Results 1 to 10 of about 141,577 (159)
Sparse signal recovery from modulo observations [PDF]
EURASIP Journal on Advances in Signal Processing, 2021 We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a relatively new imaging mechanism called modulo imaging, which can be used to extend the dynamic range ...
Viraj Shah, Chinmay Hegde
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Sparse Signal Recovery under Poisson Statistics [PDF]
2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2013 We are motivated by problems that arise in a number of applications such as Online Marketing and explosives detection, where the observations are usually modeled using Poisson statistics.
Motamedvaziri, D. +2 more
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Distributed Sparse Signal Recovery For Sensor Networks [PDF]
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 We propose a distributed algorithm for sparse signal recovery in sensor networks based on Iterative Hard Thresholding (IHT). Every agent has a set of measurements of a signal x, and the objective is for the agents to recover x from their collective ...
Eldar, Yonina C. +2 more
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Support Recovery of Sparse Signals [PDF]
, 2010 We consider the problem of exact support recovery of sparse signals via noisy measurements. The main focus is the sufficient and necessary conditions on the number of measurements for support recovery to be reliable.
Jin, Yuzhe +2 more
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Continuous-Time Sparse Signal Recovery
IEEE AccessThis study investigates a continuous-time method for sparse signal recovery, which is suitable for analog optical circuit implementation. The proposed method is defined by a nonlinear ordinary differential equation (ODE) derived from the gradient flow ...
Tadashi Wadayama, Ayano Nakai-Kasai
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Learning-based accelerated sparse signal recovery algorithms
ICT Express, 2021 In this paper, we propose an accelerated sparse recovery algorithm based on inexact alternating direction of multipliers. We formulate a sparse recovery problem with a concave regularizer and solve it with the relaxed and accelerated alternating method ...
Dohyun Kim, Daeyoung Park
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Entropy, 2023 Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying ...
Mohammad Shekaramiz, Todd K. Moon
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Shuffled multi-channel sparse signal recovery
Signal Processing, 2023 Submitted to ...
Koka, Taulant +3 more
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Adaptive algorithm for sparse signal recovery [PDF]
Digital Signal Processing, 2019 Spike and slab priors play a key role in inducing sparsity for sparse signal recovery. The use of such priors results in hard non-convex and mixed integer programming problems. Most of the existing algorithms to solve the optimization problems involve either simplifying assumptions, relaxations or high computational expenses.
Fekadu L. Bayisa +3 more
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Sparse signal recovery in Hilbert spaces [PDF]
2012 IEEE International Symposium on Information Theory Proceedings, 2012 This paper reports an effort to consolidate numerous coherence-based sparse signal recovery results available in the literature. We present a single theory that applies to general Hilbert spaces with the sparsity of a signal defined as the number of (possibly infinite-dimensional) subspaces participating in the signal's representation.
Pope, Graeme, Bölcskei, Helmut
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