By Sundarapandian Vaidyanathan (auth.), David C. Wyld, Michal Wozniak, Nabendu Chaki, Natarajan Meghanathan, Dhinaharan Nagamalai (eds.)
This booklet constitutes the complaints of the 1st overseas convention on Advances in Computing and knowledge 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 function major contributions to all significant fields of the pc technology and data expertise in theoretical and sensible aspects.
Read or Download Advances in Computing and Information Technology: First International Conference, ACITY 2011, Chennai, India, July 15-17, 2011. Proceedings PDF
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Additional info for Advances in Computing and Information Technology: First International Conference, ACITY 2011, Chennai, India, July 15-17, 2011. Proceedings
In practical applications, the dominant state xs of the reduced-order model S2 may not be directly available for measurement and hence we cannot implement the state feedback control law (26). To overcome this practical difficulty, we derive an important theorem, usually called as the Separation Principle, which first establishes that the observer-based reduced-order controller indeed stabilizes the dominant state of the given linear control system S1 and also demonstrates that the observer poles and the closed-loop controller poles are separated.
4. Tracing of Ridges Andrew  has described a classification technique based on the characteristics of the ridges. Two new classifiers have been presented in the paper. The first classification described is by using Hidden Markov Model (HMM). In fingerprint image the direction changes slowly hence HMM is suitable here for classification. The ridgelines are typically extracted directly from the directional image, then the image is binarized and thinning operation is performed, features are extracted that denotes the ridge behavior.
Jiaojiao Hu, Mei Xie  have introduced a classification technique using combination of genetic algorithm and neural network. Orientation field is given as input to genetic programming process. Features are given as input to backpropagation network and Support Vector Machine (SVM) for classification of fingerprints. 2 Core and Delta Points Within the pattern areas of loops and whorls are enclosed the focal points which are used to classify fingerprints. These points are called as core and delta.