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1005 LESSON 09-08-2013 FRIDAY FREE ONLINE eNālāndā Research and Practice UNIVERSITY run through http://sarvajan.ambedkar.org 3) School of Information Sciences and Technology; [Flag of the Kingdom of Norway] Norwegian Dhamma-biblioteket Fra buddhismens grunntekster (Kåre A. Lie) 3) School of Information Sciences and Technology;
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Posted by: @ 8:20 pm

1005 LESSON 09-08-2013 FRIDAY 

FREE ONLINE  eNālāndā Research and Practice UNIVERSITY 
run through http://sarvajan.ambedkar.org 

3)  School of Information Sciences and
Technology;

[Flag of the Kingdom of Norway] Norwegian
Dhamma-biblioteket Fra buddhismens grunntekster (Kåre A. Lie)
3)  School of Information Sciences and
Technology;

MAY
ANNA HAZARE HAVE BROAD MIND LIKE THE PEOPLE OF US WHO ELECTED OBAMA FOR
THE THE SECOND TIME, HELP BSP ACQUIRE THE MASTER KEY AS DESIRED BY DR.
AMBEDKAR, THE FATHER OF THE CONSTITUTION AND MANYAWAR KANSHIRAMJI,
FATHER OF POLITICAL AND SOCIAL REFORM TO UNLOCK ALL DOORS OF DEVELOPMENT
AND PROGRESS FOR THE WELFARE, HAPPINESS AND PEACE OF THE ENTIRE PEOPLE
AS ENSHRINED IN THE CONSTITUTION AND TO ENABLE THEM TO ACHIEVE FOR
ETERNAL BLISS AS FINAL GOAL.

THANX FOR THE INVITATION. THE ABOVE MESSAGE MAY BE DISCUSSED IN PARTY.

EID MUBARAK FOR PEOPLE  ALL OVER THE WORLD


MAY ALL BE EVER HAPPY, WELL AND SECURE
MAY ALL LIVE LONG
MAY ALL SENIENT AND NON-SENTIENT BEINGS BE EVER HAPPY
MAY ALL EVER HAVE CALM, QUIET, ALERT,ATTENTIVE AND
EQUANIMITY MIND WITH A CLEAR UNDERSTANDING THAT
EVERYTHING IS CHANGING


முஸ்லிம்கள் மகிழ்ச்சி பொங்க கொண்டாடி மகிழ வேண்டும்: ஒபாமா ரம்ஜான் வாழ்த்து

Posted Date : 17:22 (08/08/2013)Last updated : 17:23 (08/08/2013)

வாஷிங்டன்: உலகம்
முழுவதும் உள்ள முஸ்லிம்கள் ரம்ஜான் திருநாளை மகிழ்ச்சி பொங்க கொண்டாடி
மகிழ வேண்டுமென வாழ்த்துகிறேன் என அமெரிக்க அதிபர் ஒபாமா தனது வாழ்த்து
செய்தியில் கூறியுள்ளார்.

அரபு நாடுகள் மற்றும் கிழக்கு ஆசியாவிலுள்ள பல நாடுகளில் வசிக்கும்
முஸ்லிம்கள் இன்று ரம்ஜான் பண்டிகையை சிறப்பாக கொண்டாடி வருகின்றனர்.
இதையொட்டி மசூதிகளில் நடந்த தொழுகைகளில் பல்லாயிரக்கணக்கான முஸ்லிம்கள்
கலந்து கொண்டு ஒருவருக்கு ஒருவர் வாழ்த்துக்களை கூறிக்கொண்டனர்.





ரம்ஜான் பண்டிகையையொட்டி உலகம் முழுவதும் உள்ள முஸ்லிம்களுக்கு அமெரிக்க
ஜனாதிபதி ஒபாமா வாழ்த்து தெரிவித்துள்ளார். இது தொடர்பாக அவர் விடுத்துள்ள
வாழ்த்து செய்தியில்,  ‘ஈகை திருநாளை கொண்டாடும் அமெரிக்க வாழ்
சகோதரர்களுக்கு எனது இதயம் கனிந்த வாழ்த்துக்களை தெரிவித்துக்கொள்கிறேன்.
அதேபோல உலகெங்கிலும் உள்ள முஸ்லிம்கள் இவ்விழாவை மகிழ்ச்சி பொங்க கொண்டாடி
மகிழ வேண்டுமென வாழ்த்துகிறேன்’ எனக் கூறியுள்ளார்.

http://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=9&ved=0CFcQFjAI&url=http%3A%2F%2Fhal.inria.fr%2Fdocs%2F00%2F48%2F58%2F50%2FPDF%2F10_IJCSIS_MASMOUDI_PUECH.pdf&ei=PkIEUsupN4KTrgeC44GIBQ&usg=AFQjCNFDE8sMNVfguuBGB-PFiB5–gEfZA&sig2=Ap_IAB_I5aYaZv5ygERjWg

International Journal of
Computer Science
& Information Security

© IJCSIS PUBLICATION 2010
IJCSIS Vol. 8 No. 1, April 2010
ISSN 1947-5500

Editorial

Message from Managing Editor

International Journal of Computer Science and Information Security (IJCSIS)
provides a major venue for rapid publication of high quality computer science research,
including multimedia, information science, security, mobile & wireless network, data
mining, software engineering and emerging technologies etc. IJCSIS has continued to
make progress and has attracted the attention of researchers worldwide, as indicated by
the increasing number of both submissions and published papers, and also from the
web statistics.. It is included in major Indexing and Abstracting services.
We thank all those authors who contributed papers to the April 2010 issue and the
reviewers, all of whom responded to a short and challenging timetable. We are
committed to placing this journal at the forefront for the dissemination of novel and
exciting research. We should like to remind all prospective authors that IJCSIS does
not have a page restriction. We look forward to receiving your submissions and to
receiving feedback.

