What is this Book all about?
Jaideva C. Goswami, Andrew K. Chan · 2010
The concept of wavelet analysis has been in place in one form or the other since the beginning of this century. In applications to discrete data sets, wavelets may be considered basis functions generated by dilations and translations of a single function. Wavelet techniques enable us to divide a complicated function into several simpler ones and study them separately. Different types of wavelets have been used as tools to solve problems in signal analysis, image analysis, medical diagnostics, boundary - value problems, geophysical signal processing, statistical analysis, pattern recognition, and many others. A reason for the popularity of wavelets is their effectiveness in representation of nonstationary (transient) signals. The wavelet analysis is explained via a parallel with the Fourier analysis and short - time Fourier transform. Controlled Vocabulary Terms discrete Fourier transforms; signal analysis; wavelet transforms