Query: multiband adaptive filter

title: Diffusion Improved Multiband Structured Subband Adaptive Filter Algorithm With Dynamic Selection of Nodes Over Distributed Networks
abstract: In this brief, an improved multiband structured subband adaptive filter (IMSAF) algorithm is applied in diffusion networks. The introduced diffusion IMSAF (DIMSAF) utilizes the input regressors at each subband to increase the convergence speed of the diffusion normalized subband adaptive filter. This advantage is achieved at the cost of increased computational complexity and steady state error. Therefore, the DIMSAF with dynamic selection of nodes (DIMSAF DSN) is proposed. In DIMSAF DSN, the nodes are dynamically selected according to the largest decrease in mean square deviation at each iteration. The DIMSAF DSN has low computational complexity, fast convergence speed, and low steady state error. Simulation results demonstrate the good performance of the proposed algorithms.

title: Two improved multiband structured subband adaptive filter algorithms with reduced computational complexity
abstract: The improved multiband structured subband adaptive filter (IMSAF) utilizes the input regressors at each subband to speed up the convergence rate of MSAF. When the number of input regressors is increased, the convergence rate of the IMSAF algorithm improves at the cost of increased complexity. The current study introduces two new IMSAF algorithms with low computational complexity feature. In the first algorithm, a subset of input regressors at each subband is optimally picked out during the adaptation. In the second approach, the number of selected input regressors is dynamically changed at each subband for every iteration. The introduced algorithms are called selective regressor IMSAF (SR IMSAF) and dynamic selective regressor IMSAF (DSR IMSAF) The SR IMSAF and DSR IMSAF are shown to be capable of outperforming the full update IMSAF while the computational complexity is kept low. In the following, the general update equation to establishment of the family of IMSAF algorithms is presented. Accordingly, the mean square performance analysis of the algorithms is studied in a unified way and the general theoretical expressions for transient, steady state, and the stability bounds for IMSAF, SR IMSAF, and DSR IMSAF are derived. The good performance of the introduced algorithms and the validity of the derived theoretical relations are justified by presenting various experimental results.

title: Weighted Improved Multiband Structured Sub Band Adaptive Filter Algorithms
abstract: An improved multiband structured sub band adaptive filter (IMSAF) applies the input regressors at each sub band to increase the convergence speed of multiband structured sub band adaptive filter. In the conventional IMSAF algorithm, the effect of all regressors and sub bands in updating the filter coefficients is the same. In this brief, we present three weighted IMSAF (WIMSAF) algorithms so that the regressors or sub bands are weighted through the adaptation. In the weighted sub band IMSAF algorithm, the sub bands can be weighted at each iteration. In weighted regressor IMSAF, the input regressors at each sub band are weighted. In weighted sub band and regressor IMSAF, the sub bands and the regressors are jointly weighted at each adaptation. Weights in all versions of WIMSAF algorithms are selected in order to decrease the mean square deviation during the iterations. At each adaptation, the weights are set between zero and one. When the weight in the sub band/regressor is set to one, it means the maximum effect of that sub band/regressor in updating the filter coefficients. The coefficients update is not performed in sub bands/regressors with zero weights. Assigning appropriate weights leads to the fast convergence speed and low misadjustment error. Furthermore, the elimination of zero weight sub bands/regressors achieves lower computational complexity than conventional IMSAF algorithm. The mean square performance analysis of the proposed algorithms is studied and theoretical expressions for learning curve is derived. The simulation results justify the good performance of the proposed algorithms.

title: Improved Multiband Structured Subband Adaptive Filter Algorithm in Diffusion Distributed Networks
abstract: Adaptive algorithms play an important role in order to improve performance of diffusion distributed network. In comparison of diffusion Normalized least mean square algorithm, family of diffusion subband algorithms have faster convergence rate when the input signal is highly correlated. This paper solves the problem of distributed estimation in the diffusion networks based on improved multiband structured subband adaptive filter (IMSAF) and diffusion improved multiband structured subband adaptive filter (DIMSAF) is established. In proposed algorithm, convergence behavior improved due to using several input projections instead of single vector of input data. In addition, when the projection order is increased, the convergence rate of the proposed algorithm improves. The validity of the DIMSAF in comparison of DLMS, DAPA, VSS DAPA and DRLS algorithms is demonstrated by several computer simulations. The results show fastest convergence rate.

