In this paper we provide a thorough ser( symbol error rate) analysis of two well known adaptive algorithms for equalization based on a novel least squares reference model that allows to treat the equalizer problem equivalently as system identification problem. An adaptive algorithm is a procedure for adjusting the parameters of an adaptive filter to minimize a cost function chosen for the task ...

This paper presents a simple and easy implementable Least Mean Square (LMS) type approach for frequency estimation of three phase power system in an unbalanced condition. The proposed LMS type algorithm is based on a second order recursion for the complex voltage derived from Clarke's transformation which is proved in the paper. The proposed algorithm is real adaptive filter with real parameter...

Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity. In the scenarios of sparse channel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS) and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channel sparsity. However, these proposed algorithms may hard to make tradeoff between convergence speed ...

Broadband signal transmission over frequencyselective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation methods is least mean square (LMS) algorithm. However, LMS-based method is often degraded by random scaling of input training signal. To improve the estimation performance, in this paper we apply the standard l...

We present an efficient architecture for the implementation of delayed least mean square adaptive filter. We use a novel partial product generator and propose a strategy for optimized balanced pipelining across the time consuming combinational blocks of the structure .An efficient systolic architecture of the delayed least mean square adaptive filter based on the processing element We propose a...

We present an efficient architecture for the implementation of delayed least mean square adaptive filter. We use a novel partial product generator and propose a strategy for optimized balanced pipelining across the time consuming combinational blocks of the structure .An efficient systolic architecture of the delayed least mean square adaptive filter based on the processing element .We propose ...

A new adaptive lter algorithm has been developed that combines the beneets of the Least Mean Square (LMS) and Least Mean Fourth (LMF) methods. This algorithm , called LMS/F, outperforms the standard LMS algorithm judging either constant convergence rate or constant misadjustment. While LMF outperforms LMS for certain noise prooles, its stability cannot be guaranteed for known input signals even...

Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a general framework in nonstationary environment. This approach is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. This general performance analysis ca...

Various Adaptive filtering methods are using to filter Cardiac signals. The two basic adaptive filtering algorithms are LMS (Least Mean Square) and RLS (Recursive Least Square). These Adaptive Algorithms are used to filter artifacts from ECG signal. Adaptive filter minimises the mean square error between the primary input, which is ECG with noise and desired response, which is either ECG or noi...

This paper implements a VLSI based Adaptive noise canceller (ANC) using Least Mean-Square (LMS) and Normalized Least Mean-Square (NLMS). Adaptive filters are capable of adapting their filter coefficients as per the variations in characteristics of input signal and noise to achieve a noise free signal. These algorithms have been implemented on Digital Signal Processor. Platform used for implemen...