Abstract Continuous wavelet analysis has been increasingly employed in various fields of science and engineering due to its remarkable ability maintain optimal resolution both space scale. Here, we introduce wavelet-based statistics, including the power spectrum, cross correlation, bicoherence, analyze large-scale clustering matter. For this purpose, perform transforms on density distribution o...

In this paper the faults diagnosis of induction machines based on the discrete wavelet transform (DWT) is detailed. The wavelet decomposition is used to extract the information from a signal over a wide range of frequencies. This analysis is performed in both time and frequency domains. The Daubechies wavelet is selected for the analysis of the stator current. Wavelet components appear to be us...

The relation between Fourier spectra and spectra obtained from wavelet analysis is established. Small scale asymptotic analysis shows that the wavelet spectrum is meaningful only when the analysing wavelet has enough vanishing moments. We relate these results to regularity theorems in Besov spaces. For the analysis of innnitly regular signals, a new wavelet, with innnit number of cancellations ...

The WaveStat project deals with the exploratory Cluster Analysis of picture data and wavelet-built coefficients. Cluster Analysis means to collect elements (i.e. pixels) of the same property (in this context this means: gray value) to a cluster. These clusters can be visualized by colorizing them or by reconstructing them to a grayscale picture. In this article we will show that a cluster analy...

Gait is a biometric feature and gait recognition has become a challenging problem in computer vision. New wavelet velocity moments have been developed to describe and recognize gait. Wavelet moments are translation, scale and rotation invariant. Wavelet analysis has the trait of multi-resolution analysis, which strengthens the analysis ability to image subtle feature. According with the psychol...

Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in motor unit potentials (MUPs) of its motor units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial. Objective: To investigate the possibility of improv...

Wavelet-based analysis versus Gaussian smoothing in statistical parametric mapping (SPM) for detecting and analyzing brain activity from functional magnetic resonance imaging (fMRI) data is presented. Detection of activation in fMRI data can be performed in the wavelet domain by a coefficient-wise statistical t-test. The link between the wavelet analysis and SPM is based on two observations: (i...

financial companies are constantly exposed to the dangers of risk. in the last few years for various reasons, measuring value at risk (var) has become increasingly important for financial firms. the study of multiple measures of risk, var measure with a new approach provides the ground for calculation of market risk. common approaches to risk measurement due to complicated, nonlinear and changi...