The Mathematics of Signal Processing (Cambridge Texts in Applied Mathematics 48)

The Mathematics of Signal Processing (Cambridge Texts in Applied Mathematics 48)

By: Willard Miller (author), Steven B. Damelin (author)Paperback

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Description

Arising from courses taught by the authors, this largely self-contained treatment is ideal for mathematicians who are interested in applications or for students from applied fields who want to understand the mathematics behind their subject. Early chapters cover Fourier analysis, functional analysis, probability and linear algebra, all of which have been chosen to prepare the reader for the applications to come. The book includes rigorous proofs of core results in compressive sensing and wavelet convergence. Fundamental is the treatment of the linear system y= x in both finite and infinite dimensions. There are three possibilities: the system is determined, overdetermined or underdetermined, each with different aspects. The authors assume only basic familiarity with advanced calculus, linear algebra and matrix theory and modest familiarity with signal processing, so the book is accessible to students from the advanced undergraduate level. Many exercises are also included.

About Author

Steven B. Damelin is currently Full Professor at Georgia Southern University and Visiting Full Professor at the University of the Witwatersrand in Johannesburg. Willard Miller, Jr is Professor Emeritus in the School of Mathematics at the University of Minnesota.

Contents

1. Introduction; 2. Normed vector spaces; 3. Analytic tools; 4. Fourier series; 5. Fourier transforms; 6. Compressive sensing; 7. Discrete transforms; 8. Linear filters; 9. Windowed Fourier transforms, continuous wavelets, frames; 10. Multiresolution analysis; 11. Discrete wavelet theory; 12. Biorthogonal filters and wavelets; 13. Parsimonious representation of data; Bibliography; Index.

Product Details

  • ISBN13: 9781107601048
  • Format: Paperback
  • Number Of Pages: 462
  • ID: 9781107601048
  • weight: 680
  • ISBN10: 1107601045

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