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Signal Processing : A Mathematical Approach, Second Edition

By: (Author) Charles L. Byrne

Werezi Extended Catalogue
Delivery in 34 days

Ksh 34,200.00

Format: Hardback or Cased Book

ISBN-10: 1482241846

ISBN-13: 9781482241846

Edition Number: 2

Series: Chapman & Hall/CRC Monographs and Research Notes in Mathematics

Publisher: Taylor & Francis Inc

Imprint: Chapman & Hall/CRC

Country of Manufacture: CA

Country of Publication: GB

Publication Date: Nov 12th, 2014

Print length: 440 Pages

Weight: 770 grams

Dimensions (height x width x thickness): 24.10 x 15.90 x 2.80 cms

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This book explains how mathematical tools can be used to solve problems in signal processing. Assuming an advanced undergraduate- or graduate-level understanding of mathematics, this second edition contains new chapters on convolution and the vector DFT, plane-wave propagation, and the BLUE and Kalman filters. It expands the material on Fourier analysis to three new chapters to provide additional background information, presents real-world examples of applications that demonstrate how mathematics is used in remote sensing, and includes robust appendices and problems for classroom use.

Signal Processing: A Mathematical Approach is designed to show how many of the mathematical tools the reader knows can be used to understand and employ signal processing techniques in an applied environment. Assuming an advanced undergraduate- or graduate-level understanding of mathematics—including familiarity with Fourier series, matrices, probability, and statistics—this Second Edition:

  • Contains new chapters on convolution and the vector DFT, plane-wave propagation, and the BLUE and Kalman filters
  • Expands the material on Fourier analysis to three new chapters to provide additional background information
  • Presents real-world examples of applications that demonstrate how mathematics is used in remote sensing

Featuring problems for use in the classroom or practice, Signal Processing: A Mathematical Approach, Second Edition covers topics such as Fourier series and transforms in one and several variables; applications to acoustic and electro-magnetic propagation models, transmission and emission tomography, and image reconstruction; sampling and the limited data problem; matrix methods, singular value decomposition, and data compression; optimization techniques in signal and image reconstruction from projections; autocorrelations and power spectra; high-resolution methods; detection and optimal filtering; and eigenvector-based methods for array processing and statistical filtering, time-frequency analysis, and wavelets.


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