iosr-JCE   Volume-2 ~ Issue-5

Abstract: Image based steganography is a dangerous technique of hiding secret messages in the image in such a way that no one apart from the sender and intended recipient suspects the existence of the message. It is based on invisible communication and this technique strives to hide the very presence of the message itself from the observer. As a result it becomes the most preferred tool to be used by Intelligence Agencies, Terrorist Networks and criminal organizations for securely broadcasting, dead-dropping and communicating information over the internet by hiding secret information in the images. In this paper a mathematical model is designed for representing any such image based steganographic system. This mathematical model of any stego system can be used for determining vulnerabilities in the stego system as well as for steganalysing the stego images using same vulnerabilities. Based on these mathematical foundations three steganographic systems are evaluated for their strengths and vulnerabilities using MATLAB ©Image Processing Tool Box.
Key Words: Cyber Crime, Global Terrorism, Image Steganography, LSB Insertion, Mathematical Model of Image Steganographic System

[1] Kaustubh Choudhary "Image Steganography and Global Terrorism" IOSR Journal of Computer Engineering, Volum 1 Issue 2 , pp 34-48.
[2] C.Cachin, "An information-theoretic model for steganography" Proc. 2nd International Workshop Information Hiding" LNCS 1525, pp. 306–318, 1998.
[3]. J. Zollner, H. Federrath, H. Klimant, A. Pfitzman, R. Piotraschke, A. Westfeld, G. Wicke, and G. Wolf,"Modeling the security of steganographic systems," Prof. 2nd Information Hiding Workshop , pp. 345–355,April 1998.
[4] C. E. Shannon, "Communication theory of secrecy systems," Bell System Technical Journal, vol. 28, pp. 656–715, Oct. 1949.
[5] Steganography Capacity: A Steganalysis Perspective R. Chandramouli and N.D. Memon
[6]. A Mathematical Approach to Steganalysis R. Chandramouli Multimedia Systems, Networking and Communications (MSyNC) Lab Department of Electrical and Computer Engineering Stevens Institute of Technology

Abstract: Steganography is a technique of hiding secret messages in the image in such a way that no one apart from the sender and intended recipient suspects the existence of the message. Image Steganography is frequently used by Terrorist Networks for securely broadcasting, dead-dropping and communicating the secret information over the internet by hiding the secret information in the Images. As a result it becomes the most preferred tool to be used by Terrorists and criminal organizations for achieving secure CIA (Confidentiality, Integrity and Availability) compliant communication network capable of penetrating deep inside the civilian population. Steganalysis of Image (Identification of Images containing Hidden Information) is a challenging task due to lack of Efficient Algorithms, High rates of False Alarms and above all the High Computation Costs of Analyzing the Images. In this paper a Novel Technique of Image Steganalysis is devised which is not only Fast and Efficient but also Foolproof. The results shown in the paper are obtained using programs written in MATLAB© Image Processing Tool Box.
Key Words: Bit Plane Slicing, Cyber Crime, Global Terrorism, Image Steganalysis, LSB Insertion, Pixel Aberration, SDT based Image Steganography.

[1] Infosecurity Magazine article dated 02 May 2012 reports that Al-Qaeda uses Steganography to hide documents. http://www.infosecurity-magazine.com/view/25524/alqaeda-uses-steganography-documents-hidden-in-porn-videos-found-on-memory-stick
[2] Daily Mail Online, UK article dated 01 May 2012 reported that a Treasure trove of Intelligence was embedded in porn video. http://www.dailymail.co.uk/news/article-2137848/Porn-video-reveals-Al-Qaeda-planns-hijack-cruise-ships-execute-passengers.html#ixzz1uIgxpire
[3]. The New York Times article dated Oct 30, 2001 with title "Veiled Messages of Terror May Lurk in Cyberspace" claims 9/11 attacks planned using Steganography.
[4] Wired article dated 02nd July, 2001 nicknamed Bin Laden as "the Steganography Master" http://www.wired.com/politics/law/news/2001/02/41658?currentPage=all
[5] Kaustubh Choudhary "Image Steganography and Global Terrorism" IOSR Journal of Computer Engineering, Volume 1 Issue 2 , pp 34-48. http://www.iosrjournals.org/journals/iosr-jce/papers/vol1-issue2/14/N0123448.pdf
[6] Jonathan Watkins, Steganography - Messages Hidden in Bits (15th December,2001) http://mms.ecs.soton.ac.uk/mms2002/papers/6.pdf
[7] Steganalysis of LSB Based Image Steganography using Spatial and Frequence Domain Features by Hossein Malekmohamadi and Shahrokh Ghaemmaghami
[8] Kaustubh Choudhary "Mathematical Modeling of Image Steganographic System" IOSR Journal of Computer Engineering, Volume 2 Issue 5, pp 01-15.

