire automatically, which is called a smart video Fire alarm. However, conventional fire detector is generally determined using a differential image. But this method is surely introducing an error according to the noise and illumination changes. After all, that becomes the cause of the malfunction of the system.

Therefore, in this paper, we have to solve this problem, by studying the characteristics of the wavelength of the infrared ray generated from fire, heat, smoke and the like. Considering such characteristics, we study the accurate fire detection technology.

With detection technology, the most commonly used image processing methods is of using a difference between the images. This method has the advantage that the processing speed is very fast. However, this method has the disadvantage of sensitivity to dust, mist, noise, and illumination.

In this paper, we propose a smart fire simulation image method using fuzzy rules in order to solve these problems.

Fig. 1 Implementation of the Arduino sensor-based fire detection system

In Fig. 1, using the Arduino sensor, we implemented a WEB-based disaster safety systems for fire detection, where we applied the fuzzy rule in order to solve these problems. In other words, the agent detection imaging experiments were applied to smart features.

3. Computer Simulation

In this paper, it is determined real flame presence precisely in the video fire detection using fuzzy rules. In order to be able to efficiently adapt to the dust, fog, illumination changes in the actual road, the intelligent fuzzy technique was simulated.

Fig. 2 WEB-based fire detection and SMS transmission system.

In addition, in this paper, we implement a wireless sensor network and WEB-based fire safety system for detecting disaster. In Fig. 2, if there is a fire, it describes the process of notifying the fire alarm system. Fire location is transmitted to the fire department or emergency Safety Authority.

int pirState = LOW;

// Save the initial state of the flame sensor (setting the first state to LOW)

int val = 0;

// Declaration for reading the sensor values

void setup () {

pinMode (Beep, OUTPUT); // Set up output Piezo buzzer

pinMode (flame, INPUT); // Set up the input phototransistor

Serial.begin (9600); // Set up Serial Monitor

pinMode (ledPin, OUTPUT);

pinMode (inputPin, INPUT);

pinMode (pinSpeaker, OUTPUT);

Serial.begin (9600); // Prepare for serial communication (9600)

}

val = analogRead (flame);

// Retrieves a value from a phototransistor

Serial.println (val);

// Phototransistor output to input serial monitor

if (val> = 1000)

If the input value is more than 1000 // phototransistor BEEP run, need to adjust to an appropriate value depending on the environment

{

digitalWrite (Beep, HIGH); // Piezo buzzer BEEP

} Else {

digitalWrite (Beep, LOW); // Piezo buzzer OFF

}

Rule: IF PA is t1 THEN C is B2: (FBAD)

fact:

PA is t1 '

conclusion: C is t2 ': (FCON)

PA: fire conditions

CON: Inference result judgment

FBad: Mobile Phone-based

Fire detection ERROR

uncertainty, fuzzy number representing the difference image

FCON: Mobile Phone

Fire detection final results

RULE

IF TEMP = High And

FIRE = Med And

IR = High And

UV = High And

SMOG = Med

Then

POS-FIRE = High

Table 1. Fire detection conditions using fuzzy rules

Fire condition		Flame Temperature	Flame Movement	Flame Size
Flame Condition	Y1H	Big	Med	Big
	Y2YM	Big	Med	Med
	Y3S	Med	Small	Small
Smoke Condition	Y2H	Big	Med	Small
	Y2M	Med	Med	Med
	Yy2L	Small	Small	Small
Ultraviolet rays Condition	Y3H	Big	Med	Big
	Y2M	Med	Med	Med
	Y1L	Small	Small	Med

Table 1 shows fire detection conditions by using an Arduino Sensor with various disasters, and condition Name of smart algorithms that automatically detect the fire. Therefore, this can accurately determine real flame presence in the automatic fire detection image, in order to be able effectively to adapt to the actual that may occur in road dirt and fog noise and car and the sun, the lens aberration, illumination changes, UV + IR reflecting the figures were simulated using FUZZY INFERENCE SYSTEM TOOL in MATLAB.

Table 2. Fire detection Identification

ID	Fire condition
1	Temperature SMALL MED BIG
2	Smoke SMALL MED BIG
3	ultraviolet rays SMALL MED BIG
4	infrared light SMALL MED BIG
5	ROI SMALL MED BIG

The reliability of the ID of the outcome of the association rules given in Table 2 are shown below in Table 3. In this research, in order to solve this problem, the infrared ray generated from the fire, because the detection by the sensor that detects a specific wavelength of ultraviolet light was studied ways to detect the accurate consideration of the heat than the fire or smoke. However, such a minute problem, a pixel value which is used to improve based on difference image technique is advantageous in that fast access for the pixels changes in the movement, but has the disadvantage that dust and mist noise and car light changes the sensitive. Therefore, it is determined accurately real flame presence in the automatic fire detection image in this paper, in order to be able effectively to adapt to the actual that may occur in road dirt and fog noise and car and the sun, the lens aberration, illumination changes, UV + IR reflecting the figures were simulated using FUZZY INFERENCE SYSTEM TOOL in MATLAB.

Table 3 Fire detection association rules

Association Rule	ID	Reliability
Temperature->Humidity	1,4	75%
Temperature->dew point	2,3	75%
UV-rays>Flame	1,3,4	50%
UV-rays<Flame	1,2,5	50%
UV-rays>Flame	1,5	60%
Smoke >Wind direction	2,3	40%

In order to accurately determine the flame and fire detection in images in this paper, to adapt effectively to noise and illumination changes, and simulate the UV + IR figures reflect the intelligent fuzzy techniques.

Fig. 3 Fire detection simulation using fuzzy rules

Fig. 3 describes the detection of fire simulation results using fuzzy rules for improving the noise and illumination changes disadvantages of the pixel value difference imaging technique. This paper adopted the real flame presence precisely in the video fire detection using fuzzy rules.

