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The proof of the theorem concerning to the inverse cyclotomic Discrete Fourier Transform algorithm over finite field is provided.

Information Theory · Computer Science 2019-12-24 Sergei V. Fedorenko

FOCal Underdetermined System Solver (FOCUSS) is a powerful tool for sparse representation and underdetermined inverse problems, which is extremely easy to implement. In this paper, we give a comprehensive convergence analysis on the FOCUSS…

Information Theory · Computer Science 2013-04-19 Zhaoshui He , Shengli Xie , Andrzej Cichocki

Consider the problem when $X_1,X_2,..., X_n$ are distributed on a circle following an unknown distribution $F$ on $S^1$. In this article we have consider the absolute general set-up where the density can have local features such as…

Methodology · Statistics 2016-11-26 Kinjal Basu , Debapriya Sengupta

The paper develops a method for discrete computational Fourier analysis of functions defined on quasicrystals and other almost periodic sets. A key point is to build the analysis around the emerging theory of quasicrystals and diffraction…

Mathematical Physics · Physics 2008-08-14 R. V. Moody , M. Nesterenko , J. Patera

We introduce a class of integral theorems based on cyclic functions and Riemann sums approximating integrals. The Fourier integral theorem, derived as a combination of a transform and inverse transform, arises as a special case. The…

Computation · Statistics 2022-03-22 Nhat Ho , Stephen G. Walker

For a real sequence of length of m = nl, we may deduce its congruence derivative sequence with length of l. The discrete Fourier transform of original sequence can be calculated by the discrete Fourier transform of the congruence derivative…

Signal Processing · Electrical Eng. & Systems 2019-04-19 Jiasong Wang , Changchuan Yin

Fourier series approximations of continuous but nonperiodic functions on an interval suffer the Gibbs phenomenon, which means there is a permanent oscillatory overshoot in the neighbourhoods of the endpoints. Fourier extensions circumvent…

Numerical Analysis · Mathematics 2019-09-12 Marcus Webb , Vincent Coppé , Daan Huybrechs

We present an novel framework for efficiently and effectively extending the powerful continuous diffusion processes to discrete modeling. Previous approaches have suffered from the discrepancy between discrete data and continuous modeling.…

Machine Learning · Computer Science 2024-10-31 Yuxuan Gu , Xiaocheng Feng , Lei Huang , Yingsheng Wu , Zekun Zhou , Weihong Zhong , Kun Zhu , Bing Qin

This paper introduces a sparse projection matrix composed of discrete (digital) periodic lines that create a pseudo-random (p.frac) sampling scheme. Our approach enables random Cartesian sampling whilst employing deterministic and…

Image and Video Processing · Electrical Eng. & Systems 2024-01-09 Marlon Bran Lorenzana , Benjamin Cottier , Matthew Marques , Andrew Kingston , Shekhar S. Chandra

A reduced-bias nonparametric estimator of the cumulative distribution function (CDF) and the survival function is proposed using infinite-order kernels. Fourier transform theory on generalized functions is utilized to obtain the improved…

Methodology · Statistics 2009-03-18 Arthur Berg , Dimitris N. Politis

We investigate the systematic mechanism for designing fast mixing Markov chain Monte Carlo algorithms to sample from discrete point processes under the Dobrushin uniqueness condition for Gibbs measures. Discrete point processes are defined…

Machine Learning · Statistics 2015-06-09 Patrick Rebeschini , Amin Karbasi

Diffusion and flow-driven instability, or transport-driven instability, is one of the central mechanisms to generate inhomogeneous gradient of concentrations in spatially distributed chemical systems. However, verifying the transport-driven…

Systems and Control · Electrical Eng. & Systems 2020-03-05 Yutaka Hori , Hiroki Miyazako

We introduce and analyze various Regularized Combined Field Integral Equations (CFIER) formulations of time-harmonic Navier equations in media with piece-wise constant material properties. These formulations can be derived systematically…

Numerical Analysis · Mathematics 2022-04-21 Victor Dominguez , Catalin Turc

The efficient multiangle centered discrete fractional Fourier transform (MA-CDFRFT) [1] has proven to be a useful tool for time-frequency analysis; in this paper, we generalize the MA-CDFRFT to general M -periodic transforms, which, among…

Signal Processing · Electrical Eng. & Systems 2026-05-01 Christian Oswald , Franz Pernkopf

A new method is presented for solving Poisson's equation inside an open-ended rectangular pipe. The method uses Fast Fourier Transforms (FFTs) to perform mixed convolutions and correlations of the charge density with the Green function.…

Accelerator Physics · Physics 2011-11-22 Robert D. Ryne

Computationally efficient numerical methods for high-order approximations of convolution integrals involving weakly singular kernels find many practical applications including those in the development of fast quadrature methods for…

Numerical Analysis · Mathematics 2018-10-10 Akash Anand , Awanish Kumar Tiwari

The current density of states (DOS) calculations do not take into account the essential discreteness of the state space, since they rely on the unbounded continuum approximation. Recently, discrete DOS based on the quantum-mechanically…

Statistical Mechanics · Physics 2019-09-04 Alhun Aydin , Thomas Oikonomou , G. Baris Bagci , Altug Sisman

In this paper we show that a methodology based on a sampling with the Gaussian function of kind $h\,{e^{ - {{\left( {t/c} \right)}^2}}}/\left( {{c}\sqrt \pi } \right)$, where ${c}$ and $h$ are some constants, leads to the Fourier transform…

General Mathematics · Mathematics 2015-08-06 S. M. Abrarov , B. M. Quine

Recently, we have proposed a new diffusive representation for fractional derivatives and, based on this representation, suggested an algorithm for their numerical computation. From the construction of the algorithm, it is immediately…

Numerical Analysis · Mathematics 2022-04-12 Kai Diethelm

We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors. To express such models, we…

Programming Languages · Computer Science 2023-11-08 Fabian Zaiser , Andrzej S. Murawski , Luke Ong
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