A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. 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. With this ensemble mean, one can separate scales naturall...

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

During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical attempts to this decomposition problem, like synchrosqueezing, empirical wavelets or recursive variational decomposition. Here, we propose an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently. The model looks for an ensemble of modes and their respective center frequencies, such that the modes collectively reproduce the input signal, while each being smooth after demodulation into baseband. In Fourier domain, this corresponds to a narrow-band prior. We show important relations to Wiener filter denoising. Indeed, the proposed method is a generalization of the classic Wiener filter into multiple, adaptive bands. Our model provides a solution to the decomposition problem that is theoretically well founded and still easy to understand. The variational model is efficiently optimized using an alternating direction method of multipliers approach. Preliminary results show attractive performance with respect to existing mode decomposition models. In particular, our proposed model is much more robust to sampling and noise. Finally, we show promising practical decomposition results on a series of artificial and real data.

Variational Mode Decomposition

Empirical mode decomposition (EMD) has recently been pioneered by Huang et al. for adaptively representing nonstationary signals as sums of zero-mean amplitude modulation frequency modulation components. In order to better understand the way EMD behaves in stochastic situations involving broadband noise, we report here on numerical experiments based on fractional Gaussian noise. In such a case, it turns out that EMD acts essentially as a dyadic filter bank resembling those involved in wavelet decompositions. It is also pointed out that the hierarchy of the extracted modes may be similarly exploited for getting access to the Hurst exponent.

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Empirical mode decomposition as a filter bank

We survey the newly developed Hilbert spectral analysis method and its applications to Stokes waves, nonlinear wave evolution processes, the spectral form of the random wave field, and turbulence. Our emphasis is on the inadequacy of presently available methods in nonlinear and nonstationary data analysis. Hilbert spectral analysis is here proposed as an alternative. This new method provides not only a more precise definition of particular events in time-frequency space than wavelet analysis, but also more physically meaningful interpretations of the underlying dynamic processes.

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A new view of nonlinear water waves: the Hilbert spectrum

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brain-wide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a reciprocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clocklike. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, 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. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.

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Neurophysiological and Computational Principles of Cortical Rhythms in Cognition

The effect of wave breaking on the wave energy spectral shape is examined. The Stokes wave-breaking criterion is first extended to random waves and a breaking wave model is established in which the elevation of breaking waves is expressed in terms of that of the original ideal waves, which are assumed to be stationary and Gaussian. Based on this model, a simple but approximate expression for the spectrum of breaking waves is derived and applied to the case in which a deep water unidirectional wave train enters a region of adverse current steady in time and uniformly distributed in depth.

The Effect of Wave Breaking on the Wave Energy Spectrum

On the nonlinear waves in a developing process: Part II. Instability analysis

Covariance functions and spectra of components of fluid particle velocity are obtained, taking into consideration the effects of free surface fluctuations, for a Gaussian, stationary and homogeneous random gravity wave field in deep water, using infinitesimal wave solutions. Approximate representations of the covariance functions and spectra are also derived. It is shown that the covariance functions and spectra presented in this paper differ from those when the effects of free surface fluctuations are ignored, especially at, around and above the equilibrium surface.

Covariance Functions and Spectra of a Random Wave Field

The probability density function, the first three statistical moments, and the spectrum of pressure are obtained for a Gaussian, stationary and homogeneous random gravity-wave field in deep water, taking into consideration the effects of free-surface fluctuations. Approximate representation of the spectrum is also derived. It is shown that the statistical properties of pressure differ appreciably from those when free-surface fluctuations are ignored especially at, around, and above the equilibrium surface.

Statistical Properties of Pressure in a Random Wave Field

An approximate method is devised to compute the energy-containing portion of the spectrum of waves in water of finite depth, taking into account the effect of wave breaking. It is assumed that there exists a linear and Gaussian ideal wave train whose spectrum is first calculated using the wave energy flux balance equation without considering wave breaking. The Miche wave-breaking criterion for waves in water of finite depth is then applied to limit the wave elevation and establish an expression for the breaking wave elevation in terms of the elevation and elevation's second time derivative of the ideal waves. Simple expressions for the mean value, the mean square value, and the spectrum of the breaking waves are then obtained, and numerical results are presented graphically.

C. C. Tung

Papers

The Effect of Wave Breaking on the Wave Energy Spectrum

On the nonlinear waves in a developing process: Part II. Instability analysis

Covariance Functions and Spectra of a Random Wave Field

Statistical Properties of Pressure in a Random Wave Field

The effect of wave breaking on wave spectrum in water of finite depth