4. Conclusion

These days, the study to monitor various disasters based on the smart-phone has been made actively. In this study, the implementation of smart video platform on the web system in order to implement a system that can automatically determine the fire. When a fire was brought out, an SMS is automatically sent to the Fire Department and Disaster Management Agency. But the problem of the existing primary imaging method is that a malfunction can occur. In this paper, we build intelligent WEB platform that enables real-time techniques. To identify and report the malfunction for fire and other disasters, the fuzzy control algorithm was adopted.

References

[1] B. Ko, K-H Cheong and J-Y Nam, “Fire detection based on vision sensor and support vector machines,” Fire Safety Journal, Vol.44, Issue 3, pp.322-329, 2009.

[2] B.U. Töreyin, Y. Dedeoglu. U. Gudukbay and A. E. Cetin, “Computer vision based method for real-time fire and flame detection,” Pattern Recognition Letters, 27, pp.49-58, 2006.

[3] D. Han and B. Lee, “Development of Early Tunnel FirDetection Algorithm Using the Image Processing,” International Symposium on Visual Computing, pp.39-48, 2006.

[4] T. Celik, H. Ozkaramanh and H. Demirel “Fire pixel classification using Fuzzy Logic and Statistical Color model,” IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.I-1205-I-1208, 2007.

[5] Zigbee Alliance, “Zigbee Specification 2006”,

[6] B. K. Seo, “A Study on the Adaptability of Carbon Monoxide Detector”, The University of Seoul Graduate school, Academic Thesis, 2010

[7] T. Horprasert, D. Harwood, L.S Davis, “A statistical approach for real-time robust background subtraction and shadow detection,” IEEE Workshop on Frame Rate, pp.1-19, 1999.

[8] I. Kopilovic, B. Vagvolgyi, T. Sziranyi, “Application of Panoramic Annular Lens for Motion Analysis Tasks: Surveillance and Smoke Detection," IEEE International Conference on Pattern Recognition, Vol.4, pp.14-717, 2000.

License Plate Recognition Method for Inclined Plates

Young-gyu Choi*, Han-kyung Yun**, Hyeop-geon Lee***Panlinhan****

*National University of Transportation, Korea

** University of Technology and Education, Korea

*** Polytechnic University of Seoul Gangseo Campus, Korea

****Department of Information and communication Engineering Catholic Kwandong Univ Korea

Abstract

This paper presents novel methods of recognizing li-cense plates of passing vehicles outdoors. In particular, the proposed method is much robust for inclined plates caused by the changes of camera placement. To ac-quire fine images of quickly passing vehicles under a wide range of illumination conditions, we developed a sensing system having superb characteristics. We ex-panded the dynamic range and eliminated the blurring of images of fast moving vehicles by synthesizing a pair of synchronized images with different intensities. Fur-thermore, th extend the flexibility of the positioning of the TV camera, we propose a recognition algorithm that can be applied to inclined plates. The performance of the integrated system was investigated on real images of vehicles captured under various illumination conditions. The recognition rates of over 99% (conventional plates) and over 97% (highly inclined plates) shows that the developed system is effective for license plate recognition.

1 Introduction

The purposes of the vehicle recognition by the video images are classified into three, that is, the detection, the distinction, and the identification of vehicles. Those functions are essential to achieve the various applications planned in ITS (Intelligent Transport Systems). In particular, license plate recognition to identify vehicles is the most important function. Therefore a large number of techniques to recognize license

plates are being developed during the past two decades. Several systems have been applied practically into large-scale facilities, for example highways and main roads. However, current demands to apply license plate recognition into small-scale facilities, such as for managing a private parking lot and monitoring vehicle entry and exit at a factory, are increasing.

License plate recognition is realized by acquiring images of either front or near of vehicles with TV cameras then by image processing to specify license plates. Therefore, both sensing system for taking images and recognition algorithms are most significant functions.

Recognition algorithms reported in previous researches are generally composed of several processing steps as follows, extraction of the region of a license plate, segmentation of characters from the plate region, and recognition of each character. As to extraction of the plate region, the techniques based upon the binary image processing[1], neural network, and MRF are proposed. A number of techniques to segment each character after localizing the plate in the image are also proposed. In [2] histograms generated by the projection of edges onto vertical and horizontal axis are used. Neural network and fuzzy logic are adopted in [3]. For recognizing segmented characters, numerous algorithms, which are exploited mainly in OCR applications, utilizing template matching methods, statistical approach, and neural networks[4] are reported.

Not only recognition algorithms, but also a sensor is a considerable issue in practical use. The sensing system utilized for license plate recognition in outside environment has to be capable of capturing superb images under varied illumination conditions, i.e. from low brightness at twilight, up to high brightness in the sun at noon. Additionally, it has to be capable of capturing non-blurring images for fast passing vehicles.

In order to make a sensing system robust for the change of illumination conditions the expansion of the dynamic range of the sensor is required. One approach to expand the dynamic range is to improve a property of sensing devise itself. The other is to obtain an image with dynamic range by applying image processing to the images captured with a conventional TV camera.

Some imaging devices have been developed in the former approach. A typical example is

Figure 1. Various brightness measured on the road and the dynamic range required for a camera.

the CCD cameras whose devices are forced to cool at a low temperature to suppress thermal noise. In addition, logarithmic conversion CCD sensors[5, 6] and hyper-dynamic range CCD[7] have been reported. On the other hand, several studies based on the lateter approach also have been proposed[8, 9]. The studies indicate that an image with a wider dynamic range than that of a TV camera can be obtained by composing the images taken under different exposure conditions. However, no device has been realized that satisfies the requirements for license plate recognition, such as a wide dynamic range, enough resolution, frame rate, and cost.

Additionally, to capture fine images against fast passing vehicles, a sensing system is required to avert blurring of images. A conventional technique to overcome this problem is to control a shutter speed of a TV camera (e.g 1/1000 second). In this solution, usually a supplementary lighting source such as an infrared lamp is utilized to preserve brightness, therefore an entire system for license plate recognition tends to be massive and costly.

As reviewed above, although a number of techniques on both recognition algorithms and sensing systems have been proposed, they are not adequate in applications of license plate recognition, especially in small-scale facilities.