IJCSIS April 2010 Issue (Vol. 8, No. 1) has an acceptance rate of 35%.
Special thanks to our technical sponsors for their valuable service.
Available at http://sites.google.com/site/ijcsis/
IJCSIS Vol. 8, No. 1, April 2010 Edition
ISSN 1947-5500 © IJCSIS 2010, USA.
Indexed by (among others):
IJCSIS EDITORIAL BOARD
Dr. Gregorio Martinez Perez
Associate Professor - Professor Titular de Universidad, University of Murcia
(UMU), Spain
Dr. M. Emre Celebi,
Assistant Professor, Department of Computer Science, Louisiana State University
in Shreveport, USA
Dr. Yong Li
School of Electronic and Information Engineering, Beijing Jiaotong University,
P. R. China
Prof. Hamid Reza Naji
Department of Computer Enigneering, Shahid Beheshti University, Tehran, Iran
Dr. Sanjay Jasola
Professor and Dean, School of Information and Communication Technology,
Gautam Buddha University
Dr Riktesh Srivastava
Assistant Professor, Information Systems, Skyline University College, University
City of Sharjah, Sharjah, PO 1797, UAE
Dr. Siddhivinayak Kulkarni
University of Ballarat, Ballarat, Victoria, Australia
Professor (Dr) Mokhtar Beldjehem
Sainte-Anne University, Halifax, NS, Canada
Dr. Alex Pappachen James, (Research Fellow)
Queensland Micro-nanotechnology center, Griffith University, Australia
TABLE OF CONTENTS
1. Paper 29031048: Buffer Management Algorithm Design and Implementation Based on Network
Processors (pp. 1-8)
Yechang Fang, Kang Yen, Dept. of Electrical and Computer Engineering, Florida International University,
Miami, USA
Deng Pan, Zhuo Sun, School of Computing and Information Sciences, Florida International University,
Miami, USA
2. Paper 08031001: Multistage Hybrid Arabic/Indian Numeral OCR System (pp. 9-18)
Yasser M. Alginaih, Ph.D., P.Eng. IEEE Member, Dept. of Computer Science, Taibah University, Madinah,
Kingdom of Saudi Arabia
Abdul Ahad Siddiqi, Ph.D., Member IEEE & PEC, Dept. of Computer Science, Taibah University,
Madinah, Kingdom of Saudi Arabia
3. Paper 30031056: Attribute Weighting with Adaptive NBTree for Reducing False Positives in
Intrusion Detection (pp. 19-26)
Dewan Md. Farid, and Jerome Darmont, ERIC Laboratory, University Lumière Lyon 2, Bat L - 5 av.
Pierre Mendes, France, 69676 BRON Cedex, France
Mohammad Zahidur Rahman, Department of Computer Science and Engineering, Jahangirnagar
University, Dhaka – 1342, Bangladesh
4. Paper 30031053: Improving Overhead Computation and pre-processing Time for Grid Scheduling
System (pp. 27-34)
Asgarali Bouyer, Mohammad javad hoseyni, Department of Computer Science, Islamic Azad University-
Miyandoab branch, Miyandoab, Iran
Abdul Hanan Abdullah, Faculty Of Computer Science And Information Systems, Universiti Teknologi
Malaysia, Johor, Malaysia
5. Paper 20031026: The New Embedded System Design Methodology For Improving Design Process
Performance (pp. 35-43)
Maman Abdurohman, Informatics Faculty, Telecom Institute of Technology, Bandung, Indonesia
Kuspriyanto, STEI Faculty, Bandung Institute of Technology, Bandung, Indonesia
Sarwono Sutikno, STEI Faculty, Bandung Institute of Technology, Bandung, Indonesia
Arif Sasongko, STEI Faculty, Bandung Institute of Technology, Bandung, Indonesia
6. Paper 30031060: Semi-Trusted Mixer Based Privacy Preserving Distributed Data Mining for
Resource Constrained Devices (pp. 44-51)
Md. Golam Kaosar, School of Engineering and Science, Victoria University, Melbourne, Australia
Xun Yi, Associate Preofessor, School of Engineering and Science, Victoria University, Melbourne,
Australia
7. Paper 12031005: Adaptive Slot Allocation And Bandwidth Sharing For Prioritized Handoff Calls
In Mobile Netwoks (pp. 52-57)
S. Malathy, Research Scholar, Anna University, Coimbatore
G. Sudha Sadhasivam, Professor, CSE Department, PSG College of Technology, Coimbatore.
K. Murugan, Lecturer, IT Department, Hindusthan Institute of Technology, Coimbatore
S. Lokesh, Lecturer, CSE Department, Hindusthan Institute of Technology, Coimbatore
8. Paper 12031009: An Efficient Vein Pattern-based Recognition System (pp. 58-63)
Mohit Soni, DFS, New Delhi- 110003, INDIA.
Sandesh Gupta, UIET, CSJMU, Kanpur-208014, INDIA.
M.S. Rao, DFS, New Delhi-110003, INDIA
Phalguni Gupta, Professor, IIT Kanpur, Kanpur-208016, INDIA.
9. Paper 15031013: Extending Logical Networking Concepts in Overlay Network-on-Chip
Architectures (pp. 64-67)
Omar Tayan
College of Computer Science and Engineering, Department of Computer Science, Taibah University, Saudi
Arabia, P.O. Box 30002
10. Paper 15031015: Effective Bandwidth Utilization in IEEE802.11 for VOIP (pp. 68-75)
S. Vijay Bhanu, Research Scholar, Anna University, Coimbatore, Tamilnadu, India, Pincode-641013.
Dr.RM.Chandrasekaran, Registrar, Anna University, Trichy, Tamilnadu, India, Pincode: 620024.
Dr. V. Balakrishnan, Research Co-Supervisor, Anna University, Coimbatore.
11. Paper 16021024: ECG Feature Extraction Techniques - A Survey Approach (pp. 76-80)
S. Karpagachelvi, Mother Teresa Women’s University, Kodaikanal, Tamilnadu, India.
Dr. M.Arthanari, Tejaa Shakthi Institute of Technology for Women, Coimbatore- 641 659, Tamilnadu,
India.
M. Sivakumar, Anna University – Coimbatore, Tamilnadu, India
12. Paper 18031017: Implementation of the Six Channel Redundancy to achieve fault tolerance in
testing of satellites (pp. 81-85)
H S Aravinda *, Dr H D Maheshappa**, Dr Ranjan Moodithaya ***
* Department of Electronics and Communication, REVA ITM, Bangalore-64, Karnataka, India.
** Director & Principal, East Point College of Engg, Bidarahalli, Bangalore-40, Karnataka, India.
*** Head, KTMD Division, National Aerospace Laboratories, Bangalore-17, Karnataka, India.
13. Paper 18031018: Performance Oriented Query Processing In GEO Based Location Search
Engines (pp. 86-94)
Dr. M. Umamaheswari, Bharath University, Chennai-73, Tamil Nadu,India,
S. Sivasubramanian, Bharath University, Chennai-73,Tamil Nadu,India,
14. Paper 20031027: Tunable Multifunction Filter Using Current Conveyor (pp. 95-98)
Manish Kumar, Electronics and Communication, Engineering Department, Jaypee Institute of Information
Technology, Noida, India
M.C. Srivastava, Electronics and Communication, Engineering Department, Jaypee Institute of
Information Technology, Noida, India
Umesh Kumar, Electrical Engineering Department, Indian Institute of Technology, Delhi, India
15. Paper 17031042: Artificial Neural Network based Diagnostic Model For Causes of Success and
Failures (pp. 95-105)
Bikrampal Kaur, Chandigarh Engineering College, Mohali, India
Dr. Himanshu Aggarwal, Punjabi University, Patiala-147002, India
16. Paper 28031045: Detecting Security threats in the Router using Computational Intelligence (pp.
106-111)
J. Visumathi, Research Scholar, Sathyabama University, Chennai-600 119
Dr. K. L. Shunmuganathan, Professor & Head, Department of CSE, R.M.K. Engineering College, Chennai-
601 206
17. Paper 31031091: A Novel Algorithm for Informative Meta Similarity Clusters Using Minimum
Spanning Tree (pp. 112-120)
