Discrete Fourier and Wavelet Transforms: An Introduction Through Linear Algebra With Applications to Signal Processing

Goodman, Roe W.

ISBN 10: 9814725765 / ISBN 13: 9789814725767

Published by World Scientific Pub Co Inc, 2016

About this Item

300 pages. 9.61x6.69x0.87 inches. In Stock.

Seller Inventory # x-9814725765

Bibliographic Details

• Title: Discrete Fourier and Wavelet Transforms: An Introduction Through Linear Algebra With Applications to Signal Processing
• Publisher: World Scientific Pub Co Inc
• Publication Date: 2016
• Language: English
• ISBN 10: 9814725765
• ISBN 13: 9789814725767
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.7 kilograms

About this title

Synopsis

"This book is suitable as a textbook for an introductory undergraduate mathematics course on discrete Fourier and wavelet transforms for students with background in calculus and linear algebra. The particular strength of this book is its accessibility to students with no background in analysis. The exercises and computer explorations provide the reader with many opportunities for active learning. Studying from this text will also help students strengthen their background in linear algebra."

Mathematical Association of America

This textbook for undergraduate mathematics, science, and engineering students introduces the theory and applications of discrete Fourier and wavelet transforms using elementary linear algebra, without assuming prior knowledge of signal processing or advanced analysis.

It explains how to use the Fourier matrix to extract frequency information from a digital signal and how to use circulant matrices to emphasize selected frequency ranges. It introduces discrete wavelet transforms for digital signals through the lifting method and illustrates through examples and computer explorations how these transforms are used in signal and image processing. Then the general theory of discrete wavelet transforms is developed via the matrix algebra of two-channel filter banks. Finally, wavelet transforms for analog signals are constructed based on filter bank results already presented, and the mathematical framework of multiresolution analysis is examined.