This paper presents novel methods to recognize license plates of vehicles. The contributions of this work are

1. A new sensing system having a couple of superb characteristics, 1.5 x  wide dynamic range and elimination of image of image blurring against fast passing vehicles, has been developed.

Figure 2. Differences of images of license plates when the camera placements (pan and tilt) are changed up to 50 degree. It is difficult to read the registration numbers at a pan and a tilt over 40 degree.

2. An algorithm to recognize registration  numbers of vehicles, which is robust for      highly inclined plates, is proposed.

As to the contribution 2, the recognition algorithm proposed in [10] has been improved the by adopting several geometrical constraints. Consequently, the sufficient recognition rates for highly inclined plates could be achieved compared with our previous study.

Section 2 describes the problems inherent in license plate recognition. In sections 3 and 4, the sensing system and the recognition algorithm we developed are presented, respectively. In section 5, experimental results are demonstrated.

2. Problems in license plate recognition

figure 1 shows brightness of objects in outside environment. This figure indicates that the brightness is distributed from 1 up to 1.5 x cd/ under the condition where any light sources exist. Thus, a dynamic range of 1.5 x  is required for the sensing system to capture images of license of license plater without loss of information. This also indicates that the dynamic range of the conventional CCD video camera, approx-imately 300-500, is insuffcient for the sensing system under required conditions.

For applications of license plate recognition, it is necessary for the sensing system to handle passing vehicles, although unnecessary in special cases when vehicles are forced to stop. To prevent blurring of images against passing vehicles at a speed 100km/h, a shutter speed faster than 1/600 second is essentially required.

A shape of a license plate observed in an image depends on the relative placement between the sensor and vehicles. Horizontal and vertical, distortions of a plate arise from tilt and pan angles of the sensor toward passing directions of vehicles, respectively. Figure 2 shows examples of plate images where placements of the sensor are changed. The range of placements (tilt and pan) of the sensor reported in previous works is mostly restricted less than 25 degree[11, 12]. In order to apply the license plate recognition into various fields, it is necessary to develop the algorithm able to recognize more deformed plates where a sensor placement, tilt and pan, is up to 40 degree.

3. Sensing system

By combining a pair of images captured with two CCDs at the same time and under different exposure conditions, an image with a wide dynamic range and without blurring would be obtained. In this section the methods to attain those features are described and the structure of the developed sensing system is also presented.

3.1. Methods

Figure 3 shows the principle of the proposed sensing system. A prism beam splitter splits an incident ray into reflected and transmitted lights having different intensities. The intensity ratio of split rays ()is controllable with a multi-layered filter in the beam splitter. The rays with different intensities are framed in a pair of CCDs synchronously. In this figure, a transmitted light is stronger than a reflected one, thus CCD1 frames a high exposure image in comparison with CCD2. This structure allows the sensing system to capture a pair of images correspond to different exposure conditions simultaneously. To realize 1.5 x  dynamic range, the intensity ratio of the transmitted light to the reflected one is selected as 45:1 which is described later.

Figure 3. The principles of the proposed sensing system.

By synthesizing a pair of images with different intensities, an image whose dynamic range is wider than an original one can be generated. The details can be referred in [9].

As , we have the following relation

where and  represent gray levels at a pixel coordinate (x,y) in each image framed by CCD1 and CCD2 and  denotes a saturation level. In our study a signal output from a TV camera is digitized with 8 bits, thus =255. From  and , a synthesized image  with an expanded dynamic range is calculated by the following equation for each pixel.

where  and  are the coefficients determined by the exposure conditions (>) and  is also the coefficient for gamma compensation. Figure 3 depicts the idea of synthesizing a paor of images. Consequently, calculating , the image with a dynamic range which is / times as wide as one of the canera itself can be compose.

Finally the way to settle the intensity ratio of  to  is described. The dynamic range of the sensing system D is calculated from

where  represents the noise intensity of the TV camera, thus  represents S/N of the camera. The S/N of the CCD camera (SONY XC-7500) mentioned in the manual is approximately 1000, however, it is specified from the quantization error on digitization. Thus,  is derived as 255. Moreover  was approximately 0.95 from our experimental estimation. In consequence, using these parameters and this equation,  must be 43.9 to realize D= 1.5 x  dynamic range. Therefore, as described above, , which represents the intensity ratio of the transmitted light to the reflected one, is selected as (45:1).

Figure 4. The structure of the developed sensing system and the photo.

3.2. Structure of sensing system

Figure4 shows the structure of the developed sensing system and the photo. The system is composed of a zoom lens, a prism beam splotter, two CCDs, a shutter contral board, and a host computer installed a frame grabber board. Two CCDs are assembled precisely to make split incident lights go into the exact same position on each CCD. The shutter speed can be controlled from 1/60 second up to 1/10000 second. The image processing for synthesizing is executed on the host computer.

4. The algorithm to recognize license plates

Previous approaches to locate and recognize the license plate in the image have been generally utilizing inherent patterns in license plater. For example, the projection of deges onto vertical and horizontal axes is the important clue. However, a change of a position of the sensor with respect to vehicles causes a perspective distortion of the plate in the image and both vertical and horizontal axis of the image don’t coincide with those of the plates. Therefore, those approaches would easily fail in recognition.

Figure 5. Processing flow th recognize the registration numbers in the license plate.

In order to make license plate recognition more practical, the recognition algorithm robust for inclined plates has been developed. The procedure of recognition consists of four steps; (1) binarization of a whole image, (2) extraction of character regions from an image, (3) hypotheses generation of registration numbers, and (4) template matching to recognize registration numbers. Figure 5 shows the processing flow. The key of this algorithm to recognize much deformed plates is the way of generation of hypotheses, which represents the combination of segmented regions of registration numbers and unsegmented regions regarded as registration numbers. In particular, by adopting strict geometrical constraints on generation hypotheses, we improved the recognition algorithm compared with our previous study reported in [10].

The details in each step are described as follow.

