January 2023: Top 10 Cited Articles in Computer Science & Information Technology

January 2023: Top 10 Cited
Articles in Computer Science
&Information Technology
International Journal of Computer Science and
Information Technology (IJCSIT)
INSPEC,WJCI Indexed
ISSN: 0975-3826(online); 0975-4660 (Print)
https://airccse.org/journal/ijcsit.html

EDGE DETECTION TECHNIQUES FOR IMAGE SEGMENTATION
Muthukrishnan.R1
and M.Radha2
1
Assistant Professor, Department of Statistics, Bharathiar University, Coimbatore.
2
Research Scholar, Department of Statistics, Bharathiar University, Coimbatore.
ABSTRACT
Interpretation of image contents is one of the objectives in computer vision specifically in image
processing. In this era it has received much awareness of researchers. In image interpretation the partition
of the image into object and background is a severe step. Segmentation separates an image into its
component regions or objects. Image segmentation t needs to segment the object from the background to
read the image properly and identify the content of the image carefully. In this context, edge detection is a
fundamental tool for image segmentation. In this paper an attempt is made to study the performance of
most commonly used edge detection techniques for image segmentation and also the comparison of these
techniques is carried out with an experiment by using MATLAB software.
KEYWORDS
Computer Vision , Image Segmentation , Edge detection, MATLAB.
Volume URL : https://airccse.org/journal/ijcsit2011_curr.html
Source URL : https://airccse.org/journal/jcsit/1211csit20.pdf

REFERENCES
[1] Canny, J. F (1983) Finding edges and lines in images, Master's thesis, MIT. AI Lab. TR-720.
[2] Canny, J. F (1986) “A computational approach to edge detection”, IEEE Transaction on Pattern
Analysis and Machine Intelligence, 8, 679-714.
[3] Courtney. P & N. A. Thacker (2001) “Performance Characterization in Computer Vision: The Role of
Statistics in Testing and Design”, Chapter in: “Imaging and Vision Systems: Theory, Assessment and
Applications”, Jacques Blanc-Talon and Dan Popescu (Eds.), NOVA Science Books.
[4] Hanzi Wang (2004) Robust Statistics for Computer Vision: Model Fitting, Image Segmentation and
Visual Motion Analysis, Ph.D thesis, Monash University, Australia.
[5] Huber, P.J. (1981) Robust Statistics, Wiley New York. [6] Kirsch, R. (1971) “Computer determination
of the constituent structure of biological images”, Computers and Biomedical Research, 4, 315–328.
[7] Lakshmi,S & V.Sankaranarayanan (2010) “A Study of edge detection techniques for segmentation
computing approaches”, Computer Aided Soft Computing Techniques for Imaging and Biomedical
Applications, 35-41. [8] Lee, K.. M, Meer, P. & et al. (1998) “Robust Adaptive Segmentation of Range
Images”, IEEE Trans. Pattern Analysis and Machine Intelligence, 20(2), 200-205. [9] Marr, D & E.
Hildreth (1980) “Theory of edge detection”, Proc. Royal Society of London, B, 207, 187–217.
[10] Marr, D(1982) Vision, Freeman Publishers.
[11] Marr, P & Doron Mintz, D. & et al. (1991) “Robust Regression for Computer Vision: A Review”,
International Journal of Computer Vision, 6(1), 59-70.
[12] Orlando, J, Tobias & Rui Seara (2002) “Image Segmentation by Histogram Thresholding Using
Fuzzy Sets”, IEEE Transactions on Image Processing, Vol.11, No.12, 1457-1465.
[13] Punam Thakare (2011) “A Study of Image Segmentation and Edge Detection Techniques”,
International Journal on Computer Science and Engineering, Vol 3, No.2, 899-904. [14] Rafael C.
Gonzalez, Richard E. Woods & Steven L. Eddins (2004) Digital Image Processing Using MATLAB,
Pearson Education Ptd. Ltd, Singapore.
[15] Ramadevi, Y & et al (2010) “Segmentation and object recognition using edge detection techniques”,
International Journal of Computer Science and Information Technology, Vol 2, No.6, 153-161.
[16] Roberts, L (1965) “Machine Perception of 3-D Solids”, Optical and Electro-optical Information
Processing, MIT Press.
[17] Robinson. G (1977) “Edge detection by compass gradient masks”, Computer graphics and image
processing, 6, 492-501.
[18] Rousseeuw, P. J & Leroy, A (1987) Robust Regression and outlier detection, John Wiley & Sons,
New York.
[19] Senthilkumaran. N & R. Rajesh (2009) “Edge Detection Techniques for Image Segmentation – A
Survey of Soft Computing Approaches”, International Journal of Recent Trends in Engineering, Vol. 1,
No. 2, 250-254.

[20] Sowmya. B & Sheelarani. B (2009) “Colour Image Segmentation Using Soft Computing
Techniques”, International Journal of Soft Computing Applications, Issue 4, 69-80.
[21] Umesh Sehgal (2011) “Edge detection techniques in digital image processing using Fuzzy Logic”,
International Journal of Research in IT and Management, Vol.1, Issue 3, 61-66.
[22] Yu, X, Bui, T.D. & et al. (1994) “Robust Estimation for Range Image Segmentation and
Reconstruction”, IEEE trans. Pattern Analysis and Machine Intelligence, 16 (5), 530-538.

DIAGONAL BASED FEATURE EXTRACTION FOR HANDWRITTEN
ALPHABETS RECOGNITION SYSTEM USING NEURAL NETWORK
J.Pradeep1
, E.Srinivasan2
and S.Himavathi3
1,2
Department of ECE, Pondicherry College Engineering, Pondicherry, India.
3
Department of EEE, Pondicherry College Engineering, Pondicherry, India
ABSTRACT
An off-line handwritten alphabetical character recognition system using multilayer feed forward neural
network is described in the paper. A new method, called, diagonal based feature extraction is introduced
for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets
written by various people, are used for training the neural network and 570 different handwritten
alphabetical characters are used for testing. The proposed recognition system performs quite well yielding
higher levels of recognition accuracy compared to the systems employing the conventional horizontal and
vertical methods of feature extraction. This system will be suitable for converting handwritten documents
into structural text form and recognizing handwritten names.
KEYWORDS
Handwritten character recognition, Image processing, Feature extraction, feed forward neural networks.
Source URL : https://airccse.org/journal/jcsit/0211ijcsit03.pdf

