iosr-JCE   Volume-16 ~ Issue-5 ~ Sep-Oct 2014

Abstract: An occlusion is the region between two overlapping objects with disparate motion. Detecting these occluded objects is crucial for many of the video processing. The Occlusion detection is decomposed into two independent sub problems. The First is to detect foreground objects on a frame-wise basis, by labeling each pixel in an image frame as either foreground or background. The second is to couple object observations at different points in a sequence to yield the object's motion trajectory and Occlusion. The motion segmentation is based on an adaptive background subtraction method that models each pixel as mixture of Gaussians. The Gaussian distributions are then evaluated to determine which are most likely to result from a background process. This is useful to track moving objects and detect occlusion in lighting changes, repetitive motions from cluster, and long term scene changes.

Keywords: Occlusion detection, Adaptive background estimation, Gaussians model

[1]. Andrew Stein , Derek Hoiem ," Learning to Find Object Boundaries Using Motion Cues", 2005.
[2]. J. Ullmann, "Analysis of 2-D occlusion by subtracting out," IEEE Trans. Pattern Anal. Mach. Intell., vol. 14, no. 4, pp. 485–489, Apr.1992.
[3]. R. Depommier and E. Dubois, "Motion estimation with detection of occlusion areas," in Proc. IEEE Conf. Acoust., Speech, Signal Process., vol. 3, pp. 269–272, Mar. 1992.
[4]. M. J. Black and D. J. Fleet, "Probabilistic detection and tracking of motion boundaries," Int. J. Comput. Vis., vol. 38, no. 3, pp. 231–245, Jul./Aug. 2000.
[5]. V. Kolmogorov and R. Zabih, "Computing visual correspondence with occlusions using graph cuts," in Proc. Int. Conf. Comput. Vis., pp. 508–515, Sep. 2007.

Abstract: Recognizing defendant based on impressions of fingers from crime scenes is important to law enforcement agencies. Latents are partial fingerprints with small area, contain nonlinear distortion,and are usually dirty and less distinct. Due to some of these characteristics, they have a seriously smaller number of minutiae points and thus it can be distinctly difficult to automatically match latents to plain or rolled fingerprints that are stored in law enforcement databases. The goal is to develop a latent matching algorithm that uses only minutiae information. The proposed algorithm uses a robust alignment algorithm (descriptorbased Hough transform) to align fingerprints and measures similarity between fingerprints by considering both minutiae and orientation field information.Since the proposed algorithm depend only on manually marked minutiae, it can be easily used in the law enforcement application.We can added an texture feature to improve the matching performances by using a method called gray-level co-occurrence matrix.The texture features are contrast,correlation,energy and homogeneity.The proposed approach consists of following three modules: (i) align two sets of minutiae by using a descriptor-based Hough Transform; (ii) establish the correspondences between minutiae; and (iii) compute a similarity score.
Keywords: Fingerprints; Hough Transform; Latents; Minutiae; Matching

[1]. A. A. Paulino, J. Feng, and A. K. Jain, "Latent fingerprint matching using descriptor-based Hough transform," in Int'l Joint Conf. on Biometrics, October 2011, pp. 1–7
[2]. R. Cappelli, M. Ferrara, and D. Maltoni, "Minutia cylinder-code: a new representation and matching technique for fingerprint recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, no. 12,pp. 2128–2141, December 2010.
[3]. Jain.A.K, J.Feng, Nagar, Nandakumar k "On matching latent fingerprints," 2008 CVPR Workshop on biometrics.
[4]. J. Feng and A. K. Jain, "Fingerprint reconstruction: from minutiae to phase," IEEE Trans. on Pattern Analysis and Machine Intelligence,vol. 33, no. 2, pp. 209–223, February 2011.
[5]. X. Jiang, M. Liu, and A. C. Kot, "Fingerprint retrieval for identification,"IEEE Trans. on Information Forensics and Security, vol. 1, no. 4, pp.532–542, December 2006
[6]. R. Cappelli, M. Ferrara, and D. Maltoni, "Minutia cylinder-code: a new representation and matching technique for fingerprint recognition," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 32, no. 12,pp. 2128–2141, December 2010

