**Meet the editor**

Taufik Abrão received his B. S., M. Sc. and Ph.D., all in Electrical Engineering, from the Polytechnic School of the University of Sao Paulo (EPUSP), Brazil, in 1992, 1996, and 2001, respectively. Since March 1997 he has been with the Department of Electrical Engineering, Londrina State University (UEL) Brazil, where he is currently an Associate Professor in communications

engineering. In 2012 he was an Academic Visitor at CSPC University of Southampton, UK. Dr. Abrão is involved in editorial board activities of several journals and has served as TCP member in several symposium and conferences. His research interests lie in communications, including multi-user detection and estimation, MC-CDMA and MIMO systems, cooperative communication and relaying, resource allocation, heuristic and convex optimization aspects. He is co-authored of more than 180 research papers published in specialized international journals and conferences.

Contents

**Preface VII**

**Section 1 Image Reconstruction 1**

Chapter 1 **Search Algorithm for Image Recognition Based on Learning Algorithm for Multivariate Data Analysis 3**

Chapter 3 **Content-Based Image Feature Description and Retrieving 45** Nai-Chung Yang, Chung-Ming Kuo and Wei-Han Chang

Chapter 4 **Multidimensional Optimization-Based Heuristics Applied to**

Fernando Ciriaco, Taufik Abrão and Paul Jean E. Jeszensky

Lucas Hiera Dias Sampaio, Mateus de Paula Marques, Mário H. A. C.

Fábio Renan Durand, Larissa Melo, Lucas Ricken Garcia, Alysson

Chapter 5 **Ant Colony Optimization for Resource Allocation and Anomaly Detection in Communication Networks 109**

Adaniya, Taufik Abrão and Paul Jean E. Jeszensky

Chapter 6 **Optical Network Optimization Based on Particle Swarm**

José dos Santos and Taufik Abrão

**Wireless Communication Systems 81**

Chapter 2 **Ant Algorithms for Adaptive Edge Detection 23**

Aleksandar Jevtić and Bo Li

**Section 2 Telecommunication Applications 79**

**Intelligence 143**

Juan G. Zambrano, E. Guzmán-Ramírez and Oleksiy Pogrebnyak

## Contents

**Preface XI**



Preface

tions take a very long time to be computed.

Grover-type quantum search, which constitutes Part 4.

Heuristic Search is an important sub-discipline of optimization theory and finds applications in a vast variety of fields, including life science and engineering. Over the years, search meth‐ ods have made an increasing number of appearances in engineering systems, primarily be‐ cause of the capability in providing effective near-optimum solutions with low-complexity, more cost-effective and less time consuming. Heuristic Search is a method that might not al‐ ways find the best solution but is guaranteed to find a good solution in reasonable time, i.e., by sacrificing completeness it increases efficiency. Search methods have been useful in solving tough engineering-oriented problems that either could not be solved any other way or solu‐

The primary goal of this book is to provide a variety of applications for search methods and techniques in different fields of electrical engineering. By organizing relevant results and appli‐ cations, the book will serve as a useful resource for students, researchers and practitioners to further exploit the potential of search methods in solving hard non-polynomial optimization problems that arise in advanced engineering technologies, such as image and video processing issues, detection and resource allocation in telecommunication systems, security and harmonic reduction in power generation systems, as well as redundancy optimization problem and search-fuzzy learning mechanisms in industrial applications. To better explore those engineer‐ ing-oriented search methods, this book is organized in four parts. In Part 1, three search optimi‐ zation procedures applied to image and video processing are discussed. In Part 2, three specific hard optimization problems that arise in telecommunications systems are solved using guided search procedures: multiuser detection, power-rate allocation, anomaly detection and routing optical channel allocation problems are treaded deploying a collection of guided-search algo‐ rithms, such as Ant Colony, Particle Swarm, Genetic, Simulation Annealing, Tabu, Evolutionary Programming, Neighborhood Search and Hyper-Heuristic. Search methods applied to power systems and industrial processes are developed in Part 3: cognitive concepts and methods, such as fuzzy cognitive maps and adaptive fuzzy learning mechanisms are aggregated in order to efficiently model and solve optimization problems found in reliable power generation and in‐ dustrial applications. Finally, the last chapter is devoted to conceptual and formal aspects of

It is our sincere hope that the book will help readers to further explore the potential of search

**Taufik Abrão**

Electrical Engineering Department, State University of Londrina (DEEL-UEL),

Londrina, Paraná, Brazil

methods in solving efficiently hard-complexity engineering optimization problems.