Abstract :. Researchers have proposed a number of co-ordination functions in literature for improving quality of service. Each one is based on different characteristics and properties. In this paper, we evaluate the performance of wireless network using PCF, DCF & EDCF. We perform simulations using OPNET IT GURU Academic Edition simulator. In the performance evaluation of the co-ordination function, the protocols are tested under the realistic conditions. Tests are performed for various parameters (Delay, Data Dropped, Throughput) in wireless Networks. This OPNET simulation shows the impact of co-ordination function for wireless networks for different parameters.
Keywords - Data Dropped, Delay, DCF, EDCF, PCF, Throughput, Traffic Sent, Traffic Received.

[1] Ashwini Dalvi, Pamukumar Swamy, B B Meshram, DCF Improvement for Satisfactory Throughput of 802.11 WLAN, International Journal on Computer Science and Engineering , 2011 Vol: 3 Issue: 7 ,2862-2868
[2] T Madhavi , G Sasi Bhushana Rao , M Rajan Babu , K Sridevi, Analysis of Throughput and Energy Efficiency in the IEEE 802.11 Wireless Local Area Networks using Constant backoff Window Algorithm, International Journal of Computer Applications, 2011,Vol: 26 Issue: 8 ,40-47
[3] Vikram Jain , Sakshi Suhane, Performance Degradation of IEEE 802.11 MANET due to Heavy Increase and Heavy Decrease in Contention Window, International Journal of Computer Applications, 2011,Vol: 20 Issue: 6 ,26-32
[4] Ajay Dureja , Aman Dureja , Meha Khera, IEEE 802.11 Based MAC Improvements for MANET, International Journal of Computer Applications,2011, Issue: 2 ,54-57
[5] Rama Krishna CHALLA, Saswat CHAKRABARTI, Debasish DATTA, An Improved Analytical Model for IEEE 802.11 Distributed Coordination Function under Finite Load, International Journal of Communications, Network and System Sciences, 2009, Vol: 02 Issue: 03 ,237-247
[6] Yutae Lee, Min Young Chung, Tae-Jin Lee, Performance Analysis of IEEE 802.11 DCF under Nonsaturation Condition Mathematical Problems in Engineering, 2008
[7] Sunghyun Choi, Javier del Prado2, Sai Shankar N, Stefan Mangold, IEEE 802.11e Contention-Based Channel Access (EDCF) Performance Evaluation, vol.2, 2003, 1151 – 1156

ABSTRACT: IN THIS PAPER WE INTRODUCE A FRAMEWORK FOR WEB PERSONALIZATION THAT COMBINES THREE WEB DATA MINING TECHNIQUES TO PROVIDE RECOMMENDATIONS TO USER MORE ACCURATELY AND IT USES THREE ALGORITHMS ON THE RESULTS OF PREPROCESSING OF WEB DATA'S K-MEANS ALGORITHM USES VECTOR SPACE MODEL TO REPRESENT DOCUMENT AND THE SYSTEM GENERATE THE RECOMMENDATION BASED ON PATTERN ANALYSIS RANKING.
KEYWORDS: Web Mining, Personalization, Usage Mining, Structure Mining, Association Rule Mining, Content Clustering and Hybrid Recommendation.

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[3] Berendt B., Bamshad M, Spiliopoulou M., and Wiltshire J. (2001). Measuring the accuracy of sessionizers for web usage analysis, In Workshop on Web Mining, at the First SIAM International Conference on Data Mining, 7-14.
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[7] Brin, S., and Page, L. (1998). The Anatomy of a Large- Scale Hypertextual Web Search Engine, Proceedings of the 7th International World Wide Web Conference, Elsevier Science, New York, 107-117.
[8] Desikan, P., Srivastava, J., Kumar, V., and Tan, P.N. (2002). Hyperlink Analysis: Techniques and Applications, Technical Report (TR 2002-0152), Army High Performance Computing Center.
[9] Li Haigang Yin wanling "Study of Application of Web Mining Techniques in E-Business" IEEE Conference , 2006
[10] B. Mobasher, H. Dai, T. Luo, M. Nakagawa. Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization. In Data Mining and Knowledge Discovery, Kluwer Publishing, Vol. 6, No. I, pp. 61-82, January 2002.

