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A warping operator consists of an invertible axis deformation applied either in the signal domain or in the corresponding Fourier domain. Additionally, a warping transformation is usually required to preserve the signal energy, thus…

Information Theory · Computer Science 2017-07-27 Salvatore Caporale , Yvan Petillot

Microwave, submillimetre-wave, and far-infrared phased arrays are of considerable importance for astronomy. We consider the behaviour imaging phased arrays and interferometric phased arrays from a functional perspective. It is shown that…

Astrophysics · Physics 2009-11-11 Stafford Withington , George Saklatvala , Michael P. Hobson

The notion of wavelets is defined. It is briefly described {\it what} are wavelets, {\it how} to use them, {\it when} we do need them, {\it why} they are preferred and {\it where} they have been applied. Then one proceeds to the…

High Energy Physics - Phenomenology · Physics 2008-11-26 I. M. Dremin

We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in…

Machine Learning · Computer Science 2016-11-30 Shay Deutsch , Antonio Ortega , Gerard Medioni

Discrete transforms such as the discrete Fourier transform (DFT) or the discrete Hartley transform (DHT) furnish an indispensable tool in signal processing. The successful application of transform techniques relies on the existence of the…

Data Structures and Algorithms · Computer Science 2015-08-27 H. M. de Oliveira , R. J. Cintra , R. M. Campello de Souza

A constructive algorithm based on the theory of spectral pairs for constructing nonuniform wavelet basis in $L^2(\mathbb R)$ was considered by Gabardo and Nashed (J Funct. Anal. 158:209-241, 1998). In this setting, the associated…

Functional Analysis · Mathematics 2026-05-12 Owais Ahmad , Neyaz Ahmad

A graph's spectral wavelet signature determines a filtration, and consequently an associated set of extended persistence diagrams. We propose a framework that optimises the choice of wavelet for a dataset of graphs, such that their…

Signal Processing · Electrical Eng. & Systems 2023-06-28 Ka Man Yim , Jacob Leygonie

Although a lateral-shear interferometer is robust against optical component vibrations, its interferogram provides information about differential wavefronts rather than the wavefronts themselves, resulting in the loss of specific frequency…

In this paper we address the problem of constructing a feature extractor which combines Mallat's scattering transform framework with time-frequency (Gabor) representations. To do this, we introduce a class of frames, called uniform covering…

Functional Analysis · Mathematics 2018-02-09 Wojciech Czaja , Weilin Li

Recent experimental work has demonstrated the potential to combine the merits of diffractive and on-chip photonic information processing devices in a single chip by making use of planar (or slab) waveguides. Researchers have adapted key…

Optics · Physics 2023-05-26 Benjamin Wetherfield , Timothy D. Wilkinson

We consider spectral projectors associated to the Euclidean Laplacian on the two-dimensional torus, in the case where the spectral window is narrow. Bounds for their L2 to Lp operator norm are derived, extending the classical result of…

Classical Analysis and ODEs · Mathematics 2024-01-31 Ciprian Demeter , Pierre Germain

In the paper we design a Parseval wavelet frame with a compact support and many vanishing moments. The corresponding refinement mask approximates an arbitrary continuous periodic function $f$, $f(0)=1$. The refinable function has stable…

Classical Analysis and ODEs · Mathematics 2022-10-25 Elena A. Lebedeva

An explicit description of all Walsh polynomials generating tight wavelet frames is given. An algorithm for finding the corresponding wavelet functions is suggested, and a general form for all wavelet frames generated by an appropriate…

Classical Analysis and ODEs · Mathematics 2014-12-09 Yuri A. Farkov , Elena A. Lebedeva , Maria A. Skopina

New orthonormal basis of eigenfunctions for the Vladimirov operator of p-adic fractional derivation is constructed. The map of p-adic numbers onto real numbers (p-adic change of variables) is considered. This map (for p=2) provides an…

Mathematical Physics · Physics 2015-06-26 Sergei Kozyrev

Many phenomena are described by bivariate signals or bidimensional vectors in applications ranging from radar to EEG, optics and oceanography. The time-frequency analysis of bivariate signals is usually carried out by analyzing two separate…

Methodology · Statistics 2016-09-09 Julien Flamant , Nicolas Le Bihan , Pierre Chainais

Estimating accurate high-dimensional transformations remains very challenging, especially in a clinical setting. In this paper, we introduce a multiscale parameterization of deformations to enhance registration and atlas estimation in the…

Optimization and Control · Mathematics 2025-01-31 Fleur Gaudfernau , Eléonore Blondiaux , Stéphanie Allassonnière , Erwan Le Pennec

We consider the problem of elastic diffraction tomography, which consists in reconstructing elastic properties (i.e. mass density and elastic Lam\'e parameters) of a weakly scattering medium from full-field data of scattered waves outside…

Analysis of PDEs · Mathematics 2022-12-07 Bochra Mejri , Otmar Scherzer

The spectral theory for weakly stationary processes valued in a separable Hilbert space has known renewed interest in the past decade. Here we follow earlier approaches which fully exploit the normal Hilbert module property of the time…

Statistics Theory · Mathematics 2022-10-06 Amaury Durand , François Roueff

We consider the spectral definition of the fractional Laplace operator and study a basic linear problem involving this operator and singular forcing. In two dimensions, we introduce an appropriate weak formulation in fractional Sobolev…

Numerical Analysis · Mathematics 2026-02-13 Enrique Otarola , Abner J. Salgado

To investigate neural network parameters, it is easier to study the distribution of parameters than to study the parameters in each neuron. The ridgelet transform is a pseudo-inverse operator that maps a given function $f$ to the parameter…

Machine Learning · Computer Science 2024-04-22 Sho Sonoda , Isao Ishikawa , Masahiro Ikeda
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