scholarly journals DENOISING OF LIDAR ECHO SIGNAL BASED ON WAVELET ADAPTIVE THRESHOLD METHOD

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 215-220
Author(s):  
S. H. Long ◽  
G. Q. Zhou ◽  
H. Y. Wang ◽  
X. Zhou ◽  
J. L. Chen ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Threshold Method ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Signal Characteristics ◽  
Soft Threshold ◽  
Two Parameters ◽  
Unbiased Risk

Abstract. The wavelet threshold method is widely used in signal denoising. However, traditional hard threshold method or soft threshold method is deficient for depending on fixed threshold and instability. In order to achieve efficient denoising of echo signals, an adaptive wavelet threshold denoising method, absorbing the advantages of the hard threshold and the soft threshold, is proposed. Based on the advantages of traditional threshold method, new threshold function is continuous, steerable and flexibly changeable by adjusting two parameters. The threshold function is flexibly changed between the hard threshold and the soft threshold function by two parameter adjustments. According to the Stein unbiased risk estimate (SURE), this new method can determine thresholds adaptively. Adopting different thresholds adaptively at different scales, this method can automatically track noise, which can effectively remove the noise on each scale. Therefore, the problems of noise misjudgement and incomplete denoising can be solved, to some extent, in the process of signal processing. The simulation results of MATLAB show that compared with hard threshold method and soft threshold method, the signal-to-noise ratio (SNR) of the proposed de-noising method is increased by nearly 2dB, and 4dB respectively. It is safely to conclude that, when background noise eliminated, the new wavelet adaptive threshold method preserves signal details effectively and enhances the separability of signal characteristics.

Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method

2019 ◽  
Vol 71 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Jianhua Cai
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Accurate Information ◽  
Threshold Method ◽  
Content Type ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Soft Threshold ◽  
Unbiased Risk

Purpose This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which are usually used in gear fault diagnosis. Design/methodology/approach A new threshold function and a new determined method of threshold for each layer are proposed. The principle and the implementation of the algorithm are given. The simulated signal and the measured gear fault signal are analyzed, and the obtained results are compared with those from wavelet soft-threshold method, wavelet hard-threshold method and wavelet modulus maximum method. Findings The presented wavelet adaptive threshold method overcomes the defects of the traditional wavelet threshold method, and it can effectively eliminate the noise hidden in the gear fault signal at different decomposition scales. It provides more accurate information for the further fault diagnosis. Originality/value A new threshold function is adopted and the multi-resolution unbiased risk estimation is used to determine the adaptive threshold, which overcomes the defect of the traditional wavelet method.

scholarly journals Signal Denoising Method Based on Improved Wavelet Threshold Function for Microchip Electrophoresis C4D Equipment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yaonan Tong ◽  
Jingui Li ◽  
Yaohui Xu ◽  
Lichen Cao
Keyword(s):  
Microchip Electrophoresis ◽  
Threshold Function ◽  
Signal Denoising ◽  
Threshold Method ◽  
Decomposition Level ◽  
Denoising Method ◽  
Capacitively Coupled ◽  
Signal Peak ◽  
Soft Threshold ◽  
Simulation Signal

A signal denoising method using improved wavelet threshold function is presented for microchip electrophoresis based on capacitively coupled contactless conductivity detection (ME-C4D) device. The evaluation results of denoising effect for the ME-C4D simulation signal show that using Daubechies 5 (db5) wavelet at a decomposition level 4 can produce the best performance. Furthermore, the denoising effect is compared with, as well as proved to be superior to, the existing techniques, such as Savitzky–Golay, Fast Fourier Transform, and soft threshold method. This method has been successfully applied to the self-developed ME-C4D equipment. After executing this method, the noise is cleanly removed, and the signal peak shape and peak area are well maintained.