S. John Peter, Department of Computer Science and Research Center, St. Xavier’s College, Palayamkottai,
Tamil Nadu, India
S. P. Victor, Department of Computer Science and Research Center, St. Xavier’s College, Palayamkottai,
Tamil Nadu, India
18. Paper 23031032: Adaptive Tuning Algorithm for Performance tuning of Database Management
System (pp. 121-124)
S. F. Rodd, Department of Information Science and Engineering, KLS’s Gogte Institute of Technology,
Belgaum, INDIA
Dr. U. P. Kulkarni, Department of Computer Science and Engineering, SDM College of Engineering and
Technology, Dharwad, INDIA
19. Paper 26031038: A Survey of Mobile WiMAX IEEE 802.16m Standard (pp. 125-131)
Mr. Jha Rakesh, Deptt. Of E & T.C., SVNIT, Surat, India
Mr. Wankhede Vishal A., Deptt. Of E & T.C., SVNIT, Surat, India
Prof. Dr. Upena Dalal, Deptt. Of E & T.C., SVNIT, Surat, India
20. Paper 27031040: An Analysis for Mining Imbalanced Datasets (pp. 132-137)
T. Deepa, Faculty of Computer Science Department, Sri Ramakrishna College of Arts and Science for
Women, Coimbatore, Tamilnadu, India.
Dr. M. Punithavalli, Director & Head, Sri Ramakrishna College of Arts & Science for Women, Coimbatore,
Tamil Nadu, India
21. Paper 27031039: QoS Routing For Mobile Adhoc Networks And Performance Analysis Using
OLSR Protocol (pp. 138-150)
K.Oudidi, Si2M Laboratory, National School of Computer Science and Systems Analysis, Rabat, Morocco
A. Hajami, Si2M Laboratory, National School of Computer Science and Systems Analysis, Rabat, Morocco
M. Elkoutbi, Si2M Laboratory, National School of Computer Science and Systems Analysis, Rabat,
Morocco
22. Paper 28031047: Design of Simple and Efficient Revocation List Distribution in Urban Areas for
VANET’s (pp. 151-155)
Ghassan Samara , National Advanced IPv6 Center, Universiti Sains Malaysia, Penang, Malaysia
Sureswaran Ramadas, National Advanced IPv6 Center, Universiti Sains Malaysia, Penang, Malaysia
Wafaa A.H. Al-Salihy, School of Computer Science, Universiti Sains Malaysia, Penang, Malaysia
23. Paper 28031044: Software Process Improvization Framework For Indian Small Scale Software
Organizations Using Fuzzy Logic (pp. 156-162)
A. M. Kalpana, Research Scholar, Anna University Coimbatore, Tamilnadu, India
Dr. A. Ebenezer Jeyakumar, Director/Academics, SREC, Coimbatore, Tamilnadu, India
24. Paper 30031052: Urbanizing the Rural Agriculture - Knowledge Dissemination using Natural
Language Processing (pp. 163-169)
Priyanka Vij (Author) Student, Computer Science Engg. Lingayas Institute of Mgt. & Tech, Faridabad,
Haryana, India
Harsh Chaudhary (Author) Student, Computer Science Engg. Lingayas Institute of Mgt. & Tech,
Faridabad, Haryana, India
Priyatosh Kashyap (Author) Student, Computer Science Engg. Lingayas Institute of Mgt. & Tech,
Faridabad, Haryana, India
25. Paper 31031073: A New Joint Lossless Compression And Encryption Scheme Combining A
Binary Arithmetic Coding With A Pseudo Random Bit Generator (pp. 170-175)
A. Masmoudi * , W. Puech **, And M. S. Bouhlel *
* Research Unit: Sciences and Technologies of Image and Telecommunications, Higher Institute of
Biotechnology, Sfax TUNISIA
** Laboratory LIRMM, UMR 5506 CNRS University of Montpellier II, 161, rue Ada, 34392
MONTPELLIER CEDEX 05, FRANCE
26. Paper 15031012: A Collaborative Model for Data Privacy and its Legal Enforcement (pp. 176-182)
Manasdeep, MSCLIS, IIIT Allahabad
Damneet Singh Jolly, MSCLIS, IIIT Allahabad
Amit Kumar Singh, MSCLIS, IIIT Allahabad
Kamleshwar Singh, MSCLIS, IIIT Allahabad
Mr Ashish Srivastava, Faculty, MSCLIS, IIIT Allahabad
27. Paper 12031010: A New Exam Management System Based on Semi-Automated Answer Checking
System (pp. 183-189)
Arash Habibi Lashkari, Faculty of ICT, LIMKOKWING University of Creative Technology,
CYBERJAYA, Selangor,
Dr. Edmund Ng Giap Weng, Faculty of Cognitive Sciences and Human Development, University Malaysia
Sarawak (UNIMAS)
Behrang Parhizkar, Faculty of Information, Communication And Technology, LIMKOKWING University
of Creative Technology, CYBERJAYA, Selangor, Malaysia
Siti Fazilah Shamsudin, Faculty of ICT, LIMKOKWING University of Creative Technology, CYBERJAYA,
Selangor, Malaysia
Jawad Tayyub, Software Engineering With Multimedia, LIMKOKWING University of Creative Technology,
CYBERJAYA, Selangor, Malaysia
28. Paper 30031064: Development of Multi-Agent System for Fire Accident Detection Using Gaia
Methodology (pp. 190-194)
Gowri. R, Kailas. A, Jeyaprakash.R, Carani Anirudh
Department of Information Technology, Sri Manakula Vinayagar Engineering College, Puducherry – 605
107.
29. Paper 19031022: Computational Fault Diagnosis Technique for Analog Electronic Circuits using
Markov Parameters (pp. 195-202)
V. Prasannamoorthy and N.Devarajan
Department of Electrical Engineering, Government College of Technology, Coimbatore, India
30. Paper 24031037: Applicability of Data Mining Techniques for Climate Prediction – A Survey
Approach (pp. 203-206)
Dr. S. Santhosh Baboo, Reader, PG and Research department of Computer Science, Dwaraka Doss
Goverdhan Doss Vaishnav College, Chennai
I. Kadar Shereef, Head, Department of Computer Applications, Sree Saraswathi Thyagaraja College,
Pollachi
31. Paper 17021025: Appliance Mobile Positioning System (AMPS) (An Advanced mobile
Application) (pp. 207-215)
Arash Habibi Lashkari, Faculty of ICT, LIMKOKWING University of Creative Technology,
CYBERJAYA, Selangor, Malaysia
Edmund Ng Giap Weng, Faculty of Cognitive Sciences and Human Development, University Malaysia
Sarawak (UNIMAS)
Behrang Parhizkar, Faculty of ICT, LIMKOKWING University of Creative Technology, CYBERJAYA,
Selangor, Malaysia
Hameedur Rahman, Software Engineering with Multimedia, LIMKOKWING University of Creative
Technology, CYBERJAYA, Selangor, Malaysia
32. Paper 24031036: A Survey on Data Mining Techniques for Gene Selection and Cancer
Classification (pp. 216-221)
Dr. S. Santhosh Baboo, Reader, PG and Research department of Computer Science, Dwaraka Doss
Goverdhan Doss Vaishnav College, Chennai
S. Sasikala, Head, Department of Computer Science, Sree Saraswathi Thyagaraja College, Pollachi
33. Paper 23031033: Non-Blind Image Watermarking Scheme using DWT-SVD Domain (pp. 222-228)
M. Devapriya, Asst.Professor, Dept of Computer Science, Government Arts College, Udumalpet.
Dr. K. Ramar, Professor & HOD, Dept of CSE, National Engineering College, Kovilpatti -628 502.
34. Paper 31031074: Speech Segmentation Algorithm Based On Fuzzy Memberships (pp. 229-233)
Luis D. Huerta, Jose Antonio Huesca and Julio C. Contreras
Departamento de Informática, Universidad del Istmo Campus Ixtepéc, Ixtepéc Oaxaca, México
35. Paper 30031058: How not to share a set of secrets (pp. 234-237)
K. R. Sahasranand , Nithin Nagaraj, Department of Electronics and Communication Engineering, Amrita
Vishwa Vidyapeetham, Amritapuri Campus, Kollam-690525, Kerala, India.
Rajan S., Department of Mathematics, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam-690525,
Kerala, India.
