Linear algebra for signal processing
著者
書誌事項
Linear algebra for signal processing
（The IMA volumes in mathematics and its applications, v. 69）
Springer-Verlag, c1995
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注記
"With 37 illustrations."
"Papers based on lectures presented at the IMA Workshop on Linear Algebra for Signal Processing held at the Institute for Mathematics and its Applications, University of Minnesota, Minneapolis, April 6-10, 1992"--Pref
"Based on proceedings of a workshop that was an integral part of the 1991-92 IMA program on 'Applied Linear Algebra'"--Foreword
Includes bibliographical references
内容説明・目次
内容説明
Signal processing applications have burgeoned in the past decade. During the same time, signal processing techniques have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This trend will continue as many new signal processing applications are opening up in consumer products and communications systems.
In particular, signal processing has been making increasingly sophisticated use of linear algebra on both theoretical and algorithmic fronts. This volume gives particular emphasis to exposing broader contexts of the signal processing problems so that the impact of algorithms and hardware can be better understood; it brings together the writings of signal processing engineers, computer engineers, and applied linear algebraists in an exchange of problems, theories, and techniques. This volume will be of interest to both applied mathematicians and engineers.
目次
Structured matrices and inverses.- Structured condition numbers for linear matrix structures.- The canonical correlations of matrix pairs and their numerical computation.- Continuity of the joint spectral radius: Application to wavelets.- Inversion of generalized Cauchy matrices and other classes of structured matrices.- Wavelets, filter banks, and arbitrary tilings of the time-frequency plane.- Systolic algorithms for adaptive signal processing.- Adaptive algorithms for blind channel equalization.- Square-root algorithms for structured matrices, interpolation, and completion problems.
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