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.

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.

An Improved Online De-Noising Method for Ultrasonic Echo Signal of Wear Debris in Oil

2014 ◽  
Vol 631-632 ◽  
pp. 490-493
Author(s):  
Yi Ning Li ◽  
Pei Lin Zhang ◽  
Chao Xu ◽  
Yun Qiang Zhang ◽  
Long Yun Li
Keyword(s):  
Wear Debris ◽  
Optimal Solution ◽  
Threshold Function ◽  
Complex Wavelet Transform ◽  
Echo Signal ◽  
Obvious Effect ◽  
Swarm Optimization ◽  
Linear Threshold ◽  
Ultrasonic Echo ◽  
Non Linear

The ultrasonic echo signal of wear debris is influenced by many matters. It causes so much more noise. Therefore, it puts forward an improved online de-noising method for ultrasonic echo signal of wear debris in oil. In the dual-tree complex wavelet transform (DTCWT) field, a method, which combines the new non-linear threshold function with adaptive threshold, utilizes particle swarm optimization (PSO) for optimizing the parameter of non-linear threshold function to get the optimal solution. The result of de-noising method can be evaluated. Experimental results show that the proposed method has obvious effect on signal de-noising.

Feature Extraction of Underwater Target Ultrasonic Echo Based on Wavelet Transform

2014 ◽  
Vol 599-601 ◽  
pp. 1517-1522 ◽  
Author(s):  
Zheng Long Wu ◽  
Jie Li ◽  
Zhen Yu Guan
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Signal Amplitude ◽  
Main Task ◽  
Echo Signal ◽  
Denoising Method ◽  
Ultrasonic Echo ◽  
Envelope Extraction ◽  
Underwater Target ◽  
Breaking Points

Ultrasonic detection has been widely used in underwater detectoscopes as an important method for underwater detection. Feature extraction of echo signal time-delay and amplitude is the main task of processing underwater ultrasonic signal. Underwater target ultrasonic echo signal is influenced by reverberation and noise from the sea and system itself, reverberation interference of signal background is the main difficulty for target echo detection. So we use denoising algorithm to denoise echo signal. At first this paper denoises the measured weighted background clutter data using wavelet threshold denoising method, then the paper extracts breaking points of echo signal through wavelet transform, at last the paper makes an envelope extraction using Hilbert transform combined with wavelet transform methods, and acquires the feature information of echo signal amplitude.

Improved Wavelet Threshold Denoising Method

2014 ◽  
Vol 602-605 ◽  
pp. 3177-3180
Author(s):  
Wei Ping Cui ◽  
Li Juan Du
Keyword(s):  
Traditional Method ◽  
Wavelet Denoising ◽  
Threshold Function ◽  
Signal Denoising ◽  
Improved Method ◽  
Denoising Method ◽  
Comparison And Analysis ◽  
Selection Of

In this paper, through comparison and analysis of various wavelet denoising methods, a new threshold function is constructed, and the selection of threshold is improved. Signal denoising simulation is made by the software MATLAB, the results show that the improved method is superior to the traditional method, and obtain a better denoising effect.

Ultrasonic echo signal modeling and identification for minor defects in metallic materials

2015 ◽  
Vol 23 (9) ◽  
pp. 2635-2644
Author(s):  
杨辰龙 YANG Chen-long ◽  
陈越超 CHEN Yue-chao ◽  
叶钱 YE Qian ◽  
郑慧峰 ZHENG Hui-feng
Keyword(s):  
Metallic Materials ◽  
Echo Signal ◽  
Signal Modeling ◽  
Ultrasonic Echo

scholarly journals Arrival-Time Detection in Wind-Speed Measurement: Wavelet Transform and Bayesian Information Criteria

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 269 ◽  
Author(s):  
Wei Zhang ◽  
Zhipeng Li ◽  
Xuyang Gao ◽  
Yanjun Li ◽  
Yibing Shi
Keyword(s):  
Wavelet Transform ◽  
Wind Speed ◽  
Arrival Time ◽  
Time Window ◽  
Signal To Noise Ratio ◽  
Information Criteria ◽  
Echo Signal ◽  
Time Frequency ◽  
Ultrasonic Echo ◽  
Ultrasonic Anemometer

The time-difference method is a common one for measuring wind speed ultrasonically, and its core is the precise arrival-time determination of the ultrasonic echo signal. However, because of background noise and different types of ultrasonic sensors, it is difficult to measure the arrival time of the echo signal accurately in practice. In this paper, a method based on the wavelet transform (WT) and Bayesian information criteria (BIC) is proposed for determining the arrival time of the echo signal. First, the time-frequency distribution of the echo signal is obtained by using the determined WT and rough arrival time. After setting up a time window around the rough arrival time point, the BIC function is calculated in the time window, and the arrival time is determined by using the BIC function. The proposed method is tested in a wind tunnel with an ultrasonic anemometer. The experimental results show that, even in the low-signal-to-noise-ratio area, the deviation between mostly measured values and preset standard values is mostly within 5 μs, and the standard deviation of measured wind speed is within 0.2 m/s.

Application of Wavelet Transform in Signal Compression and Signal Recombination

2014 ◽  
Vol 986-987 ◽  
pp. 2056-2059
Author(s):  
Zhe Yuan Wang ◽  
Li Jiang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Packet ◽  
Influence Factors ◽  
Wavelet Packet Transform ◽  
Signal Compression ◽  
Matlab Simulation ◽  
Paper Briefly ◽  
Simulation Experiments

This paper discusses the application of wavelet transform in signal compression and signal recombination detailedly. This paper briefly introduces the principle of wavelet transform in signal compression and signal recombination, this paper also introduces the wavelet MATLAB simulation experiments. This paper researches the differences the wavelet transform and wavelet packet transform in signal compression, this paper also briefly discusses the influence factors of signal compression.

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.

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