4.1. Binarization

As preprocessing to extract character regions inscribed on the license plates, a synthesized image with a wide dynamic range is binarized. A static thresholding for a whole image causes miss-binarization when an image includes local changes of brightness. Thus, small window regions are defined over an image, then thresh values in each region are calculated by the dynamic thresholding method[13]. Finally, thresh values for each pixel are derived by interpolating in 4-neightbour regions linearly. Note that the horizontal and vertical sizes of small regions were defined as 10 x 10(pixel) empirically. The larger window fails to binarize characters and the smaller window generates many pieces of noise fragments.

Figure 6. Korean license plate: geometrical properties for each character are defined.

Figure 7. Normal and inclined plates: relations between each geometrical property in an inclined plate would be approximately as same as those of a normal plate.

4.2. Extraction of character regions

At the beginning of this step, the binarized image is labeled and segmented. From the configurations of the sensor (e.g. a focal length) and the range of the distance between the sensor and vehicles, the sizes of characters observed in images can be estimated in advance. Figure 6 shows an example of a Japanese license plate. It consists of a district office name, a classification number, a prefix (Hiragana), and registration numbers. According to the regulations, however inclined the plate is in the image, the height of registration numbers are twice as large as the other characters in the plate as shown in Figure 7. Utilizing the range of sizes of each character (including registration numbers), both the candidate regions of registration numbers and other characters are extracted, respectively. Additionally, a hyphen and a couple of cots that are screws to fix a plate on a vehicle, which are helpful to specify registration numbers, are also extracted from an image. Note that in this paper only registration numbers are the targets to recognize. Empirically, the range of width of registration numbers was defined from 3 up to 40 pixel and the range of height from 25 up to 65 pixel.

As shown in Figure 6, , which is composed of the center position, width, and height, are defined for each region. The inclined plate would be preserved the relations between each  of a normal plate as shown in Figure 7. Therefore, from all of the candidate regions of registration numbers, hypotheses compounded from these candidates are generated in consideration of following geometrical constraints.

Figure 8. Segmented regions of registration numbers. (a) All of registration numbers are segmented properly. (b) Number 0 can not be extracted as the candidate of the registration numbers, therefore the region between numbers 1 and 5 is defined as a compensated region.

Figure 9. Examples of hypotheses with tree representation:《n》 denotes the candidate segmented correctly. ◇ denotes the node which must be compensated.

1.All the candidate regions in one hypothesis must align.

2.Geometrical relations between each candidate region must be proportional to these of the actual license plates. These geometrical relations can be derived from each .

3.Around candidate regions of registration numbers included in one hypothesis, several candidates of other characters, which are assigned to a district office name, a classification number, or a prefix, must exist. In a ddition, a hyphen and a couple of dots must also exist in a right place.

Furthermore, each hypothesis must be composed of two, three, or four candidates. For real applications, a few registration numbers could not be segmented properly. In particular, this failure frequently arises from inclined plates. To cope with unsegmented registration numbers. this hypothesis generator compensates for lack of regions of unsegmented registration numbers. Also, to deal with all the prossible combinations of candidates of registration numbers efficiently, hypotheses are represented with the tree interpretation.

Examples illustrating compensation of the unsegmented region and the tree representation are shown in Figure 8 and Figure9. Firstly the way of hypothesis generation is described while focusing on the number5. For a simplest case that all the registration numbers are segmented successfully as shown in Figure 8(a), a hypothesis depicted in Figure 9(a) is generated However, for example, in the case where the number 4 is not segmented as shown in Figure 8(b), several hypotheses must, be generated carefully. If the numbers 5, 3, and 2 satisfy the geometrical constraints as the node-1 (root), node-2, and node-3 respectively, a hypothesis shown in Figure 9(b) is generated. In this case the node-4 is not specified currently. Thus, although this hypothesis is not correct, a region whose position and size are estimated from the defined nodes (1,2,3) is compensated, then this region is assigned to the node-4 in the hypothesis. Additionally if the numbers 5, 3, and 2 satisfy the constraints as the node-1, node-3, and node-4 respectively, a compensated region between the numbers 5 and 3 shown in Figure 8(b) is defined by the same manner, then another hypothesis shown in Figure 9(c), which is possibly correct, is generated.

In these examples, the number 5 is always marked as the node-1 (root). However, the number 5 should not be necessarily root. For instance, it is possible that another candidates of registration numbers at right of the number 5 may exist. Therefore, after generatinf all possible hypotheses whose node-1 is the number 5, another hypotheses in which the number 5 is assigned to the node-2 (and/or the node-3) are generated as shown in Figure 9(d).

So far the candidate number 5 is focused, thus all hypotheses in clude the number 5. But it might be possible that the number 5 was not segmented properly or this candidate does not correspond to the registration number of the license plate actually. So hypothesis generation with focusing on another candidates (the numbers 4, 3, and 2) must be executed.

As a result of combination both of segmented candidates and compensated regions, a number of hypotheses would be generated. For recognizing registration numbers efficiently at the final step, each hypothesis is ranked according to the confidence alue calculated from follwing crieria;

● A hypothesis composed of more segmented candidates is given a larger value.

● A hypothesis around which there exist more cdandidates of other characters (e.g. a distinct office name) is given a larger value.

In our experiments described in Section 5.2, the confidence value C was calculated from a following equation.

where  represents the number of registration numbers included in a hypothesis. Also  and  represent the number of other characters and a hyphen/dots extracted around a hypothesis, respectively.

4.3 Template matching for recognition

To recognize registration numbers, the technique of template matching is utilized for each hypothesis in order of the confidence values, For example, a hypothesis shown in Figure 9(a), which is generated from the plate shown in Figure 8(1), is matched with a set of templates composed of ten numerals, 0 to 9. If all of four candidates in one hypothesis can be matched and recognized successfully, this recognition step is terminated. Contrarily, in the case that the matching is failed even for one cadidate, the template matching is applied to a hypothesis having the following confidence value.

For inclined plates, characters extracted from plates are also distorted. Therefore, according to the alignment of the candidates forming a hypothesis, the appropriate templates are utilized for the template matching. To reduce computational time, in our experiments described at the next section, several sets of templates are prepared in advance. Note that as a result of templates are prepared in advance. Note that as a result of preliminary experiments, typical eleven sets of templates whose horizontal inclined angles are distributed from – 50 up to 50 degree are sufficient practically. Figure 10 shows the templates used in our experiments.

