The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time...The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.展开更多
Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical mean...Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical meanings. This paper studies the single trial extraction of visual evoked potential by combining EMD and wavelet threshold filter. Experimental results showed that the EMD based method can separate the noise out of the event related potentials (ERPs) and effectively extract the weak ERPs in strong background noise, which manifested as the waveform characteristics and root mean square error (RMSE).展开更多
Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding unde...Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding underlying factors or components from multivariate statistical data based on dimension reduction methods,and it is applicable to extract the non-stationary signals.FastICA based on negentropy is presented to effectively extract and separate the vibration signals caused by human activity in this paper.A new method combined empirical mode decomposition(EMD) technique with the adaptive threshold method is applied to extract the vibration pulses,and suppress the interference signals.The practical tests demonstrate that the method proposed in the paper is effective in separating and extracting the vibration signals.展开更多
基金funded by the National Basic Research Program of China(973 Program)(No.2011 CB201002)the National Natural Science Foundation of China(No.41374117)the great and special projects(2011ZX05005–005-008HZ and 2011ZX05006-002)
文摘The Gabor and S transforms are frequently used in time-frequency decomposition methods. Constrained by the uncertainty principle, both transforms produce low-resolution time-frequency decomposition results in the time and frequency domains. To improve the resolution of the time-frequency decomposition results, we use the instantaneous frequency distribution function（IFDF） to express the seismic signal. When the instantaneous frequencies of the nonstationary signal satisfy the requirements of the uncertainty principle, the support of IFDF is just the support of the amplitude ridges in the signal obtained using the short-time Fourier transform. Based on this feature, we propose a new iteration algorithm to achieve the sparse time-frequency decomposition of the signal. The iteration algorithm uses the support of the amplitude ridges of the residual signal obtained with the short-time Fourier transform to update the time-frequency components of the signal. The summation of the updated time-frequency components in each iteration is the result of the sparse timefrequency decomposition. Numerical examples show that the proposed method improves the resolution of the time-frequency decomposition results and the accuracy of the analysis of the nonstationary signal. We also use the proposed method to attenuate the ground roll of field seismic data with good results.
基金Natural Science Fund for Colleges and Universities in Jiangsu Province of China grant number: 10 KJB510003+2 种基金Natural Science Fund in Changzhou grant number: CJ20110023 Open Project of the State Key Laboratory of Robotics and System (HIT), and the State Key Laboratory of Cognitive Neurosciences and Learning
文摘Empirical mode decomposition(EMD) is a new signal decomposition method, which could decompose the non-stationary signal into several single-component intrinsic mode functions (IMFs) and each IMF has some physical meanings. This paper studies the single trial extraction of visual evoked potential by combining EMD and wavelet threshold filter. Experimental results showed that the EMD based method can separate the noise out of the event related potentials (ERPs) and effectively extract the weak ERPs in strong background noise, which manifested as the waveform characteristics and root mean square error (RMSE).
文摘Monitoring transmission towers is of great importance to prevent severe thefts on them and ensure the reliability and safety of the power grid operation.Independent component analysis(ICA) is a method for finding underlying factors or components from multivariate statistical data based on dimension reduction methods,and it is applicable to extract the non-stationary signals.FastICA based on negentropy is presented to effectively extract and separate the vibration signals caused by human activity in this paper.A new method combined empirical mode decomposition(EMD) technique with the adaptive threshold method is applied to extract the vibration pulses,and suppress the interference signals.The practical tests demonstrate that the method proposed in the paper is effective in separating and extracting the vibration signals.
