Data Mining - Concepts and Techniques.

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

International Journal of Computer and Applications

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Differential evolution (DE) is a vector population-based stochastic search optimization algorithm. DE converges faster, finds the global optimum independent to initial parameters, and uses few control parameters. The exploration and exploitation are the two important diversity characteristics of population-based stochastic search optimization algorithms. Exploration and exploitation are compliment to each other, i.e., a better exploration results in worse exploitation and vice versa. The objective of an efficient algorithm is to maintain the proper balance between exploration and exploitation. This paper focuses on a comparative study based on diversity measures for DE and its prominent variants, namely JADE, jDE, OBDE, and SaDE.

A Diversity-Based Comparative Study for Advance Variants of Differential Evolution

Speech based systems may be used for a variety of tasks that we use in everyday life The applications using speech may be broadly classified into speaker recognition and speaker verification The recognition deals with the identification of the person who is speaking The task of verification systems is to verify the claimed identity of the person Here we make use of speech as a biometric modality Besides, speech is extensively used for word identification whose application includes the speech to text conversion The variety of applications has a large number of issues associated with them In this chapter we give a broad overview of these systems, their working and design issues Some of the issues addressed in the chapter include the variation of speech, effect of noise, text dependent and independent recognition, start and end of signal detection, etc Most of the discussions revolve around the pattern recognition approach for the recognition systems This would give an application base of the discussed approaches that we have studied so far or would be studying later in this book

Intelligent System Design in Speech

To make smart card much faster, we need efficient data structure. Access time of on chip data depends on how and where we stored. Some Data Structure take maximum time and some take minimum time depending on the space and time complexity of data structure. In this work, we have taken some data structures and find that BST is the best suitable data structure for performing smart card operations in compare to other possible data structures.

http://ieeexplore.ieee.org/iel7/6657460/6657931/06658037.pdf

Efficient Data Structure Based Smart Card Implementation

Speech forms a major biometric that makes it possible to reap the numerous fruits of automation and verification. The later of the book focus on the shortcomings of speech that motivate the development of hybrid systems using face along with speech. Hence it is important to have a brief idea about face as a biometric that uniquely couples with speech. This chapter would present various techniques of using face as a biometric. We deal with two kinds of systems. The first class of systems uses dimensionality reduction techniques over the visual information that face represents. Here we would discuss the Principle Component Analysis (PCA) and Regularized Linear Discriminant Analysis (R-LDA). All these methods have their own mechanisms for dimensionality reduction. The other class of algorithm identifies landmarks in face and uses these for the recognition purpose. We also analyze the various advantages and shortcomings of these systems.

Intelligent System Design in Face

Evolutionary Algorithms are good optimizing agents that use the evolutionary concepts to evolve systems. The complex fitness landscapes are a problem that restricts the performance of these algorithms. Besides specifying the correct parameter is always very important. In this chapter we study the means by which we can execute a number of evolutionary algorithms in a variety of models to give an overall optimal performance to the entire network. We first discuss the Island Model Algorithm where the individuals are evolved in multiple islands with regular interaction. We then study a related algorithm called the Hierarchical Fair Competition algorithm. Here we would separate the various individuals into classes as per their fitness values. Each of these would have a separate evolution. We would then discuss the Nested Evolutionary Strategies where we fix the parameters of an Evolutionary Strategy by some technique. Towards the end we discuss the Hybrid Genetic Algorithm Particle Swarm Optimization. This algorithm generates individuals by twin usage of Genetic Algorithm and Particle Swarm Optimization.

Anupam Shukla

Papers

A Diversity-Based Comparative Study for Advance Variants of Differential Evolution

Intelligent System Design in Speech

Efficient Data Structure Based Smart Card Implementation

Intelligent System Design in Face

Parallel and Hierarchical Evolutionary Algorithms