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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High-frequency, long-range coupling between prefrontal and visual cortex during attention.
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Past, present and future of nonlinear system identification in structural dynamics
TL;DR: In this article, a review of the past and recent developments in system identification of nonlinear dynamical structures is presented, highlighting their assets and limitations and identifying future directions in this research area.
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Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method
TL;DR: Though this new approach yields IMF with the similar RMS noise as EEMD, it effectively eliminated residue noise in the IMFs.
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Review Paper: Health Monitoring of Civil Infrastructure:
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Infrared and visible image fusion methods and applications: A survey
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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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