Denoising method for shear probe signal based on wavelet thresholding

2012 ◽  
Vol 18 (2) ◽  
pp. 135-140 ◽  
Author(s):  
Shuxin Wang ◽  
Xuezhong Xiao ◽  
Yanhui Wang ◽  
Zilong Wang ◽  
Baokuo Chen
Keyword(s):  
Wavelet Thresholding ◽  
Probe Signal ◽  
Denoising Method ◽  
Shear Probe

scholarly journals A New Wavelet Thresholding Function Based on Hyperbolic Tangent Function

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Can He ◽  
Jianchun Xing ◽  
Juelong Li ◽  
Qiliang Yang ◽  
Ronghao Wang
Keyword(s):  
Shape Parameter ◽  
Wavelet Thresholding ◽  
Optimization Strategy ◽  
Hyperbolic Tangent ◽  
Denoising Method ◽  
Hyperbolic Tangent Function ◽  
Artificial Fish Swarm Algorithm ◽  
Tangent Function ◽  
Artificial Fish Swarm ◽  
Thresholding Function

Thresholding function is an important part of the wavelet threshold denoising method, which can influence the signal denoising effect significantly. However, some defects are present in the existing methods, such as function discontinuity, fixed bias, and parameters determined by trial and error. In order to solve these problems, a new wavelet thresholding function based on hyperbolic tangent function is proposed in this paper. Firstly, the basic properties of hyperbolic tangent function are analyzed. Then, a new thresholding function with a shape parameter is presented based on hyperbolic tangent function. The continuity, monotonicity, and high-order differentiability of the new function are theoretically proven. Finally, in order to determine the final form of the new function, a shape parameter optimization strategy based on artificial fish swarm algorithm is given in this paper. Mean square error is adopted to construct the objective function, and the optimal shape parameter is achieved by iterative search. At the end of the paper, a simulation experiment is provided to verify the effectiveness of the new function. In the experiment, two benchmark signals are used as test signals. Simulation results show that the proposed function can achieve better denoising effect than the classical hard and soft thresholding functions under different signal types and noise intensities.

Denoising Method for Calibration Data of Landing Guidance Radar Based on EMD and Wavelet

2014 ◽  
Vol 962-965 ◽  
pp. 2856-2862
Author(s):  
De Yi Sang ◽  
Jian Jun Zhao ◽  
Li Bin Yang
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Wavelet Denoising ◽  
Calibration Data ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Calibration Process ◽  
Frequency Components

The noise resulted in the calibration process of the landing guidance radar can cause serious accidents. Analyse the principle of the EMD and wavelet denoising method. Points out the deficiencies of pure EMD or pure wavelet denoising method. Propose a denoising method based on EMD and wavelet. Improved the discriminanting method for high or low frequency components and the discriminanting method for wavelet thresholding. First EMD the signal, then denoise the high frequency components by wavelet, finally, combined the low frequency components and the denoised high frequency components to get the denoised data.

A Denoising Method Based on EEMD and Interval-Thresholding Strategy

2014 ◽  
Vol 902 ◽  
pp. 336-340 ◽  
Author(s):  
Zhi Zhou ◽  
Xing Man Yang ◽  
Gang Chen
Keyword(s):  
Simulation Experiment ◽  
Adaptive Signal Processing ◽  
Basis Functions ◽  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Signal Processing Method ◽  
Adaptive Signal ◽  
Conventional Signal ◽  
Selection Of

As a conventional signal denoising method, wavelet thresholding denoising has problems including selection of basis vectors and poor denoising effect. EMD is an expansion of basis functions that are signal-dependent, but with the problem of mode mixing. In order to solve these problems, a denoising method based on EEMD and interval-thresholding strategy, an adaptive signal processing method is proposed, which can achieve good effects for signal denoising. Firstly, investigated signal is decomposed into IMFs by EEMD adaptively. Then, each IMF is denoising by interval-thresholding method based on sparse code shrinkage. Lastly, the denoised signal is reconstructed by denoised IMFs. Moreover, the presented method is validated by numerical simulation experiment.

