Sampling theory in Fourier and signal analysis : advanced topics
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Bibliographic Information
Sampling theory in Fourier and signal analysis : advanced topics
（Oxford science publications）
Oxford University Press, 1999
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Includes bibliographical references and index
Description and Table of Contents
Description
This is the second of a two-volume series on sampling theory. The mathematical foundations were laid in the first volume, and this book surveys the many applications of sampling theory both within mathematics and in other areas of science. Many of the topics covered here are not found in other books, and all are given an up to date treatment bringing the reader's knowledge up to research level. This book consists of ten chapters, written by ten different teams of
authors, and the contents range over a wide variety of topics including combinatorial analysis, number theory, neural networks, derivative sampling, wavelets, stochastic signals, random fields, and abstract harmonic analysis. There is a comprehensive, up to date bibliography.
Table of Contents
- 1. Applications of sampling theory to combintorial analysis, Stirling numbers, special functions and the Riemann zeta function
- 2. Sampling theory and the arithmetic Fourier transform
- 3. Derivative sampling - a paradigm example of multi-channel methods
- 4. Computational methods in linear prediction for band-limited signals based on past samples
- 5. Interpolation and sampling theories, and linear ordinary boundary value problems
- 6. Sampling by generalized kernels
- 7. Sampling theory and wavelets
- 8. Approximation by translates of a radial function
- 9. Almost sure sampling restoration of band-limited stochastic signals
- 10. Abstract harmonic analysis and the sampling theorem
by "Nielsen BookData"