430 research outputs found
Scalar and vector Slepian functions, spherical signal estimation and spectral analysis
It is a well-known fact that mathematical functions that are timelimited (or
spacelimited) cannot be simultaneously bandlimited (in frequency). Yet the
finite precision of measurement and computation unavoidably bandlimits our
observation and modeling scientific data, and we often only have access to, or
are only interested in, a study area that is temporally or spatially bounded.
In the geosciences we may be interested in spectrally modeling a time series
defined only on a certain interval, or we may want to characterize a specific
geographical area observed using an effectively bandlimited measurement device.
It is clear that analyzing and representing scientific data of this kind will
be facilitated if a basis of functions can be found that are "spatiospectrally"
concentrated, i.e. "localized" in both domains at the same time. Here, we give
a theoretical overview of one particular approach to this "concentration"
problem, as originally proposed for time series by Slepian and coworkers, in
the 1960s. We show how this framework leads to practical algorithms and
statistically performant methods for the analysis of signals and their power
spectra in one and two dimensions, and, particularly for applications in the
geosciences, for scalar and vectorial signals defined on the surface of a unit
sphere.Comment: Submitted to the 2nd Edition of the Handbook of Geomathematics,
edited by Willi Freeden, Zuhair M. Nashed and Thomas Sonar, and to be
published by Springer Verlag. This is a slightly modified but expanded
version of the paper arxiv:0909.5368 that appeared in the 1st Edition of the
Handbook, when it was called: Slepian functions and their use in signal
estimation and spectral analysi
Swinging and tumbling of elastic capsules in shear flow
The deformation of an elastic micro-capsule in an infinite shear flow is
studied numerically using a spectral method. The shape of the capsule and the
hydrodynamic flow field are expanded into smooth basis functions. Analytic
expressions for the derivative of the basis functions permit the evaluation of
elastic and hydrodynamic stresses and bending forces at specified grid points
in the membrane. Compared to methods employing a triangulation scheme, this
method has the advantage that the resulting capsule shapes are automatically
smooth, and few modes are needed to describe the deformation accurately.
Computations are performed for capsules both with spherical and ellipsoidal
unstressed reference shape. Results for small deformations of initially
spherical capsules coincide with analytic predictions. For initially
ellipsoidal capsules, recent approximative theories predict stable oscillations
of the tank-treading inclination angle, and a transition to tumbling at low
shear rate. Both phenomena have also been observed experimentally. Using our
numerical approach we could reproduce both the oscillations and the transition
to tumbling. The full phase diagram for varying shear rate and viscosity ratio
is explored. While the numerically obtained phase diagram qualitatively agrees
with the theory, intermittent behaviour could not be observed within our
simulation time. Our results suggest that initial tumbling motion is only
transient in this region of the phase diagram.Comment: 20 pages, 7 figure
Swinging and tumbling of elastic capsules in shear flow
The deformation of an elastic micro-capsule in an infinite shear flow is
studied numerically using a spectral method. The shape of the capsule and the
hydrodynamic flow field are expanded into smooth basis functions. Analytic
expressions for the derivative of the basis functions permit the evaluation of
elastic and hydrodynamic stresses and bending forces at specified grid points
in the membrane. Compared to methods employing a triangulation scheme, this
method has the advantage that the resulting capsule shapes are automatically
smooth, and few modes are needed to describe the deformation accurately.
Computations are performed for capsules both with spherical and ellipsoidal
unstressed reference shape. Results for small deformations of initially
spherical capsules coincide with analytic predictions. For initially
ellipsoidal capsules, recent approximative theories predict stable oscillations
of the tank-treading inclination angle, and a transition to tumbling at low
shear rate. Both phenomena have also been observed experimentally. Using our
numerical approach we could reproduce both the oscillations and the transition
to tumbling. The full phase diagram for varying shear rate and viscosity ratio
is explored. While the numerically obtained phase diagram qualitatively agrees
with the theory, intermittent behaviour could not be observed within our
simulation time. Our results suggest that initial tumbling motion is only
transient in this region of the phase diagram.Comment: 20 pages, 7 figure
Inversion of the Debye-Wolf diffraction integral using an eigenfunction representation of the electric fields in the focal region
Published versio
Fast and direct inversion methods for the multivariate nonequispaced fast Fourier transform
The well-known discrete Fourier transform (DFT) can easily be generalized to
arbitrary nodes in the spatial domain. The fast procedure for this
generalization is referred to as nonequispaced fast Fourier transform (NFFT).
Various applications such as MRI, solution of PDEs, etc., are interested in the
inverse problem, i.e., computing Fourier coefficients from given nonequispaced
data. In this paper we survey different kinds of approaches to tackle this
problem. In contrast to iterative procedures, where multiple iteration steps
are needed for computing a solution, we focus especially on so-called direct
inversion methods. We review density compensation techniques and introduce a
new scheme that leads to an exact reconstruction for trigonometric polynomials.
In addition, we consider a matrix optimization approach using Frobenius norm
minimization to obtain an inverse NFFT
Applied Harmonic Analysis and Data Processing
Massive data sets have their own architecture. Each data source has an inherent structure, which we should attempt to detect in order to utilize it for applications, such as denoising, clustering, anomaly detection, knowledge extraction, or classification. Harmonic analysis revolves around creating new structures for decomposition, rearrangement and reconstruction of operators and functions—in other words inventing and exploring new architectures for information and inference. Two previous very successful workshops on applied harmonic analysis and sparse approximation have taken place in 2012 and in 2015. This workshop was the an evolution and continuation of these workshops and intended to bring together world leading experts in applied harmonic analysis, data analysis, optimization, statistics, and machine learning to report on recent developments, and to foster new developments and collaborations
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