36. Paper 30031057: Secure Framework for Mobile Devices to Access Grid Infrastructure (pp. 238-
243)
Kashif Munir, Computer Science and Engineering Technology Unit King Fahd University of Petroleum
and Minerals HBCC Campus, King Faisal Street, Hafr Al Batin 31991
Lawan Ahmad Mohammad, Computer Science and Engineering Technology Unit King Fahd University of
Petroleum and Minerals HBCC Campus, King Faisal Street, Hafr Al Batin 31991
37. Paper 31031076: DSP Specific Optimized Implementation of Viterbi Decoder (pp. 244-249)
Yame Asfia and Dr Muhamamd Younis Javed, Department of Computer Engg, College of Electrical and
Mechanical Engg, NUST, Rawalpindi, Pakistan
Dr Muid-ur-Rahman Mufti, Department of Computer Engg, UET Taxila, Taxila, Pakistan
38. Paper 31031089: Approach towards analyzing motion of mobile nodes- A survey and graphical
representation (pp. 250-253)
A. Kumar, Sir Padampat Singhania University, Udaipur , Rajasthan , India
P.Chakrabarti, Sir Padampat Singhania University, Udaipur , Rajasthan , India
P. Saini, Sir Padampat Singhania University, Udaipur , Rajasthan , India
39. Paper 31031092: Recognition of Printed Bangla Document from Textual Image Using Multi-
Layer Perceptron (MLP) Neural Network (pp. 254-259)
Md. Musfique Anwar, Nasrin Sultana Shume, P. K. M. Moniruzzaman and Md. Al-Amin Bhuiyan
Dept. of Computer Science & Engineering, Jahangirnagar University, Bangladesh
40. Paper 31031081: Application Of Fuzzy System In Segmentation Of MRI Brain Tumor (pp. 261-
270)
Mrigank Rajya, Sonal Rewri, Swati Sheoran
CSE, Lingaya’s University, Limat, Faridabad India, New Delhi, India
41. Paper 30031059: E-Speed Governors For Public Transport Vehicles (pp. 270-274)
C. S. Sridhar, Dr. R. ShashiKumar, Dr. S. Madhava Kumar, Manjula Sridhar, Varun. D
ECE dept, SJCIT, Chikkaballapur.
42. Paper 31031087: Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of
Analystical Methodology (pp. 275-280)
Arutchelvan. G, Department of Computer Science and Applications Adhiparasakthi College of Arts and
Science G. B. Nagar, Kalavai , India
Dr. Srivatsa S. K., Dept. of Electronics Engineering, Madras Institute of Technology, Anna University,
Chennai, India
Dr. Jagannathan. R, Vinayaka Mission University, Chennai, India
43. Paper 30031065: Selection of Architecture Styles using Analytic Network Process for the
Optimization of Software Architecture (pp. 281-288)
K. Delhi Babu, S.V. University, Tirupati
Dr. P. Govinda Rajulu, S.V. University, Tirupati
Dr. A. Ramamohana Reddy, S.V. University, Tirupati
Ms. A.N. Aruna Kumari, Sree Vidyanikethan Engg. College, Tirupati
44. Paper 27031041: Clustering Time Series Data Stream – A Literature Survey (pp. 289-294)
V.Kavitha, Computer Science Department, Sri Ramakrishna College of Arts and Science for Women,
Coimbatore, Tamilnadu, India.
M. Punithavalli, Sri Ramakrishna College of Arts & Science for Women, Coimbatore ,Tamil Nadu, India.
45. Paper 31031086: An Adaptive Power Efficient Packet Scheduling Algorithm for Wimax
Networks (pp. 295-300)
R Murali Prasad, Department of Electronics and Communications, MLR Institute of technology,
Hyderabad
P. Satish Kumar, professor, Department of Electronics and Communications, CVR college of engineering,
Hyderabad
46. Paper 30041037: Content Base Image Retrieval Using Phong Shading (pp. 301-306)
Uday Pratap Singh, LNCT, Bhopal (M.P) INDIA
Sanjeev Jain, LNCT, Bhopal (M.P) INDIA
Gulfishan Firdose Ahmed, LNCT, Bhopal (M.P) INDIA
47. Paper 31031090: The Algorithm Analysis of E-Commerce Security Issues for Online Payment
Transaction System in Banking Technology (pp. 307-312)
Raju Barskar, MANIT Bhopal (M.P)
Anjana Jayant Deen,CSE Department, UIT_RGPV, Bhopal (M.P)
Jyoti Bharti, IT Department, MANIT, Bhopal (M.P)
Gulfishan Firdose Ahmed, LNCT, Bhopal (M.P)
48. Paper 28031046: Reduction in iron losses In Indirect Vector-Controlled IM Drive Using FLC (pp.
313-317)
Mr. C. Srisailam , Electrical Engineering Department, Jabalpur Engineering College, Jabalpur, Madhya
Pradesh,
Mr. Mukesh Tiwari, Electrical Engineering Department, Jabalpur Engineering College, Jabalpur, Madhya
Pradesh,
Dr. Anurag Trivedi, Electrical Engineering Department, Jabalpur Engineering College, Jabalpur, Madhya
Pradesh
49. Paper 31031071: Bio-Authentication based Secure Transmission System using Steganography (pp.
318-324)
Najme Zehra, Assistant Professor, Computer Science Department, Indira Gandhi Institute of Technology,
GGSIPU, Delhi.
Mansi Sharma, Scholar, Indira Gandhi Institute of Technology, GGSIPU, Delhi.
Somya Ahuja, Scholar, Indira Gandhi Institute of Technology, GGSIPU, Delhi.
Shubha Bansal, Scholar, Indira Gandhi Institute of Technology, GGSIPU, Delhi.
50. Paper 31031068: Facial Recognition Technology: An analysis with scope in India (pp. 325-330)
Dr.S.B.Thorat, Director, Institute of Technology and Mgmt, Nanded, Dist. - Nanded. (MS), India
S. K. Nayak, Head, Dept. of Computer Science, Bahirji Smarak Mahavidyalaya, Basmathnagar, Dist. -
Hingoli. (MS), India
Miss. Jyoti P Dandale, Lecturer, Institute of Technology and Mgmt, Nanded, Dist. - Nanded. (MS), India
51. Paper 31031069: Classification and Performance of AQM-Based Schemes for Congestion
Avoidance (pp. 331-340)
K.Chitra Lecturer, Dept. of Computer Science D.J.Academy for Managerial Excellence Coimbatore, Tamil
Nadu, India – 641 032
Dr. G. Padamavathi Professor & Head, Dept. of Computer Science Avinashilingam University for Women,
Coimbatore, Tamil Nadu, India – 641 043
A new joint lossless compression and encryption
scheme combining a binary arithmetic coding
with a pseudo random bit generator
A. MASMOUDI #1, W. PUECH ∗2, M.S. BOUHLEL #3
# Research Unit: Sciences and Technologies of Image and Telecommunications, Higher Institute of Biotechnology
Sfax TUNISIA
1 atef.masmoudi@lirmm.fr
3 medsalim.bouhlel@enis.rnu.tn
∗ Laboratory LIRMM, UMR 5506 CNRS University of Montpellier II
161, rue Ada, 34392 MONTPELLIER CEDEX 05, FRANCE
2 william.puech@lirmm.fr
Abstract—In this paper, we propose a new scheme which
performs both lossless compression and encryption of data.
The lossless compression is based on the arithmetic coding
(AC) and the encryption is based on a pseudo random
bit generator (PRBG). Thus, the plaintext is compressed
with a binary arithmetic coding (BAC) whose two mapping
intervals are swapped randomly by using a PRBG. In this
paper, we propose a PRBG based on the standard chaotic
map and the Engel Continued Fraction (ECF) map to
generate a keystream with both good chaotic and statistical
properties. To be used in cryptography, a PRBG may need
to meet stronger requirements than for other applications.
In particular, various statistical tests can be applied to the
outputs of such generators to conclude whether the generator
produces a truly random sequence or not. The numerical
simulation analysis indicates that the proposed compression
and encryption scheme satisfies highly security with no loss
of the BAC compression efficiency.

