In the clinic. the traditional method to obtain Evoked Potential (EP) is the calculated average value of repeated measurements. This is infficient and inaccurate. An adaptive signal enhancement filter embedded with Least Mean Square (LMS) or Recursive Least Square (RLS) algorithm is an ffictive tool. It significantly decreases the number of repeat measurements needed to obtain satisfactory resu...

Adaptive filters are primary methods to remove the power line interference from the ECG signal. The frequency range of ECG signal is generally 0.05 Hz to 100 Hz, and that of the power line interference is 50 Hz which lies in the ECG signal band. So, it has become very crucial to remove the power line interference from the ECG signal. In this paper Kalman based least mean square (KLMS) filter ha...

Adaptive smart antenna arrays are used for cellular communication. This paper presents a comparative study of beamforming techniques for adaptive smart antenna using hybrid algorithms like Simple Matrix Inversion with Recursive Least Square (SMI-RLS) and Least Mean Square with Recursive Least Square (LMS-RLS) algorithm. The results are compared on the basis of null depth and error plot for diff...

Abstract. Adaptive inverse control of linear system with fixed learning rate least mean square (LMS) algorithm is improved by varying the learning rate. This variable learning rate LMS algorithm is proved to be convergent by using Lyapunov method. It has better performance especially when there is noise in command input signal. And it is simpler than the Variable Step-size Normalized LMS algori...

In this paper, we present the convergence analysis of proportionate-type least mean square (Pt-LMS) algorithm that identifies the sparse system effectively and more suitable for real time VLSI applications. Both first and second order convergence analysis of Pt-LMS algorithm is studied. Optimum convergence behavior of Pt-LMS algorithm is studied from the second order convergence analysis provid...

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 (...

An adaptive transversal equalizer based on the least-mean-square (LMS) algorithm, operating in an environment with a temporally correlated interference, can exhibit better steady-state mean-square-error (MSE) performance than the corresponding Wiener filter. This phenomenon is a result of the nonlinear nature of the LMS algorithm and is obscured by traditional analysis approaches that utilize t...

A neural network design – the adaptive resonance theory least mean square (ART-LMS) neural network – is proposed for the restoration of images corrupted by impulse noise. The network design is based on the concept of a counterpropagation network (CPN). The ART network automatically uses a vigilance parameter to generate the cluster layer node for the Kohonen learning algorithm in CPN. In additi...

In this paper we designed a least mean square (LMS) adaptive filter to remove the unwanted noise which might occur during music recordings, echo in telephone networks, etc. Generally all LMS algorithm starts with an assumption of weight vector as zero initially and iteration continues till the error is minimized till its optimum level. This takes much more time to compute the optimized coeffici...

This paper explores the potential of several popular equalization techniques and proposes new approaches to overcome their disadvantages. Such as the conventional least-mean-square (LMS ) algorithm, the recursive least-squares ( RLS ) algorithm, the filtered-X LMS algorithm and their development. An H 2 optimal initialization has been proposed to overcome the slow convergence problem while keep...