Svd And Signal Processing
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SVD and Signal Processing
Compiled in this book is a selection of articles written by internationally recognized experts in the fields of matrix computation and signal processing. In almost all digital signal processing (DSR) problems, the available data is corrupted by (measurement) noise or is incomplete. Classical techniques are unable to separate ''signal'' spaces and ''noise'' spaces. However, the information hidden in the data can be made explicit through singular value decomposition (SVD). SVD based signal processing is making headway and will become feasible soon, thanks to the progress in parallel computations and VLSI implementation. The book is divided into six parts. Part one is a tutorial, beginning with an introduction, including (VLSI) parallel algorithms and some intriguing problems. It describes several applications of SVD in system identification and signal detection. It also deals with the fundamental harmonic retrieval problem and principal component analysis. Part two discusses details of model reduction, system identification and detection of multiple sinusoids in white noise, while part three is devoted to the total-least-squares and generalized singular value decomposition problems. The fourth section deals with real-time and adaptive algorithms, the fifth examines fast algorithms and architectures, such as block-algorithms, computational arrays, systolic arrays, hypercubes and connection machines, and the final part addresses some open problems.
SVD and Signal Processing, III
Matrix Singular Value Decomposition (SVD) and its application to problems in signal processing is explored in this book. The papers discuss algorithms and implementation architectures for computing the SVD, as well as a variety of applications such as systems and signal modeling and detection.The publication presents a number of keynote papers, highlighting recent developments in the field, namely large scale SVD applications, isospectral matrix flows, Riemannian SVD and consistent signal reconstruction. It also features a translation of a historical paper by Eugenio Beltrami, containing one of the earliest published discussions of the SVD.With contributions sourced from internationally recognised scientists, the book will be of specific interest to all researchers and students involved in the SVD and signal processing field.
SVD and Signal Processing, II
Author: Richard J. Vaccaro
language: en
Publisher: Elsevier Publishing Company
Release Date: 1991
This volume is an outgrowth of the 2nd International Workshop on SVD and Signal Processing which was held in Kingston, Rhode Island, 25-27 June, 1990. The singular value decomposition (SVD) has been applied to signal processing problems since the late 1970's, although it has been known in various forms for over 100 years. SVD filtering has been shown to give better results at lower signal-to-noise ratios than classical techniques based on linear filtering. This explains in part the recent interest in SVD techniques for signal processing. This book is a compilation of papers that examine in detail the singular decomposition of a matrix and its application to problems in signal processing. Algorithms and implementation architectures for computing the SVD are discussed, and analysis techniques for predicting and understanding the performance of SVD-based algorithms are given. The volume will provide both a stimulus for future research in this field as well as useful reference material for many years to come.