Wavelet transforms are recent mathematical techniques, based on group theory and square
integrable representations, which allows one to unfold a signal, or a field, into both space …

Most of the real-life signals are non-stationary in nature. The examples of such signals
include biomedical signals, communication signals, speech, earthquake signals, vibration …

Wheelsets are important components of railway vehicles, and their health directly affects
running safety, operation efficiency, and passenger comfort, so it is particularly important to …

We discuss the basic ideas and motivations of time-frequency analysis from the point of view
of what is known, what is not, what is mathematically possible, and what is not. We address …

A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and
tutorial purposes. The discussion includes nonstationary signal analysis, scale versus …

It has been well understood that a given signal can be represented in an infinite number of
different ways. Different signal representations can be used for different applications. For …

Interest in parameterized time-frequency analysis for non-stationary signal processing is
increasing steadily. An important advantage of such analysis is to provide highly …

The continuous wavelet transform has deep mathematical roots in the work of Alberto P.
Calderon. His seminal paper on complex method of interpolation and intermediate spaces …

This paper develops the theory of a new general class of signal energy representations
depending on time and scale. Time-scale analysis has been introduced recently as a …

A new time-frequency distribution (TFD) that adapts to each signal and so offers a good
performance for a large class of signals is introduced. The design of the signal-dependent …