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Advances in heuristic signal processing and applications
Chatterjee A., Nobahari H., Siarry P., Springer Publishing Company, Incorporated, New York, NY, 2013. 394 pp. Type: Book (978-3-642378-79-9)
Date Reviewed: Feb 20 2014

Signal detection in the presence of noise is a difficult optimization problem. To obtain computationally reasonable algorithms, it is necessary to use heuristics to reduce the computations. This book is an edited series of chapters by different authors on recent work in signal processing. The editors contribute an initial overview that summarizes the individual contributions. The chapters do not appear to be organized into areas of application; some chapters focus on specific application areas, while others concentrate on specific techniques. In particular, topics include detecting objects, tracking moving objects, and distinguishing component signals in a received signal. A number of the chapters make use of heuristics derived from artificial intelligence, such as in particle swarm optimization.

While the organization of the book is not based on common topics, the individual chapters tend to fall into one of a handful of categories. One group relates to techniques, including general applications such as nonconvex optimization applied to the design of a two-channel linear phase finite impulse response quadrature mirror filter; robust reduced rank linearly constrained beam-forming algorithms; the iterative design of finite impulse response filters that uses particle swarm optimization; 2D recursive filter design that uses a modified version of an interesting technique called invasive weed optimization to better explore the design space; and a heuristic optimal design of multiplier-less digital filters. This group also features the introduction of a genetic algorithm that preserves the canonic signed digit code structure. The last chapter in this group uses a priori knowledge and a dictionary of potential sources for signal separation.

Another group deals with applications, with chapters on detecting objects and designing radar wave forms for target detection using a multi-objective optimization technique. Other chapters in this group explore the use of particle swarm optimization for multi-object tracking; how meta-heuristics can be used to determine optimal power allocation in wireless sensor networks; and the use of the interaction between detection and tracking in adaptive radar systems. Another chapter surveys the ways in which kurtosis optimization schemes can be used for source separation when the known sources are observed as a linearly distorted mixture at a sensor array. The authors apply swarm intelligence and ant colony optimization to estimate the state of nonlinear systems, treating the problem as a stochastic dynamic optimization. The last chapter in this group uses hybrid correlation-neutral network synergy for gait signal classification, and applies this to the problem of distinguishing healthy subjects from those suffering from conditions such as amyotrophic lateral sclerosis (ALS) based on how they walk.

The last group contains chapters on imaging applications, with such topics as the use of wavelets for image denoising in medical applications; a particular concern here is to balance the noise reduction and the retention of significant details. Another chapter describes a discrete color monogenic wavelet transform for object recognition, generalizing the way in which monogenic wavelet transforms are used to convert grayscale images to color images. The final chapter in this group presents a “state-of-the-art” survey for image matching and feature matching in two and three dimensions, with the restriction that only methods suitable for embedded or wearable real-time systems are considered.

Each chapter concludes with its own comprehensive bibliography, so that the interested reader can dip into the book and then follow the references for more information on topics of particular interest. The book should be of interest to both practitioners and students interested in the current state of signal processing.

Reviewer:  J. P. E. Hodgson Review #: CR142019 (1405-0297)
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Signal Processing Systems (C.3 ... )
 
 
Edge And Feature Detection (I.4.6 ... )
 
 
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