I. INTRODUCTION

In recent years, a variety of lossless data compression
methods have been proposed [4], [3], [23], [31]. All of
these methods can not perform both lossless compression
and encryption of data. This paper presents a new scheme
which combines arithmetic coding (AC) with a pseudo random
bit generator (PRBG) to perform both compression
and encryption of data.

AC has been widely used as an efficient compression
algorithm in the new standards such JBIG2, JPEG2000
and H.264/AVC. For some specific applications, AC is
also considered as an encryption algorithm. In [5], Cleary
et al. considered the AC as an encryption scheme and
they demonstrated that it is vulnerable against chosen
plaintext attack and known plaintext attack. In [8], Bergen
et al. studied the data security provided by an adaptive
arithmetic coding (AAC). The improved algorithm based
on regular re-initialisation and adjustment of one of the
model parameters provides significant data security, but
is vulnerable to a chosen plaintext attack. In [27], Wen
et al. designed the binary arithmetic coding (BAC) with
key-based interval splitting. They proposed to use a key
for splitting the interval associated with the symbol to be
encoded. Thus, the traditional assumption in AC that a single
contignous interval is used for each symbol is not preserved.
The repeated splitting at each encoding/decoding
step allowing both encryption and compression. In [12],
Kim et al. demonstrated the insecurity of the interval
splitting AC against a known plain-text attack and a chosen
plain-text attack. They also provided an improved version
called the secure AC by applying a series of permutations
at the input symbol sequence and output codeword. It
should be noticed that due to the permutations process, the
scheme has a high complexity and it is difficult to extend
the secure AC to the context-based AC that exploits the
input symbol redundancy to encode messages. In [34],
Zhou et al. demonstrated that the secure AC is vulnerable
against a chosen cipher-text attack. The basic idea is to
progressively design codewords input to the decoder, and
establish the correspondance of the bit location before and
after the codeword permutation step. In [35], Zhou et al.
presented a new scheme for joint security and performance
enhancement of secure AC. They proposed to incorporate
the interval splitting AC scheme with the bit-wise XOR
operation. This scheme can be extended to any adaptive
and context-based AC due to the elimination of the input
symbol permutation step. In addition, the implementation
is lower complexity than the original secure AC. Zhou
et al. also presented a selective encryption scheme with
even lower complexity. In [6], Grangetto et al. proposed
a novel multimedia security framework by means of AC.
The scheme is based on a random organization of the
encoding intervals using a secret key. This technique
can be applied to any multimedia coder employing AC
as entropy coding stage, including static, adaptive and
context-based AC. They proposed an implementation for
their scheme tailored to the JPEG2000 standard. Mi et
al. [17] proposed a new chaotic encryption scheme based
on randomized AC using the logistic map for pseudo
random bit generator. However, the logistic map is weak
in security because it does not satisfy uniform distribution
property and it has a small key space with only one control
parameter [1], [2].