Abstract : In this paper we put forward a modified approach towards skeletonization of English alphabets and characters. This algorithm has been designed to find the skeleton of all the typeface of Modern English as present in the Microsoft database. The algorithm has been kept simple and optimized for efficient skeletonization. Finally, the performance of the algorithm after testing has been aptly demonstrated.
Keywords –Digital Library, Microsoft Visual C++, Skeletonization, Structuring Element, Thresholding, Typeface

[1] Aarti Desai, Latesh Malik and Rashmi Welekar, "A New Methodology for Devnagari Character Recognisation", JM International Journal on Information Technology, Volume-1 Issue-1, ISSN: Print 2229-6115, January 2011, pp. 56-60
[2] G. Sanniti di Baja, "Well-shaped, stable and reversible skeletons from the (3, 4)-distance transform", J. Visual Comm. Image Representation, 1994, pp. 107-115
[3] Gisela Klette, "Skeletons in Digital Image Processing", Centre for Image Technology and Robotics, Tamaki, CITR-TR-112, July 2002
[4] Khalid Saeed, Marek Tabedzki, Mariusz Rybnik and Marcin Adamski, "K3M: A Universal Algorithm for Image Skeletonization and a Review of Thinning Techniques", International Journal of Applied Mathematics and Computer Science, Volume-20, No. 2, 2010, pp. 317-335
[5] Rafael C. Gonzalez and Richard E. Woods, " Digital Image Processing", Second Edition, Prentice-Hall, 2002, pp. 541-545
[6] D. Ballard and C. Brown, "Computer Vision", Prentice-Hall, 1982, Chapter-8
[7] E. Davies, "Machine Vision: Theory, Algorithms and Practicalities", Academic Press, 1990, pp. 149 – 161
[8] R. Haralick and L. Shapiro, "Computer and Robot Vision", Vol. 1, Addison-Wesley Publishing Company, 1992, Chapter-5

Abstract : Diabetic retinopathy is a kind of disorder which occurs due to high blood sugar level. This disorder affects retina in many ways. Blood vessels in the retina gets altered . Exudates are secreted, hemorrhages occur, swellings appear in the retina. Diabetic Retinopathy (DR) is the major cause of blindness. Automatic Recognition of DR lesions like Exudates, in digital fundus images can contribute to the diagnosis and screening of this disease. In this approach, an automatic and efficient method to detect the exudates is proposed. The real time retinal images are obtained from a nearby hospital. The retinal images are pre-processed via. Contrast Limited Adaptive Histogram Equalization (CLAHE). The preprocessed colour retinal images are segmented using K-Means Clustering technique. The segmented images establish a dataset of regions. To classify these segmented regions into Exudates and Non-Exudates, a set of features based on colour and texture are extracted. Classification is done using support Vector Machine This method appears promising as it can detect the very small areas of exudates.
Keywords – Diabetic Retinopathy, Exudates, fundus image, k means clustering, SVM

