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Jul 26, 2012 · This allows us to detect when aliasing between two or more frequencies has occurred, as well as to determine the value of unaliased frequencies.
Underlying our algorithm are a few simple observations relating the Fourier coefficients of time-shifted samples to unshifted samples of the input function.
Jan 2, 2016 · The main idea is to recover entries of multivariate frequencies by using equispaced evaluations of the function along a coordinate axis as well ...
A new deterministic algorithm for the sparse Fourier transform problem, in which the algorithm seeks to identify k ≪ N significant Fourier coefficients from ...
Feb 4, 2017 · This paper revisits the sparse FFT problem with the added twist that the sparse coefficients approximately obey a (k_0,k_1)-block sparse model.
Apr 9, 2013 · This allows us to detect when aliasing between two or more frequencies has occurred, as well as to determine the value of unaliased frequencies.
This paper revisits the sparse FFT problem with the added twist that the sparse coefficients approximately obey a (k0,k1)-block sparse model. In this model, ...
In this paper, we discuss the development of a sublinear sparse Fourier algorithm for high-dimensional data. In ``Adaptive Sublinear Time Fourier Algorithm" ...
Feb 4, 2017 · To the best of our knowledge, our result is the first sublinear-time algorithm for model based compressed sensing, and the first sparse FFT ...
ABSTRACT. The problem of approximately computing the k dominant Fourier coefficients of a vector X quickly, and using few samples in time do-.