REFERENCES
[1] S. Mori, C.Y. Suen and K. Kamamoto, “Historical review of OCR research and development,” Proc.
of IEEE, vol. 80, pp. 1029-1058, July 1992.
[2] S. Impedovo, L. Ottaviano and S. Occhinegro, “Optical character recognition”, International Journal
Pattern Recognition and Artificial Intelligence, Vol. 5(1-2), pp. 1-24, 1991.
[3] V.K. Govindan and A.P. Shivaprasad, “Character Recognition – A review,” Pattern Recognition, vol.
23, no. 7, pp. 671- 683, 1990 International Journal of Computer Science & Information Technology
(IJCSIT), Vol 3, No 1, Feb 2011 37
[4] R. Plamondon and S. N. Srihari, “On-line and off- line handwritten character recognition: A
comprehensive survey,”IEEE. Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 1,
pp. 63-84, 2000.
[5] N. Arica and F. Yarman-Vural, “An Overview of Character Recognition Focused on Off-line
Handwriting”, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews,
2001, 31(2), pp. 216 - 233.
[6] U. Bhattacharya, and B. B. Chaudhuri, “Handwritten numeral databases of Indian scripts and
multistage recognition of mixed numerals,” IEEE Transaction on Pattern analysis and machine
intelligence, vol.31, No.3, pp.444-457, 2009.
[7] U. Pal, T. Wakabayashi and F. Kimura, “Handwritten numeral recognition of six popular scripts,”
Ninth International conference on Document Analysis and Recognition ICDAR 07, Vol.2, pp.749-753,
2007.
[8] R.G. Casey and E.Lecolinet, “A Survey of Methods and Strategies in Character Segmentation,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No.7, July 1996, pp. 690-706.
[9] Anil.K.Jain and Torfinn Taxt, “Feature extraction methods for character recognition-A Survey,”
Pattern Recognition, vol. 29, no. 4, pp. 641-662, 1996.
[10] R.G. Casey and E.Lecolinet, “A Survey of Methods and Strategies in Character Segmentation,”
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No.7, July 1996, pp. 690-706.
[11] C. L. Liu, H. Fujisawa, “Classification and Learning for Character Recognition: Comparison of
Methods and Remaining Problems”, Int. Workshop on Neural Networks and Learning in Document
Analysis and Recognition, Seoul, 2005.
[12] F. Bortolozzi, A. S. Brito, Luiz S. Oliveira and M. Morita, “Recent Advances in Handwritten
Recognition”, Document Analysis, Umapada Pal, Swapan K. Parui, Bidyut B. Chaudhuri, pp 1-30.
[13] Anita Pal & Dayashankar Singh, “Handwritten English Character Recognition Using Neural,”
Network International Journal of Computer Science & Communication.vol. 1, No. 2, July-December
2010, pp. 141-144.
[14] Dinesh Acharya U, N V Subba Reddy and Krishnamurthy, “Isolated handwritten Kannada numeral
recognition using structural feature and K-means cluster,” IISN-2007, pp-125 -129.

[15] N. Sharma, U. Pal, F. Kimura, "Recognition of Handwritten Kannada Numerals", 9th International
Conference on Information Technology (ICIT'06), ICIT, pp. 133-136.
[16] Rafael C. Gonzalez, Richard E. woods and Steven L.Eddins, Digital Image Processing using
MATLAB, Pearson Education, Dorling Kindersley, South Asia, 2004.
[17] S.V. Rajashekararadhya, and P.VanajaRanjan, “Efficient zone based feature extraction algorithm for
handwritten numeral recognition of four popular south-Indian scripts,” 38 Journal of Theoretical and
Applied Information Technology, JATIT vol.4, no.12, pp.1171-1181, 2008.
Authors
J.Pradeep received his B.Tech degree in Electronics and Communication Engineering from Barathiyar
college of Engineering and Technogy affiliated to Pondicherry University in the year
2005. He obtained his M.Tech degree in Electronics and Communication Engineering
from Podicherry Engineering College in the year 2009. He is currently a Ph.D
candidate in the Department of Electronics and Communication Engineering in
Podicherry Engineering College. He has published two papers in International Journal.
He is a life member of ISTE. His areas of interest are Wireless Communication, Image
proceesing and Neural networks.
E.Srinivasan obtained his B.E. degree in Electrical and Electronics Engineering from
P.S.G. College of Technology, Coimbatore, India, in the year 1984. He received his
M.E. degree in Instrumentation Technology in the year 1987 from Madras Institute of
Technology, Chennai, India. He was awarded with Ph.D. degree by the Anna
University, Chennai, India in the year 2003 for his research contributions in Nonlinear
Signal Processing. Currently, he is serving as Professor and Head of the Department
of Electronics and Communication Engineering, Pondicherry Engineering College,
Pondicherry, India. He has published 30 research papers in national/international journals/conferences. He
is a reviewer of the AMSE journal of Signal Processing. His research interests include nonlinear signal
processing and pattern recognition and their applications.
S.Himavathi completed her BE degree in Electrical and Electronics Engineering from
College of Engineering, Guindy, Chennai, India, in the year 1984. She obtained her
M.E. degree in Instrumentation Technology in the year 1987 from Madras Institute of
Technology, Chennai, India. She completed her Ph.D. degree in the area of Fuzzy
modeling in the year 2003 from Anna University, Chennai, India. She is a Professor and
Head of the Department of Electrical and Electronics Engineering, Pondicherry
Engineering College. She has around 50 publications to her credit. She is a reviewer of
the AMSE journal of Modeling, IEEE Industrial Electronics Society and Asian Neural Networks Society.
Her research interests are Fuzzy systems, Neural Networks, Hybrid systems and their applications.

SECURITY THREATS ON CLOUD COMPUTING VULNERABILITIES
Te-Shun Chou
Department of Technology Systems, East Carolina University, Greenville, NC, U.S.A.
ABSTRACT
Clouds provide a powerful computing platform that enables individuals and organizations to perform
variety levels of tasks such as: use of online storage space, adoption of business applications,
development of customized computer software, and creation of a “realistic” network environment. In
previous years, the number of people using cloud services has dramatically increased and lots of data has
been stored in cloud computing environments. In the meantime, data breaches to cloud services are also
increasing every year due to hackers who are always trying to exploit the security vulnerabilities of the
architecture of cloud. In this paper, three cloud service models were compared; cloud security risks and
threats were investigated based on the nature of the cloud service models. Real world cloud attacks were
included to demonstrate the techniques that hackers used against cloud computing systems. In addition,
countermeasures to cloud security breaches are presented.
KEYWORDS
Cloud computing, cloud security threats and countermeasures, cloud service models.

REFERENCES
1. DataLossDB Open Security Foundation. http://datalossdb.org/statistics
2. Sophos Security Threat Report 2012. http://www.sophos.com/
3. Amazon.com Server Said to Have Been Used in Sony Attack, May 2011.
http://www.bloomberg.com/news/2011-05-13/sony-network-said-to-have-been-invaded-by-hackersusing-
amazon-com-server.html
4. D. Jamil and H. Zaki, “Security Issues in Cloud Computing and Countermeasures,” International
Journal of Engineering Science and Technology, Vol. 3 No. 4, pp. 2672-2676, April 2011.
5. K. Zunnurhain and S. Vrbsky, “Security Attacks and Solutions in Clouds,” 2nd IEEE International
Conference on Cloud Computing Technology and Science, Indianapolis, December 2010.
6. W. A. Jansen, “Cloud Hooks: Security and Privacy Issues in Cloud Computing,” 44th Hawaii
International Conference on System Sciences, pp. 1–10, Koloa, Hawaii, January 2011.
7. T. Roth, “Breaking Encryptions Using GPU Accelerated Cloud Instances,” Black Hat Technical
Security Conference, 2011.
8. CERT Coordination Center, Denial of Service.
http://www.packetstormsecurity.org/distributed/denial_of_service.htm
9. M. Jensen, J. Schwenk, N. Gruschka, and L. L. Iacono, “On Technical Security Issues in Cloud
Computing,” IEEE International Conference in Cloud Computing, pp. 109-116, Bangalore, 2009.
10. Thunder in the Cloud: $6 Cloud-Based Denial-of-Service Attack, August 2010.
http://blogs.computerworld.com/16708/thunder_in_the_cloud_6_cloud_based_denial_of_service_att ack
11. DDoS Attack Rains Down on Amazon Cloud, October 2009.
http://www.theregister.co.uk/2009/10/05/amazon_bitbucket_outage/
12. 2011 CyberSecurity Watch Survey, CERT Coordination Center at Carnegie Mellon University.
13. D. Catteddu and G. Hogben, “Cloud Computing Benefits, Risks and Recommendations for
Information Security,” The European Network and Information Security Agency (ENISA), November
2009.
14. Insider Threats Related to Cloud Computing, CERT, July 2012. http://www.cert.org/
15. Data Breach Trends & Stats, Symantec, 2012. http://www.indefenseofdata.com/data-breach-
trendsstats/
88
16. 2012 Has Delivered Her First Giant Data Breach, January 2012.
http://www.infosecisland.com/blogview/19432-2012-Has-Delivered-Her-First-Giant-DataBreach.html 17.
A Few Wrinkles Are Etching Facebook, Other Social Sites, USA Today, 2011.
http://www.usatoday.com/printedition/life/20090115/socialnetworking15_st.art.htm