Figure 10. Composition of the template

Figure 11. Examples of images captured by the developed sensing system and the conventional camera.

Figure 12. Prototype system for recognizing license plates.

5. Experiments

5.1. Evaluation of the developed sensing system

To evaluate the efficacy of the developed sensing system, images for passing vehicles were captured under the direct sunshine at noon in comparison with a conventional CCD camera. The brightness around plates was 1.0×approximately and the shutter speed was set to 1/10000s for both cameras.

In Figure 11(b), some characters on the plate are much saturated and indistinct. On the other hand the image captured by the developed sensing system are fairly clear withour blurring and all of characters are easily readable.

Thes examples prove that the developed system is competent for capturing the scense with a wide dynamic range including the environment under the direct sunshine. Furthermore, it is confirmed that this system can be utilized for passing vehicles.

5.2. Performance of license plate recognition system

Figure 12shows the experimental systen for license plate recognition utilizing the developed sensing system. The magnetic loop was laid underground to detect vegicles. The signal from this magnetic sensor triggered the sensing system to frame a pair of images including the front of vehicles. Captured images were synthesized on the host computer to expand a dynamic range, then recognized. This system was set up at the gate of the parking area.

Table 1. Experimental conditions.

shutter speed	1/125sec.
focal length	50mm
image size	640(H)×480(V)(pix)
size of license plate in images	160(H)×80(V)(pix) on average
total number of vehicles	949
environment	outdoors, taken from 8am to 5pm (sunny and rainy days)
brightness	approx
camera placement	pan -15, tilt 10 degree

Experiments were conducted for three days, includeing sunny and rainy days, from 8 AM to 5 PM. The distribution of brightness on license plates through the experiments was approximately estimated from  up to 1×. The number of vehicles to be recognized was 949. The camera placement angles, pan and tilt, was about –15 and 10 degree, respectively. The details of experimental conditions are summarized in Table 1.

To evaluate the robustness for the camera placement, the competence for recognizing the inclined plates was investigated. For reasons of the difficulty to place the TV camera at many positions, simulated images were generated by transforming original images. Figure 13 shows examples of transformed images as if they had been captured from camera placements whose pan and tilt were () : (20,20), () : (-20,20), () : (40,40) and () : (-40,40) degree.

Figure 13. Examples of image transformations : (top) original images. (b), (c), (d) and (e) are transformed images according to each camera placement.

The results of recognition for original images and transformed images are depicted in Figure 14 and enumerated in Table 2. For the original images (non-transformed images), 99.3% of license plates were recognized successfully. On the other hand, for the slightly inclined plates (=±20,  = 20.), recognition rate was about 98%. Furthermore, for highly inclined plates( = ±40,  = 40.), the developed system achieved the recognition rate of over 97%.

As plates inclined greatly, there were many cases where a few regions of registration numbers could not be segmented because of the distorions of license plates. Figure 15 illustrates those cases where some registration numbers were not segmented properly. However, by compensating unsegmented registration numbers, those were recognized successfully. Those examples prove the function to estimate and compensate unsegmented regions is much helpful to recognize inclined plates.

Note that the reducion of the recognition rate for the inclined plaes resulted from that all of the regions of registration numbers could not be segmented propdrly due to the distortion of the images in several cases, as shown in Figure 16.

For practical reasons in this experiment, the speed of vehicles had to be less than 10. Our separate experiment confirmed that for vehicles at a speed of more than 100 the images captured by the developed sensing system with a shutter speed of 1/1000 were as same quality as those for vehicles at a low speed.

6. Conclusion

In this paper the novel sensing system, which utilizes both two CCDs and the prism to plit an incident ray into two lights with different intensities, has been presented. One of the main features of this sensing system is that it covers wide illumination conditions from twilight to noon under sunshine. The dynamic range of the developed sensing system is 1.5×, which is about thirty times as wide as ones of the conventional cameras. Simultaneously, this system is capable to capture images of fast passing vehicles without blurring.

Furthermore, the algorithm robust for recognizing inclined plates was propesed. Several geometrical constraints inherent in license plates are adopted to generate hypotheses efficiently. Additionally, to cope with the case that registraion numbers can not be segmented, the unsegmented regions are compensted in consideration of those constraints. The camera placement, pan and tilt, can be enlarged up to 40 degree. This characteristic makes the system flexible for the camera placement in practical use.

Table 2. Results of recognition of registration numbers for original images and transformed images.

camera placement[deg]	percentage (success/total)	average
original	99.3%(942/949)	-
pan:20, tilt:20	98.1%(931/949)	98.3% (1866/1898)
pan:-20, tilt:20	98.5%(935/949)
pan:40, tilt:40	97.2%(922/949)	97.3% (1846/1898)
pan:-40, tilt:40	97.4%(924/949)

The obtained results for both the sensing system and the recognition system are sufficient. At the conventional camera placement. recognition rate over 99% could be achieved in the outside enviroment. Even if the sensor is placed much obliquely relative to vehicles, over 97% of the license plates could be recognized successfully. Thus, this system is much qualified for many fields of license plate recognition, especially for small-scale facilities.

References

[1] M. Takatoo etal. Gray scale image processing technology applied to vehicle license number recognition system. In IEEE int.

Workshop on Industrial Qpplocations of Machine Vision  pages 76-79,1987.

[2] Kenji Kanayama, Yoshimasa Fujikawa, Koichi Fujimoto, and Masanobu Horino. Development of vehicle-licnese number recognition system using real-time image processing and its application to travel-rime measurement. In IEEE Vehicle Technology, pages 798-804, 1991.

[3] J.A.G Nijihuis, M.H. ter Brugge, K.A Helmholt, J.P.W. Pluim, L. Spaanenburg, R.S.Venema, and M.A. Westenberg. Car license plate recognition with neural networks and fuzzy logic. In int. Conf. Neural Network, volume 5, pages 2232-2236, 1995

[4] Sorin Draghici. Aneural network based artificial vision system for license plate recognition. Int. J. of Neural systems, 8(1):113-126, February 1997.

