Near-field time-frequency localization method using sparse representation

2012 ◽  
Vol 19 (6) ◽  
pp. 29-34
Author(s):  
Bo WANG ◽  
Juan-juan LIU ◽  
Xiao-ying SUN ◽  
Yan-jun ZHANG
Keyword(s):  
Sparse Representation ◽  
Near Field ◽  
Localization Method ◽  
Time Frequency ◽  
Time Frequency Localization

Detection of the Third Heart Sound Based on Nonlinear Signal Decomposition and Time–Frequency Localization

2016 ◽  
Vol 63 (8) ◽  
pp. 1718-1727 ◽  
Author(s):  
Shovan Barma ◽  
Bo-Wei Chen ◽  
Wen Ji ◽  
Seungmin Rho ◽  
Chih-Hung Chou ◽  
...  
Keyword(s):  
Heart Sound ◽  
Signal Decomposition ◽  
Time Frequency ◽  
The Third ◽  
Third Heart Sound ◽  
Time Frequency Localization ◽  
Nonlinear Signal

scholarly journals Orthogonal-based wavelet analysis of wind turbulence and correlation between turbulence and forces

2007 ◽  
Vol 29 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Le Thai Hoa ◽  
Nguyen Dong Anh
Keyword(s):  
Wavelet Transforms ◽  
Discrete Wavelet ◽  
Time Frequency ◽  
Existing Problems ◽  
Physical Measurements ◽  
Force Relation ◽  
Wind Turbulence ◽  
Time Frequency Localization ◽  
Physical Simulations ◽  
Wavelet Decompositions

Recent models of wind turbulence and turbulence-force relation as well still contain uncertainties. Further studies on them are needed to gain the better knowledge to refine the existing problems from analytical computations to wind tunnel's physical simulations in the wind engineering. The continuous and discrete wavelet transforms have been applied as powerful transformation tools to represent time series into the time-frequency localization. This paper will apply the orthogonal-based wavelet decomposition to investigate the intermittency of the turbulence and to detect the turbulence-force correlation in the both temporal-spectral information using proposed cross energy of wavelet decompositions. Analyzing data have been obtained by physical measurements on model from the wind tunnel tests.

Voltage Flicker Extraction Based on Wavelet Analysis

2013 ◽  
Vol 385-386 ◽  
pp. 1389-1393 ◽  
Author(s):  
Lin Chai ◽  
Jun Ru Sun
Keyword(s):  
Low Frequency ◽  
Matlab Simulation ◽  
Time Frequency ◽  
Multi Resolution Analysis ◽  
Voltage Signal ◽  
Time Frequency Localization ◽  
Resolution Analysis ◽  
Modulus Maximum ◽  
Voltage Flicker ◽  
Non Stationary Signal

Extracting voltage flicker from the sampling voltage signal is a precondition for management of flicker. Voltage flicker signal is a low frequency time-varying non-stationary signal. The traditional fourier transform has great limitations when analyze the non-stationary signal for not having the time resolution. As wavelet transform has good property of time-frequency localization, it become a powerful tool for analyze this kind of signal. This paper adopts multi-resolution analysis of wavelet to extract voltage flicker signal. Furthermore, according to the characteristics of wavelet function, this paper selects Daubechies wavelet to accomplish the multi-level decomposition and reconstruction of signal, in order to get the frequency and amplitude of voltage flicker signals. Based on the principle of modulus maximum, it can be determined what time the voltage flicker happen and over. The results of MATLAB simulation indicate that voltage flicker signal can be effectively extracted by wavelet multi-resolution analysis. Wavelet multi-resolution analysis is considerably ideal for voltage flicker extraction.

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