Wavelet threshold based on Stein's unbiased risk estimators of restricted location parameter in multivariate normal

Karamikabir, Hamid; Afshari, Mahmoud; Lak, Fazlollah

doi:10.1080/02664763.2020.1772209

Cited by 14 publications

(5 citation statements)

References 27 publications

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“…Zellner [16] extended the problem of estimating the multivariate normal mean in large dimension, and then he suggested the BLF that generalizes the quadratic loss function. Te published papers in this direction include Sanjari Farsipour and Asgharzadeh [17], Selahattin and Issam [18], Nimet and Selahattin [19], Lahoucine et al [20], Karamikabir and Afsahri [21], and Karamikabir et al [22].…”

Section: Introductionmentioning

confidence: 99%

On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator

Hamdaoui

Benkhaled

Alshahrani

et al. 2023

Journal of Mathematics

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This work consists of developing shrinkage estimation strategies for the multivariate normal mean when the covariance matrix is diagonal and known. The domination of the positive part of James–Stein estimator (PPJSE) over James–Stein estimator (JSE) relative to the balanced loss function (BLF) is analytically proved. We introduce a new class of shrinkage estimators which ameliorate the PPJSE, and then we construct a series of polynomial shrinkage estimators which improve the PPJSE; also, any estimator of this series can be ameliorated by adding to it a new term of higher degree. We end this paper by simulation studies which confirm the performance of the suggested estimators.

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Section: Introductionmentioning

confidence: 99%

On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator

Hamdaoui

Benkhaled

Alshahrani

et al. 2023

Journal of Mathematics

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“…In the wavelet threshold denoising algorithm, the common functions were soft threshold, hard threshold, adaptive threshold, etc. [ 21 ]. In this work, the wavelet threshold denoising algorithm with improved threshold function, which was between soft threshold and hard threshold, was selected for SEMG signal denoising as the Eq (1) .…”

Section: Methodsmentioning

confidence: 99%

Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis

Wang

2023

PLoS ONE

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Objective This work was aimed to analyze the effect of vitamin energy drink on muscle fatigue by surface electromyography (SEMG) and ultrasonic bioimaging (USBI). Methods 20 healthy men were selected to do increasing load fatigue test. Surface electromyographic signals and ultrasonic biological images were collected based on wavelet threshold function with improved thresholds. Time domain and frequency domain characteristic integrated electromyography (IEMG), root mean square amplitude (RMS), average power frequency (MPF), and surface and deep muscle morphological changes were analyzed. Hemoglobin concentration (HB), red blood cell number (RBC), mean volume of red blood cell (MCV), blood lactic acid (BLA), malondialdehyde (MDA), and phosphocreatine kinase (CK) were measured. Results 1) the Accuracy (94.10%), Sensitivity (94.43%), Specificity (93.75%), and Precision (94.07%) of the long and short-term memory (LSTM) specificity for muscle fatigue recognition were higher than those of other models. 2) Compared with the control group, the levels of BLA, MDA, and CK in the experimental group were decreased and HB levels were increased after exercise (P < 0.05). 3) IEMG and RMS of the experimental group were higher than those of the control group, and increased with time (P < 0.05). 4) The mean amplitude of the response signal decreased with time. Compared with the control group, the surface muscle thickness, deep muscle thickness, total muscle thickness, contrast, and homogeneity (HOM) decreased in the experimental group; while the angular second moment (ASM) and contrast increased, showing great differences (P < 0.05). Conclusion Surface electromyographic signal and ultrasonic biological image can be used as auxiliary monitoring techniques for muscle fatigue during exercise. Drinking vitamin energy drinks before exercise can relieve physical fatigue to a certain extent and promote the maintenance of muscle microstructure.

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“…Ji et al (2016) proposed a denoising algorithm using wavelet threshold method and exponential adaptive window width fitting to remove the non-stationary electromagnetic noise in airborne transient electromagnetic data of ground power supply on unmanned aircraft. Karamikabir et al (2021) proposed that the wavelet threshold based on Stein's unbiased risk estimators of restricted location parameter in multivariate normal and had verified the effectiveness of the proposed method by simulation research. Kong et al (2021) proposed an early fault diagnosis method for wind turbine bearings based on improved CPSO adaptive exponential wavelet threshold function.…”

Section: Introductionmentioning

confidence: 94%

“…Karamikabir et al. (2021) proposed that the wavelet threshold based on Stein's unbiased risk estimators of restricted location parameter in multivariate normal and had verified the effectiveness of the proposed method by simulation research. Kong et al.…”

Section: Introductionmentioning

confidence: 96%

Removal of strong noise in magnetotelluric data using grey wolf optimized wavelet threshold

Liu

Zhang

et al. 2022

Geophysical Prospecting

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Traditional magnetotelluric signal processing usually uses time–frequency transformation method. Wavelet is also a time–frequency transformation method that used to suppress the magnetotelluric noise. However, the selection of the threshold is very significant, and the unsuitable threshold will lead to excessive distortion of the reconstructed signal. Thus, we propose a method for magnetotelluric noise suppression using grey wolf optimized wavelet threshold. First, the magnetotelluric signal is decomposed by wavelet with appropriate wavelet basis and decomposition layers. The generalized cross‐validation criterion is used as the fitness function of grey wolf optimizer algorithm, which optimizes the threshold of each decomposition layer. Then, the detail coefficients of each layer and the maximum layer of the approximation coefficients are used in the optimized threshold. Next, the inverse wavelet transform is performed. Finally, the noise contour is obtained through iteratively searching for the optimal threshold, and the useful magnetotelluric signal is reconstructed. Simulation experiments and measured magnetotelluric data processing show that the large‐scale interference can effectively be suppressed, and the reconstructed magnetotelluric signal retains the more abundant low‐frequency of useful information. Compared with the remote reference method, fixed threshold method and Birge–Massart layered threshold method, the proposed method realizes the wavelet denoising with adaptive threshold selection in the magnetotelluric noise suppression. The results obtained show smoother and more continuous apparent resistivity–phase curves, which verifies the effectiveness of the optimization.

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Wavelet threshold based on Stein's unbiased risk estimators of restricted location parameter in multivariate normal

Cited by 14 publications

References 27 publications

On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator

On Some Classes of Estimators Derived from the Positive Part of James–Stein Estimator

Effect of vitamin energy drinks on relieving exercise-induced fatigue in muscle group by ultrasonic bioimaging data analysis

Removal of strong noise in magnetotelluric data using grey wolf optimized wavelet threshold

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