title: Two Low Computational Complexity Improved Multiband Structured Subband Adaptive Filter Algorithms
abstract: The improved multiband structured subband adaptive filter (IMSAF) applies the input regressors at each subband to speed up the convergence rate of MSAF. When the projection order is increased, the convergence rate of the IMSAF algorithm improves at the cost of increased complexity. The present research introduces two new IMSAF algorithms with low computational complexity feature. In the first algorithm, the selective partial update approach (SPU) is extended to IMSAF algorithms and SPU IMSAF is established. In SPU IMSAF, the filter coefficients are partially updated at each subband for every adaptation. In the second algorithm, the set membership (SM) strategy is utilized in IMSAF and SM IMSAF is established. The SM IMSAF has fast convergence rate, low steady state error and low computational complexity features at the same time. Also, by combining SM and SPU methods, the SM SPU IMSAF is introduced. Simulation results demonstrate the good performance of the proposed algorithms.

title: Baseline Wander Removal from ECG Using Multiband Structured Sub band Adaptive Filter LMS Algorithm
abstract: This paper investigates the new detailed adaptive noise canceller (ANC) system for Electrocardiogram (ECG) enhancement with robustness based on multiband structured sub band adaptive filter (MSAF) The proposed design is probably a new realization form of algorithm which guaranteed a more stable transformation and solves the structured problems in conventional sub band adaptive filter (SAF) The objective of the examination is to give arrangement keeping in mind the end goal to improve the execution of ANC as far as filter parameters which are procured with the assistance of uniform filter banks (UFB) and non uniform filter banks (NUFB) structured MSAF's using LMS algorithm. Computer simulation demonstrates that the proposed system gives improved performance and achieves good adaptation. NUFB structured MSAF algorithms are applied on ECG records obtained from standard MIT BIH data base and the performance is compared with UFB structured MSAF algorithms in terms of parameters SNR, MSE, RMSE and distortion. The SNR for various NUFB structured MSAF's was found to be higher than the UFB structured MSAF's. The five channel NUFB structures with decimation factors (16, 16, 8, 4, 2) has on average SNR of 17.67064 dB is obtained using LMS algorithm which is superior to existing algorithms.

title: Diffusion improved multiband structured subband adaptive filter algorithms with dynamic selection of regressors and subbands over distributed networks
abstract: The present study solves the problem of distributed estimation in the diffusion networks based on the family of improved multiband structured subband adaptive filters (IMSAFs) The diffusion IMSAF (DIMSAF) the DIMSAF with dynamic selection of regressors (DIMSAF DSR) and the DIMSAF with dynamic selection of subbands (DIMSAF DSS) are established. The DIMSAF DSS and the DIMSAF DSR algorithms, while benefiting from high convergence speed in DIMSAF, have lower computational complexity and lower steady state error. During the weight coefficients adaptation in DIMSAF DSR, the input signal regressors are dynamically selected at each subband of different nodes. In DIMSAF DSS, the subbands are dynamically selected at each node. In the following, the introduced algorithms are established based on a general update equation. Accordingly, the mean square performance analysis of the algorithms is studied in a unified way. The theoretical results and the good performance of proposed algorithms are justified by several computer simulations in adaptive diffusion networks.

title: Diffusion improved multiband structured subband adaptive filter algorithms with dynamic selection of regressors and subbands over distributed networks

title: Power line interference cancellation from ECG signal using multiband structured sub band adaptive filter algorithm
abstract: The Electrocardiogram (ECG) record is a procedural electrical activity of the heart which is noninvasive recording and is acquired by surface electrodes at designated locations on the skin of patient's body during which noise such as power line interference (PLI) with 60 Hz frequency is acquired from power mains. In this research paper a new ECG enhancement design using multiband structured RESEARCH Vol. 15, 2018 Indian Journal of Engineering ISSN 2319 7757 EISSN 2319 7765

title: Mean Square Deviation Analysis of Multiband Structured Subband Adaptive Filter Algorithm
abstract: A multiband structured subband adaptive filter (MSAF) algorithm was introduced to achieve a fast convergence rate for the correlated input signal. The convergence analysis of the adaptive filter algorithm is an important concept because it provides a guideline to design the adaptive filter. However, the convergence analysis of the MSAF algorithm has not been researched as extensively as that of the normalized least mean square algorithm. Therefore, it needs to be researched. In this paper, we present a new approach to the mean square deviation (MSD) analysis of the MSAF algorithm by using the persistently exciting input and the practical assumption that the stopband attenuation of the prototype filter is high. Unlike the previous analysis, the proposed analysis is possible to be applied to the long length adaptive filter such as the acoustic echo cancellation. The proposed analysis is also applied to a non stationary model with a random walk of the optimal weight vector. The simulation results match with the theoretical results in both the transient state and steady state MSD.