Nonlinear Signal Processing: A Statistical Approach focuses on
unifying the study of a broad and important class of nonlinear
signal processing algorithms which emerge from statistical
estimation principles, and where the underlying signals are
non-Gaussian, rather than Gaussian, processes. Notably, by
concentrating on just two non-Gaussian models, a large set of tools
is developed that encompass a large portion of the nonlinear signal
processing tools proposed in the literature over the past several
decades.
Key features include:
* Numerous problems at the end of each chapter to aid development
and understanding
* Examples and case studies provided throughout the book in a wide
range of applications bring the text to life and place the theory
into context
* A set of 60+ MATLAB software m-files allowing the reader to
quickly design and apply any of the nonlinear signal processing
algorithms described in the book to an application of interest is
available on the accompanying FTP site.
Circa l’autore
GONZALO R. ARCE received a Ph D degree in electrical engineering from Purdue University in 1982. Since 1982, he has been with the faculty of the Department of Electrical and Computer Engineering at the University of Delaware where he is currently Charles Black Evans Distinguished Professor and Chairman. He has held visiting professor appointments at the Unisys Corporate Research Center and at the International Center for Signal and Image Processing, Tampere University of Technology, in Tampere, Finland. He holds seven U.S. patents, and his research has been funded by Do D, NSF, and numerous industrial organizations. He is an IEEE Fellow for his contributions to the theory and applications of nonlinear signal processing.
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