164 research outputs found
Modulated Oscillations in Three Dimensions
The analysis of the fully three-dimensional and time-varying polarization
characteristics of a modulated trivariate, or three-component, oscillation is
addressed. The use of the analytic operator enables the instantaneous
three-dimensional polarization state of any square-integrable trivariate signal
to be uniquely defined. Straightforward expressions are given which permit the
ellipse parameters to be recovered from data. The notions of instantaneous
frequency and instantaneous bandwidth, generalized to the trivariate case, are
related to variations in the ellipse properties. Rates of change of the ellipse
parameters are found to be intimately linked to the first few moments of the
signal's spectrum, averaged over the three signal components. In particular,
the trivariate instantaneous bandwidth---a measure of the instantaneous
departure of the signal from a single pure sinusoidal oscillation---is found to
contain five contributions: three essentially two-dimensional effects due to
the motion of the ellipse within a fixed plane, and two effects due to the
motion of the plane containing the ellipse. The resulting analysis method is an
informative means of describing nonstationary trivariate signals, as is
illustrated with an application to a seismic record.Comment: IEEE Transactions on Signal Processing, 201
Generalized Morse Wavelets as a Superfamily of Analytic Wavelets
The generalized Morse wavelets are shown to constitute a superfamily that
essentially encompasses all other commonly used analytic wavelets, subsuming
eight apparently distinct types of analysis filters into a single common form.
This superfamily of analytic wavelets provides a framework for systematically
investigating wavelet suitability for various applications. In addition to a
parameter controlling the time-domain duration or Fourier-domain bandwidth, the
wavelet {\em shape} with fixed bandwidth may be modified by varying a second
parameter, called . For integer values of , the most symmetric,
most nearly Gaussian, and generally most time-frequency concentrated member of
the superfamily is found to occur for . These wavelets, known as
"Airy wavelets," capture the essential idea of popular Morlet wavelet, while
avoiding its deficiencies. They may be recommended as an ideal starting point
for general purpose use
On the Analytic Wavelet Transform
An exact and general expression for the analytic wavelet transform of a
real-valued signal is constructed, resolving the time-dependent effects of
non-negligible amplitude and frequency modulation. The analytic signal is first
locally represented as a modulated oscillation, demodulated by its own
instantaneous frequency, and then Taylor-expanded at each point in time. The
terms in this expansion, called the instantaneous modulation functions, are
time-varying functions which quantify, at increasingly higher orders, the local
departures of the signal from a uniform sinusoidal oscillation. Closed-form
expressions for these functions are found in terms of Bell polynomials and
derivatives of the signal's instantaneous frequency and bandwidth. The analytic
wavelet transform is shown to depend upon the interaction between the signal's
instantaneous modulation functions and frequency-domain derivatives of the
wavelet, inducing a hierarchy of departures of the transform away from a
perfect representation of the signal. The form of these deviation terms
suggests a set of conditions for matching the wavelet properties to suit the
variability of the signal, in which case our expressions simplify considerably.
One may then quantify the time-varying bias associated with signal estimation
via wavelet ridge analysis, and choose wavelets to minimize this bias
Analysis of Modulated Multivariate Oscillations
The concept of a common modulated oscillation spanning multiple time series
is formalized, a method for the recovery of such a signal from potentially
noisy observations is proposed, and the time-varying bias properties of the
recovery method are derived. The method, an extension of wavelet ridge analysis
to the multivariate case, identifies the common oscillation by seeking, at each
point in time, a frequency for which a bandpassed version of the signal obtains
a local maximum in power. The lowest-order bias is shown to involve a quantity,
termed the instantaneous curvature, which measures the strength of local
quadratic modulation of the signal after demodulation by the common oscillation
frequency. The bias can be made to be small if the analysis filter, or wavelet,
can be chosen such that the signal's instantaneous curvature changes little
over the filter time scale. An application is presented to the detection of
vortex motions in a set of freely-drifting oceanographic instruments tracking
the ocean currents
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals
There are three equivalent ways of representing two jointly observed
real-valued signals: as a bivariate vector signal, as a single complex-valued
signal, or as two analytic signals known as the rotary components. Each
representation has unique advantages depending on the system of interest and
the application goals. In this paper we provide a joint framework for all three
representations in the context of frequency-domain stochastic modeling. This
framework allows us to extend many established statistical procedures for
bivariate vector time series to complex-valued and rotary representations.
These include procedures for parametrically modeling signal coherence,
estimating model parameters using the Whittle likelihood, performing
semi-parametric modeling, and choosing between classes of nested models using
model choice. We also provide a new method of testing for impropriety in
complex-valued signals, which tests for noncircular or anisotropic second-order
statistical structure when the signal is represented in the complex plane.
Finally, we demonstrate the usefulness of our methodology in capturing the
anisotropic structure of signals observed from fluid dynamic simulations of
turbulence.Comment: To appear in IEEE Transactions on Signal Processin
A Power Variance Test for Nonstationarity in Complex-Valued Signals
We propose a novel algorithm for testing the hypothesis of nonstationarity in
complex-valued signals. The implementation uses both the bootstrap and the Fast
Fourier Transform such that the algorithm can be efficiently implemented in
O(NlogN) time, where N is the length of the observed signal. The test procedure
examines the second-order structure and contrasts the observed power variance -
i.e. the variability of the instantaneous variance over time - with the
expected characteristics of stationary signals generated via the bootstrap
method. Our algorithmic procedure is capable of learning different types of
nonstationarity, such as jumps or strong sinusoidal components. We illustrate
the utility of our test and algorithm through application to turbulent flow
data from fluid dynamics
Bivariate Empirical Mode Decomposition
10 pages, 3 figures. Submitted to Signal Processing Letters, IEEE. Matlab/C codes and additional material are downloadable from http://perso.ens-lyon.fr/patrick.flandrinThe Empirical Mode Decomposition (EMD) has been introduced quite recently to adaptively decompose nonstationary and/or nonlinear time series. The method being initially limited to real-valued time series, we propose here an extension to bivariate (or complex-valued) time series which generalizes the rationale underlying the EMD to the bivariate framework. Where the EMD extracts zero-mean oscillating components, the proposed bivariate extension is designed to extract zero-mean rotating components. The method is illustrated on a real-world signal and properties of the output components are discussed. Free Matlab/C codes are available at http://perso.ens-lyon.fr/patrick.flandrin
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