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The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

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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...

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Journal ArticleDOI

Ensemble empirical mode decomposition: a noise-assisted data analysis method

TL;DR: The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.
Journal ArticleDOI

Variational Mode Decomposition

TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Journal ArticleDOI

Empirical mode decomposition as a filter bank

TL;DR: It turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions, and the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent.
Journal ArticleDOI

A new view of nonlinear water waves: the Hilbert spectrum

TL;DR: In this paper, Hilbert spectral analysis is proposed as an alternative to wavelet analysis, which provides not only a more precise definition of particular events in time-frequency space, but also more physically meaningful interpretations of the underlying dynamic processes.
Journal ArticleDOI

Neurophysiological and Computational Principles of Cortical Rhythms in Cognition

TL;DR: A plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory, and selective attention, and implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.
References
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Book

Probabilistic Structural Dynamics: Advanced Theory and Applications

Y. K Lin
TL;DR: Spectral analysis evolutionary spectral analysis Markov processes exact solutions for multi-dimensional nonlinear systems stability of stochastic systems approximate solutions for first-excursion failure disordered structures as discussed by the authors.
Journal ArticleDOI

A product theorem for Hilbert transforms

TL;DR: In this article, a mathematical theorem for finding the Hilbert transform of a product of functions in a simplified fashion is presented under certain conditions, and a mathematical application of this theorem for engineering applications is discussed.
Journal ArticleDOI

Stochastic prediction of ground motion and spectral response parameters at hard-rock sites in eastern north america

TL;DR: The stochastic model has been successfully applied to the prediction of ground motions in the Western United States and to short-period magnitudes from large to great earthquakes worldwide as mentioned in this paper.
Journal ArticleDOI

The instabilities of gravity waves of finite amplitude in deep water I. Superharmonics

TL;DR: In this paper, the authors extended the analysis of the normal-mode perturbation of steep irrotational gravity waves to subharmonic perturbations, namely those having horizontal scales greater than the basic wavelength (2π/k).
Book ChapterDOI

The Mechanism for Frequency Downshift in Nonlinear Wave Evolution

TL;DR: Huang et al. as discussed by the authors used a Hilbert transform to produce the phase-amplitude diagram and the Hilbert Spectrum, and found that the wave fusion event could occur in finite band widths and wind wave fields as well; and it is indeed the mechanism responsible for the frequency downshift in nonlinear wave evolution in general.
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