18. An Update on LinkedIn Member Passwords Compromised, LinkedIn Blog, June, 2012.
http://blog.linkedin.com/2012/06/06/linkedin-member-passwords-compromised/
19. Dropbox: Yes, We Were Hacked, August 2012. http://gigaom.com/cloud/dropbox-yes-we-
werehacked/
20. Web Based Attacks, Symantec White Paper, February 2009.
21. Symantec Internet Security Threat Report, 2011 Trends, Vol. 17, April 2012.
22. P. P. Ramgonda and R. R. Mudholkar, “Cloud Market Cogitation and Techniques to Averting SQL
Injection for University Cloud,” International Journal of Computer Technology and Applications, Vol. 3,
No. 3, pp. 1217-1224, January, 2012.
23. A. S. Choudhary and M. L. Dhore, “CIDT: Detection of Malicious Code Injection Attacks on Web
Application,” International Journal of Computer Applications, Vol. 52, No. 2, pp. 19-26, August 2012.
24. Web Application Attack Report For The Second Quarter of 2012
http://www.firehost.com/company/newsroom/web-application-attack-report-second-quarter-2012
25. Visitors to Sony PlayStation Website at Risk of Malware Infection, July 2008.
http://www.sophos.com/en-us/press-office/press-releases/2008/07/playstation.aspx
26. N. Provos, M. A. Rajab, and P. Mavrommatis, “Cybercrime 2.0: When the Cloud Turns Dark,” ACM
Communications, Vol. 52, No. 4, pp. 42–47, 2009.
27. S. S. Rajan, Cloud Security Series | SQL Injection and SaaS, Cloud Computing Journal, November
2010.
28. Researchers Demo Cloud Security Issue With Amazon AWS Attack, October 2011.
http://www.pcworld.idg.com.au/article/405419/researchers_demo_cloud_security_issue_amazon_aw
s_attack/
29. M. McIntosh and P. Austel, “XML Signature Element Wrapping Attacks and Countermeasures,”
2005 workshop on Secure web services, ACM Press, New York, NY, pp. 20–27, 2005.
30. N. Gruschka and L. L. Iacono, “Vulnerable Cloud: SOAP Message Security Validation Revisited,”
IEEE International Conference on Web Services, Los Angeles, 2009.
31. A. Tripathi and A. Mishra, “Cloud Computing Security Considerations Interface,” 2011 IEEE
International Conference on Signal Processing, Communications and Computing, Xi'an, China,
September 2011.
32. H. C. Li, P. H. Liang, J. M. Yang, and S. J. Chen, “Analysis on Cloud-Based Security Vulnerability
Assessment,” IEEE International Conference on E-Business Engineering, pp.490-494, November 2010.
33. Amazon: Hey Spammers, Get Off My Cloud!
http://voices.washingtonpost.com/securityfix/2008/07/amazon_hey_spammers_get_off_my.html
34. W. Jansen and T. Grance, “Guidelines on Security and Privacy in Public Cloud Computing,”
Computer Security Division, Information Technology Laboratory, National Institute of Standards and
Technology, Special Publication 800-144, December 2011.

35. Tackling the Insider Threat http://www.bankinfosecurity.com/blogs.php?postID=140
36. “Cloud Security Risks and Solutions,” White Paper, BalaBit IT Security, July 2010.
37. S. J. Stolfo, M. B. Salem, and A. D. Keromytis, “Fog computing: Mitigating Insider Data Theft
Attacks in the Cloud,” IEEE Symposium on Security and Privacy Workshops, pp. 125-128, San
Francisco, CA, 2012.
38. M. Jensen, C. Meyer, J. Somorovsky, and J. Schwenk, “On the Effectiveness of XML Schema
Validation for Countering XML Signature Wrapping Attacks,” First International Workshop on Securing
Services on the Cloud, Milan, Italy, September 2011.
39. S. Gajek, M. Jensen, L. Liao, and J. Schwenk, “Analysis of Signature Wrapping Attacks and
Countermeasures,” IEEE International Conference on Web Services, pp. 575–582, Miami, Florida, July
2009.

COMMON PHASES OF COMPUTER FORENSICS INVESTIGATION
MODELS
Yunus Yusoff, Roslan Ismail and Zainuddin Hassan
College of Information Technology, Universiti Tenaga Nasional, Selangor, Malaysia
ABSTRACT
The increasing criminal activities using digital information as the means or targets warrant for a
structured manner in dealing with them. Since 1984 when a formalized process been introduced, a great
number of new and improved computer forensic investigation processes have been developed. In this
paper, we reviewed a few selected investigation processes that have been produced throughout the years
and then identified the commonly shared processes. Hopefully, with the identification of the commonly
shard process, it would make it easier for the new users to understand the processes and also to serve as
the basic underlying concept for the development of a new set of processes. Based on the commonly
shared processes, we proposed a generic computer forensics investigation model, known as GCFIM.
KEYWORDS
Computer Forensic Models, Computer Forensic Investigation

