Anastasios N. Venetsanopoulos – författare

Describing the relevant detection and estimation theory, this detailed guide provides the background knowledge needed to tackle the design of practical WLAN positioning systems. It sets out key system-level challenges and design considerations in increasing positioning accuracy and reducing computational complexity, and it also examines design trade-offs and experimental results. Radio characteristics in real environments are discussed, as are the theoretical aspects of non-parametric statistical tools appropriate for modeling radio signals, statistical estimation techniques and the model-based stochastic estimators often used for positioning. A historical account of positioning systems in also included, giving graduate students, researchers and practitioners alike the perspective needed to understand the benefits and potential applications of WLAN positioning.

This book is devoted to the study of fuzzy reasoning as applied to decision-making and control processes. It contains a collection of contributions covering a wide range of topics within the field. It contains 21 papers, written by 34 contributors and is divided into five parts. Part 1 involves four chapters providing background material together with useful techniques for the validation of fuzzy knowledge bases and the software representation of fuzzy sets and fuzzy logic. Part 2 presents an overview of neuro-fuzzy expert systems along with an important case study, and a neural network model which is suitable for fuzzy reasoning. Part 3 presents the state of the art of fuzzy controllers, including design and implementation aspects. This part starts with a chapter on the demystification of fuzzy control and includes critical evaluations of fuzzy controllers, along with new types of fuzzy controllers such as the sliding mode fuzzy controller.Part 4 involves a chapter on fuzzy parameter and state estimation, which is of fundamental importance in a variety of applications, a chapter on fuzzy reasoning as used in rule-based systems, and a chapter on computing the multivariable shape of an n-D pattern class. Finally, Part 5 presents six important applications dealing with industrial robotic systems, mechanical systems, manipulators with artificial rubber muscles, Petri nets, biomedical engineering, and non-destructive fruit collection. The book is suitable for researchers and practitioners, as well as for teachers and students in related master and doctoral courses.

The function of a filter is to transform a signal into another one more suit­ able for a given purpose. As such, filters find applications in telecommunica­ tions, radar, sonar, remote sensing, geophysical signal processing, image pro­ cessing, and computer vision. Numerous authors have considered deterministic and statistical approaches for the study of passive, active, digital, multidimen­ sional, and adaptive filters. Most of the filters considered were linear although the theory of nonlinear filters is developing rapidly, as it is evident by the numerous research papers and a few specialized monographs now available. Our research interests in this area created opportunity for cooperation and co­ authored publications during the past few years in many nonlinear filter families described in this book. As a result of this cooperation and a visit from John Pitas on a research leave at the University of Toronto in September 1988, the idea for this book was first conceived. The difficulty in writing such a mono­ graph was that the area seemed fragmented and no general theory was available to encompass the many different kinds of filters presented in the literature. However, the similarities of some families of nonlinear filters and the need for such a monograph providing a broad overview of the whole area made the pro­ ject worthwhile. The result is the book now in your hands, typeset at the Department of Electrical Engineering of the University of Toronto during the summer of 1989.

The recent interest in artificial neural networks has motivated the publication of numerous books, including selections of research papers and textbooks presenting the most popular neural architectural and learning schemes. "Artificial Neural Networks: Learning Algorithms, Performance Evaluation and Applications" presents recent developments which can have a very significant impact on neural network research, in addition to the selective review of the existing vast literature on artificial neural networks. This book can be read in different ways, depending on the background, the specialization and the ultimate goals of the reader. A specialist should find in this book well-defined and easily reproducible algorithms which perform the training of neural networks much faster than existing algorithms, along with the performance evaluation of various neural network architectures and training schemes. "Artificial Neural Networks" can also help a beginner interested in the development of neural network systems to build the necessary background in an organized and comprehensive way.The presentation of the materials in this book is based on the belief that the successful application of neural networks to real-world problems depends strongly on the knowledge of their learning properties and performance. Neural networks are introduced as trainable devices which have the unique ability to generalize. The pioneering work on neural networks which appeared during the past decade is presented, together with the current developments in the field, through a comprehensive and unified review of the most popular neural network architectures and learning schemes. Efficient LEarning Algorithms for Neural NEtworks (ELEANNE), which can achieve much faster convergence than existing learning algorithms, are among the recent developments explored in this book. A new generalized criterion for the training of neural networks is presented, which leads to a variety of fast learning algorithms. Finally, the book presents the development of learning algorithms which determine the minimal architecture of multi-layered neural networks while performing their training. The text is a valuale source of information to all researchers and engineers interested in neural networks.The book may also be used as a text for an advanced course on the subject.

