In this paper, a new spatio-temporal filtering method for removing noise from image sequences is proposed. This method combines the use of motion compensation and signal decomposition to account for the effects of object motion. Because of object motion, image sequences are temporally nonstationary, which requires the use of adaptive filters. By motion compensating the sequence prior to filteri...

Parametric modeling of non-stationary signals is addressed in this article. We present several models based on the characteristic features of the modelled signal, together with the methods for accurate estimation of model parameters. Non-stationary signals, viz. transient system response, speech phonemes, and electrocardiograph signal are fitted by these feature-based models.

A new approach to modified S-transform based non-stationary power signal de-noising is presented in this paper. Modified S-transform is employed in de-noising techniques to separate high frequency noise which is prevalent in practical signals like power disturbance signals from the low frequency undistorted signals. In this we have tried to bring out the advantage of the modified S-transform an...

In analyzing a signal, especially a non-stationary signal, it is often necessary the desired signal to be segmented into small epochs. Segmentation can be performed by splitting the signal at time instances where signal amplitude or frequency change. In this paper, the signal is initially decomposed into signals with different frequency bands using wavelet transform. Then, fractal dimension of ...

This paper presents a new lossy compression algorithm for stationary signal based on Discrete Walsh Hadamard Transform (DWHT). The principle of compression algorithm consists in framing the original speech signal into stationary frames and applying the DWHT. Then, the obtained coefficients are thresholded in order to truncate all coefficients below a given thresholds values. Compression is achi...

Biomedical signals are non-stationary and a research topic of practical interest as the signal has time varying statistics. The problem of time varying is usually circumvented by assuming local stationary over a short time interval, where stationary techniques are applied. However, features extracted from these methods are not always suitable and methods for non-stationary process are needed. T...

In this paper, we propose a unique approach to enhance speech signals that have been corrupted by non-stationary noises. This approach is not based on a spectral subtraction algorithm, but on an algorithm that separates the speech signal and noise signal contributions in the autocorrelation domain. We call this technique the AR-HASE speech enhancement algorithm. In this initial study, we evalua...