We propose a maximum likelihood estimation approach for the recovery of continuously-defined sparse signals from noisy measurements, in particular periodic sequences of Diracs, derivatives of Diracs and piecewise polynomials. The conventional approach for this problem is based on least-squares (a.k.a. annihilating filter method) and Cadzow denoising. It requires more measurements than the numbe...

This paper studies sparse spikes deconvolution over the space of measures. For non-degenerate sums of Diracs, we show that, when the signalto-noise ratio is large enough, total variation regularization (which the natural extension of ` norm of vector to the setting of measures) recovers the exact same number of Diracs. We also show that both the locations and the heights of these Diracs converg...

in this paper, two inverse problems of stephen kind with local (dirichlet) boundary conditions are investigated. in the first problem only a part of boundary is unknown and in the second problem, the whole of boundary is unknown. for the both of problems, at first, analytic expressions for unknown boundary are presented, then by using these analytic expressions for unknown boundaries and bounda...

In this work we consider the problem of recovering an ensemble of Diracs on the sphere from its projection onto spaces of spherical harmonics. We show that under an appropriate separation condition on the unknown locations of the Diracs, the ensemble can be recovered through Total Variation norm minimization. The proof of the uniqueness of the solution uses the method of ‘dual’ interpolating po...

in this letter we have proposed a new regularization scheme to deal with the divergent integralsoccurring in the quantum mechanical problem of calculating the bound state energy of the delta-functionpotential in two and three dimensions. based on the schwinger parameterization technique we argue thatthere are no infinities even in d dimensions. in this way we were able to compare our proposal w...

The aim of this paper is to introduce and study a new concept ofstrong double $(A)_ {Delta}$-convergent sequence offuzzy numbers with respect to an Orlicz function and also someproperties of the resulting sequence spaces of fuzzy   numbers areexamined. In addition, we define the double$(A,Delta)$-statistical convergence of fuzzy  numbers andestablish some connections between the spaces of stron...

the aim of this paper is to introduce and study a new concept ofstrong double $(a)_ {delta}$-convergent sequence offuzzy numbers with respect to an orlicz function and also someproperties of the resulting sequence spaces of fuzzy   numbers areexamined. in addition, we define the double$(a,delta)$-statistical convergence of fuzzy  numbers andestablish some connections between the spaces of stron...

Consider classes of signals which have a ̄nite number of degrees of freedom per unit of time, and call this number the rate of innovation of a signal. Examples of signals with ̄nite rate of innovation include stream of Diracs (e.g. the Poisson process), non-uniform splines and piecewise polynomials. Eventhough these signals are not bandlimited, we show that they can be sampled uniformly at (or ab...

We address a problem of sampling and reconstructing periodic piecewise polynomials based on the theory for signals with a finite rate of innovation (FRI signals) from samples acquired by a sinc kernel. This problem was discussed in a previous paper. There was, however, an error in a condition about the sinc kernel. Further, even though the signal is represented by parameters, these explicit val...