REFERENCES
[1] M. G. Noblett, M. M. Pollitt & L. A. Presley, (2000) “Recovering and Examining Computer Forensic
Evidence”, Forensic Science Communications, Vol. 2, No. 4.
[2] M. M. Pollitt, (1995) “Computer Forensics: An Approach to Evidence in Cyberspace”, in Proceeding
of the National Information Systems Security Conference, Baltimore, MD, Vol. II, pp. 487-491.
[3] M. M. Pollitt, (2007) “An Ad Hoc Review of Digital Forensic Models”, in Proceeding of the Second
International Workshop on Systematic Approaches to Digital Forensic Engineering (SADFE‟07),
Washington, USA.
[4] G. Palmer, (2001) "DTR-T001-01 Technical Report. A Road Map for Digital Forensic Research",
Digital Forensics Workshop (DFRWS), Utica, New York.
[5] M. Reith, C. Carr & G. Gunsh, (2002) “An Examination of Digital Forensics Models”, International
Journal of Digital Evidence, Vol. 1, No. 3.
[6] B. Carrier & E. H. Spafford, (2003) “Getting Physical with the Digital Investigation Process”,
International Journal of Digital Evidence, Vol. 2, No. 2
[7] V. Baryamereeba & F. Tushabe, (2004) “The Enhanced Digital Investigation Process Model”, in
Proceeding of Digital Forensic Research Workshop, Baltimore, MD.
[8] M. K. Rogers, J. Goldman, R. Mislan, T. Wedge & S. Debrota, (2006) “Computer Forensic Field
Triage Process Model”, presented at the Conference on Digital Forensics, Security and Law, pp. 27-40.
[9] P. Sundresan, (2009) “Digital Forensic Model based on Malaysian Investigation Process”,
International Journal of Computer Science and Network Security, Vol. 9, No. 8. [10] S. Ciardhuain,
(2004) “An Extended Model of Cybercrime Investigation”, International Journal of Digital Evidence,
Vol. 3, No. 1, pp. 1-22.
[11] P. Stephenson, (2003) "A Comprehensive Approach to Digital Incident Investigation.", Information
Security Technical Report, Vol. 8, Issue 2, pp 42-52. International Journal of Computer Science &
Information Technology (IJCSIT), Vol 3, No 3, June 2011 31
[12] N. L. Beebe & J. G. Clark, (2004) “A Hierarchical, Objective-Based Framework for the Digital
Investigations Process”, in Proceeding of Digital Forensic Research Workshop (DFRWS), Baltimore,
Maryland.
[13] M. Kohn, J. H. P. Eloff, & M. S. Olivier, (2006) “Framework for a Digital Forensic Investigation”,
in Proceedings of the ISSA 2006 from Insight to Foresight Conference, Sandton, South Africa.
[14] F. C. Freiling & B. Schwittay, (2007) “Common Process Model for Incident and Computer
Forensics”, in Proceedings of Conference on IT Incident Management and IT Forensics, Stuttgard,
Germany, pp. 19-40.
[15] D. Bem & E. Huebner, (2007) “Computer Forensic Analysis in a Virtual Environment”,
International Journal of Digital Evidence, vol. 6, no. 2, pp. 1-13.

[16] E. S. Pilli, R. C. Joshi, & R. Niyogi, (2010) “Network Forensic frameworks: Survey and research
challenges,” Digital Investigation, Vol. 7, pp. 14-27.
Author
Yunus Yusoff is currently pursuing a PhD in the field of computer forensics focusing
on the trustworthiness of digital evidence. Prior to joining education field, he has
extensive working experience in banking industry, managing a department specializing
in the information security and disaster recovery.

A Novel Technique For Image Steganography Based On Block-DCT And
Huffman Encoding
A.Nag ! , S. Biswas * , D. Sarkar * , P.P. Sarkar *
!Academy of Technology, Hoogly – 721212
*USIC, University of Kalyani, Kalyani, Nadia – 741 235, West Bengal, India
ABSTRACT
Image steganography is the art of hiding information into a cover image. This paper presents a novel
technique for Image steganography based on Block-DCT, where DCT is used to transform original image
(cover image) blocks from spatial domain to frequency domain. Firstly a gray level image of size M × N
is divided into no joint 8 × 8 blocks and a two dimensional Discrete Cosine Transform(2-d DCT) is
performed on each of the P = MN / 64 blocks. Then Huffman encoding is also performed on the secret
messages/images before embedding and each bit of Huffman code of secret message/image is embedded
in the frequency domain by altering the least significant bit of each of the DCT coefficients of cover
image blocks. The experimental results show that the algorithm has a high capacity and a good
invisibility. Moreover PSNR of cover image with stego-image shows the better results in comparison with
other existing steganography approaches. Furthermore, satisfactory security is maintained since the secret
message/image cannot be extracted without knowing decoding rules and Huffman table.
KEYWORDS
Steganography, Frequency Domain, DCT, Huffman Coding, Information Hiding.

REFERENCES
[1] DES Encryption Standard (DES), National Bureau of Standard (U.S.). Federal Information Processing
Standards Publication 46, National Technical Information Service, Springfield, VA, 1997.
[2] Daemen,J., and Rijmen, V. “Rijndael: The Advanced Encryption Standard”, Dr. Dobb‟s Journal,
March 2001.
[3] R. Rivest, A. Shamir, and L. Adleman, 1978. A method for obtaining digital signatures and public-
key cryptosystems. Communication of the ACM: 120-126.
[4] Pfitzmann, B. 1996. Information hiding terminology,” Proc. First Workshop of Information Hiding
Proceedings, Cambridge, U.K., Lecture Notes in Computer Science, Vol.1174: 347-350.
[5] Wang, H & Wang, S, “Cyber warfare: Steganography vs. Steganalysis”, Communications of the
ACM, 47:10, October 2004
[6] Jamil, T., “Steganography: The art of hiding information is plain sight”, IEEE Potentials, 18:01,
1999.
[7] Moerland, T, “Steganography and Steganalysis”, Leiden Institute of Advanced Computing Science,
www.liacs.nl/home/ tmoerl/privtech.pdf
[8] N. F. Johnson and S. Katzenbeisser, A survey of steganographic techniques., in S. Katzenbeisser and
F. Peticolas (Eds.): Information Hiding, pp.43-78. Artech House, Norwood, MA, 2000.
[9] Li, Zhi., Sui, Ai, Fen., and Yang, Yi, Xian. 2003 “A LSB steganography detection algorithm”, IEEE
Proceedings on Personal Indoor and Mobile Radio Communications: 2780-2783.
[10] J. Fridrich and M. Goljan, "Digital image steganography using stochastic modulation", SPIE
Symposium on Electronic Imaging, San Jose, CA, 2003.
[11] Alkhrais Habes , “4 least Significant Bits Information Hiding Implementation and Analysis” ,
ICGST Int. Conf. on Graphics, Vision and Image Processing (GVIP-05), Cairo, Egypt, 2005.
[12] Krenn, R., “Steganography and Steganalysis”, http://www.krenn.nl/univ/cry/steg/article.pdf
[13] C.-C. Chang, T.-S. Chen and L.-Z. Chung, “A steganographic method based upon JPEG and
quantization table modification”, Information Sciences, vol. 141, 2002, pp. 123-138.
[14] R. Chu, X. You, X. Kong and X. Ba, “A DCT-based image steganographic method resisting
statistical attacks”, InProceedings of (ICASSP '04), IEEE International Conferenceon Acoustics, Speech,
and Signal Processing, 17-21 May.vol.5, 2004, pp V-953-6.
[15] H.-W. Tseng and C.-C. Chang, “Steganography using JPEG-compressed images”, The Fourth
InternationalConference on Computer and Information Technology, CIT'04, 14-16 Sept 2004, pp. 12-17.
[16] Chen, B. and G.W. Wornell, 2001. Quantization index modulation: A class of provably good
methods for digital watermarking and information embedding.IEEE Trans. Inform. Theor., 47: 1423-
1443. DOI: 10.1109/18.923725.

