Books in the Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control series

A comprehensive treatment of cognitive radio networks and the specialized techniques used to improve wireless communications The human brain, as exemplified by cognitive radar, cognitive radio, and cognitive computing, inspires the field of Cognitive Dynamic Systems.

Presents a detailed description of practical methods to control echo and noise. This work develops a statistical theory for optimal control parameters and presents practical estimation and approximation methods.

* Emphasizes important applications and theoretical advances, e.g. , complex-valued signal processing * Examines the seven most important topics in adaptive filtering that will define the next generation adaptive filtering solutions.

Describes and discusses the variants of kernel analysis methods for data types that have been intensely studied in recent years This book covers kernel analysis topics ranging from the fundamental theory of kernel functions to its applications.

Providing an extensive overview of the radio resource management problem in femtocell networks, this invaluable book considers both code division multiple access femtocells and orthogonal frequency-division multiple access femtocells.

* On-line learning is a fundamental tool in adaptive signalprocessing * Presents on-line learning from a signal processingperspective.

Using a pedagogical style along with detailed proofs and illustrative examples, this book opens a view to the largely unexplored area of nonlinear systems with uncertainties. The focus is on adaptive nonlinear control results introduced with the new recursive design methodology--adaptive backstepping.

Focuses on robust control, currently a very important topic in control research and engineering. The interest in this area is motivated by the need to achieve greater accuracy and predictability in modern control systems, as are found in aircraft and rocket navigation systems, for example.

Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. Researchers are applying neural networks and fuzzy systems in series, from the use of fuzzy inputs and outputs for neural networks to the employment of individual neural networks to quantify the shape of a fuzzy membership function.

This text presents an approach developed by the author, to handle some of the most common types of component imperfections encountered in industrial automation, consumer electronics, and defence and transportation systems.

Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks.

Because Wideband Multiple Input and Multiple Output (MIMO) technology is just now being implemented in hardware, there is currently a great desire for knowledge of both the theory and practicality of its channels.

Correlative Learning: A Basis for Brain and Adaptive Systems provides a bridge between three disciplines: computational neuroscience, neural networks, and signal processing. First, the authors lay down the preliminary neuroscience background for engineers.

The first book to deal with key technology for the next generation of radar systems with important military and homeland defence applications. Knowledge-based techniques are widely considered by the radar community to represent the key area of advancement for the next generation of radar systems.

Space-time communication is a hot topic in academia and industry, but, current research only appears in journal articles. This comprehensive book provides coverage of cutting-edge research in the field, with an emphasis on Multiple-Input Multiple-Output (MIMO) wireless communication systems.

An introduction to the concept of backpropagation, or "dynamic feedback", an algorithm developed by the author to be used in neural networking. The text initiates a body of background tools capable of generating applications across a range of fields, from engineering to political forecasting.

Neural networks consist of interconnected groups of neurons which function as processing units and aim to reconstruct the operation of the human brain.

Intelligent Image Processing describes the EyeTap technology that allows non-invasive tapping into the human eye through devices built into eyeglass frames. This isn't merely about a computer screen inside eyeglasses, but rather the ability to have a shared telepathic experience among viewers.

This title deals with the design of Radial Basis Function Networks (RBFNs), one of two classes of feedforward networks with applications in artificial neural networks, for particular tasks. These applications are in such engineering problems as nonlinear process estimation and control.

Examining a specialised part of neural networks, with applications in control, signal processing and time series analysis, this title provides an up-to-date treatment of a class of nonlinear dynamical systems using feed forward neural network structures.

This well organized guide treats adaptive control design and analysis in an authoritative, rigorous manner. Gives both continuous-time and discrete-time adaptive control designs and their analysis. Deals with both single-input single-output and muli-input multi-output systems.

From elininating outside interference to separate data stream traveling together, filters are used for a range of applications in communications. The Least Mean Square (LMS) filter has established itself as the workhorse for the design of linear adaptive systems. This book deals with this topic.

Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. Researchers are applying neural networks and fuzzy systems in series, from the use of fuzzy inputs and outputs for neural networks to the employment of individual neural networks to quantify the shape of a fuzzy membership function.

The filtering of real world signals requires an adaptive mode of operation to deal with the statistically nonstationary nature of the data. Feedback and nonlinearity within filtering architectures are needed to cater for long time dependencies and possibly nonlinear signal generating mechanisms.

A collection of tutorial articles on recent advancements and state-of-the-art results Provides a comprehensive overview of sensor and array processing. Covers fundamental principles as well as applications. Features some of the most prominent researchers from different centers in North America and Europe.

Model-Based Signal Processing develops the "model-based approach" to signal processing for a variety of useful model sets including the popularly termed "physics-based" models. It presents a unique viewpoint of signal processing from the model-based perspective.

A highly accessible and unified approach to the design and analysis of intelligent control systems Adaptive Approximation Based Control is a tool every control designer should have in his or her control toolbox.

A complete, one-stop reference on the state of the art of unsupervised adaptive filtering While unsupervised adaptive filtering has its roots in the 1960s, more recent advances in signal processing, information theory, imaging, and remote sensing have made this a hot area for research in several diverse fields.

This book describes the use of neural networks and fuzzy methods for identifying and controlling nonlinear dynamical systems. It combines advanced concepts from traditional control theory with the intuitive properties of intelligent systems to solve real-world control problems.

Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the design of neural networks. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear.