6 papers
Reconstructing Graph Signals from Noisy Dynamical Samples
Akram Aldroubi, Victor Bailey, Ilya Krishtal +2
We investigate the dynamical sampling space-time trade-off problem within a graph setting. Specifically, we derive necessary and sufficient conditions for space-time sampling that…
Orthogonally weighted regularization for rank-aware joint sparse recovery: algorithm and analysis
Armenak Petrosyan, Konstantin Pieper, Hoang Tran
We propose and analyze an efficient algorithm for solving the joint sparse recovery problem using a new regularization-based method, named orthogonally weighted ($\mat…
Neural network integral representations with the ReLU activation function
Armenak Petrosyan, Anton Dereventsov, Clayton Webster
In this effort, we derive a formula for the integral representation of a shallow neural network with the ReLU activation function. We assume that the outer weighs admit a finite $L…
A Weighted -Minimization Approach For Wavelet Reconstruction of Signals and Images
Joseph Daws, Armenak Petrosyan, Hoang Tran +1
In this effort, we propose a convex optimization approach based on weighted -regularization for reconstructing objects of interest, such as signals or images, that are spar…
Local-to-global frames and applications to dynamical sampling problem
Akram Aldroubi, Carlos Cabrelli, Ursula Molter +1
In this paper we consider systems of vectors in a Hilbert space of the form where and are countable sets o…
An Operator theoretic approach to the convergence of rearranged Fourier series
Keaton Hamm, Ben Hayes, Armenak Petrosyan
This article studies the rearrangement problem for Fourier series introduced by P.L. Ulyanov, who conjectured that every continuous function on the torus admits a rearrangement of…