7,108 research outputs found
Relationships among Interpolation Bases of Wavelet Spaces and Approximation Spaces
A multiresolution analysis is a nested chain of related approximation
spaces.This nesting in turn implies relationships among interpolation bases in
the approximation spaces and their derived wavelet spaces. Using these
relationships, a necessary and sufficient condition is given for existence of
interpolation wavelets, via analysis of the corresponding scaling functions. It
is also shown that any interpolation function for an approximation space plays
the role of a special type of scaling function (an interpolation scaling
function) when the corresponding family of approximation spaces forms a
multiresolution analysis. Based on these interpolation scaling functions, a new
algorithm is proposed for constructing corresponding interpolation wavelets
(when they exist in a multiresolution analysis). In simulations, our theorems
are tested for several typical wavelet spaces, demonstrating our theorems for
existence of interpolation wavelets and for constructing them in a general
multiresolution analysis
Adaptive multiresolution analysis based on synchronization
We propose an adaptive multiscale approach to data analysis based on synchronization. The approach is nonlinear, data driven in the sense that it does not rely on a priori chosen basis, and automatically determines the data scale. Numerical results for one- and two-dimensional cases illustrate that the method works effectively for the usual modulated signals such as chirps, etc., as well as for more complicated data with multiple scales. The method extends straightforwardly to functions defined on weighted graphs and grids in high dimensions. Connections with some other recent approaches to multiscale analysis are briefly discussed
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
- …