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Titlebook: Computational Signal Processing with Wavelets; Anthony Teolis Book 1998 Birkhäuser Boston 1998 Fourier transform.Gabor transform.MATLAB.Sh

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书目名称Computational Signal Processing with Wavelets
编辑Anthony Teolis
视频video
概述Includes supplementary material:
丛书名称Applied and Numerical Harmonic Analysis
图书封面Titlebook: Computational Signal Processing with Wavelets;  Anthony Teolis Book 1998 Birkhäuser Boston 1998 Fourier transform.Gabor transform.MATLAB.Sh
描述Overview For over a decade now, wavelets have been and continue to be an evolving subject of intense interest. Their allure in signal processing is due to many factors, not the least of which is that they offer an intuitively satisfying view of signals as being composed of little pieces of wa‘ues. Making this concept mathematically precise has resulted in a deep and sophisticated wavelet theory that has seemingly limitless applications. This book and its supplementary hands-on electronic: component are meant to appeal to both students and professionals. Mathematics and en­ gineering students at the undergraduate and graduate levels will benefit greatly from the introductory treatment of the subject. Professionals and advanced students will find the overcomplete approach to signal represen­ tation and processing of great value. In all cases the electronic component of the proposed work greatly enhances its appeal by providing interactive numerical illustrations. A main goal is to provide a bridge between the theory and practice of wavelet-based signal processing. Intended to give the reader a balanced look at the subject, this book emphasizes both theoretical and practical issues of
出版日期Book 1998
关键词Fourier transform; Gabor transform; MATLAB; Shannon; Symbol; code; discrete Fourier transform; information;
版次1
doihttps://doi.org/10.1007/978-1-4612-4142-3
isbn_softcover978-1-4612-8672-1
isbn_ebook978-1-4612-4142-3Series ISSN 2296-5009 Series E-ISSN 2296-5017
issn_series 2296-5009
copyrightBirkhäuser Boston 1998
The information of publication is updating

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Overcomplete Wavelet Transform,e few constraints on the spacing between sample points throughout the time—scale plane; however, computational consideration is restricted here to an interesting subclass of sampling sets that allow for the fast computation of the forward and inverse transforms.. In this case, the freedom of choice
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