397 research outputs found
Recent Advances in Theory and Methods for Nonstationary Signal Analysis
Cataloged from PDF version of article.All physical processes are nonstationary. When analyzing
time series, it should be remembered that nature can
be amazingly complex and that many of the theoretical
constructs used in stochastic process theory, for example,
linearity, ergodicity, normality, and particularly stationarity,
are mathematical fairy tales. There are no stationary time
series in the strict mathematical sense; at the very least, everything
has a beginning and an end. Thus, while it is necessary
to know the theory of stationary processes, one should not
adhere to it dogmatically when analyzing data from physical
sources, particularly when the observations span an extended
period. Nonstationary signals are appropriate models for
signals arising in several fields of applications including
communications, speech and audio, mechanics, geophysics,
climatology, solar and space physics, optics, and biomedical
engineering. Nonstationary models account for possible time
variations of statistical functions and/or spectral characteristics
of signals. Thus, they provide analysis tools more general
than the classical Fourier transform for finite-energy signals
or the power spectrum for finite-power stationary signals.
Nonstationarity, being a “nonproperty” has been analyzed
from several different points of view. Several approaches
that generalize the traditional concepts of Fourier analysis
have been considered, including time-frequency, time-scale,
and wavelet analysis, and fractional Fourier and linear
canonical transforms
Recent Advances in Theory and Methods for Nonstationary Signal Analysis
Cataloged from PDF version of article.All physical processes are nonstationary. When analyzing
time series, it should be remembered that nature can
be amazingly complex and that many of the theoretical
constructs used in stochastic process theory, for example,
linearity, ergodicity, normality, and particularly stationarity,
are mathematical fairy tales. There are no stationary time
series in the strict mathematical sense; at the very least, everything
has a beginning and an end. Thus, while it is necessary
to know the theory of stationary processes, one should not
adhere to it dogmatically when analyzing data from physical
sources, particularly when the observations span an extended
period. Nonstationary signals are appropriate models for
signals arising in several fields of applications including
communications, speech and audio, mechanics, geophysics,
climatology, solar and space physics, optics, and biomedical
engineering. Nonstationary models account for possible time
variations of statistical functions and/or spectral characteristics
of signals. Thus, they provide analysis tools more general
than the classical Fourier transform for finite-energy signals
or the power spectrum for finite-power stationary signals.
Nonstationarity, being a “nonproperty” has been analyzed
from several different points of view. Several approaches
that generalize the traditional concepts of Fourier analysis
have been considered, including time-frequency, time-scale,
and wavelet analysis, and fractional Fourier and linear
canonical transforms
Localization of Multi-Dimensional Wigner Distributions
A well known result of P. Flandrin states that a Gaussian uniquely maximizes
the integral of the Wigner distribution over every centered disc in the phase
plane. While there is no difficulty in generalizing this result to
higher-dimensional poly-discs, the generalization to balls is less obvious. In
this note we provide such a generalization.Comment: Minor corrections, to appear in the Journal of Mathematical Physic
Nonlinear internal wave penetration via parametric subharmonic instability
6 pages, 5 figuresInternational audienceWe present the results of a laboratory experimental study of an internal wave field generated by harmonic, spatially-periodic boundary forcing from above of a density stratification comprising a strongly-stratified, thin upper layer sitting atop a weakly-stratified, deep lower layer. In linear regimes, the energy flux associated with relatively high frequency internal waves excited in the upper layer is prevented from entering the lower layer by virtue of evanescent decay of the wave field. In the experiments, however, we find that the development of parametric subharmonic instability (PSI) in the upper layer transfers energy from the forced primary wave into a pair of subharmonic daughter waves, each capable of penetrating the weakly-stratified lower layer. We find that around of the primary wave energy flux penetrates into the lower layer via this nonlinear wave-wave interaction for the regime we study
Regional coherence evaluation in mild cognitive impairment and Alzheimer's disease based on adaptively extracted magnetoencephalogram rhythms
This study assesses the connectivity alterations caused by Alzheimer's disease (AD) and mild cognitive impairment (MCI) in magnetoencephalogram (MEG) background activity. Moreover, a novel methodology to adaptively extract brain rhythms from the MEG is introduced. This methodology relies on the ability of empirical mode decomposition to isolate local signal oscillations and constrained blind source separation to extract the activity that jointly represents a subset of channels. Inter-regional MEG connectivity was analysed for 36 AD, 18 MCI and 26 control subjects in δ, θ, α and β bands over left and right central, anterior, lateral and posterior regions with magnitude squared coherence—c(f). For the sake of comparison, c(f) was calculated from the original MEG channels and from the adaptively extracted rhythms. The results indicated that AD and MCI cause slight alterations in the MEG connectivity. Computed from the extracted rhythms, c(f) distinguished AD and MCI subjects from controls with 69.4% and 77.3% accuracies, respectively, in a full leave-one-out cross-validation evaluation. These values were higher than those obtained without the proposed extraction methodology
A time frequency analysis of wave packet fractional revivals
We show that the time frequency analysis of the autocorrelation function is,
in many ways, a more appropriate tool to resolve fractional revivals of a wave
packet than the usual time domain analysis. This advantage is crucial in
reconstructing the initial state of the wave packet when its coherent structure
is short-lived and decays before it is fully revived. Our calculations are
based on the model example of fractional revivals in a Rydberg wave packet of
circular states. We end by providing an analytical investigation which fully
agrees with our numerical observations on the utility of time-frequency
analysis in the study of wave packet fractional revivals.Comment: 9 pages, 4 figure
Spectral Analysis of Multi-dimensional Self-similar Markov Processes
In this paper we consider a discrete scale invariant (DSI) process with scale . We consider to have some fix number of
observations in every scale, say , and to get our samples at discrete points
where is obtained by the equality
and . So we provide a discrete time scale
invariant (DT-SI) process with parameter space . We find the spectral representation of the covariance function of
such DT-SI process. By providing harmonic like representation of
multi-dimensional self-similar processes, spectral density function of them are
presented. We assume that the process is also Markov
in the wide sense and provide a discrete time scale invariant Markov (DT-SIM)
process with the above scheme of sampling. We present an example of DT-SIM
process, simple Brownian motion, by the above sampling scheme and verify our
results. Finally we find the spectral density matrix of such DT-SIM process and
show that its associated -dimensional self-similar Markov process is fully
specified by where is
the covariance function of th and th observations of the process.Comment: 16 page
Tomograms and other transforms. A unified view
A general framework is presented which unifies the treatment of wavelet-like,
quasidistribution, and tomographic transforms. Explicit formulas relating the
three types of transforms are obtained. The case of transforms associated to
the symplectic and affine groups is treated in some detail. Special emphasis is
given to the properties of the scale-time and scale-frequency tomograms.
Tomograms are interpreted as a tool to sample the signal space by a family of
curves or as the matrix element of a projector.Comment: 19 pages latex, submitted to J. Phys. A: Math and Ge
A wavelet analysis of the Rosenblatt process: chaos expansion and estimation of the self-similarity parameter
By using chaos expansion into multiple stochastic integrals, we make a
wavelet analysis of two self-similar stochastic processes: the fractional
Brownian motion and the Rosenblatt process. We study the asymptotic behavior of
the statistic based on the wavelet coefficients of these processes. Basically,
when applied to a non-Gaussian process (such as the Rosenblatt process) this
statistic satisfies a non-central limit theorem even when we increase the
number of vanishing moments of the wavelet function. We apply our limit
theorems to construct estimators for the self-similarity index and we
illustrate our results by simulations
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