The function of a filter is to transform a signal into another one more suit­ able for a given purpose. As such, filters find applications in telecommunica­ tions, radar, sonar, remote sensing, geophysical signal processing, image pro­ cessing, and computer vision. Numerous authors have considered deterministic and statistical approaches for the study of passive, active, digital, multidimen­ sional, and adaptive filters. Most of the filters considered were linear although the theory of nonlinear filters is developing rapidly, as it is evident by the numerous research papers and a few specialized monographs now available. Our research interests in this area created opportunity for cooperation and co­ authored publications during the past few years in many nonlinear filter families described in this book. As a result of this cooperation and a visit from John Pitas on a research leave at the University of Toronto in September 1988, the idea for this book was first conceived. The difficulty in writing such a mono­ graph was that the area seemed fragmented and no general theory was available to encompass the many different kinds of filters presented in the literature. However, the similarities of some families of nonlinear filters and the need for such a monograph providing a broad overview of the whole area made the pro­ ject worthwhile. The result is the book now in your hands, typeset at the Department of Electrical Engineering of the University of Toronto during the summer of 1989.

The recent interest in artificial neural networks has motivated the publication of numerous books, including selections of research papers and textbooks presenting the most popular neural architectures and learning schemes. Artificial Neural Networks: Learning Algorithms, Performance Evaluation, and Applications presents recent developments which can have a very significant impact on neural network research, in addition to the selective review of the existing vast literature on artificial neural networks. This book can be read in different ways, depending on the background, the specialization, and the ultimate goals of the reader. A specialist will find in this book well-defined and easily reproducible algorithms, along with the performance evaluation of various neural network architectures and training schemes. Artificial Neural Networks can also help a beginner interested in the development of neural network systems to build the necessary background in an organized and comprehensive way.The presentation of the material in this book is based on the belief that the successful application of neural networks to real-world problems depends strongly on the knowledge of their learning properties and performance. Neural networks are introduced as trainable devices which have the unique ability to generalize. The pioneering work on neural networks which appeared during the past decades is presented, together with the current developments in the field, through a comprehensive and unified review of the most popular neural network architectures and learning schemes. Efficient LEarning Algorithms for Neural NEtworks (ELEANNE), which can achieve much faster convergence than existing learning algorithms, are among the recent developments explored in this book. A new generalized criterion for the training of neural networks is presented, which leads to a variety of fast learning algorithms. Finally, Artificial Neural Networks presents the development of learning algorithms which determine the minimal architecture of multi-layered neural networks while performing their training.Artificial Neural Networks is a valuable source of information to all researchers and engineers interested in neural networks. The book may also be used as a text for an advanced course on the subject.

Reporting the state of the art of color image processing, this monograph fills an existing gap in the literature on digital signal and image processing. It can serve the needs of different users at different levels: as a textbook which covers a graduate image processing course, as a up-to-date reference for researchers since it offers a broad survey of the relevant literature, and as a relevant information source for development engineers who work in the design and the implementation of various image processing tasks. Part of the material in the book was the basis of seminars at the University of Toronto. The book contains numerous examples and pictures of color image processing results, as well as tables which summarize the results of the analysis. Algorithms implemented in JAVA can be downloaded from the author's website .

Reporting the state of the art of color image processing, this monograph fills an existing gap in the literature on digital signal and image processing. It can serve the needs of different users at different levels: as a textbook which covers a graduate image processing course, as a up-to-date reference for researchers since it offers a broad survey of the relevant literature, and as a relevant information source for development engineers who work in the design and the implementation of various image processing tasks. Part of the material in the book was the basis of seminars at the University of Toronto. The book contains numerous examples and pictures of color image processing results, as well as tables which summarize the results of the analysis. Algorithms implemented in JAVA can be downloaded from the author's website .