The Study of Signal Denoising for Laser Cutting Based on Adaptive Wavelet Denoising

2012 ◽  
Vol 546-547 ◽  
pp. 686-690
Author(s):  
Hui Juan Hao ◽  
Ji Yong Xu ◽  
Juan Li
Keyword(s):  
Laser Cutting ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Denoising ◽  
Quality Analysis ◽  
Signal Denoising ◽  
Threshold Method ◽  
Denoising Method ◽  
Swarm Optimization ◽  
Adaptive Wavelet ◽  
Soft Threshold

In order to reduce the noise of acquisition signal in laser cutting, an adaptive wavelet denoising method is proposed in this paper. Based on the analysis of the limitations of traditional threshold method, the particle swarm optimization algorithm is used to select the optimal threshold of wavelet. Compared with the commonly hard and soft threshold method, the experiment results show that the method used in this paper is relatively stable, and can reduce noise excellently. The method can provide more accurate signal for quality analysis in laser cutting .So the method can be used in noise denoising of pulse-induced acoustic sound.

scholarly journals Empirical Mode Decomposition De-Noising Method Based on Improved Soft-Threshold

2011 ◽  
Vol 1 ◽  
pp. 421-425 ◽  
Author(s):  
Jian Hui Xi ◽  
Jia Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Soft Threshold ◽  
Simulation Results ◽  
Improved Function ◽  
Constant Deviation

In this paper, an improved soft-threshold function is constructed, combined the improved function and empirical mode decomposition (EMD) methods, a new de-noising method has been proposed. Set the adaptive threshold for the intrinsic mode functions (IMFs) of the EMD, and then de-noise the each IMF respectively. Finally, the de-noised signal is reconstructed by the de-noised IMF components. Through the simulation results of quantitative analysis by signal-to-noise ratio (SNR) and mean square error (MSE), the algorithm in this paper has better de-noising effect. Also, this method can effectively improve the constant deviation between the original signal and the de-noised signal by traditional soft-threshold.

Wavelet Image Denoising Based on the New Threshold Function

2013 ◽  
Vol 347-350 ◽  
pp. 2231-2235
Author(s):  
Hui Tang ◽  
Zeng Li Liu ◽  
Lin Chen ◽  
Zai Yu Chen
Keyword(s):  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Threshold Function ◽  
Decomposition Level ◽  
Denoising Method ◽  
Global Threshold ◽  
Derivative Discontinuity ◽  
Soft Threshold ◽  
Wavelet Image ◽  
Constant Deviation

A new threshold function was proposed to overcome that hard threshold function is not continuous, soft threshold function has constant deviation and derivative discontinuity defects. It will be applied to using different thresholds denoising method with different decomposition level based on the D.J global threshold. Experimental results shows that the denoising result of new threshold function is superior to the traditional soft and hard threshold function in minimum mean square error (MSE) and peak signal to noise ratio (PSNR).

The Research of a Critical Threshold Denoising Method and its Parameters Selection

2013 ◽  
Vol 475-476 ◽  
pp. 263-267
Author(s):  
Qian Xiao ◽  
Yan Hui Jiang ◽  
Bin Wang ◽  
Mei Jia Liu ◽  
Mei Xia Song
Keyword(s):  
Threshold Function ◽  
Critical Threshold ◽  
Threshold Method ◽  
Simulation Experiments ◽  
Denoising Method ◽  
Parameters Selection ◽  
Soft Threshold ◽  
Selection For ◽  
Constant Deviation ◽  
Selection Of

For soft threshold function are likely to cause a constant deviation with the original signal, hard threshold function can not fully remove noise and the selection of semi threshold function parameters is complex, we presented a critical threshold function, and analyzed the parameter selection for the new threshold. The simulation experiments prove that the denoising of critical threshold method is much better, and it also can make up for the deficiencies of traditional threshold.

scholarly journals Research on Wavelet Threshold Denoising Method for UWB Tunnel Personnel Motion Location

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ning Liu ◽  
Ranqiao Zhang ◽  
Zhong Su ◽  
Guodong Fu ◽  
Jingang He
Keyword(s):  
Low Frequency ◽  
Threshold Function ◽  
Personal Safety ◽  
Threshold Estimation ◽  
Denoising Method ◽  
Positioning Accuracy ◽  
Motion Signal ◽  
Soft Threshold ◽  
Special Environment ◽  
Positioning Algorithm