In addition, chaotic systems have been used for several
applications [14], [32], [29], [30], [33] and some of these
novel chaotic systems have designed pseudo random bit
generators (PRBG) for stream cipher applications [10],
[20]. The chaotic systems used in cryptography generate
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ISSN 1947-5500
a keystream with good properties such as ergodicity,
sensitivity to initial values and sensitivity to control
parameters. However, some of them are not very suitable
to be used in cryptography due to their density function
which is not uniform distributed or due to their small key
space. To be used in cryptography, a PRBG may need to
meet stronger requirements than for other applications.
In particular, various statistical tests [21], [16] can be
applied to the outputs of such generators to conclude
whether the generator produces a truly random sequence
or not. The use of ECF-map increases the complexity of a
cryptosystem based on only one chaotic system and thus
makes difficult to extract information about it [20]. In
addition, ECF-map conserves the cryptography properties
of the chaotic system; like sensitivity to initial conditions
and control parameters; non periodicity and randomness;
and add interesting statistical properties such uniform
distribution density function and zero co-correlation.
In this paper, we propose a new joint compression
and encryption scheme based on AC and PRBG. The
proposed PRBG is based on the use of the standard
chaotic map coupled with the Engle Continued Fractions
(ECF) map. The outputs of the standard map are used
as the inputs of ECF-map. The standard map with good
chaotic properties and the ECF-map which possesses good
statistical properties motivate us to design a new PRBG
for secure AC.

The rest of this paper is organized as follows. In Section
2, we briefly discuss the AC. Section 3 details the proposed
PRBG which is based on the standard chaotic map and the
engel continued fraction map. In Section 4, we describe
the proposed algorithm for secure AC. In Section 5, we
analyze the security of the proposed scheme and we
discuss experiment results. Finally, conclusions of this
paper will be discussed in Section 6.
II. OVERVIEW OF ARITHMETIC CODING
AC [13], [28], [9], [18] is a statistical coder and is
very efficient for data compression. In addition, AC has
been widely used in many standards including JPEG2000,
JBIG2 and H.264/AVC. The principe of AC is that it
assigns one codeword to the entire input data symbols and
this codeword is a real number in the interval [0, 1). To
calculate the appropriate codeword for input data symbols,
the AC works with a modeler that estimates the probability
of each symbol at the encoding/decoding process. The
model used by AC can be either static or adaptive.
Let S = {s1, …, sn} be an independent and identically
distributed binary sequence of n random symbols. During
the encoding process, we firstly estimate the probability
of each symbol and we calculate the cumulative distribution
vector (CDV) by assigning, for each symbol si, a
subinterval with a size proportional to its probability in the
interval [0, 1). Next, for any new symbol si from the input
sequence, we select the subinterval for si and we define
it as the new current interval. We iterate this step until all
input sequence has been processed and we finally generate
the codeword that uniquely identifies the final interval.
There are many types of AC. Thus, the binary arithmetic
coding (BAC) is an important type of encoder due to its
ability to reduce the complexity created with the dynamic
update of the CDV when we use an adaptive models.
In addition, BAC has universal applications because data
symbols which are putted out from any alphabet can be
coded as a sequence of binary symbols. When we work
with a binary source alphabet, the CDV is [0, p0, 1], with
p0 the probability of the symbol ”0”. The interval [0, 1)
is partitionned in two parts. In this case, the symbol ”0”
is represented by the range [0, p0) and the symbol ”1” is
represented by the range [p0, 1). The Algorithms 1 and 2
illustrate the encoding and decoding procedures for the
BAC.
Algorithm 1 Binary encoder
Initialize base ← 0, length ← 2N − 1
for i ← 1 to n do
x ← length × p(0)
if bi = 0 then
length ← x
else
init base ← base
base ← base + x
length ← length − x
if init base > base then
propagate carry()
end if
end if
if length < length min then
renorm enc interval()
end if
end for
Algorithm 2 Binary Decoder
Initialize base ← 0, length ← 2N − 1, code = input 4
bytes from compressed file
while Not end of compressed file do
x ← length × p(0)
if code ≥ x then
bi ← 1
else
bi ← 0
end if
if bi = 0 then
length ← x
else
code ← code − x
length ← length − x
end if
if length < length min then
renorm dec interval()
end if
output bi
end while
III. PSEUDO RANDOM BITS GENERATED FROM THE
STANDARD CHAOTIC MAP AND THE ECF-MAP
In this section, we describe the process of the proposed
PRBG. In this PRBG, we suggest to use the standard
chaotic map which is defined by:
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xj = xj−1 + p0 × sin(yj−1)
yj = yj−1 + xj
, (1)
where xj and yj are taken modulo 2π. The secret key in
the proposed PRBG is a set of three floating point numbers
and one integer (x0, y0, p0,N0), where {x0, y0} ∈ [0, 2π)
is the initial values set, p0 is the control parameter which
can have any real value greater than 18.0 and N0 is
the number of initial iterations of the standard chaotic
map [19]. The standard map has good chaotic properties
and a large key space of the order 157 bits [19] with an
accuracy of 10−14. This key space is sufficient enough
to resist the brute-force attack. However, the standard
chaotic map generates a sequence with non uniform
density function. The experimental results presented in
Table I, show that sequences generated from standard
chaotic map failed some tests of the NIST statistical test
suite [21] and these sequences are not good enough to
be used in cryptographic applications. It seems a good
idea to transform the chaotic sequence generated from the
standard chaotic map to a new sequence which satisfies
uniform distribution property and have many important
characteristics of cryptography such as zero co-correlation,
randomness and ideal nonlinearity. In [7], a new nonlinear
dynamical system has been proposed which called Engel
Continued Fraction map.
The Engel continued fraction (ECF) map TE : [0, 1] →
[0, 1) is given by:
TE(x) =

1
 1
x
 ( 1
x
− 1
x
) if x = 0
0 if x = 0.
(2)
For any x ∈ [0, 1), the ECF-map generates a new and
unique continued fraction expansion [15], [22], [25], [24],
[11] of x of the form:
x =
1
b1 + b1
b2+ b2
b3+
…+
bn−1
bn+