[5] R.Ginosar and Y.Y.Zeevi. Adaptive sensitivity/intelligent scan imaging sensor chips. In SPIE: Visual Communications and image Processing,  volume 1001, pages 462-468, 1988.

[6] K.Takada et al. Logarithmic-converting ccd line sensor. J. Instit. Television Eng. Japan, 49(2):169-175, 1995.

[7] H.Komobuchi etal 1/4 inch NTSC format HYPER-D range IL-CCD. In IEEE Workshop CCD Advanced image Sensors, volume SS-1, Apr, 1995.

[8] K.Moriwak. Adaptive exposure image input system for obtaining high quality color information. IEICE, J76-KII(9):1894-1901, Sept. 1993.

[9] Keiichi Yamada, Tomoaki Nakano. and Shin Yamamoto. A vision sensor having an expanded dynamic range for autonomous vehicles. IEEE　。Trans. Vehicular Technology, 47(1):332+341, February 1998.

[10] T.Naito, T.Tsukada, K.Yamada, K.Kozuka, and S.Yamamoto. License plate recognition method for passing vehicles outside environment. In Int. Cont. Quality Control by Artificial Vision, pages 86-93, 1998.

[11] Hisashi KUROSAKI, Makoto YAGI, and Hisanori YOKOSUKA. Vehicle license number recognition system for measuring travel time. Journal of Robotics and Mechatronics,  5(2):192-197, 1993.

[12] Glen Auty et. al. An image acquition system for traffic monitoring applications. In SPIE, number 2416, pages 119-133, 1995.

[13] N. Otsu. A threshold selection method from gray-level histograms. IEEE. Trans. sys. Man. and Cybern., SMC-9(1):62-66, January 1979.

A Hand Tracking System Based On the Kalman Filter

Seok-Han Lee*, Kyung-Ho Kim**

*Dept. of Information and Communication Engineering, Jeon-Ju University, Jeon-Ju, Korea.

seokhan@jj.ac.kr

**Dept. of Image, Chung-Ang University, Seoul, Korea.

Abstract

In this paper, we present a hand tracking and gesture recognition framework. Our system employs a depth capture device in order to acquire 3D geometric information of user’s bare hand. The user’s hand position and gesture information is estimated from the depth clues. In particular, we build a flexible tracking ellipsoid and restrict the hand tracking area within the tracking area in order to avoid diverse problems caused by conventional object detection/tracking systems. This ellipsoidal area is actively adjusted according to the statistics of user’s hand gesture and motion history. The proposed system can be quite helpful for the systems for which conventional interface is not suitable. The experimental results verify accuracy and efficiency of the proposed system.

Key words: Hand Shape Representation, Human-Computer Interaction, Hand Gesture Recognition.

1. Introduction

Typically, user-computer interfaces are based on conventional interaction devices, such as mouse, keyboard, touch screen, tablet, etc., which all rely on user’s physical contact and operation. However, in many situations, such operations and interactions are quite restrictive, and in such case, user’s natural body motion can be used as very useful piece of information for the human-machine interaction. Therefore, there have been considerable research efforts for the techniques known as natural user interface (NUI), in which the system attempts to track and recognize user’s action and this then provide very efficient method for the interaction. In particular, the NUI technique which makes use of user’s bare hand information is known as one of the most efficient way which enables users to interact with devices. Even though the estimation of hand information is steadily gaining in importance due to the drive from various applications, most of conventional approaches are quite restrictive in real applications. In addition, they often require complicated and expensive devices which are operated under the laboratory condition by experts. In this paper, we present a hand tracking and gesture recognition system. Our system employs a depth capture device to acquire 3D information of user’s bare hand, so that the user’s hand position and gesture information is estimated from the depth clues. In particular, we build a flexible ellipsoid and restrict the hand tracking area within the ellipsoid in order to avoid tracking failure problems which are often caused by conventional object tracking frameworks. This tracking area is actively controlled according to the statistical data of user’s hand motion history. This approach ensures very stable tracking even in the case that multiple objects are present in the scene simultaneously. Our system can be used in various applications for which conventional interface cannot be employed.

Figure 1. Block diagram of the proposed framework

(a)

(b)

Figure 2. Hand detection and finger tip counting: (a) hand detection (reft) and the tracking ellipsoid(right) (b) time-series curve of the hand contour and detected finger tips.

2. Proposed Framework

A. 3D Tracking Ellipsoid

The proposed system computes running averages of center point of the hand in order to remove the jitter problem. The running average is also used to compute the uncertainty of the hand motion in the 3D space. The uncertainty is used to control the shape of the ellipsoidal area for the next tracking period. Then the hand position is assumed to be present in the tracking area, and the system detects the user’s hand position in the defined area. Fig.2-(a) shows an example of hand detection and the ellipsoid controlled by the statistical data of hand motion.

B. Hand Shape Representation and Gesture Recognition

Once the position and uncertainty of user’s hand motion are estimated, the system then attempts to detect the user’s hand and counts the number of fingers in order to recognize user’s hand gesture. Fig.2-(b) shows the time series curves which illustrate the extracted hand contour curve. From this curves we can get topological information of the hand which is used to recognize user’s hand gesture.

(a)

(b)

Figure 3. Hand detection results: (a) conventional approaches (b) proposed method

(a)                    (b)

Figure 4. Center points of the hand(x-axis): (a) distance transform  (b) center of mass

Figure 5. Uncertainties of the hand motion(x-axis)

Figure 6.Application examples of the proposed framework

3. Exeprimental Result

Fig.3 illustrates examples of user’s hand detection. Red regions represent that user’s hand is detected. In case of the conventional methods, the hand tracking is not achieved successfully. On the other hand, the proposed system is not affected by any other objects resident in the scene. Fig. 3-(b) also shows that the ellipsoid expands toward the direction of hand motion. Fig. 4 presents the results of center point estimation. Fig.4-(a) shows the results by the simple distance transform, and Fig.4-(b) by the center of mass technique, which verify that the results are quite stable. Therefore, we employ the COM to obtain the center position. Fig.5 shows the uncertainty of x axis hand position used to control the attribute of the ellipsoid. Fig. 6 shows that the proposed system can be used for real applications. We build a test application in which a user can perform interactions with bare hand. This also verifies that our system can be helpful for the situation in which conventional interface is not suitable.

