VLSI Systolic Architecture Implementation for Noise Elimination from ECG Signal

Different forms of noise are caused by electrocardiogram (ECG) signals, which vary founded on frequency content. To enhance accurateness and dependability, the elimination of such a trouble is necessary. Denoising ECG pointers is difficult as it is difficult to add secure coefficient filter. It is possible to use adaptive filtering techniques, in which the feature vectors can be changed to top dynamic signal changes. With a degree of sparsity, such as non-sparse, partial sparse and sparse, the framework shifts. The Least Mean Square (LMS) and Zero Attractor LMS (ZA-LMS) convex filtering combination is ideal for both Sparse and Non-Sparse settings. Popular the proposed design, the Systolic Architecture is introduced in direction to improve device efficiency and to reduce the combinational delay path. Systolic architectures are developed using the Xilinx device generator tool for normal Least Mean Square (LMS), Zero Attractor LMS (ZA-LMS) and Convex combinations of Least Mean Square (LMS) and Zero Attractor LMS (ZA-LMS) interfaces.Simulation remains performed with various ECG signals obtained from MIT-BIH database as input to designed filtering and its SNR is obtained. The study shows that the SNR value in systolic architectures is higher than in filter bank structures. For systolic LMS buffers, the SNR value is 4.5 percent greater than the structure of the Lms algorithm. The SNR for the systolic separation technology of ZA-LMS is 2.5 percent higher than the separation technology of ZA-LMS. The SNR value for LMS and ZA-LMS filtering structure systolic convex combinations is 6% higher than that for LMS and ZA-LMS filtering structure convex combinations.

Adaptive Channel Estimation Based on an Improved Norm-Constrained Set-Membership Normalized Least Mean Square Algorithm

An improved norm-constrained set-membership normalized least mean square (INCSM-NLMS) algorithm is proposed for adaptive sparse channel estimation (ASCE). The proposed INCSM-NLMS algorithm is implemented by incorporating an lp-norm penalty into the cost function of the traditional set-membership normalized least mean square (SM-NLMS) algorithm, which is also denoted as lp-norm penalized SM-NLMS (LPSM-NLMS) algorithm. The derivation of the proposed LPSM-NLMS algorithm is given theoretically, resulting in a zero attractor in its iteration. By using this proposed zero attractor, the convergence speed is effectively accelerated and the channel estimation steady-state error is also observably reduced in comparison with the existing popular SM-NLMS algorithms for estimating exact sparse multipath channels. The estimation behaviors are investigated via a typical sparse wireless multipath channel, a typical network echo channel, and an acoustic channel. The computer simulation results show that the proposed LPSM-NLMS algorithm is better than those corresponding sparse SM-NLMS and traditional SM-NLMS algorithms when the channels are exactly sparse.

Robust Adaptive Algorithm by an Adaptive Zero Attractor Controller of ZA-LMS Algorithm

This paper proposes a new approach to identify time varying sparse systems. The proposed approach uses Zero-Attracting Least Mean Square (ZA-LMS) algorithm with an adaptive optimal zero attractor controller which can adapt dynamically to the sparseness level and provide appreciable performance in all environments ranging from sparse to nonsparse conditions. The optimal zero attractor controller is derived based on the criterion that confirms largest decrease in mean square deviation (MSD) error. A simple update rule is also proposed to change the zero attractor controller based on the level of sparsity. It is found that, for nonsparse system, the proposed approach converges to LMS (as ZA-LMS cannot outperform LMS when the system is nonsparse) and, for highly sparse system, as the proposed approach is based on optimal zero attractor controller, it converges either similar to ZA-LMS or even better than ZA-LMS (depending on the value of zero attractor controller chosen for ZA-LMS algorithm). The performance of the proposed algorithm is better than ZA-LMS and LMS when the system is semisparse. Simulations were performed to prove that the proposed algorithm is robust against variable sparsity level.