1,768 research outputs found
Multiresolution Analysis with Non-Nested Spaces
International audienceTwo multiresolution analyses (MRA) intended to be used in scientiic visualization, and that are both based on a non-nested set of approximating spaces, are presented. The notion of approximated r eenement is introduced to deal with non nested spaces. The rst MRA scheme, referred to as BLaC (Blending of Linear and Constant) wavelets is based on a one parameter family of wavelet bases that realizes a blend between the Haar and the linear wavelet bases. The approximated reenement is applied in the last part to build a second MRA scheme for data deened on an arbitrary planar triangular mesh
Orthogonal Wavelets via Filter Banks: Theory and Applications
Wavelets are used in many applications, including image processing, signal analysis and seismology. The critical problem is the representation of a signal using a small number of computable functions, such that it is represented in a concise and computationally efficient form. It is shown that wavelets are closely related to filter banks (sub band filtering) and that there is a direct analogy between multiresolution analysis in continuous time and a filter bank in discrete time. This provides a clear physical interpretation of the approximation and detail spaces of multiresolution analysis in terms of the frequency bands of a signal. Only orthogonal wavelets, which are derived from orthogonal filter banks, are discussed. Several examples and applications are considered
Shannon Multiresolution Analysis on the Heisenberg Group
We present a notion of frame multiresolution analysis on the Heisenberg
group, abbreviated by FMRA, and study its properties. Using the irreducible
representations of this group, we shall define a sinc-type function which is
our starting point for obtaining the scaling function. Further, we shall give a
concrete example of a wavelet FMRA on the Heisenberg group which is analogous
to the Shannon
MRA on \RR.Comment: 17 page
Simulating full-sky interferometric observations
Aperture array interferometers, such as that proposed for the Square
Kilometre Array (SKA), will see the entire sky, hence the standard approach to
simulating visibilities will not be applicable since it relies on a tangent
plane approximation that is valid only for small fields of view. We derive
interferometric formulations in real, spherical harmonic and wavelet space that
include contributions over the entire sky and do not rely on any tangent plane
approximations. A fast wavelet method is developed to simulate the visibilities
observed by an interferometer in the full-sky setting. Computing visibilities
using the fast wavelet method adapts to the sparse representation of the
primary beam and sky intensity in the wavelet basis. Consequently, the fast
wavelet method exhibits superior computational complexity to the real and
spherical harmonic space methods and may be performed at substantially lower
computational cost, while introducing only negligible error to simulated
visibilities. Low-resolution interferometric observations are simulated using
all of the methods to compare their performance, demonstrating that the fast
wavelet method is approximately three times faster that the other methods for
these low-resolution simulations. The computational burden of the real and
spherical harmonic space methods renders these techniques computationally
infeasible for higher resolution simulations. High-resolution interferometric
observations are simulated using the fast wavelet method only, demonstrating
and validating the application of this method to realistic simulations. The
fast wavelet method is estimated to provide a greater than ten-fold reduction
in execution time compared to the other methods for these high-resolution
simulations.Comment: 16 pages, 9 figures, replaced to match version accepted by MNRAS
(major additions to previous version including new fast wavelet method
Multiresolution approximation of the vector fields on T^3
Multiresolution approximation (MRA) of the vector fields on T^3 is studied.
We introduced in the Fourier space a triad of vector fields called helical
vectors which derived from the spherical coordinate system basis. Utilizing the
helical vectors, we proved the orthogonal decomposition of L^2(T^3) which is a
synthesis of the Hodge decomposition of the differential 1- or 2-form on T^3
and the Beltrami decomposition that decompose the space of solenoidal vector
fields into the eigenspaces of curl operator. In the course of proof, a general
construction procedure of the divergence-free orthonormal complete basis from
the basis of scalar function space is presented. Applying this procedure to MRA
of L^2(T^3), we discussed the MRA of vector fields on T^3 and the analyticity
and regularity of vector wavelets. It is conjectured that the solenoidal
wavelet basis must break r-regular condition, i.e. some wavelet functions
cannot be rapidly decreasing function because of the inevitable singularities
of helical vectors. The localization property and spatial structure of
solenoidal wavelets derived from the Littlewood-Paley type MRA (Meyer's
wavelet) are also investigated numerically.Comment: LaTeX, 33 Pages, 3 figures. submitted to J. Math. Phy
Classification of Generalized Multiresolution Analyses
We discuss how generalized multiresolution analyses (GMRAs), both classical
and those defined on abstract Hilbert spaces, can be classified by their
multiplicity functions and matrix-valued filter functions . Given a
natural number valued function and a system of functions encoded in a
matrix satisfying certain conditions, a construction procedure is described
that produces an abstract GMRA with multiplicity function and filter
system . An equivalence relation on GMRAs is defined and described in terms
of their associated pairs . This classification system is applied to
classical examples in as well as to previously studied
abstract examples.Comment: 18 pages including bibliograp
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