, bn ∈ N, bn ≤ bn+1 (3)
Let x ∈ [0, 1), and define:
b1 = b1(x) =  1
x

bn = bn(x) = b1(Tn−1
E (x)), n≥ 2, Tn−1
E (x) = 0,
(4)
where T0E
(x) = x and Tn
E(x) = TE(Tn−1
E (x)) for n ≥ 1.
From definition of TE it follows that:
x = 1
b1+b1TE(x)
= 1
b1+ b1
b2+
b2
b3+
…+
bn−1
bn+bnTn
E
(x)
. (5)
The method used for generating the ECF-continued
fraction expansion of x is described in Algorithm 3.
From the theorem presented in [7], if we let x ∈ [0, 1),
then x has a finite ECF-expansion (i.e., Tn
E(x) = 0 for
some n ≥ 1) if and only if x ∈ Q. Thus, all floating
number has a unique and finite ECF-expansion. Note that,
we paid most attention to the following sequence:
Algorithm 3 ECF expansion
Initialize x0 ← x, i ← 0
while xi = 0 do
i ← i + 1
bi ←  1
xi−1

xi ← 1
 1
xi−1
 ( 1
xi−1
−  1
xi−1
)
end while
Zn(x) = bn(x)Tn
E(x), n ≥ 1. (6)
The sequence {Zi(x)}ni
=1 is in [0, 1) and uniformly
distributed for almost all points x (for a proof see [7]).
So, the ECF-map generates a random and unpredictable
sequence {Zi(x)}ni
=1 with a uniform distribution. These
properties, which are very useful in cryptography, motivate
us to use ECF-map in our PRBG.
The use of the standard chaotic map make the output
very sensitive to the input and in our PRGB, the outputs of
this chaotic map are used as the input to the ECF-map for
generating sequences with desirable chaotic and statistical
properties.
In the following paragraph, we give the detailed procedure
to generate pseudo random binary sequences using
the standard and ECF maps.
We define a function G : [0, 1) → [0, 1) such that:
G(xi) =

j
Zj(xi) − 

j
Zj(xi), (7)
where {Zj} is the set calculated according to (6) using
ECF-map. In addition, assume that we have defined a
function F : [0, 1] → {0, 1} that converts the real number
xi to a discrete bit symbol as follows:
F(xi) =

0 if xi < 0.5
1 otherwise
. (8)
We propose to use the 2-D standard map, with
{x0, y0} the initial values and p0 the control parameter
of the chaotic map. The majority of cryptosystems with
keystreams independent of plaintexts are vulnerable under
known plaintext attacks [26]. Thus, to enhance the security
of our encryption method, we propose to use the plaintext
when producing keystreams. In our scheme, we firstly iterate
the chaotic map N0 times and the operation procedures
of the proposed PRBG are described as follows:
• Step 1: The standard map is iterated continuously.
For the jth iteration, the output of the standard map
is a new set {xj, yj}.
• Step 2: Assuming that the plaintext is a binary
sequence B = b1…bn. For the jth bit of the
plaintext we calculate Sj the decimal representation
of bj−8…bj−1. Note that for the first 8 bits of the
plaintext, Sj equals to a secret value S0. In addition,
the standard map generates a set {xj, yj} ∈ [0, 2π).
So we propose to calculate the set {aj}nj
=1 using the
relation:
aj = (xj + yj +
Sj
256
) − (xj + yj +
Sj
256
). (9)
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• Step 3: Finally, the sequence Kn = {kj}nj
=1 represents
the random binary sequence and it is generated
by:
kj = F(G(aj)). (10)
The standard and ECF maps are iterated until the generation
of a keystream with length n. In order to generate
the random binary sequence {kj}nj
=1, an initial sequence
{aj}nj
=1 has to be created using the standard map. To
test the randomness of the sequence generated by using
the standard map, we propose to calculate the sequence
{Mj}nj
=1 as follows: Mj = F(aj) for 1 ≤ j ≤ n. From
a cryptographic point of view, the sequence {Mj}nj
=1 is
not good enough for designing a PRBG because it does
not pass all statistical tests designed by the NIST [21].
Therefore, we propose to use the ECF-map to convert the
generated sequence {aj}nj
=1 to a binary sequence {kj}nj
=1
of the same length by applying (10). Table I shows the
passing rate of the sequences without and with using ECFmap.
A noticeable improvement is observed after mixing
standard map with the ECF-map and all the tests are
passed. Figures 1 and 2 present respectively the chaotic
trajectory and the distribution function of the proposed
PRBG for a keystream of length 10, 000 bits generated
with a random encryption key. In these two figures, we
have supposed that the keystream acts as byte, so the range
of the keystream is 0 − 255.

Fig. 1. Distribution function of the generated keystream by using our
PRBG.
Fig. 2. The uniform property of the generated keystream by using our
PRBG