Reference

[1] D. Lee and Y. Park, “Vision-Based Remote Control System by Motion Detection and Open Finger Counting,” IEEE Trans Consumer Electronics., vol. 55, no. 4, Nov. 2009, pp.2308-2313.

[2] Z. Ren, J. Yuan, and Z. Zhang, “Robust hand gesture recognition based on finger-earth movers distance with a commodity depth camera,” In Proc. of ACM Multimedia,2011.

[3] M. Van den Bergh, L. Van Gool, “Combining RGB and ToF Cameras for Real-time 3D Hand Interaction,” IEEE Workshop on Applications of Computer Vision,pp.66-72, Jan., 2011.

A Study on Measurements of Hydrogen Permeation Time using the Pressure Sensor and Wavelet Threshold

Tae-Soo Kim*, Joong-Chang Chun**

*Department of Steel IT Engineering Uiduk University Kyeongju, Korea

Email : tskim@uu.ac.kr

**Department of Electronic Engineering Gyeong Nam National University of  Science and Technology, Jinju, Korea

Email : jcchun@gntech.ac.kr

Abstract

We measured the time for hydrogen permeation in steel plate. In here, we eliminated the noise through threshold method of Donoho and measure the source signal if the additive noise was mixed in the hydrogen permeation signal. The results from eliminating the additive noise seemed to be sufficiently valid compared with the conventional methods which didn’t remove the noise.

Keywords : hydrogen permeation, enamel steel, pressure sensor, wavelet threshold

1. Introduction

The use of enamel steel in the industry such as hot water tanks has been increased as well as in the filed on household or construction such as kitchen utensils or blackboard recently.

In order to manufacture these enamel steel, the processes of drying, curing and cooling are required after coating the steel plate with enamel materials. However, there is a serious problem that crack occurs by the hydrogen from the steel organization when manufacturing the enamel steel, called as fish scale. The range that fish scale does not happen is the ability of hydrogen management of enamel steel. This is the main parameters to determine the quality of enamel steel.[1]

In addition, the probability of occurring the fish scale is decreased when the ability of hydrogen employment is larger. The ability of hydrogen employment is determined by measuring the time when the hydrogen occurred from testing steel permeate into experimental bath. In this case, the enamel steel has better property when the hydrogen permeation time is longer. There are two conventional methods for the test of hydrogen transmission to material test.[2] First method is the image processing, which measures the bubble of hydrogen occurred by the digital camera. However, this method is inconsistent with the standard and makes an error such as large deviation when calculating the number of hydrogen bubbles. Second method is using the capillary wall, which the volume of hydrogen occurred is measured by glass capillary wall with 1 mm diameter through CCD camera. However, the range of measurement is limited and maintenance is much difficult due to the leak pressure problem. Thus, in here, this report suggests novel methods using the pressure sensor, which can last the accuracy of measurement with large range but no leak pressure in order to improve previous problems. Furthermore, we applied wavelet threshold method which considered the other components except hydrogen bonds affected on the pressure sensor as noise and eliminated them.

2. Instrument for measuring hydrogen permeation

A. Design of the reactor vessel bath of inserting section for hydrogen generation

The reactor vessel was consisted of feed sections to produce hydrogen, electrodes to electrolyze the solvents, and temperature controller for vessel.

Solution cycle/

saving/

filtering system

Specimen automatic supply/pass

Hydrogen insertion bath

Hydrogen insertion bath

Transmi-ssion hydrogen testing bath

Transmi-ssion hydrogen testing bath

Reactor(I)

Reactor(II)

Processing &Control Computer System

L/C System

L/C network interface

Fig 1. The scheme for instrument measuring hydrogen permeation

B. Structural design of measuring section for permeating hydrogen

The measuring section for hydrogen permeation was filled with deionized distilled water to convert the amount of inserted hydrogen to the pressure and composed of temperature controller and pressure sensor to systemically regulate the condition of vessel.

C. Control structures for automatical measurement

This system had the system to automatically supply and emit the test specimen. In particular, it should be fabricated from durable materials for solvents such as the sulfuric acid and consisted of the structure which can automatically emit the solvents after supplying solvents to the hydrogen generating reactor vessel and testing the pressure measurement.

3 .Wavelet treshold method

The wavelet threshold method has been known by the threshold denoising method. There are two methods with hard threshold method and soft threshold method.[3]-[5]

Assume that a signal is corrupted with additive noise

g = f + n                                                  (1)

Where f is original signal, n is an additive white Gaussian noise function with the distribution of N(0,1).

For well-known conventional methods, the threshold method-based wavelet eliminating noise process developed by Donoho and Jonstone was classified into following three steps.[4]

(step 1) Transform the noisy observation signal f into an orthogonal domain by discrete wavelet transform.

(step  2)  Apply hard or soft thresholding to the resulted wavelet coefficients by using the threshold method.

(step 3) Perform inverse discrete wavelet transform on the estimated wavelet coefficients to obtain the denoised signal.

If the wavelet scaling functions, ,  were chosen, it can be shown that the signal is given by the equation (2) , g∈  for any integer  in finite real section.

(2)

Donoho and Johnstone have presented the standard thresholding method that is shown by the figure 1.

(3)

Where  is threshold. And the soft threshold is

(4)

The relationship of hard-thresholding and soft-thresholding is shown by figure 1.[4]

(a)                   (b)

Fig. 2. Donoho and Johnstone's standard threshold : (a) hard threshold, (b) soft threshold

The signal waveform  denoised by threshold method give

(5)

4. Simulation and discussion

The flux of hydrogen measured by hydrogen permeation current analysis is followed by

(6)

In here, I(t) is hydrogen permeation current, A is an area, and F is Faraday constant.[1],[6],[7]

Also, induction value developed from the second law of diffusion is given by.

