Signal Denoising Using Optimized Trimmed Thresholding

Hassanein, Maha A.; Hanna, M.T.; Seif, N. P.; Elbarawy, Menna T. M. M.

doi:10.1007/s00034-017-0665-8

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…The value of r i is calculated using Eq. ( 5 ), and the risk vector R is created 28 . where 1 ≤ i ≤ N , N is the number of wavelet coefficients in the j -th layer, and CD j,i is the i -th element in the vector P .…”

Section: Methodsmentioning

confidence: 99%

“…The useful signal damage in the denoised signal obtained by the hard threshold method is relatively small, but due to its discontinuity in the real number domain, the obtained denoised signal is prone to the pseudo-Gibbs phenomenon. The soft threshold denoising method makes up for the deficiencies of the hard threshold denoising method, but in the actual filtering process, the soft threshold strategy faces the difficulty of removing the interference signals in the adjacent range of singular values; the effective waveform in the prominent area of the processed signal is easily deformed and damaged 28 , 30 . The analysis shows that the waveform deformation and damage in the protruding area are caused by the excessive contraction of the effective signal; the setting of parameter a is too large.…”

Section: Methodsmentioning

confidence: 99%

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Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

Zhang

Luo

et al. 2022

Sci Rep

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There are high- and low-frequency noise signals in a microseismic signal that can lead to the distortion and submersion of an effective waveform. At present, effectively removing high- and low-frequency noise without losing the effective signal of local waveform spikes remains a challenge. This work addresses this issue with an improved wavelet adaptive thresholding method. Because a denoised signal conceptually approximates the minimum error, a dynamic selection model is established for the optimal threshold. On this basis, an adaptive correction factor aj is proposed to reflect the noise intensity, which uses the 1/2 power of the ratio of the median absolute value to the amplitude of the monitoring data to reflect the noise intensity of the wavelet detail signal and corrects the size of the denoising scale. Finally, the performance of the improved method is quantitatively evaluated in terms of the denoising quality and efficiency using the signal-to-noise ratio, root-mean-square error, sample entropy and running time.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

Zhang

Luo

et al. 2022

Sci Rep

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“…It is necessary to calculate the frequency corresponding to each level of load amplitude according to the distribution model of the load amplitude after dividing the load amplitude. The statistical method is shown in equation (11) or equation (12)…”

Section: One-dimensional Load Spectrum Preparationmentioning

confidence: 99%

“…The signals collected by the sensors are often mixed with other noises that disturb analysis of these signals. Hassanein et al 12 used optimized trimmed thresholding for signal denoising. Zhang and Zhang 13 put forward a signal denoising method based on empirical mode decomposition.…”

Section: Introductionmentioning

confidence: 99%

Load spectrum for automotive wheels hub based on mixed probability distribution model

Geng

Liu

Yang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In actual engineering, the actual road test or indoor bench test is usually used to collect the data of the road load of the parts to acquire the fatigue life estimation of the auto parts. This paper proposes a method for load spectrum construction based on the mixed distribution probability model using the data of the road load spectrum collected in the test site. Pau Ta criteria outlier elimination and wavelet signal denoising are applied to analyze the original road load spectrum data. Then the maximum likelihood estimation method is used to estimate the generalized Pareto distribution parameters of all excesses. The Pareto distribution is also employed to extrapolate the load spectrum. Through the characteristic analysis of the load spectrum, the one-dimensional and two-dimensional program load spectrum of the hub is established based on the mixed probability distribution model, which provides a theoretical basis for the life prediction of the hub. In addition, the research results of this paper provide inspirations for the fatigue life prediction and fatigue durability bench test of automotive parts subjected to the complexity and variability of random loads.

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An improved wavelet threshold denoising approach for surface electromyography signal

Ouyang,

Cai,

Liu

et al. 2023

EURASIP J. Adv. Signal Process.

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Background The surface electromyography (sEMG) signal presents significant challenges for the dynamic analysis and subsequent examination of muscle movements due to its low signal energy, broad frequency distribution, and inherent noise interference. However, the conventional wavelet threshold filtering techniques for sEMG signals are plagued by the Gibbs-like phenomenon and an overall decrease in signal amplitude, leading to signal distortion. Purpose This article aims to establish an improved wavelet thresholding method that can filter various types of signals, with a particular emphasis on sEMG signals, by adjusting two independent factors. Hence, it generates the filtered signal with a higher signal-to-noise ratio (SNR), a lower mean square error (MSE), and better signal quality. Results After denoising Doppler and Heavysine signals, the filtered signal exhibits a higher SNR and lower MSE than the signal generated from traditional filtering algorithms. The filtered sEMG signal has a lower noise baseline while retaining the peak sEMG signal strength. Conclusion The empirical evaluation results show that the quality of the signal processed by the new noise reduction algorithm is better than the traditional hard thresholding, soft thresholding, and Garrote thresholding methods. Moreover, the filtering performance on the sEMG signal is improved significantly, which enhances the accuracy and reliability of subsequent experimental analyses.

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Signal Denoising Using Optimized Trimmed Thresholding

Cited by 3 publications

References 17 publications

Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

Investigation of microseismic signal denoising using an improved wavelet adaptive thresholding method

Load spectrum for automotive wheels hub based on mixed probability distribution model

An improved wavelet threshold denoising approach for surface electromyography signal

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