[17] Chan, C.K. and Cheng. L.M. 2003. Hiding data in image by simple LSB substitution. Pattern
Recognition, 37: 469 – 474.
[18] Chang, C.C and Tseng, H.W. 2004. A Steganographic method for digital images using side match.
Pattern Recognition Letters, 25: 1431 – 1437.
[19] SWANSON, M.D., KOBAYASHI, M., and TEWFIK, A.H.: 'Multimedia data embedding and
watermarking technologies', Proc. IEEE, 1998, 86(6), pp. 1064-1087
[20] Chen, T.S., Chang C.C., and Hwang, M.S. 1998. A virtual image cryptosystem based upon vector
quantization. IEEE transactions on Image Processing, 7,10: 1485 – 1488.
[21] Chung, K.L., Shen, C.H. and Chang, L.C. 2001. A novel SVD- and VQ-based image hiding scheme.
Pattern Recognition Letters, 22: 1051 – 1058.
[22] Iwata, M., Miyake, K., and Shiozaki, A. 2004. Digital Steganography Utilizing Features of JPEG
Images, IEICE Transfusion Fundamentals, E87-A, 4:929 – 936. International Journal of Computer
Science and Information Technology, Volume 2, Number 3, June 2010 112
[23] Chen, P.Y. and Wu, W.E. 2009. A Modified Side Match Scheme for Image Steganography,
International Journal of Applied Science and Engineering, 7,1: 53 – 60.
[24] Gonzalez, R.C. and Woods, R.E., Digital Image Processing using MATLAB, Pearson Education,
India,2006.
[25] Jayaraman, S., Esakkirajan, S. and Veerakumar, T. Digital Image Processing, Tata McGraw Hill
Education Private Limited, India, 2009.

Hybrid GPS-GSM Localization of Automobile Tracking System
Mohammad A. Al-Khedher
Mechatronics Engineering Department, Al-Balqa Applied University, Amman 11134, Jordan.
ABSTRACT
An integrated GPS-GSM system is proposed to track vehicles using Google Earth application. The remote
module has a GPS mounted on the moving vehicle to identify its current position, and to be transferred by
GSM with other parameters acquired by the automobile‟s data port as an SMS to a recipient station. The
received GPS coordinates are filtered using a Kalman filter to enhance the accuracy of measured position.
After data processing, Google Earth application is used to view the current location and status of each
vehicle. This goal of this system is to manage fleet, police automobiles distribution and car theft cautions.
KEYWORDS
Automobile Tracking, GPS, GSM, Microcontroller, Kalman filter, Google Earth.

REFERENCES
[1] M. A. Al-Taee, O. B. Khader, and N. A. Al-Saber,“ Remote monitoring of Automobile diagnostics
and location using a smart box with Global Positioning System and General Packet Radio Service,” in
Proc. IEEE/ACS AICCSA, May 13–16, 2007, pp. 385–388.
[2] J. E.Marca, C. R. Rindt,M.Mcnally, and S. T. Doherty, “A GPS enhanced in-Automobile extensible
data collection unit,” Inst. Transp. Studies, Univ.California, Irvine, CA, Uci-Its- As-Wp-00-9, 2000.
[3] C. E. Lin, C.-W. Hsu, Y.-S. Lee, and C.C.Li, “Verification of unmanned air Automobile flight control
and surveillance using mobile communication,”J. Aerosp. Comput. Inf. Commun., vol. 1, no. 4, pp. 189–
197, Apr. 2004.
[4] Hapsari, A.T., E.Y. Syamsudin, and I. Pramana, “Design of Automobile Position Tracking System
Using Short Message Services And Its Implementation on FPGA”, Proceedings of the Conference on
Asia South Pacific Design Automation, Shanghai, China, 2005.
[5] Fan, X., W. Xu, H. Chen, and L. Liu, “CCSMOMS:A Composite Communication Scheme for Mobile
Object Management System”, 20th International Conference on Advanced Information Networking and
Applications, Volume 2, Issue 18-20, April 2006, pp. 235–239 .
[6] Hsiao, W.C.M., and S.K.J. Chang, “The Optimal Location Update Strategy of Cellular Network
Based Traffic Information System”, Intelligent Transportation Systems Conference, 2006.
[7] Tamil, E.M., D.B. Saleh, and M.Y.I. Idris, “A Mobile Automobile Tracking System with GPS/GSM
Technology”, Proceedings of the 5th Student Conference on Research and Development (SCORED),
Permala Bangi, Malaysia, May 2007.
[8] Ioan Lita, Ion Bogdan Cioc and Daniel Alexandru Visan, “A New Approach of Automobile
Localization System Using GPS and GSM/GPRS Transmission,” Proc. ISSE ' 06, pp. 115-119, 2006.
[9] T. Krishna Kishore, T.Sasi Vardhan, N.Lakshmi Narayana, „Automobile Tracking Using a Reliable
Embedded Data Acquisition Sysytem With GPS and GSM‟, International Journal of Computer Science
and Network Security, VOL.10 No.2, 286-291, 2010. [10] Wen Leng and Chuntao Shi, “The GPRS-based
location system for the long-distance freight”, ChinaCom '06, pp1-5, Oct.2006.
[11] C. E. Lin, C. C. Li, S. H. Yang, S. H. Lin; C. Y. Lin, “Development of On-Line Diagnostics and
Real Time Early Warning System for Automobiles,” in Proc. IEEE Sensors for Industry Conference,
Houston, 2005, pp. 45-51.
[12] C. E. Lin and C. C. Li, “A Real Time GPRS Surveillance System using the Embedded System,”
AIAA J. Aerosp. Comput., Inf. Commun., vol. 1, no.1, pp. 44-59, Jan. 2004. 85
[13] J. Lin, S. C. Chen, Y. T. Shin, and S. H. Chen, “A Study on Remote On-Line Diagnostic System for
Automobiles by Integrating the Technology of OBD, GPS, and 3G,” in World Academy of Science,
Engineering and Technology, 2009, aug. 2009, pp. 435–441.
[14] National Marine Electronics Association, “NMEA 0183 Standard For Interfacing Marine Electronic
Devices,” Version 3.01, January 1, 2002.
[15] N. Kamarudin and Z. M. Amin, “Multipath error detection using different GPS receiver's antenna,"

in Proc. 3rd FIG Regional Conf. Jakarta, Indonesia, October 3-7, 2004 [16] Melgard, T. E., G.
Lachapelle, and H. Gehue. “GPS Signal Availability in an Urban AreaReceiver Performance Analysis”.
IEEE, 1994.
[17] Nayak R. A., Cannon M. E., Wilson C., Zhang G. (2000): “Analysis of Multiple GPS Antennas for
Multipath Mitigation in Vehicular Navigation”, Institute of Navigation National Technical
Meeting/Anaheim, CA/January 26-28, 2000.
[18] Rempel, RS; Rodgers, AR (1997): “Effects of differential correction on accuracy of a GPS animal
location system”, Journal of Wildlife Management [J. WILDL. MANAGE.]. Vol. 61, no. 2, pp. 525-530.
Apr 1997.
[19] Malleswari B.L, MuraliKrishna I.V and LalKishore K (Jan 2007) “Kalman filter for GPS Datum
conversion”, Mapworld Forum, Hyderabad. [20] D. McNeil Mayhew, Multi-rate sensor fusion for GPS
navigation using Kalman filtering, PhD Thesis, Dpt of electrical engineering, Virginia Polytechnic
Institute and State University, 1999.
[21] Trond Nypan, Kenneth Gade, Oddvar Hallingstad, “Vehicle positioning by database comparison
using the Box-Cox metric and Kalman filtering”, VTC 2002, Birmingham, USA, May 6-9, 2002.
[22] R.G. Brown, P.Y.C. Hwang, “Introduction to Random Signals and Applied Kalman Filtering”, 3 ed:
John Wiley & Sons, 1997.
[23] U. S. C. G. N. Center, "Navstar GPS User Equipment Introduction," United States Coast Guard
Navigation Center, Tech. Rep., DoD Joint Program Office, September 1996.

