Data preprocessing is a crucial step for mining and learning from data, and one of its primary
activities is the transformation of data. This activity is very important in the context of time …

In this tutorial, we review recent wavelet denoising techniques for medical ultrasound and for
magnetic resonance images. We evaluate their implementation via MATLAB package and …

This second edition of The Illustrated Wavelet Transform Handbook: Introductory Theory and
Applications in Science, Engineering, Medicine and Finance has been fully updated and …

Given its broad applications, time series analysis has gained substantial research attention
but remains a very challenging task. Recent years have witnessed the great success of deep …

This study proposes a methodology for rolling element bearings fault diagnosis which gives
a complete and highly accurate identification of the faults present. It has two main stages …

Since the COVID-19 epidemic, people's attention has returned to the issue of their health,
through fitness and physical exercise to improve their resistance. The existing sports …

The paper provides a spatio-temporal change detection framework for the analysis of image
time series. In this framework, the detection of changes in time is addressed at the image …

This brief derives a 2-D spectrum estimator from some recent results on the statistical
properties of wavelet packet coefficients of random processes. It provides an analysis of the …

This paper first provides statistical properties of wavelet operators when the observation
model can be seen as the product of a deterministic piecewise regular function (signal) and …

In real-world situations, recorded signals are often contaminated by noise. These signals
and noise can be non-stationary and multi-component, which makes their time-frequency …