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 booklet constitutes the lawsuits of the 1st overseas convention on Advances in Computing and knowledge know-how, ACITY 2011, held in Chennai, India, in July 2011. The fifty five revised complete papers awarded have been conscientiously reviewed and chosen from quite a few submissions. The papers function major contributions to all significant fields of the pc technological know-how and data know-how in theoretical and sensible 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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A novel hyperchaotic system and its control. J. Uncertain Sys. 3, 137–144 (2009) 27. : The Stability of Motion. Springer, New York (1967) Stabilization of Large Scale Discrete-Time Linear Control Systems by Observer-Based Reduced Order Controllers Sundarapandian Vaidyanathan and Kavitha Madhavan R & D Centre, Vel Tech Dr. RR & Dr. org/ Abstract. This paper investigates the stabilization of large scale discrete-time linear control systems by observer-based reduced order controllers. Sufficient conditions are derived for the design of observer-based reduced order controllers for the large scale discrete-time linear control systems by obtaining a reduced order model of the original linear plant using the dominant state of the system.

Kim, J. Bae, I. Yoo, 2000 • Y. Liu, S. Yuan, X. Zhu, Y. Wang, W. Zhang and Y. Wang, 2002 • • • • 4. 5. Tico, 2002 C. Klimanee and D. Nguyen • • • • • 6. Rakesh,20 06 • • • Disadvantages Poincare Index is used to detect core. Number and curvature of cores are used for classification. • Delta is not required for classification. • False core point is not eliminated completely It uses thresholding for preprocessing. Poincare index is modified to improve accuracy. • Efficient Classification results.

Fingerprint Classification based on learned feature. IEEE Transactions on Systems, Man and Cybernetics 35(3), 287–300 (2005) 13. : Fingerprint Identification with Exclusive and Continuous Classification. IEEE Conference on IEA, pp. 1–6 (2006) 14. : Fingerprint Classification using Improved Directional Field and Fuzzy Wavelet Neural Network. IEEE Intelligent Control and Automation, 9961–9964 (2006) 15. : SVM-based Fingerprint Classification using Orientation Field. In: International Conference on Natural Computation, pp.

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