(7)

Where ,  is the hydrogen permeation current at the stationary state, and  is the hydrogen permeation time.

Continuous time at Equation (7) is sampled by a sampling time Ts in order to simulate. Then Equation(7) is given by

(8)

The signal added the noise for the flux converting value is followed by

(9)

(10)

Where  is a noise factor and  is a white Gaussian noise.

Figure 3 represents the example for the case  is 1.0. The signal which adapt the threshold methods for additive noise is shown in Figure  4.

(a)

(b)

Fig. 3. Hydrogen permeation signals, (a) original signal, (b) noisy signal

(a)

(b)

Fig. 4. Result signals adapted the wavelet threshold methods,

(a) soft threshold,  (b) high threshold

At upper figures, y axis represents P(n) and x axis represents the time of the hydrogen permeation.

From the figures, the measurement results eliminating the noise was shown. Thus, applying threshold methods for calculating signal was more effective than applying the conventional methods.

5. Conclusions

In this paper, we proposed sensor signal denoising by using the discrete wavelet transformation. According to the noise level estimated in the sensor to avoid noisy data, we selected the number of wavelet coefficients. Simulation results showed a good sensor signal that is possible to use detecting and analyzing. Next we will make a real device. Then we will research for a hydrogen transport time using the device.

Acknowledgments

This study was supported by an Industry-Academic Cooperation technology R&D project, Small & Medium Business Administration, Republic of Korea(C0395548)

References

[1] ASTM international, “Standard Practice for Evaluation of Hydrogen Uptake, Permeation, and Transport in Metals by an Electrochemical Techique”, Designation: G148-97, 2011.

[2] S.M. Oh, “Test Device for Hydrogen Permetaiton of Steel Plate”, KPRIS, Patent, Posco, 2013.

[3] G. Song and R. Zhao, “Three novel models of threshold edtimatior for wavelet coefficients,” 2nd Inter. Conf. on Wavelet Analysis and Its Applications, Berlin: Springer-Velag, pp. 145-150, 2001.

[4] Donoho D. L., “De-noising by soft-thresholding. IEEE Trans. On IT”, 41(3), pp. 613-627, 1995.

[5] Jeong Yup Han, Su Kyung Lee, and Hong Bae Park, “Denosing ECG using Translation Invariant Multiwavelet”, Proc. Of world academy of science, engineering and technology”, Vol. 31, pp. 140-144, 2008

[6] BSI Standards publication, “Method of measurement of hydrogen permeation and determination of hydrogen uptake and transport in metals by an electrochemical technique”, BS EN ISO, 17081, 2014

[7] Michiya Okada, “ Hydrogen Permeation in Iron by Electrochemical Measurement”, J.Japan Imst. Metals, Vol. 50, No. 2, pp. 201-207, 1986

Object Detection Based on Corresponding Points Estimation

Dong-wook Kim*

*Dept. Information & Communication Eng.,Jeonju University, Jeonju, Republic of Korea

E-mail: dwkim@jj.ac.kr

Abstract

In order to use the general-purpose camera for stereo vision, the correction for distortion is needed.  In this study, a new technology to achieve the distortion correction is proposed, which is based on the block matching method.

Keywords: Stereo Vision, Disparity, Block Matching

1. Introduction

Stereo vision is widely used in various fields. Along with development of unmanned vehicle system recently, various sensors are used for detecting the existence of objects and obstacles in front of a vehicle. Especially, the image processing technology is being used widely for application of stereo vision [1-2].

In applications of stereo vision technology, some fields such as face recognition require very accurate 3D shape information. A camera system with high performance is necessary to obtain accurate 3D shape information but it is very expensive. Meanwhile, there are also many application fields that require rough depth information, not precise information. For example, access sensing of entrance and exit and obstacle detection of unmanned ground vehicle are included in such application fields [3-4]. This paper aims to the latter, rough applications. In this study, a new correction technology to need for acquisition depth information using the general-purpose camera is suggested.

2. The proposed algorithm

Fig. 1 The flow for the proposed algorithm

The flow diagram of algorithm suggested in this study is shown in Figure 1. The algorithm can be divided into two parts including Part-A and Part-B, and Part-A is the step to create vector map for distortion correction and Part-B is the step to detect an actual object or measure the depth. Each step can be explained as follows

The vector map for correction of left and right stereo images is obtained using the reference images and these are used to correct input images. The most part of camera distortion can be solved through this procedure. This method is somewhat insufficient for obtaining a very precise result but it is enough for obtaining main characteristics only.

The vector map obtained from the previous step is used for measuring the depth or detecting an object. This is the step to obtain disparity. There are various methods for detecting disparity from a stereo image such as pixel unit, block unit and area unit, but in this study, it is detected in block unit. At this time, the size of block to be used is .

Based on the distortion correction information obtained through the reference image, simulations were carried out. Figure 2 shows a right image when the person stands on the side. Figure 3 shows the final result of detecting parts with depth from the change area.

Fig. 2  The right image

Fig.3 Detected object blocks

3. Conclusion

In this study, a method to use the block matching technology was suggested in order to solve the lens distortion occurred when the stereo technology was applied based on general-purpose camera. The reference image was produced and the vector map was prepared based on such reference image, and the depth information was extracted based on the prepared vector map.

References

[1] Y. Furukawa, J. Ponce, “Accurate, dense, and robust multiview stereopsis”, IEEE Trans. Pattern Anal. Mach. Intell. 32(8) 1362–1376 (2010)

[2] H. Vu, R. Keriven, P. Labatut, and J . Pons, “Towards high-resolution large-scale multi-viewstereo”, Proceedings of CVPR, pp. 1430–1437 (2009)

[3] R. Hartley and A. Zisserman, Multiple view geometry in computer vision. Cambridge Univ. Press, 2000

[4] G. Unal and S. Soatto, “A variational approach to problems in calibration of multiple cameras, “IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 8, AUGUST 2007