SEGMENTATION AND OBJECT RECOGNITION USING EDGE
DETECTION TECHNIQUES
Y.Ramadevi, T.Sridevi, B.Poornima, B.Kalyani
Department of CSE , Chaitanya Bharathi Institute of Technology Gandipet, Hyderabad.
ABSTRACT
Image segmentation is to partition an image into meaningful regions with respect to a particular
application. Object recognition is the task of finding a given object in an image or video sequence. In this
paper, interaction between image segmentation (using different edge detection methods) and object
recognition are discussed. Edge detection methods such as Sobel, Prewitt, Roberts, Canny, Laplacian of
Guassian(LoG) are used for segmenting the image. Expectation-Maximization (EM) algorithm, OSTU
and Genetic algorithms were used to demonstrate the synergy between the segmented images and object
recognition.
KEYWORDS
EM algorithm, OSTU, Genetic Algorithm, Image Segmentation, Object Recognition.

REFERENCES
[1] Iasonas Kokkinos, and Petros Maragos (2009),”Synergy between Object Recognition and image
segmentation using Expectation and Maximization Algorithm”., IEEE Trans. on Pattern Analysis and
Machine Intelligence (PAMI), Vol. 31(8), pp. 1486-1501, 2009.
[2] Wen-Xiong Kang, Qing-Qiang Yang, Run-Peng Liang (2009), “The Comparative Research on Image
Segmentation Algorithms,” First International Workshop on Education Technology and Computer
Science.
[3] V. Ferrari, T. Tuytelaars, and L.V. Gool(2004), “Simultaneous Object Recognition and Segmentation
by Image Exploration,” Proc. Eighth European Conf. Computer Vision, 2004.
[4] B. Leibe, A. Leonardis, and B. Schiele(2004), “Combined Object Categorization and Segmentation
with an Implicit Shape Model,” Proc. ECCV Workshop Statistical Learning in Computer Vision, 2004.
[5] Y.Ramadevi, B.Kalyani, T.Sridevi(2010), “ Synergy between Object Recognition and Image
Segmentation”, International Journal on Computer Science and Engineering, Vol. 02, No. 08, 2010, 2767-
2772.
[6] N.Senthilkumarn, R.Rajesh(2009), ”Edge Detection Techniques for Image Segmentation- A Survey of
Soft Computing Approaches”, IJRTE, vol1,No2, 2009 250-254.

AUTHORS
Y Rama Devi received B.E. from Osmania University in 1991 and M.Tech (CSE) degree
from JNT University in 1997. She received his Ph.D. degree Central University,
Hyderabad in 2009. She is Professor, Chaitanya Bharathi Institute of Technology,
Hyderabad. Her research interests include Image Processing, Soft Computing, Data
Mining, and Bio-Informatics. She is a member for IEEE, ISTE, IETE, and IE. She has
published more than 25 research publications in various National, Inter-national
conferences, proceedings and Journals.
T.Sridevi received B E from Osmania University in 1992 and M.Tech (CSE) degree from
JNT University in 2002. She is pursuing her Ph D from Osmania University in Computer
Science under the guidance of Dr V. Vijaya Kumar. She has 14 years of teaching/industry
experience. She joined as Assistant Professor in Chaitanya Bharathi Institute of
Technology, Hyderabad, India in 2002. Presently she is an Associate Professor, Chaitanya
Bharathi Institute of Technology, Hyderabad. Her research areas include Water Marking,
Image Processing and Soft Computing. She is a life member of IETE. She has published more than 5
research publications in various National, Inter-national conferences, proceedings and Journals.
B.Poornima received her B.Tech from J.N.T.U in 2005. She is pursuing her M.Tech from
CBIT under Osmania University. Her area of interest is image processing.
B.Kalyani received B.Sc computers from Sri Krishnadevaraya University, Anantapur in
2003, M.Sc Mathematics from Sri Krishnadevaraya University, Anantapur in 2006 and
M.Tech(C.S.E) from Osmania University. Her area of interest is image processing.

MACHINE LEARNING METHODS FOR SPAM E-MAIL
CLASSIFICATION
W.A. Awad1
and S.M. ELseuofi2
1
Math.&Comp.Sci.Dept., Science faculty, Port Said University
2
Inf. System Dept.,Ras El Bar High inst.
ABSTRACT
The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable
anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a
very successful rate. In this paper we review some of the most popular machine learning methods
(Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their
applicability to the problem of spam Email classification. Descriptions of the algorithms are presented,
and the comparison of their performance on the SpamAssassin spam corpus is presented.
KEYWORDS
Spam, E-mail classification, Machine learning algorithms

REFERENCES
[1] M. N. Marsono, M. W. El-Kharashi, and F. Gebali, “Binary LNS-based naïve Bayes inference engine
for spam control: Noise analysis and FPGA synthesis”, IET Computers & Digital Techniques, 2008
[2] Muhammad N. Marsono, M. Watheq El-Kharashi, Fayez Gebali “Targeting spam control on
middleboxes: Spam detection based on layer-3 e-mail content classification” Elsevier Computer
Networks, 2009
[3] Yuchun Tang, Sven Krasser, Yuanchen He, Weilai Yang, Dmitri Alperovitch ”Support Vector
Machines and Random Forests Modeling for Spam Senders Behavior Analysis” IEEE GLOBECOM,
2008 184
[4] Guzella, T. S. and Caminhas, W. M. ”A review of machine learning approaches to Spam filtering.”
Expert Syst. Appl., 2009 [5] Wu, C. ”Behavior-based spam detection using a hybrid method of rule-based
techniques and neural networks” Expert Syst., 2009
[6] Khorsi. “An overview of content-based spam filtering techniques”, Informatica, 2007
[7] Hao Zhang, Alexander C. Berg, Michael Maire, and Jitendra Malic. "SVM-KNN: Discriminative
nearest neighbour classification for visual category recognition", IEEE Computer Society Conference on
Computer Vision and Pattern Recognition, 2006
[8] Carpinteiro, O. A. S., Lima, I., Assis, J. M. C., de Souza, A. C. Z., Moreira, E. M., & Pinheiro, C. A.
M. "A neural model in anti-spam systems.", Lecture notes in computer science.Berlin, Springer, 2006
[9] El-Sayed M. El-Alfy, Radwan E. Abdel-Aal "Using GMDH-based networks for improved spam
detection and email feature analysis"Applied Soft Computing, Volume 11, Issue 1, January 2011
[10] Li, K. and Zhong, Z., “Fast statistical spam filter by approximate classifications”, In Proceedings of
the Joint international Conference on Measurement and Modeling of Computer Systems. Saint Malo,
France, 200
6 [11] Cormack, Gordon. Smucker, Mark. Clarke, Charles " Efficient and effective spam filtering and re-
ranking for large web datasets" Information Retrieval, Springer Netherlands. January 2011
[12] Almeida,tiago. Almeida, Jurandy.Yamakami, Akebo " Spam filtering: how the dimensionality
reduction affects the accuracy of Naive Bayes classifiers" Journal of Internet Services and Applications,
Springer London , February 2011
[13] Yoo, S., Yang, Y., Lin, F., and Moon, I. “Mining social networks for personalized email
prioritization”. In Proceedings of the 15th ACM SIGKDD international Conference on Knowledge
Discovery and Data Mining (Paris, France), June 28 - July 01, 2009

