The LMS adaptive algorithm is the most popular algorithm for adaptive ltering because of its simplicity and robustness. However, its main drawback is slow convergence whenever the adaptive lter input auto-correlation matrix is ill-conditioned i.e. the eigenvalue spread of this matrix is large 2, 4]. Our goal in this paper is to develop an adaptive signal transformation which can be used to spee...

Extracting clean fetal electrocardiogram (ECG) signals is very important in fetal monitoring. In this paper, we proposed a new method for fetal ECG extraction based on wavelet analysis, the least mean square (LMS) adaptive filtering algorithm, and the spatially selective noise filtration (SSNF) algorithm. First, abdominal signals and thoracic signals were processed by stationary wavelet transfo...

LMS algorithm is simple and is well suited for continuous transmission systems since it is a continuously adaptive algorithm. However, it is not known for its convergence speed in the presence of Gaussian, spatially white, of null mean and variance which has prompted people to use other complicated algorithms. In the above scenario LMS has maximum mean square error and minimum error stability. ...

Electrocardiogram (ECG) signal is affected by many noise interferences. Out of all the noise effects the power line interference is the predominant one. In this paper the implementation of the adaptive algorithm techniques for reduction in this power line interference is shown and a comparison of these techniques is performed. The adaptive filters used have shown a good improvement in the SNR (...

The least mean squares (LMS) algorithm is one of the most popular recursive parameter estimation methods. In its standard form it does not take into account any special characteristics that the parameterized model may have. Assuming that such model is sparse in some domain (for example, it has sparse impulse or frequency response), we aim at developing such LMS algorithms that can adapt to the ...

In this paper we develop a modified filtered-x least mean squares (MFX-LMS) method to synthesis an adaptive repetitive controller for rejecting periodic disturbances at selective frequencies. We show how a MFX-LMS algorithm can be utilized when the reference signal is deterministic and periodic. A new adaptive step size is proposed with the motivation to improve the convergence rate of the MFX-...

This paper presents an efficient design of Adaptive filters which uses enhanced NLMS algorithm for eliminating noise added by mean of various communication media or any other noise sources. By using the appropriate weights, Adaptive filter estimates and remove the estimated noise signal from the available information. LMS and Normalized LMS are two most efficient algorithm for noise cancelation...

The main goal of this paper is to study and to compare the performance of different adaptive filter algorithms for noise cancellation. Adaptive noise cancellation method is used for estimating a speech signal which is corrupted by an additive noise. The reference input containing noise is adaptively filtered and subtracted from the primary input signal to obtain the de-noised signal. The desire...

The demand for increased capacity in wireless communication networks has motivated recent research activities toward wireless systems that exploit the concept of smart antenna and space selectivity. Efficient utilization of limited radio frequency spectrum is only possible to use smart/adaptive antenna system. Smart antenna radiates not only narrow beam towards desired users exploiting signal p...