Wavelet Transform Selection Method for Biological Signal Treatment

2020 ◽  
pp. 23-34 ◽  
Author(s):  
Gonzalo Jiménez ◽  
Carlos Andrés Collazos Morales ◽  
Emiro De-la-Hoz-Franco ◽  
Paola Ariza-Colpas ◽  
Ramón Enrique Ramayo González ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Selection Method ◽  
Biological Signal ◽  
Signal Treatment

Application of a New Kind of Wavelet Threshold De-Noising Method in Partial Discharge Signals

2014 ◽  
Vol 889-890 ◽  
pp. 780-785
Author(s):  
Cheng Long Xu ◽  
Hong Yu ◽  
Bi Qiang Du ◽  
Jun Li ◽  
Ze Kun Liu ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Simulation Experiment ◽  
Wavelet Coefficient ◽  
Partial Discharge ◽  
Noise Signal ◽  
Selection Method ◽  
Wavelet Coefficients ◽  
Threshold Selection ◽  
Whole Process ◽  
Maximum Value

In order to more effectively remove noise in partial discharge signals, it is proposed a new threshold selection method in this paper. This method firstly takes the signals before the partial discharge starting to happen as only contain noise signal, and then applies a wavelet transform to the only contain noise signal. Secondly record every detail part and the maximum value of wavelet coefficients of last layer approximation part, and take this value as its layer threshold. And then applies a wavelet transform to the partial discharge signals which contains noises. Next is to process wavelet coefficient of each layer using the selected threshold. Finally, the already handled wavelet coefficients is used to reconstruction the signals. The whole process of threshold choosing is automatic without human intervention. Simulation experiment show that compared with the traditional threshold selection method, this method can be better to remove the noise of the partial discharge signals, and it has a strong practical value.

Comparing Different Means of Signal Treatment for Improving the Detection Power in HPLC-UV-HG-AFS

2012 ◽  
Vol 239-240 ◽  
pp. 1045-1051
Author(s):  
Jian Cui ◽  
Yan Wang ◽  
Xue Hong Zhao ◽  
Li Dai
Keyword(s):  
Fourier Transform ◽  
Wavelet Transform ◽  
Gaussian Distribution ◽  
Analytical Methods ◽  
Arsenic Species ◽  
Detection Power ◽  
Arsanilic Acid ◽  
Organic Arsenic ◽  
Trace Concentrations ◽  
Signal Treatment

The purpose of detecting trace concentrations of analytes often is hindered by occurring noise in the signal curves of analytical methods. This is also a problem when different arsenic species (organic arsenic species such as arsanilic acid, nitarsone and roxarsone) are to be determined in animal meat by HPLC-UV-HG-AFS, which is the basis of this work. In order to improve the detection power, methods of signal treatment may be applied. We show a comparison of convolution with Gaussian distribution curves, Fourier transform, and wavelet transform. It is illustrated how to estimate decisive parameters for these techniques. All methods result in improved limits of detection. Furthermore, applying baselines and evaluating peaks thoroughly is facilitated. However, there are differences. Fourier transform may be applied, but convolution with Gaussian distribution curves shows better results of improvement. The best of the three is wavelet transform, whereby the detection power is improved by factors of about 2.4.

Wavelet transform-based fault diagnosis and line selection method of small current grounding system

2008 ◽  
Author(s):  
Ni Yang ◽  
Shuqing Zhang ◽  
Liguo Zhang ◽  
Kexin Zhang ◽  
Lingyun Sun
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Selection Method ◽  
Small Current ◽  
Grounding System ◽  
Line Selection
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