Advances in Computing and Information Technology: First by Sundarapandian Vaidyanathan (auth.), David C. Wyld, Michal

By Sundarapandian Vaidyanathan (auth.), David C. Wyld, Michal Wozniak, Nabendu Chaki, Natarajan Meghanathan, Dhinaharan Nagamalai (eds.)

This ebook constitutes the complaints of the 1st foreign convention on Advances in Computing and data expertise, ACITY 2011, held in Chennai, India, in July 2011. The fifty five revised complete papers provided have been rigorously reviewed and chosen from various submissions. The papers characteristic major contributions to all significant fields of the pc technology and data expertise in theoretical and functional aspects.

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Extra resources for Advances in Computing and Information Technology: First International Conference, ACITY 2011, Chennai, India, July 15-17, 2011. Proceedings

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Fract. 23, 1245–1251 (2005) 21. : Global chaos synchronization for R¨ossler and Arneodo chaotic systems. Far East J. Math. Sci. 44, 137–148 (2010) 22. : Analysis and characterization of the hyperchaos generated by a semiconductor laser object. IEEE J. Quantum Electr. 41, 541–548 (2005) 23. : Hyperchaos from cellular neural networks. Electronics Lett. 31, 250–251 (1995) 24. : A hyperchaos in a modified canonical Chua’s circuit. Internat. J. Bifurcat. Chaos. 14, 221–243 (2004) 25. : A hyperchaotic attractor coined from chaotic L¨u system.

The well-known Henry’s Classification scheme divides a fingerprint structure into three major classes or patterns namely Arch, Loop and Whorl. These classes are further divided by researchers into arch, tented arch, left loop, right loop, double loop and whorl. Figure. 1 displays an algorithmic flow for selection of features and classification of fingerprint. Beginning with generation of orientation map or ridge flow, it follows the flow to be used for different methods for classification. C. Wyld et al.

5. Spurious Singular Points (yellow) Zhou et al. [20] work is based on DORIC (Differences of Orientation values along a Circle) feature an extended form of Poincare Index. These are given to Support Vector Machine to design classifier. [29] uses constraints to remove extra singular points by treating them as centeroid if they are too close and deleting a pair of core and delta if distance between them is less than threshold. [30] presents an algorithm to fine tune orientation map by finding the direction of gravity.

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