Chapter 8 **Application of Harmony Search Algorithm in Power Engineering 201**

H. R. Baghaee, M. Mirsalim and G. B. Gharehpetian

	- **Section 4 Grover-Type Quantum Search 259**

## Preface

**Section 3 Power Systems and Industrial Processes Applications 173**

Chapter 8 **Application of Harmony Search Algorithm in Power**

Chapter 9 **Heuristic Search Applied to Fuzzy Cognitive Maps**

Chapter 10 **Optimal Allocation of Reliability in Series Parallel**

H. R. Baghaee, M. Mirsalim and G. B. Gharehpetian

Chapter 11 **Geometry and Dynamics of a Quantum Search Algorithm for**

**an Ordered Tuple of Multi-Qubits 261**

Ruppert

**VI** Contents

**Engineering 201**

**Learning 221**

Yamani

Yoshio Uwano

Arruda and Taufik Abrão

**Production System 241**

**Section 4 Grover-Type Quantum Search 259**

Chapter 7 **An Adaptive Neuro-Fuzzy Strategy for a Wireless Coded Power Control in Doubly-Fed Induction Aerogenerators 175**

I. R. S. Casella, A. J. Sguarezi Filho, C. E. Capovilla, J. L. Azcue and E.

Bruno Augusto Angélico, Márcio Mendonça, Lúcia Valéria R. de

Rami Abdelkader, Zeblah Abdelkader, Rahli Mustapha and Massim

Heuristic Search is an important sub-discipline of optimization theory and finds applications in a vast variety of fields, including life science and engineering. Over the years, search meth‐ ods have made an increasing number of appearances in engineering systems, primarily be‐ cause of the capability in providing effective near-optimum solutions with low-complexity, more cost-effective and less time consuming. Heuristic Search is a method that might not al‐ ways find the best solution but is guaranteed to find a good solution in reasonable time, i.e., by sacrificing completeness it increases efficiency. Search methods have been useful in solving tough engineering-oriented problems that either could not be solved any other way or solu‐ tions take a very long time to be computed.

The primary goal of this book is to provide a variety of applications for search methods and techniques in different fields of electrical engineering. By organizing relevant results and appli‐ cations, the book will serve as a useful resource for students, researchers and practitioners to further exploit the potential of search methods in solving hard non-polynomial optimization problems that arise in advanced engineering technologies, such as image and video processing issues, detection and resource allocation in telecommunication systems, security and harmonic reduction in power generation systems, as well as redundancy optimization problem and search-fuzzy learning mechanisms in industrial applications. To better explore those engineer‐ ing-oriented search methods, this book is organized in four parts. In Part 1, three search optimi‐ zation procedures applied to image and video processing are discussed. In Part 2, three specific hard optimization problems that arise in telecommunications systems are solved using guided search procedures: multiuser detection, power-rate allocation, anomaly detection and routing optical channel allocation problems are treaded deploying a collection of guided-search algo‐ rithms, such as Ant Colony, Particle Swarm, Genetic, Simulation Annealing, Tabu, Evolutionary Programming, Neighborhood Search and Hyper-Heuristic. Search methods applied to power systems and industrial processes are developed in Part 3: cognitive concepts and methods, such as fuzzy cognitive maps and adaptive fuzzy learning mechanisms are aggregated in order to efficiently model and solve optimization problems found in reliable power generation and in‐ dustrial applications. Finally, the last chapter is devoted to conceptual and formal aspects of Grover-type quantum search, which constitutes Part 4.

It is our sincere hope that the book will help readers to further explore the potential of search methods in solving efficiently hard-complexity engineering optimization problems.

> **Taufik Abrão** Electrical Engineering Department, State University of Londrina (DEEL-UEL), Londrina, Paraná, Brazil

**Section 1**

**Image Reconstruction**

**Image Reconstruction**

**Chapter 1**

**Search Algorithm for Image Recognition Based on**

**Learning Algorithm for Multivariate Data Analysis**

An image or a pattern can be recognized using prior knowledge or the statistical informa‐ tion extracted from the image or the pattern. The systems for image recognition and classifi‐ cation have diverse applications, e.g. autonomous robot navigation[1], image tracking radar [2], face recognition [3], biometrics [4], intelligent transportation, license plate recognition,

The problem of automatic image recognition is a composite task that involves detection and localization of objects in a cluttered background, segmentation, normalization, recognition and verification. Depending on the nature of the application, e.g. sizes of training and test‐ ing database, clutter and variability of the background, noise, occlusion, and finally, speed requirements, some of the subtasks could be very challenging. Assuming that segmentation and normalization haven been done, we focus on the subtask of object recognition and veri‐

Diverse paradigms have been used in the development of algorithms for image recognition, some of them are: artificial neural networks [7, 8], principal component analysis [9, 10], fuz‐ zy models [11, 12], genetic algorithms [13, 14] and Auto-Associative memory [15]. The fol‐

Abrishambaf *et al* designed a fingerprint recognition system based in Cellular Neural Net‐ works (CNN). The system includes a preprocessing phase where the input fingerprint image is enhanced and a recognition phase where the enhanced fingerprint image is matched with the fingerprints in the database. Both preprocessing and recognition phases are realized by means of CNN approaches. A novel application of skeletonization method is used to per‐

> © 2013 Zambrano et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Zambrano et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

fication, and demonstrate the performance using several sets of images.

lowing paragraphs describe some work done with these paradigms.

Juan G. Zambrano, E. Guzmán-Ramírez and

Additional information is available at the end of the chapter

character recognition [5] and fingerprints [6].

Oleksiy Pogrebnyak

**1. Introduction**

http://dx.doi.org/10.5772/ 52179