Journal Papers:
[1] Gwenole Quellec, Stephen R. Russell, and Michael D. Abramoff, Senior Member, IEEE―Optimal Filter Framework for Automated,Instantaneous Detection of Lesions in Retinal Images‖ IEEE Transactions on medical imaging, vol. 30, no. 2,pp. 523-533,February 2011.
[2] Alireza Osareh, Bita Shadgar, and Richard Markham ―A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images‖ IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 4,pp.535-545,July 2009.
[3] Carla Agurto, Eduardo Barriga, Sergio Murillo, Marios Pattichis, Herbert Davis, Stephen Russell, Michael Abrrmoff, and Peter Soliz ―Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection‖. IEEE Trans. Medical. Imaging Vol. 29, No. 2,pp.502-512, February 2010.
[4] T. Walter, J. Klein, P. Massin, and A. Erginary,. ―A contribution of image processing to the diagnosis of diabetic retinopathy, detection of exudates in colour fundus images of the human retina‖. IEEE Trans. Medical. Imaging,Vol. 21, No. 10, pp.1236–1243, October. 2002.
[5] Akara Sopharak , Bunyarit Uyyanonvara and Sarah Barman "Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering‖.Journal of sensors/2009. ISSN 1424-8220.www.mdpi.com/journal/sensors.
[6] Niemeijer, B.V Ginnekan S.R, Russell. M and M.D. Abramoff ―Automated detection and differentiation of drusen, exudates and Cotton wool spots in digital color fundus photographs for diabetic retinopathy diagnosis ‖, Invest. Ophthalmol Vis. Sci., Vol.48,pp. 2260-2267, 2007.
[7] Niemeijer.M, Abramoff.M.D, Van Ginneken.B, ―Information fusion for Diabetic Retinopathy CAD in Digital color fundus photographs‖ IEEE Transactions on medical imaging, vo7l. 26, no. 10,pp. 1357-1365, October, 2007.
[8] Ricci.E, Perfetti.R, ―Retinal Blood vessel segmentation using Line operators and Support Vector Classification‖ IEEE Transactions on medical imaging, vol. 28, no. 5,pp. 775-785, March 2009.
[9] Goatman.K.A, Fleming. A.D, Philip. S, William. G.T, ―Detection of New vessels on the Optic Disc using Retinal photographs‖ IEEE Transactions on medical imaging, vol. 30, no. 4,pp. 972-979, April 2011.
[10] Deepak.K.S, Sivaswamy. J, ―Automatic assessment of macular edema from color retinal images‖ IEEE Transactions on medical imaging, vol. 31, no. 3,pp. 766-776, March 2012.
[11] Huiqili, Chutatape. O, ―Automated feature extraction in color retinal images by a model based approach‖ IEEE Transactions on Bio-Medical Engineering, vol. 51, no. 2,pp. 246-254, February 2004..
[12] Aquine. A, Gegundez, Aries. M.E, Marin.D, ―Detecting the Optic Disc boundary in digital fundus images using morphological, edge detection and feature extraction technique‖ IEEE Transactions on medical imaging, vol. 29, no. 11,pp. 1860-1869, November 2011.
[13] Tobin.K.N, Chaum.E, Govindasamy.V.P, ―Detection of anatomic structures in human retinal imagery‖ IEEE Transactions on medical imaging, vol. 26, no. 12,pp. 1729-1739, December 2007.
[14] Akara Sopharak, Mathew N. Dailey, Bunyarit Uyyanonvara, Sarah Barman, Tom Williamson,Yin Aye Moe, ―Machine Learning approach to automatic Exudates detection in retinal images from diabetic patients‖, Journal of Modern optics,2009.
[15] Fleming. AD, Philips. S, Goatman. KA, Williams. GJ, Olson. JA, sharp. PF, ―Automated detection of exudates for Diabetic Retinopathy Screening‖, Journal on Phys Med.and Bio., vol. 52, no. 24, pp. 7385-7396, 2007.
Proceedings Papers:
[16] Doaa Youssef, Nahed Solouma, Amr El-dib, Mai Mabrouk, ―New Feature-Based Detection of Blood Vessels and Exudates in Color Fundus Images―IEEE conference on Image Processing Theory, Tools and Applications,2010,vol.16,pp.294-299.
[17] Sanchez. C.I, Mayo.A, Garcia. M, Lopez.M.I, Hornero. R, ―Automatic Image processing Algorithm to detect hard exudates based on Mixture models‖ IEEE conference on Engineering in medicine and Biology society, pp. 4453-4456, September 2006.
[18] Pradeep Kumar. A. V, Prashanth. C, Kavitha.G, ―Segmentation and grading of Diabetic retinopathic exudates using error boost feature selection method ‖World Congress on Information and Communication Technologies, pp. 518-523, December 2011.
[19] C. Sinthanayothin, ―Image analysis for automatic diagnosis of Diabetic Retinopathy‖, World Congress on Information and Communication Technologies, pp. 522-532, December 2000.

Abstract: Speech quality evaluation is an extremely important problem in modern communication networks. Service providers always strive to achieve a certain Quality of Service (QoS) in order to ensure customer satisfaction. Modeling the speech quality becomes an urgent issue. In this project a computable model for different time scale speech quality evaluation, called E-Model is developed. The results indicate that subjects can monitor speech quality variations very accurately with a delay of approximately 1 second. Non-intrusive speech quality is measured at the receiver from a degraded signal using G.107 (E-model) which is a parameter based model and calculate MOS values with quality rating factor. The quality rating factor is calculated by network impairments (loudness rating) of a speech. The output from the model described here is a scalar quality rating value, R, which varies directly with the overall conversational quality. The key contribution of this paper is to explore the use of G-107 (E-model) based features for different time scale non-intrusive speech quality evaluation using time varying loudness of a speech for long stimuli. Sectional speech quality is obtained by E-model, which is called instantaneous quality of the section that will be constant for each section. Overall perceived quality can be calculated by using average of instantaneous speech quality.
Keywords: Critical bands, E-model, loudness, MOS, non-intrusive speech quality,

[1] ITU-T Rec. G.107, The E-Model, a Computational Model for Use in Transmission Planning, 2003.
[2] ITU-T Rec. P.800, Methods for Subjective Determination of Transmission Quality, Int. Telecomm. Union, Aug. 1996.
[3] W. C. Hardy, "QoS Measurement and Evaluation of telecommunications Quality of Service.," John Wiley & Sons, ISBN 0-471-
49957-9, 2001.
[4] ITU-T Rec. P.862, Perceptual Evaluation of Speech Quality (PESQ): An Objective Method for End-to-End Speech Quality
Assessment of Narrow-Band Telephone Networks and Speech Codecs, Geneva, Switzerland, Feb.2001.
[5] ITU-T Rec. P.880, Methods for objective and subjective assessment of quality, August 2004.
[6] Contribution ITU-T[COM 12-94], Continuous assessment of time-varying subjective vocal quality and its relationship with overall
subjective quality, 1999.