Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Walia, Ranjan; Ghosh, Swaroop

doi:10.3390/app8050686

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…In the case of any problem, the deviation of the level of bias and even if the drift is slow enough, the weight of the bias adapts alternately to track and remove the drift. By a biased weight together with normal weights when the noise is cancelled to fit, bias or drift removal can be achieved at the same time through random interference or the elimination of periodic [10,11].…”

Section: The Proposed Methodsmentioning

confidence: 99%

Adaptive Symlet filter based on ECG baseline wander removal

Nahar

2020

Serb J Electr Eng

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In this paper, proposed a new approach of combining the hybrid soft computing technique called Adaptive Symlet Wavelet Transform (ASWT) filter. The baseline wanders (BW) noise removal from an ECG signals to minimize distortion of the S-T segment of the ECG signal specially that have high sampling frequencies. Therefore, when using Symlet Wavelet Transform (SWT) to analysis the ECG signal can cause problems to analysis, exclusively when examining the content of the ECG signal at low-frequency such as S-T segment. The corresponding frequency components of the approximation coefficients at level number seven are (0-3.9) Hz. Since the BW frequency is below 0.5 Hz and ST segment frequency between (0.67-4) Hz. The adaptive filter with a unity reference signal used to remove the BW noise below 0.5 Hz from the lowest level of the approximation coefficient of the decomposed ECG signal. The denoising output from adaptive filter and the output from SWT (the other detail coefficients) will use as an input to ISWT for reconstruction ECG signals with the remove BW signal. This method represents a very effective filter for BW noise removal, as it does not need for any computation process of reference point.

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Section: The Proposed Methodsmentioning

confidence: 99%

Adaptive Symlet filter based on ECG baseline wander removal

Nahar

2020

Serb J Electr Eng

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“…To ensure a high speech quality and speech intelligibility, cutting-edge metaheuristic algorithms have emerged and are considered as potential solutions, since conventional step-descent adaptive filtering algorithms offer limited performance. Recent studies have proven that the use of metaheuristic algorithms has increased the performance of advanced filtering applications, such as active noise control (ANC) [3][4][5][6][7][8][9][10][11][12][13][14], enhancement of speech or suppression of noise [15][16][17] and acoustic echo cancellation. Regarding the latter application, Diana et al [18] proposed a hybrid metaheuristic technique based on the artificial bee colony (ABC) and the Kernel Adaptive Improved Proportionate and Normalized Least Mean Square (KIPNLMS) algorithm.…”

Section: Introductionmentioning

confidence: 99%

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms

Pichardo,

Avalos,

Sánchez

et al. 2024

Biomimetics

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Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced.

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“…The practical running shows that the Improved Gene Expression Programming has high efficiency. Implementations of hybrid algorithms have also been reported (Bhullar and Ghosh, 2018; Walia and Ghosh, 2018).…”

Section: Introductionmentioning

confidence: 99%

Effective design and development of hybrid ABC-CSO-based capacitor placement with load forecasting based on artificial neural network

Sharma¹,

Ghosh

2019

Self Cite

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Purpose This paper aims to develop a capacitor position in radial distribution networks with a specific end goal to enhance the voltage profile, diminish the genuine power misfortune and accomplish temperate sparing. The issue of the capacitor situation in electric appropriation systems incorporates augmenting vitality and peak power loss by technique for capacitor establishments. Design/methodology/approach This paper proposes a novel strategy using rough thinking to pick reasonable applicant hubs in a dissemination structure for capacitor situation. Voltages and power loss reduction indices of distribution networks hubs are shown by fuzzy enrollment capacities. Findings A fuzzy expert system containing a course of action of heuristic rules is then used to ascertain the capacitor position appropriateness of each hub in the circulation structure. The sizing of capacitor is solved by using hybrid artificial bee colony–cuckoo search optimization. Practical implications Finally, a short-term load forecasting based on artificial neural network is evaluated for predicting the size of the capacitor for future loads. The proposed capacitor allocation is implemented on 69-node radial distribution network as well as 34-node radial distribution network and the results are evaluated. Originality/value Simulation results show that the proposed method has reduced the overall losses of the system compared with existing approaches.

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Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Cited by 5 publications

References 33 publications

Adaptive Symlet filter based on ECG baseline wander removal

Adaptive Symlet filter based on ECG baseline wander removal

A High-Speed Acoustic Echo Canceller Based on Grey Wolf Optimization and Particle Swarm Optimization Algorithms

Effective design and development of hybrid ABC-CSO-based capacitor placement with load forecasting based on artificial neural network

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