Recently the Wavelet Transform has gained a lot of popularity in the field of signal and image processing. This is due to its capability of providing both time and frequency information simultaneously, hence giving a timefrequency representation of the signal. The traditional Fourier Transform can only provide spectral information about a signal. Moreover, the Fourier method only works for stat...

This chapter investigates the use of advanced signal processing techniques especially wavelet transforms to extract additional information from a two dimensional surface profile. The wavelet transform is able to aid the user in quickly assessing, visually, if the surface profile has a periodic or non-periodic component as well as if the profile signal is stationary or non-stationary. In additio...

Traditional Fast Fourier Transform algorithm makes the implementation of the multicarrier system feasible and attractive with respect to single-carrier systems. However, when the channel is doubly selective (i.e. time-frequencyselective), this traditional methodology fails. Since the channel frequency response is rapidly time-varying as a result the optimal transmission/reception methodology no...

The purpose of the study is to develop a recurrent neural network for detecting moment beginning transient process random non-stationary signal in sliding window. possibility using apparatus artificial networks (ANN) analyzing processes investigated. Rapid detection at which changes its behavior an urgent task electrical engineering.
 
 Materials and methods. A comparison made between...

We use the analytic signal approach based on the Hilbert transform to compute the phase diierence between two non-stationary signals and nd out epochs of phase locking.

For n ∈ N, r ∈ (0,∞) and a Radon random vector X with values in a Banach space E let en,r(X,E) = inf(Emina∈α ‖X − a‖), where the infimum is taken over all subsets α of E with card(α) ≤ n (n-quantizers). We investigate the existence of optimal n-quantizers for this L-quantization propblem, derive their stationarity properties and establish for L-spaces E the pathwise regularity of stationary qua...

A time continuous statistical model of multiple chirp signal observed against the background strongly-dependent stationary Gaussian noise is considered in paper. Strong consistency least squares estimates for such a trigonometric regression parameters proved.

Segmentation of a nonstationary process consists in assuming piecewise stationarity and in detecting the instants of change. We consider here that all the data is available in a same time and perform a global segmentation instead of a sequential procedure. We build a change process and define arbitrarily its prior distribution. That allows us to propose the MAP estimate as well as some minimum ...