The Volterra and Wiener Theories of Nonlinear Systems
This text presents a complete and detailed development of the analysis, design and characterization of non-linear systems using the Volterra and Wiener theories, as well as gate functions, thus yielding new insights and a better comprehension of the subject. The Volterra and Wiener theories are useful in the study of systems in biological, mechanical, and electrical fields.
Biomedical Signal Processing and Signal Modeling
A biomedical engineering perspective on the theory, methods, and applications of signal processing This book provides a unique framework for understanding signal processing of biomedical signals and what it tells us about signal sources and their behavior in response to perturbation. Using a modeling-based approach, the author shows how to perform signal processing by developing and manipulating a model of the signal source, providing a logical, coherent basis for recognizing signal types...
The Volterra Series and its Application
Modeling of weakly nonlinear systems by means of Volterra series analysis is presented. Necessary conditions for representing nonlinearities by a Volterra series are developed analytically as well as heuristically. A two-condition convergence criterion for Volterra series and a method for determining Volterra transfer functions are established. For systems with multiple nodes, an extension of Volterra series analysis; method of nonlinear currents is developed and applied to a MESFET...
Neural Networks: A Comprehensive Foundation
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
Nonlinear Model-Based Image/Video Processing and Analysis
A comprehensive survey of techniques and applications in image and video processing and analysis A widely varied selection of experts provides extensive coverage of nonlinear model-based techniques in image and video processing and analysis. This volume not only details new techniques in still image and digital video but also discusses applications in computer vision, multimedia, and visual information retrieval systems. All nonlinear, model-based techniques are detailed, and a complete and...
Mathematical Problems in Image Processing
Partial differential equations and variational methods were introduced into image processing about 15 years ago, and intensive research has been carried out since then. The main goal of this work is to present the variety of image analysis applications and the precise mathematics involved. It is intended for two audiences. The first is the mathematical community, to show the contribution of mathematics to this domain and to highlight some unresolved theoretical questions. The second is the...
Polynomial Signal Processing
Despite our growing understanding of the properties and capabilities of nonlinear filters, there persists the belief among engineers that these filters are too complex to implement. This book debunks the myth that all nonlinear filters are complex with its coverage of the polynomial filter. It examines all major aspects of the technology, including system modeling, speed analysis, image processing, communications, biological signal processing, semiconductor modeling, neutral sets, and more.
Nonlinear Distortion in Wireless Systems: Modeling and Simulation with MATLAB
This book covers the principles of modeling and simulation of nonlinear distortion in wireless communication systems with MATLAB simulations and techniques
In this book, the author describes the principles of modeling and simulation of nonlinear distortion in single and multichannel wireless communication systems using both deterministic and stochastic signals. Models and simulation methods of nonlinear amplifiers explain in detail how to analyze and evaluate the performance of data...
Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches
Focuses on System Identification applications of the adaptive methods presented. but which can also be applied to other applications of adaptive nonlinear processes.
Covers recent research results in the area of adaptive nonlinear system identification from the authors and other researchers in the field.
Recurrent Neural Networks for Prediction
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real--time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters. ? Analyses the...
The Volterra Series and its Application
Modeling of weakly nonlinear systems by means of Volterra series analysis is presented. Necessary conditions for representing nonlinearities by a Volterra series are developed analytically as well as heuristically. A two-condition convergence criterion for Volterra series and a method for determining Volterra transfer functions are established. For systems with multiple nodes, an extension of Volterra series analysis; method of nonlinear currents is developed and applied to a MESFET...
Nonlinear Distortion in Wireless Systems: Modeling and Simulation with MATLAB
This book covers the principles of modeling and simulation of nonlinear distortion in wireless communication systems with MATLAB simulations and techniques
In this book, the author describes the principles of modeling and simulation of nonlinear distortion in single and multichannel wireless communication systems using both deterministic and stochastic signals. Models and simulation methods of nonlinear amplifiers explain in detail how to analyze and evaluate the performance of data...
Kernel Adaptive Filtering: A Comprehensive Introduction
Reproducing kernel Hilbert spaces is a topic of great current interest for applications in signal processing, communications, and controls The first book to explain real-time learning algorithms in reproducing kernel Hilbert spaces, On-Line Kernel Learning includes simulations that illustrate the ideas discussed and demonstrate their applicability as well as MATLAB codes for simulations. This book is ideal for professionals and graduate students interested in nonlinear adaptive systems for...
Adaptive Nonlinear System Identification: The Volterra and Wiener Model Approaches
Focuses on System Identification applications of the adaptive methods presented. but which can also be applied to other applications of adaptive nonlinear processes.
Covers recent research results in the area of adaptive nonlinear system identification from the authors and other researchers in the field.
The Volterra and Wiener Theories of Nonlinear Systems
This text presents a complete and detailed development of the analysis, design and characterization of non-linear systems using the Volterra and Wiener theories, as well as gate functions, thus yielding new insights and a better comprehension of the subject. The Volterra and Wiener theories are useful in the study of systems in biological, mechanical, and electrical fields.
Nonlinear Model-Based Image/Video Processing and Analysis
A comprehensive survey of techniques and applications in image and video processing and analysis A widely varied selection of experts provides extensive coverage of nonlinear model-based techniques in image and video processing and analysis. This volume not only details new techniques in still image and digital video but also discusses applications in computer vision, multimedia, and visual information retrieval systems. All nonlinear, model-based techniques are detailed, and a complete and...
Mathematical Problems in Image Processing
Partial differential equations and variational methods were introduced into image processing about 15 years ago, and intensive research has been carried out since then. The main goal of this work is to present the variety of image analysis applications and the precise mathematics involved. It is intended for two audiences. The first is the mathematical community, to show the contribution of mathematics to this domain and to highlight some unresolved theoretical questions. The second is the...
Recurrent Neural Networks for Prediction
New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real--time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters. ? Analyses the...
Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.
Polynomial Signal Processing
Despite our growing understanding of the properties and capabilities of nonlinear filters, there persists the belief among engineers that these filters are too complex to implement. This book debunks the myth that all nonlinear filters are complex with its coverage of the polynomial filter. It examines all major aspects of the technology, including system modeling, speed analysis, image processing, communications, biological signal processing, semiconductor modeling, neutral sets, and more.