In the process of tunnel construction, the problems of strong sealing, inconvenient communication, and harsh environment pose a serious threat to the personal safety of construction workers. Therefore, personnel positioning technology has important application value in tunnel safety construction. A special environment for tunnel personnel positioning and the ultrawideband (UWB) positioning system are affected by personnel movement, which leads to the problem of lowering positioning accuracy. A wavelet threshold denoising method for motion positioning of people in tunnels is proposed. The positioning algorithm of the method adopts a three-sided positioning algorithm based on symmetric double-sided two-way ranging. The wavelet analysis is used to decompose the motion signal of the personnel in the tunnel, and the low frequency coefficient and high frequency coefficient of the signal are decomposed to determine the influence of the motion noise of the personnel on the UWB positioning. The soft threshold function and the hard threshold function are, respectively, selected to perform wavelet threshold denoising on the motion positioning result in the tunnel. According to the denoising effect, the db5 wavelet 5-layer decomposition, under the heuristic threshold estimation criterion, the soft threshold function denoising is the best denoising method. The verification by the positioning experiment shows that the method is suitable for tunnel personnel positioning. The wavelet threshold denoising method can weaken the influence of outliers in the motion positioning of UWB personnel and improve the positioning accuracy.

Doppler Signal Denoising Based on Feature Adaptive Wavelet Shrinkage Method

2012 ◽  
Vol 532-533 ◽  
pp. 702-707
Author(s):  
Zhao Yin ◽  
Jing Jin ◽  
Yan Wang ◽  
Yi Shen
Keyword(s):  
Circulatory System ◽  
Doppler Signal ◽  
Signal Spectrum ◽  
Wavelet Shrinkage ◽  
Threshold Method ◽  
Denoising Method ◽  
Adaptive Wavelet ◽  
Doppler Signals ◽  
Envelope Extraction ◽  
Flow Detection

The envelope extraction of Doppler signal spectrum is very important in ultrasonic blood flow detection, due to the fact that it can provide the diagnosis information of blood circulatory system. Doppler signals are often polluted by noises, which will affect the performance of the envelope extraction. Therefore, it is necessary to remove the noises before extracting the spectrum envelope. In this paper, a Doppler denoising method based on the Feature Adaptive Wavelet Shrinkage is proposed. The advantage of this method is that the threshold of each coefficient is set by using the coefficient at the current location and its two neighbor coefficients. Simulation results demonstrate that the proposed method can remove the noises of Doppler signals more effectively compared to the traditional wavelet threshold method.

A Improved Algorithm of Wavelet Image De-Noising Based on Threshold Function

2013 ◽  
Vol 756-759 ◽  
pp. 1674-1678
Author(s):  
Zhen Xian Lin
Keyword(s):  
Theoretical Analysis ◽  
Experimental Results ◽  
Threshold Function ◽  
Noisy Image ◽  
Threshold Method ◽  
Higher Derivatives ◽  
Soft Threshold ◽  
Wavelet Image ◽  
Improved Algorithm

The relevant properties of the selected classical threshold function and selected threshold were compared and analyzed for the methods of noisy-image wavelet threshold de-noising. On the basis of this, a new wavelet threshold de-noising function was given in this paper. The new defined threshold function overcomes the shortcoming which the hard threshold function and the soft threshold function on higher derivatives are discontinuous by adding a variable. Theoretical analysis and experimental results show that, the constructed wavelet threshold de-noising function possess the better adaptability and de-noising effect. In the case of strong Gauss noise for the image, relatively soft threshold method, the PSNR of a new threshold de-noising method can be improved from 4dB to 6dB.

An Adaptive Wavelet Shrinkage and its Application in Image De-Noising

2011 ◽  
Vol 128-129 ◽  
pp. 500-503
Author(s):  
Tian Jie Cao
Keyword(s):  
Linear Function ◽  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Computing Time ◽  
Adaptive Method ◽  
Risk Estimate ◽  
Wavelet Coefficients ◽  
Adaptive Wavelet ◽  
Proportional Relation ◽  
Unbiased Risk

In this paper an adaptive method of shrinkage of the wavelet coefficients is presented. In the method, the wavelet coefficients are divided into two classes by a threshold. One class of them with the smaller absolute values at a scale is transformed with a proportional relation，another class with the larger absolute values at the same scale is transformed with a linear function. The threshold and the coefficient in the proportional relation or in the linear function are determined by the principle of minimizing the Stein’s unbiased risk estimate. In the paper, the method of estimation of the threshold and the coefficient is given and the adaptive method of shrinkage of the wavelet coefficients is applied to image denoising. Examples in the paper show that the presented method has an advantage over SureShrink from the point of view of both the Stein’s unbiased risk estimate and the signal-to-noise ratio. In addition, the method takes almost the same computing time as the SureShrink in image denoising.

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