IV. THE PROPOSED COMPRESSION AND ENCRYPTION
SCHEME

We assume that the plaintext is a binary sequence
B = b1…bn. Let p0 the probability of symbol ”0” and
p1 the probability of symbol ”1”. We propose to use the
keystream Kn = {kj}nj
=1 generated from our PRBG to
randomly exchange the two intervals of the CDV used in
BAC encoding/decoding process. Thus, before encoding
the bit bj of the plaintext B, we propose to generate
the jth key kj using our PRBG. In the encryption and
decryption algorithms, we suggest to use two variables
called lower and upper which initially equal to 0 and
1 respectively, and the CDV is [0, p0, 1]. If the generated
key kj equals to 1, then these two variables are
permuted and the CDV becomes [0, p1, 1]. So, we only
suggest to permute the probabilities p0 and p1 in the
CDV according to the generated key kj . The encryption
and decryption procedures are illustrated in Algorithms 4
and 5 respectively. The proposed scheme leads to make
BAC performing both lossless compression and encryption
simultaneously. In addition, AC is very sensitive to
errors in the compressed data, and this undesired property
ameliorates the security of the proposed method. The
cryptographic properties of the proposed PRBG lead to
perform maximum randomization in the swapping intervals
process. The decryption process is similar to the
encryption one. It should be noted that the proposed
scheme conserves the compression efficiency of the BAC
because we use the same probabilities when encoding the
binary symbols without and with the permutation process.
The most advantage of the work presented in this paper is
the use of the chaos theory with the use of the ECF-map
into arithmetic coding to provide a new scheme which
performs both compression and encryption of data.
Algorithm 4 Encryption algorithm
Initialize base ← 0, length ← 2N − 1 , lower ← 0 ,
upper ← 1 ,
for i ← 1 to n do
generate ki using the PRBG
if Ki = 1 then
permute(lower, upper)
end if
x ← length × p(lower)
if bi = lower then
length ← x
else
init base ← base
base ← base + x
length ← length − x
if init base > base then
propagate carry()
end if
end if
if length < length min then
renorm enc interval()
end if
end for
V. EXPERIMENT RESULTS AND SECURITY ANALYSIS
The BAC implementation used during the experiment
analysis was downloaded from the website
(http://www.cipr.rpi.edu/∼said/fastac.html) and it was implemented
using C++. In this paper, we propose to analyze
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Test No. Test Name x0 = 3.59587469543 x0 = 5.02548745491
y0 = 0.8512974635 y0 = 2.9654128766
p0 = 120.9625487136 p0 = 100.6
N0 = 250 N0 = 250
{kj}Nj
=1
{aj}Nj
=1
{kj}Nj
=1
{aj}Nj
=1
1 FT 0.950563 0.000000 0.571394 0.000000
2 BFT (m = 128) 0.487702 0.004997 0.606546 0.025579
3 RT 0.852448 0.000000 0.588039 0.000000
4 LROT 0.909896 0.217013 0.676629 0.419327
5 MRT 0.931527 0.406179 0.104819 0.760720
6 SPT 0.760384 0.417304 0.067271 0.019833
7 NOTMT (m = 9, B = 000000001) 0.976154 0.004070 0.285350 0.000407
8 OTMT (m = 9, B = 111111111) 0.528047 0.000343 0.509185 0.198951
9 MUST (L=7, Q= 1280) 0.189804 0.026644 0.087637 0.296153
10 LZT 0.537151 0.234318 0.061457 0.002342
11 LCT (M = 500) 0.482937 0.275970 0.685647 0.829220
12 ST (m = 16) 0.442602 0.115116 0.252451 0.952714
13 AET 0.182287 0.000000 0.784454 0.000000
14 CST (Forward) 0.837613 0.000000 0.606517 0.000000
CST(Reverse) 0.801266 0.000000 0.223216 0.000000
15 RET (x = +1) 0.938621 0.000000 0.403319 0.000000
16 REVT (x = -1) 0.241429 0.000000 0.764309 0.000000
TABLE I
STATISTICAL TESTS ON THE SEQUENCES {kj}nj
=1 AND {Mj}nj
=1 WITH DIFFERENT KEYS.
Lena bit plane 512 × 512 Static model Adaptive model
Traditional AC Our method Traditional AC Our method
Bit plane 8 32780 32780 27622 27622
Bit plane 7 32182 32182 30085 30085
Bit plane 6 32786 32786 31151 31151
Bit plane 5 32790 32790 32295 32295
TABLE II
THE COMPRESSION EFFICIENCY (IN BYTE) OF BIT PLANE WITH DIFFERENT INFORMATION ENTROPY.
Algorithm 5 Decryption algorithm
Initialize base ← 0, length ← 2N − 1, code = input 4
bytes from compressed file
while Not end of compressed file do
generate ki using the PRBG
if Ki = 1 then
permute(lower, upper)
end if
x ← length × p(lower)
if code ≥ x then
bi ← upper
code ← code − x
length ← length − x
else
bi ← lower
length ← x
end if
if length < length min then
renorm dec interval()
end if
output bi
end while
our method in multimedia application and especially to
each binary bit plane of the gray-scale images of different
size with 8-bits per pixel. Table II shows the compression
results of the Lena binary bit plane images for both
traditional BAC and our approach in static model and
Image size in pixels Total elapsed time(s) using our method
in static model in adaptive model
256 × 256 2.30 3.00
512 × 512 9.23 12.00
1024 × 1024 34.30 45.50
TABLE III
THE COMPRESSION AND ENCRYPTION SPEEDS OF OUR METHOD IN
BOTH STATIC AND ADAPTIVE MODEL.
adaptive model. From Table II, the obtained bytes using
both static and adaptive model are the same with and
without using the encryption process. Thus, our proposed
scheme conserves the compression efficiency.
There is an other important issue on a compression
and encryption scheme which is the running speed. The
analysis has been done using a machine with Intel core
2 Duo 2.93 GHZ CPU and 2 GB RAM running on
Windows XP Professional Edition. The execution times
of our method for images with different size are shown in
Table III.
The proposed compression and encryption scheme is
based on a BAC whose two mapping intervals are exchanged
randomly by using a PRBG. This scheme is sensitive
to both plaintext and key. As shown in Figure 3, the
ciphertext has uniform distribution for both on static model
and adaptive model. Therefore, the proposed scheme does
not provide any clue to employ any statistical attack on
the ciphertext.
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(a) Static model
(b) Adaptive model
Fig. 3. The uniform property of the ciphertext for the first 10, 000 bits
of the encrypted Lena in (a) Static model and (b) Adaptive model.
VI. CONCLUSIONS
In this paper, we proposed a new scheme which combines
BAC with a PRBG to perform both lossless compression
and encryption of data. In our scheme, we exploit
both the efficiency of the BAC in lossless data compression
and the advantages of chaos theory in data encryption
to provide a scheme which can be very useful in many
applications such as multimedia applications and medical
imaging.
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[Flag of the Kingdom of Norway] Norwegian
Dhamma-biblioteket Fra buddhismens grunntekster (Kåre A. Lie)

Buddhism in Norway

File:Buddhisttempel Løvenstad.JPG

The Vietnamese “Khuông Việt” pagoda at Løvenstad near Oslo, the only of its kind in Norway.

Buddhism in Norway has existed since the beginning of the 1970s, after immigration from countries with Buddhist populations, mainly Vietnam. Buddhistforbundet (The Buddhist Federation) in Norway was established as a religious society in 1979 by two Buddhist groups (The Zen School and Karma Tashi Ling buddhistsenter)
who wanted to create a common organization to preserve issues of common
interest. As of 2013, there are over 30 to 50 thousand (between 0.7%[1] and up to 1%[2] of the total population) registered Buddhists in Norway. Around 5% of them are ethnic Norwegians[3].

Population

By county

County Total population Buddhist population Percent buddhist
Oslo 575,475 2,912 0.5%
Akershus 527,625 1,767 0.3%
Østfold 268,584 1,143 0.4%
Hordaland 469,681 952 0.2%
Rogaland 420,574 844 0.2%
Sør-Trøndelag 286,729 801 0.2%
Buskerud 254,634 774 0.3%
Vestfold 229,134 538 0.2%
Vest-Agder 168,233 512 0.3%
Telemark 167,548 379 0.2%
Hedmark 190,071 350 0.1%
Oppland 184,288 274 0.1%
Møre og Romsdal 248,727 246 0.0%
Aust-Agder 107,359 227 0.2%
Troms 155,553 177 0.1%
Nordland 235,380 131 0.0%
Nord-Trøndelag 130,708 104 0.0%
Finnmark 72,492 81 0.1%
Sogn og Fjordane 106,457 40 0.0%

By region

County Total population Buddhist population Percent buddhist
Eastern Norway 2,397,359 8,137 0.3%
Western Norway 1,245,439 2,082 0.1%
Trøndelag 417,437 905 0.2%
Southern Norway 275,592 739 0.2%
Northern Norway 463,425 389 0.0%
Year Buddhists Percent
1990 3,012 0.07%
2000 7,031 0.16%
2005 9,471 0.20%
2010 13,376 0.27%

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