ENHANCEMENT OF IMAGES USING MORPHOLOGICAL
TRANSFORMATIONS
K.Sreedhar1
and B.Panlal2
1
Department of Electronics and communication Engineering, VITS (N9) Karimnagar, Andhra Pradesh,
India
2
Department of Electronics and communication Engineering, VCE (S4) Karimnagar, Andhra Pradesh,
India
ABSTRACT
This paper deals with enhancement of images with poor contrast and detection of background. Proposes a
frame work which is used to detect the background in images characterized by poor contrast. Image
enhancement has been carried out by the two methods based on the Weber‟s law notion. The first method
employs information from image background analysis by blocks, while the second transformation method
utilizes the opening operation, closing operation, which is employed to define the multi-background gray
scale images. The complete image processing is done using MATLAB simulation model. Finally, this
paper is organized as follows as Morphological transformation and Weber‟s law. Image background
approximation to the background by means of block analysis in conjunction with transformations that
enhance images with poor lighting. The multibackground notion is introduced by means of the opening
by reconstruction shows a comparison among several techniques to improve contrast in images. Finally,
conclusions are presented.
KEYWORDS
Image Background Analysis by blocks, Morphological Methods, Weber‟s law notion, Opening Operation,
Closing Operation, Erosion-Dilation method, Block Analysis for Gray level images.

REFERENCES
[1]. I. R. Terol-Villalobos, “A multiscale contrast approach on Morphological connected contrast
mappings” Opt. Eng., vol. 43, no. 7, pp. 1577–1595, 2009
. [2]. J. Kasperek, “Real time morphological image contrast enhancement in FPGA,” in LNCS, New
York: Springer, 2008.
[3]. I.R. Terol-Villalobos, “Morphological image enhancement and segmentation with analysis,” P. W.
Hawkes, Ed. New York: Academic, 2005, pp. 207–273. [4]. F. Meyer and J. Serra, “Contrast and Activity
Lattice,” Signal Processing, vol. 16, pp. 303–317, 1989.
[5]. J. D. Mendiola-Santibañez and I. R. Terol-Villalobos, “Morphological contrast mappings based on
the flat zone notion,” vol. 6, pp. 25–37, 2005.
[6]. A. Toet, “Multiscale contrast enhancement with applications to image fusion,” Opt. Eng., vol. 31, no.
5, 1992.
[7]. S. Mukhopadhyay and B. Chanda, “A multiscale morphological approach to local contrast
enhancement,” Signal Process. vol. 80, no. 4, pp. 685–696, 2000.
[8]. A. K. Jain, Fundamentals of Digital Images Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
[9]. J. Short, J. Kittler, and K. Messer, “A comparison of photometric normalization algorithms for face
verification,” presented at the IEEE Int. Conf. Automatic Face and Gesture Recognition, 2004.
[10]. C. R. González and E.Woods, Digital Image Processing. Englewood Cliffs, NJ: Prentice Hall, 1992.
[11]. R. H. Sherrier and G. A. Johnson, “Regionally adaptive histogram equalization of the chest,” IEEE
Trans. Med. Imag., vol. MI-6, pp. 1–7, 1987.
[12]. A. Majumder and S. Irani, “Perception-based contrast enhancement of images,” ACM Trans. Appl.
Percpt., vol. 4, no. 3, 2007, Article 17. International Journal of Computer Science & Information
Technology (IJCSIT) Vol 4, No 1, Feb 2012 50
[13]. Z. Liu, C. Zhang, and Z. Zhang, “Learning-based perceptual image quality improvement for video
conferencing,” presented at the IEEEInt. Conf. Multimedia and Expo (ICME), Beijing, China, Jul. 2007.
[14]. E. H. Weber, “De pulsu, resorptione, audita et tactu,” in Annotationesanatomicae et physiologicae.
Leipzig, Germany: Koehler, 1834.
[15]. J. Serra and P. Salembier, “Connected operators and pyramids,” presented at the SPIE. Image
Algebra and Mathematical Morphology, SanDiego, CA, Jul. 1993.
[16]. P. Salembier and J. Serra, “Flat zones filtering, connected operators and filters by reconstruction,”
IEEE Trans. Image Process., vol. 3, no.8, pp. 1153–1160, Aug. 1995.
[17]. J. Serra, Mathematical Morphology Vol. I. London, U.K.: Academic, 1982.
[18]. P. Soille, Morphological Image Analysis: Principle and Applications. New York: Springer-Verlag,
2003. [19]. H. Heijmans, Morphological Image Operators. New York: Academic, 1994.

[20]. L. Vincent and E. R. Dougherty, “Morphological segmentation for textures and particles,” in
Digital Image Processing Methods, E. R. Dougherty, Ed. New York: Marcel Dekker, 1994, pp. 43– 102.
[21]. E. Peli, “Contrast in complex images,” J. Opt. Soc. Amer., vol. 7, no. 10, pp. 2032–2040, 1990.
[22]. Morphological Image Processing by Steven W. Smith, www.dspguide.com/ch25/4.htm
[23]. Erik R. Urbach and Michael H. F. Wilkinson “Efficient 2-D Grayscale Morphological
Transformations With Arbitrary Flat Structuring Elements‟‟ IEEE TRANSACTIONS ON IMAGE
PROCESSING, VOL. 17, NO. 1, JANUARY 2008, www.cs.rug.nl/~michael/tip20082dse.pdf.
AUTHORS
K.Sreedhar received the B.Tech. degree in Electronics and Communication Engineering
from JNTUH University, Hyderabad, India and M.Tech degree in Communication Systems
from JNTUH University, Hyderabad, India . He attended the International Conference on
Technology and Innovation at Chennai. He also attended the National Conference at
Coimbatore, Tamilnadu, India on INNOVATIVE IN WIRELESS TECHNOLOGY. He is
working as a Assistant Professor in Electronics and Communication Engineering department
at Vivekananda Institute of Science and Technology, Karimnagar, Andhra Pradesh, India He
has a Life Member ship in ISTE. He published four International Research papers.
B.Panlal received the B.Tech. degree in Electronics and Communication Engineering from
JNTUH University, Hyderabad, India and M.Tech degree from KU University, Warangal,
India . He has a Life Member ship in ISTE. Presently, He is working at Vaageswari College
of engineering, AndhraPradesh, India.

