Simulation for noise cancellation using LMS adaptive filter

Lee, Jia-Haw; Ooi, Lu Ean; Ko, Ying-Hao; Teoh, Choe-Yung

doi:10.1088/1757-899x/211/1/012003

Cited by 7 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Importantly, the effect gradually improves with time because the column weight vector is initialized to the zero vector at first, so it can hardly take effect at the beginning. With the operation of the filter system, the column weight vector will come into effect to make the approximate noise as close to the real noise as possible [36] . Here, we focus on the section where the adaptive filter works efficiently, which means the region where enough sample numbers have been obtained.…”

Section: Signal Analysismentioning

confidence: 99%

SNR enhancement of magnetic fields measurement with the diamond NV center using a compound filter system

Ye,

Fu,

et al. 2023

中国光学快报

View full text Add to dashboard Cite

Nitrogen-vacancy (NV) centers in diamond are progressively favored for room-temperature magnetic field measurement. The signal to noise ratio (SNR) optimization for NV diamond magnetometry generally concentrates on signal amplitude enhancement rather than efficient noise processing. Here, we report a compound filter system combining a wavelet denoising method and an adaptive filter for the realization of an efficient weak magnetic measurement with a high SNR. It allows enhanced magnetic field measurement with an average SNR enhancement of 17.80 dB at 50 nT within 500 mHz to 100 Hz and 14.76 dB at 500 mHz within 50 nT to 1100 nT. The introduction of this system in NV diamond magnetometry is aimed to improve signal quality by effectively eliminating the noise and retaining ideal signals.

show abstract

Section: Signal Analysismentioning

confidence: 99%

SNR enhancement of magnetic fields measurement with the diamond NV center using a compound filter system

Ye,

Fu,

et al. 2023

中国光学快报

View full text Add to dashboard Cite

show abstract

“…The weight update equation using Feintuch's LMS algorithm is + 1 = + @ * (10) Noise cancellation is one of the most important applications of adaptive filters, in which it desired to recover a useful signal, from a noisy one, + (Dixit, 2017;Lee, et al, 2017;Qureshi, et al, 2017). In adaptive noise cancellation, the noisy signal, + is employed as the reference signal for the adaptive filter whose input must be E , another version of the noise signal which is strongly correlated to as illustrated in Figure 2.…”

Section: ;mentioning

confidence: 99%

“…The adaptive mechanism adjusts the filter coefficients in such a manner that the filter output approximates , thus forcing the error signal, to resemble signal (Apolinario Jr. & Netto, 2009). Vol.10, No.2, 2019 Most of the recent works in adaptive noise cancellation employed FIR adaptive filter structure based on either traditional LMS or one of its numerous (Chhikara & Singh, 2012;Lee, et al, 2017;Thakkar, 2017;Huang, et al, 2017). These approaches have been reported to be inefficient in the presence of impulsive noise.…”

Section: ;mentioning

confidence: 99%

Development of an Adaptive IIR Filter Based on Modified Robust Mixed-Norm Algorithm for Adaptive Noise Cancellation

2019

CEIS

View full text Add to dashboard Cite

Noise cancellation is one of the most important applications of adaptive filters. The employment of adaptive filtering in most digital signal processing tasks is currently an area of growing interest as adaptive filters, due to their dynamic nature, perform better than the traditional filters in compensating for random noise in their environment. However, the compensation for impulsive interference or noise is desired since most adaptive algorithms earlier proposed modelled noise as a random process of the White Gaussian distribution. A modified robust mixed-norm (MRMN) algorithm recently proposed to compensate for impulsive interference has been found to be hardware efficient, however the MRMN algorithm has only been tested on adaptive FIR system identification task. In this paper, an adaptive IIR filter based on MRMN adaptive algorithm is proposed and tested for noise cancellation task. The developed filter structure was modelled and simulated in MATLAB environment. The results obtained showed that the MRMN algorithm does in fact compensate for the presence of impulsive interference, however, at a higher computational complexity relative to the LMS algorithm.

show abstract

Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG

Abdulwahid

Karetla

Ganesh

et al. 2023

2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)

View full text Add to dashboard Cite

Simulation for noise cancellation using LMS adaptive filter

Cited by 7 publications

References 11 publications

SNR enhancement of magnetic fields measurement with the diamond NV center using a compound filter system

SNR enhancement of magnetic fields measurement with the diamond NV center using a compound filter system

Development of an Adaptive IIR Filter Based on Modified Robust Mixed-Norm Algorithm for Adaptive Noise Cancellation

Evaluation of Deep Training and Adapting Algorithms in Medical with De-Noising EEG

Contact Info

Product

Resources

About