Abstract: Mobile phones are among the most popular consumer devices, and the recent development of 3G networks, rapidly increasing power of personal mobile devices (smart phones, tablets, etc.) is providing much richer contents and social interactions to users. This trend however is restricted by the limited battery lifetime of mobile devices and unstable wireless connectivity, making the highest possible quality of service experienced by mobile users not feasible .The recent cloud computing technology, with its rich resources to compensate for the limitations of mobile devices and connections, can potentially provide an ideal platform to support the desired mobile services. In this paper, we propose the design of a Cloud-based, Mobile social tv system (CloudMoV). The system effectively utilizes both PaaS (Platform-as-a-Service) and IaaS (Infrastructure-as-a-Service) cloud services to offer the living-room experience of video watching to a group of disparate mobile users who can interact socially while sharing the video. To guarantee good streaming quality as experienced by the mobile users with time varying wireless connectivity, we employ a surrogate for each user in the IaaS cloud for video downloading and social exchanges on behalf of the user. Given the battery life as a key performance bottleneck, we advocate the use of burst transmission from the surrogates to the mobile users, and carefully decide the burst size which can lead to high energy efficiency and streaming quality. Social interactions among the users, in terms of spontaneous textual exchanges, are effectively achieved by efficient designs of data storage with Bitable. These various designs for flexible transcending capabilities, battery efficiency of mobile devices and spontaneous social interactivity together provide an ideal platform for Mobile social tv services. We have implemented CloudMoV on Amazon EC2 and Google App Engine and verified its superior performance based on real world experiments.

[1]. M. Satyanarayanan, P. Bahl, R. Caceres, and N. Davies, "The case for vm-based cloudlets in mobile computing," IEEE Pervasive Computing, vol. 8, pp.14–23, 2009.
[2]. S. Kosta, A. Aucinas, P. Hui, R. Mortier, and X. Zhang, "Thinkair: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading," in Proc. of IEEE INFOCOM, 2012.
[3]. W. Zhu, C. Luo, J. Wang, and S. Li, "Multimedia cloud computing," IEEE Signal Processing Magazine, vol. 28, pp. 59–69, 2011.
[4]. T. Coppens, L. Trappeniners, and M. Godon, "AmigoTV: towards a social TV experience," in Proc. of EuroITV, 2004.
[5]. N. Ducheneaut, R. J. Moore, L. Oehlberg, J. D. Thornton, and E. Nickell, "Social TV: Designing for Distributed, Sociable Television Viewing," International Journal of Human-Computer Interaction, vol. 24, no. 2, pp. 136–154, 2008

Abstract: Concept mining is a process that focuses on extracting ideas and concepts found in documents. The approach is somewhat similar to text mining, with the main difference being that mining a text focuses on the extraction of information rather than ideas. In this paper, we propose concept-based text representation, with an accent on using the proposed representation in different applications such as information retrieval, text summarization, and question answering. This work presents a new prototype for concept mining by extracting the concept-based information from a raw text using leximancer. At the text representation level, we introduce a sentence based conceptual ontological representation that builds concept-based representations for the whole document. A new concept-based similarity measure is proposed to measure the similarity of texts based on their meaning. The proposed approach is domain independent and it could be applied to general domain applications. The proposed approach is going to apply to the domain of information retrieval, and give an assertion for proceeding in the right directions of this research.

Keywords: Concepts, Similarity, Extraction, Information, Leximancer,Mining.

[1]. D. Jurafsky and J. H. Martin, Speech and Language Processing, Prentice Hall Inc., 2000
[2]. D. Gildea and D. Jurafsky, "Automatic labeling of semantic roles," Computational Linguistics, vol. 28, no. 3,2002.
[3]. V. Bharanipriya & V. Kamakshi Prasad, "Web Content Mining Tools: A Comparative Study" International Journal of Information Technology and Knowledge Management January-June 2011, Volume 4, No. 1, pp. 211-215.

[4]. Dunham, M. H. 2003. Data Mining Introductory and Advanced Topics. Pearson Education.
[5]. www.leximancer.com

Abstract: Internet has become an inevitable part of our lives. While internet offers a mass of useful services which makes communication easier and faster than ever, it presents some threats too along the way. Over the last few years, botnet has risen to become the primary source for various internet attacks such as DDos attacks, spamming, phishing etc. Accordingly, a great deal of research has focused on methods to detect and extenuate the effects of botnets. In this paper, we have analyzed the feasibility of outgoing and incoming traffic i.e. intrusions and extrusions, to detect P2P based botnets. We present an approach that uses a network perimeter mentoring system called bothunter. As a part of the research work, a botnet detection system for peer to peer botnets called P2P-BDS has been proposed.

