Journal ArticleDOI
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
Norden E. Huang,Zheng Shen,Steven R. Long,Man-Li C. Wu,Hsing H. Shih,Quanan Zheng,Nai-Chyuan Yen,C. C. Tung,Henry H. Liu +8 more
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TLDR
In this paper, a new method for analysing nonlinear and nonstationary data has been developed, which is the key part of the method is the empirical mode decomposition method with which any complicated data set can be decoded.Abstract:
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the empirical mode decomposition method with which any complicated data set can be dec...read more
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Adaptive data analysis via sparse time-frequency representation
Thomas Y. Hou,Zuoqiang Shi +1 more
TL;DR: A new adaptive method for analyzing nonlinear and nonstationary data inspired by the empirical mode decomposition (EMD) method and the recently developed compressed sensing theory that is less sensitive to noise perturbation and the end effect compared with the original EMD method.
Journal ArticleDOI
Speech pitch determination based on Hilbert-Huang transform
Hai Huang,Jiaqiang Pan +1 more
TL;DR: The results show that, compared with conventional methods, the pitch determination method based on HHT well improves the accuracy and resolution of pitch recognition.
Journal ArticleDOI
A novel approach for automated detection of focal EEG signals using empirical wavelet transform
Abhijit Bhattacharyya,Manish Sharma,Ram Bilas Pachori,Pradip Sircar,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +6 more
TL;DR: An automatic approach has been presented to detect electroencephalogram (EEG) signals of non-focal and focal groups to determine the area linked to the focal epilepsy and the developed prototype can be used for the epileptic patients and aid the clinicians to confirm diagnosis.
Journal ArticleDOI
Natural demodulation of two-dimensional fringe patterns. II. Stationary phase analysis of the spiral phase quadrature transform.
TL;DR: It is shown that the spiral phase (or vortex) transform approaches the ideal quadrature transform asymptotically and that the approximation errors increase with the relative curvature of the fringes.
Journal ArticleDOI
Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis
TL;DR: In this paper, the authors consider the iterative filtering (IF) approach as an alternative to EMD and provide sufficient conditions on the filters that ensure the convergence of IF applied to any L 2 signal.
References
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TL;DR: In this paper, the authors used the representations of the noise currents given in Section 2.8 to derive some statistical properties of I(t) and its zeros and maxima.
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