Research Progress of Wavelet Denoising Method of Transformer Partial Discharge Signal

2012 ◽  
Vol 571 ◽  
pp. 584-588
Author(s):  
Ying Zhang ◽  
Fu Cheng You
Keyword(s):  
Partial Discharge ◽  
Wavelet Denoising ◽  
Research Progress ◽  
Reconstruction Method ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Translation Invariant ◽  
Thresholding Method ◽  
Advantages And Disadvantages ◽  
Modulus Maxima

Wavelet analysis has been widely used in the denoising of partial discharge signal of transformer. This paper introduces the main method of partial discharge signal denoising, which focuses on the studying of wavelet denoising methods. The main wavelet denoising methods are introduced herein including wavelet decomposition and reconstruction method, wavelet thresholding method, the translation invariant wavelet thresholding method, the wavelet denoising based on modulus maxima method, and the most widely used wavelet thresholding is introduced primarily. The analysis of their advantages and disadvantages is helpful to choose a proper wavelet denoising method.

Research of Filtering Algorithm for Echo of Ultrasonic Wave Based on Wavelet Transformation

2011 ◽  
Vol 52-54 ◽  
pp. 1212-1217
Author(s):  
Xiao Li Zhu ◽  
Ze Zhang
Keyword(s):  
Wavelet Transformation ◽  
Threshold Function ◽  
Wavelet Thresholding ◽  
Noise Detection ◽  
Matlab Simulation ◽  
Noisy Signal ◽  
Echo Signal ◽  
Simulation Experiments ◽  
Denoising Method ◽  
Ultrasonic Echo

This paper proposes an improved wavelet threshold denoising method, threshold function by selecting different thresholds to filter out the noisy signal. Firstly, do the wavelet transformation for noisy signal, and then use soft threshold, hard threshold and the improved threshold algorithm to denoising the signal, finally realized by Matlab simulation of wavelet thresholding. Threshold denoising via different methods of simulation experiments show that the improved threshold denoising algorithm can effectively filter plate bonding ultrasonic echo signal in the noise detection and can be a good feature to retain the original signal.

A New Denoising Method via Empirical Mode Decomposition

2014 ◽  
Vol 651-653 ◽  
pp. 2090-2093 ◽  
Author(s):  
Shou Cheng Zhang ◽  
Li Li Sui
Keyword(s):  
Empirical Mode Decomposition ◽  
Basic Principle ◽  
Random Noise ◽  
Doppler Signal ◽  
Threshold Function ◽  
Signal Denoising ◽  
Ecg Signal ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Mode Decomposition

In non-parametric signal denoising area, empirical mode decomposition is potentially useful. In this paper, the wavelet thresholding principle is directly used in EMD-based denoising. The basic principle of the method is to reconstruct the signal with IMFs previously thresholded. A novel threshold function is proposed to improve denoising effect by exploiting the special characteristics of the hard and soft thresholding method. The denoising method is validated through experiments on the “Doppler” signal and a real ECG signal from MIT-BIH databases corrupted by additive white Gaussian random noise. The simulations show that the proposed EMD-based method provides very good results for denoising.

Denoising of seismic signals based on empirical mode decomposition-wavelet thresholding

2020 ◽  
pp. 107754632092684
Author(s):  
Li Long ◽  
Xiulan Wen ◽  
Yixue Lin
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Waves ◽  
Low Frequency ◽  
Wavelet Thresholding ◽  
Seismic Signals ◽  
Denoising Method ◽  
Intrusion Prevention ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Feature Information

Unattended object detection systems have seen full applications in military surveillance, object recognition, and intrusion prevention. When applied to actual work scenarios, these systems have problems such as low recognition accuracy, low positioning accuracy, and weak detection effect of distant objects. Obtainment of enough feature information concerning the effective signals is critical to target recognition. This work focuses on interference in seismic signals and the way to store the feature information of effective signals. First, the authors analyzed the frequency and attenuation characteristics of seismic waves of typical target sites, in which the Rayleigh wave is suitable for the detection of the energy of seismic signals produced by human targets and vehicles. As seismic signals are low-frequency waves, the authors researched the performance of the empirical mode decomposition method and the wavelet thresholding method in denoising seismic signals, and an improved empirical mode decomposition-wavelet threshold denoising method is proposed. The test result shows that the improved denoising method can effectively remove noise in seismic signals and preserve the effective signals of the target.

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