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

Robotics is a highly multi-disciplinary field which attracts the attention of many researchers from diverse fields. One of the major studied problems in Robotics is the problem of Robotic Path Planning. The problem deals with finding of the path that can be used by the robot for navigation purpose without any collision. The output of this algorithm is then implemented for physically moving the real robot on the desired path. In this paper we have used Multi-Neuron Heuristic Search (MNHS) which is an advanced form of A* algorithm. The MNHS was earlier proposed by the authors for special cases where the heuristics changes sharply and it was shown to be a powerful algorithm in the same context. In this paper we apply the MNHS for the Robot Path Planning. The motivation is to make the problem robust against the uncertainties that might arise like the sudden discovery that the path being followed does not lead to the goal. The MNHS has better capabilities to solve maze-like maps where the uncertainty is extremely high. Another such area is when the robot enters into a highly chaotic area. Here it might be better to go with a path that is less chaotic or has lesser number of obstacles. We tested the algorithm for numerous test cases. In all the cases, the MNHS was able to solve the problem of path planning well.

/pdf/robotic-path-planning-using-multi-neuron-heuristic-search-1th7ukuku8.pdf

Robotic path planning using multi neuron heuristic search

Robot path planning is about finding a collision free motion from one position to another. Efficient algorithms for solving problems of this type have important applications in areas such as: industrial robotics, computer animation, drug design, and automated surveillance. In this paper, a modified A* algorithm is used for optimizing the path. Different from the approaches that only choose the shortest routes, this method estimates the energy consumption and chooses the most energy efficient routes. As mobile robots are powered by batteries, their energy is limited. Therefore, how to minimize energy consumption is an important problem. The basic idea is to minimize unnecessary stops and turns for mobile robots that cause acceleration and deceleration and consumes significant energy. Simulation results are presented on various environments with different levels of complexity depending on the density of the obstacles. The effectiveness of the proposed approach is evaluated in terms of number of movement steps, path length, energy consumption, number of turns and time. The experimental results show that our approach can provide effective path by reducing the number of turns compared to A*, thus saving energy. All paths generated were optimal in terms of length and smoothness.

Modified A* Algorithm for Mobile Robot Path Planning

In this paper an attempt is made to develop speaker identification system which is used to determine the identity of an unknown speaker among several speakers of known speech characteristics, from a sample of his or her voice. Every speaker has different individual characteristics embedded in his /her speech utterances. These characteristics can be extracted from utterances and different neural network models are used to get the desired results. To evaluate speech characteristics from utterances they are stored in digitized form. Speech features namely LPC, RC, APSD, Number of zero crossing and Formant frequencies are extracted from speech signal and formed speech feature vectors. These data features are fed into Artificial Neural Network using back propagation learning algorithm and clustering algorithm for training and identification processes of different speakers. The database used for this system consists of 20 speakers including both male and female from different parts of India and languages are Hindi, Sanskrit, Punjabi and Telugu. The average identification rate 83.29% is achieved when the network is trained using back propagation algorithm and it is improved by about 9% and reached up to 92.78% when using clustering algorithm.

Text-Dependent Multilingual Speaker Identification for Indian Languages Using Artificial Neural Network

This paper presents a multi-robot exploration approach for application in wireless environments. The challenges generally faced by a robot team are to maintain network connectivity among themselves, in order to have an accurate map of the environment at each instant and have an efficient navigation plan for moving toward the unexplored area. To address these issues, we focus on the integration of such connectivity constraints and take navigation plan problems into account. A modified A* based algorithm is proposed for planning the navigation of the robots. A communication protocol based on the concept of leader-follower is developed for maintaining network connectivity. Mobile robots typically use a wireless connection to communicate with the other team members and establishes a Mobile Ad Hoc NETwork among themselves. A communication route is established between each robot pair for exchanging local map data, in order to achieve consistent global map of the environment at each instant. If the routes have multiple hops, this raises the problem of message delaying because time delay accumulates per hop traveled. The purpose of the proposed Leader Follower Interaction Protocol is to reduce the total number of hop counts required for all transmissions between robot pairs. This is different from the centralized approach where the leader is a fixed base station. The role of leader in the proposed approach switches from one robot to others as network’s wireless topology changes as robots move. Simulation results show the effectiveness of communication protocol, as well as the navigation mechanism.

Multi-Robot Exploration in Wireless Environments

Travelling Salesman Problem (TSP) is a NP-Hard combinatorial optimization problem and many real life problems are constrained replica of it which possesses exponential time complexity and requires heavy combination capability. In this work a new nature inspired meta-heuristics called Egyptian Vulture Optimization Algorithm (EVOA) is being introduced and presented for the first time and illustrated with examples how it can be utilized for the constrained graph based problems and is utilized to solve the various dimensional datasets of the traditional travelling salesman problem. There are not many discrete optimization bio-inspired algorithms available in the literature and in that respect it is a novel one which can readily utilized for the graph based and assignment based problems. This EVOA is inspired by the natural and skilled phenomenal habits, unique perceptions and intelligence of the Egyptian Vulture bird for carry out the livelihood and acquisition of food which is inevitable for any kind of organisms. The Egyptian Vulture bird is one of the few birds who are known for showing dexterous capability when it comes to its confrontation with tough challenges and usage of tools with combinations of force and weakness finding ability. The results show that the Egyptian Vulture Optimization meta-heuristics has potential for deriving solutions for the TSP combinatorial problem and it is found that the quality and perfection of the solutions for the datasets depend mainly on the number of dimensions when considerable for the same number of iterations.

Anupam Shukla

Papers

Robotic path planning using multi neuron heuristic search

Modified A* Algorithm for Mobile Robot Path Planning

Text-Dependent Multilingual Speaker Identification for Indian Languages Using Artificial Neural Network

Multi-Robot Exploration in Wireless Environments

Solving Travelling Salesman Problem Using Egyptian Vulture Optimization Algorithm – A New Approach