Keywords: Attacker, Bot, Botmaster, Extrusion, Intrusion, IRC, peer to peer, Zombie

[1] Honeynet Project. Know your Enemy: TrackingBotnets, March 2005. http://www.honeynet.org/ papers/bots.

[2] G. Schaffer, "Worms and Viruses and Botnets, Oh My! : Rational Responses to Emerging Internet Threats", IEEE Security & Privacy, 2006.
[3] P. Barford and V.Yagneshwaran, "An Inside Look at Botnets". In Special Workshop on Malware Detection, Advances in Information Security, Springer, Heidelberg(2006)
[4] B. Saha and A, Gairola, " Botnet: An overview," CERT-In White Paper(CIWP)-2005-05,2005
[5] Liu, J., Xiao, Y., Ghaboosi, K., Deng, H., and Zhang, J., 2009. Botnet: Classification, Attacks, Detection, Tracing, and Preventive Measures. EURASIP Journal on Wireless Communications and Networking, Vol.2009

Abstract: Q-SPIM is an organized advance and incessant progress to enhance software organization's ability throughput quality software that balances time and cost [2]. Such a model developed is verified and validated by the researcher in the course of this research. To improve the overall throughput this approach proves to be a good software up gradation model. This paper work concentrates on upgrading the process as well as organization through Quadrant based approach. This model depends upon the experience on research of software companies and plots the same in different quadrants. The ultimate aim is to develop of a model which would be useful in practice for software development companies to develop quality product. The principle of models for improvement and software process improvement model is described in this paper. The purposed model is a generic model which is beneficial for small firm as well as large firm.

Keywords: CMM- Throughput, Capture maturity model, Q-SPIM : Quadrant based Software process improvement model

[1] Jesper Arent, Jacob Nørbjerg, Software Process Improvement as Organizational Knowledge Creation: A Multiple Case Analysis, Proceedings of the 33rd Hawaii International Conference on System Sciences – 2000.
[2] Ian Sommerville, Lancaster University, UK, "Software Documentation, Printed 7/11/01.This paper is a revised version of Chapter 30 from my book Software Engineering, 4th edition, published by Addison Wesley in 1992".
[3] http://en.wikipedia.org/wiki/Inspection.

Abstract: One of the most vexing problems in wireless sensor network security is the node Clone attack. In this attack, an adversary breaks into a sensor node, reprograms it, and inserts several copies of the node back into the sensor network. Cloning gives the adversary an easy way to build an army of malicious nodes that can cripple the sensor network. A few distributed solutions to address this fundamental problem have been recently proposed. However, these solutions are not satisfactory. Therefore first, the desirable properties of a distributed mechanism for the detection of node Clone attacks have been analyzed. Second, the known solutions for this problem do not completely meet our requirements. Third, a new self healing, Randomized, Efficient, and Distributed (RED) protocol for the detection of node Clone attacks has been proposed, and it satisfies the intended requirements.

Index Terms: clone attack, RED, witness distribution, oblivious, performance, WSN.

[1]. Maneesha V. Ramesh, Aswathy B. Raj and Hemalatha T, "Wireless Sensor Network Security: Real-Time Detection and Prevention of Attacks", 2012 Fourth International Conference on Computational Intelligence and Communication Networks, IEEE
[2]. Chia-Mu Yu, Chun-Shien Lu, Sy-Yen Kuo, "CSI: Compressed Sensing-Based Clone Identification in Sensor Networks", 8th IEEE International Workshop on Sensor Networks and Systems for Pervasive Computing 2012, Lugano
[3]. Yan-Xiao Li, Lian-Qin, Qian-Liang, "Research On Wireless Sensor Network Security", 2010 International Conference on Computational Intelligence and Security, IEEE
[4]. Yilin Wang1 and Maosheng Qin, "Security for Wireless Sensor Networks", International Conference on Control, Automation and Systems 2010Oct. 27-30, 2010
[5]. Heesook Choi, Sencun Zhu, Thomas F. La Porta, "SET: Detecting node clones in Sensor Networks", [8] KuthadiVenuMadhav, Rajendra.CAnd Raja Lakshmi Selvaraj (2010), "A Study Of Security Challenges In Wireless Sensor Networks", Journal of Theoretical and Applied Information Technology © 2005 - 2010 JATIT& LLS