UBIQUITOUS MOBILE HEALTH MONITORING SYSTEM FOR
ELDERLY (UMHMSE)
Abderrahim BOUROUIS1
,Mohamed FEHAM2
and Abdelhamid BOUCHACHIA3
1
STIC laboratory, Abou-bekr BELKAID University, Tlemcen, Algeria
2
STIC laboratory, Abou-bekr BELKAID University, Tlemcen, Algeria
3
Research Group, Software Engineering and Soft Computing, University of Klagenfurt, Austria
ABSTRACT
Recent research in ubiquitous computing uses technologies of Body Area Networks (BANs) to monitor
the person's kinematics and physiological parameters. In this paper we propose a real time mobile health
system for monitoring elderly patients from indoor or outdoor environments. The system uses a biosignal
sensor worn by the patient and a Smartphone as a central node. The sensor data is collected and
transmitted to the intelligent server through GPRS/UMTS to be analyzed. The prototype (UMHMSE)
monitors the elderly mobility, location and vital signs such as Sp02 and Heart Rate. Remote users (family
and medical personnel) might have a real time access to the collected information through a web
application.
KEYWORDS
Ubiquitous health monitoring, Mobile Health Monitoring, Smartphone. Intelligent central sever, Location.

REFERENCES
[1] CN Scanaill, B Ahearne and GM Lyons, “Long-Term Telemonitoring of Mobility Trends of Elderly
People Using SMS Messaging”, IEEE Communications, 2006.
[2] http://www.ons.dz/index-en.php
[3] World Health Organization 2010, WORLD HEALTH STATISTICS 2010 International Journal of
Computer Science & Information Technology (IJCSIT), Vol 3, No 3, June 2011 81
[4] Phillip Olla and Joseph Tan, “Mobile Health Solutions for Biomedical Applications”, Medical
inforMation science reference, 2009, pp. 129-140. [5] Shimizu, K ,”Telemedicine by Mobile
Communication”, IEEE Engineering in Medicine and Biology, 1999, pp. 32-44.
[6] C. N. Scanaill , S. Carew ,P. Barralon, N. Noury , D. Lyons and G. M. Lyons, “A review of
approaches to mobility telemonitoring of the elderly in their living environment”, Annals of Biomedical
Engineering, 2006,vol. 34, pp. 545-565.
[7] E. Jovanov , A. Milenkovic, C. Otto and P. C. De Groen, “A wireless body area network of intelligent
motionsensors for computer assisted physical rehabilitation” , Journal of NeuroEngineering and
Rehabilitation, 2005, vol. 2.
[8] A Van Halteren , R Bults ,K Wac , D Konstantas , I Widya , N Dokovsky , G Koprinkov , V Jones and
R Herzog “ Mobile Patient Monitoring: The MobiHealth System” ,The Journal on Information
Technology in Healthcare 2004; 2(5); pp. 365–373.
[9] D Konstantas , A Van Halteren1,R Bults , K Wac , V Jones , I Widya and R Herzog, “
MOBIHEALTH : AMBULANT PATIENT MONITORING OVER PUBLIC WIRELESS NETWORKS
”, Mediterranean Conference on Medical and Biological Engineering MEDICON 2004.
[10] J. M. Choi, B. H. Choi, J. W. Seo ,R. H. Sohn, M. S. Ryu and W. Yi,A, “System for Ubiquitous
Health Monitoring in the Bedroom via a Bluetooth Network and Wireless LAN". Proc. The 26th Annual
International Conference of the IEEE EMBS, San Fransisco, CA, USA: Engineering in Medicine and
Biology Society, vol. 2, 2004, pp. 3362-3365.
[11] E. Farella, A. Pieracci , D. Brunelli , L. Benini , B. Ricco and A. Acquaviva, "Design and
implementation of WiMoCA node for a body area wireless sensor network," in Proceedings of the 2005
Systems Communications, 2005, pp. 342-347.
[12] M. J. Morón ,J. R. Luque , A. A. Botella , E. J. Cuberos ,E. Casilari and A. Diaz-Estrella, “A Smart
Phone-based Personal Area Network for Remote Monitoring of Biosignals”, 4th International Workshop
on Wearable and Implantable Body Sensor Networks (BSN 2007) IFMBE Proceedings, 2007, Volume
13, 3rd Session, pp. 116-121.
[13] S. Dai and Y. Zhang ,”Wireless Physiological Multi-parameter Monitoring System Based on Mobile
Communication Networks”, In 19th IEEE Symposium on Computer-Based Medical Systems Based on
Mobile Communication Networks, Washington, DC, USA: IEEE Computer Soceity, , 2006, pp. 473-478.
[14] J. W. Lee and J. Y. Jung , “ ZigBee Device Design and Implementation for Context-Aware

UHealthcare System”,The IEEE 2nd International Conference on Systems and Networks
Communications, Cap Esterel, French Riviera, 2007, IEEE Computer Society, pp. 22.
[15] Guang-Zhong Yang , “Body Sensor Networks” (Ed) Springer; 1st Edition. 2006, pp.147-149.
[16] M. J. Morón , J. R. Luque , A. A. Botella , E. J. Cuberos , E. Casilari , A. Diaz-Estrella and J. A.
Gázquez , “Development of wireless Body Area Network based on J2ME for M-Health applications”, 2nd
European Computing Conference , 2008.
[17] N. Deblauwe and L. V. Biesen, "An event-driven lbs for public transport: design and feasibility
study of gsm-based positioning," in Proceedings of the 45th FICE congress Athens, 2005, pp. 29-35.
[18] Nonin Medical ,http://www.nonin.com/
[19] http://www.forum.nokia.com/Devices/Device_specifications.
[20] M. J. Morón, J. R. Luque, A. Gómez-Jaime, E. Casilari, and A. Díaz-Estrella, “Prototyping of a
remote monitoring system for a medical Personal Area Network using Python,” in 3rd International
Conference on Pervasive Computing Technologies for Healthcare, 2009. PervasiveHealth pp. 1 –5.
[21] http://wiki.forum.nokia.com/index.php/Category:Python International Journal of Computer Science
& Information Technology (IJCSIT), Vol 3, No 3, June 2011 82
[22] M Saipunidzam, I Mohammad Noor and M.T Shakirah , “M-LEARNING: A NEW PARADIGM OF
LEARNING MATHEMATICS IN MALAYSIA ”, International journal of computer science &
information Technology (IJCSIT) Vol.2, No.4, 2010,pp. 76-86.

AUTHORS
Abderrahim Bourouis received the B.E. and M.E..degrees in telecommunication from
Abou-bekr BELKAID university , Algeria, in 2007 and 2009 respectively. He joined STIC
laboratory in 2010. He has been engaged in the design and development of Locationbased
service (LBS) and Body Sensor Networks (BSN).
Mohammed Feham received the Dr. Eng. degree in Optical and Microwave
Communications from the University of Limoges (France) in 1987, and his PhD in
Science from the University of Tlemcen (Algeria) in 1996. Since 1987, he has been
Assistant Professor and Professor of Microwave, Communication Engineering and
Telecommunication Networks. He has served on the Scientific Council and other
committees of the Electronics and Telecommunication Departments of the University of Tlemcen. His
research interest now is mobile networks and services.
Abdelhamid Bouchachia is currently an Associate Professor at the University of
Klagenfurt, Department of Informatics, Austria. He obtained his Doctorate in Computer
Science from the same University in 2001. He then spent one year as a post-doc at the
University of Alberta, Canada. His major research interests include soft computing and
machine learning encompassing nature-inspired computing, neurocomputing, fuzzy
systems, incremental learning, semi-supervised learning and uncertainty modeling.. He is a
member of the IEEE task force for adaptive and evolving fuzzy systems and member of
the Evolving Intelligent Systems (EIS) Technical Commmittee of the Systems, Man and Cybernetics
(SMC) Society of IEEE.

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