Distributed multi-view sparse vector recovery
In this paper, we consider a multi-view compressed sensing problem, where each sensor
can only obtain a partial view of the global sparse vector. Here the partial view means that …
can only obtain a partial view of the global sparse vector. Here the partial view means that …
Measurement matrix design for sample-efficient binary compressed sensing
P Sarangi, P Pal - IEEE Signal Processing Letters, 2022 - ieeexplore.ieee.org
This letter investigates the problem of recovering a binary-valued signal from compressed
measurements of its convolution with a known finite impulse response filter. We show that it …
measurements of its convolution with a known finite impulse response filter. We show that it …
Recovery of binary sparse signals from compressed linear measurements via polynomial optimization
SM Fosson, M Abuabiah - IEEE Signal Processing Letters, 2019 - ieeexplore.ieee.org
The recovery of signals with finite-valued components from few linear measurements is a
problem with widespread applications and interesting mathematical characteristics. In the …
problem with widespread applications and interesting mathematical characteristics. In the …
A non-convex adaptive regularization approach to binary optimization
Binary optimization is a long-time problem ubiquitous in many engineering applications, eg,
automatic control, cyber-physical systems and machine learning. From a mathematical …
automatic control, cyber-physical systems and machine learning. From a mathematical …
[图书][B] Sparse Recovery Under Side Constraints Using Null Space Properties
F Matter - 2022 - wwwopt.mathematik.tu-darmstadt.de
Zusammenfassung Ein Kernaspekt von Compressed Sensing ist die Rekonstruktion von
Signalen mithilfe von möglichst wenigen Messungen. Dies wird unter Ausnutzung der …
Signalen mithilfe von möglichst wenigen Messungen. Dies wird unter Ausnutzung der …
Non-convex Lasso-kind approach to compressed sensing for finite-valued signals
SM Fosson - arXiv preprint arXiv:1811.03864, 2018 - arxiv.org
In this paper, we bring together two trends that have recently emerged in sparse signal
recovery: the problem of sparse signals that stem from finite alphabets and the techniques …
recovery: the problem of sparse signals that stem from finite alphabets and the techniques …
Sparse linear regression with compressed and low-precision data via concave quadratic programming
We consider the problem of the recovery of a k-sparse vector from compressed linear
measurements when data are corrupted by a quantization noise. When the number of …
measurements when data are corrupted by a quantization noise. When the number of …
[图书][B] Super-resolution under Extreme Sampling Constraints: Theory and Algorithms
P Sarangi - 2023 - search.proquest.com
High dimensional inverse problems are at the heart of numerous modern signal processing
and machine learning applications, where the goal is to sense the physical environment and …
and machine learning applications, where the goal is to sense the physical environment and …
Opportunistic communications in large uncoordinated networks
J Borràs Pino - 2023 - upcommons.upc.edu
(English) The increase of wireless devices offering high data rate services limits the
coexistence of wireless systems sharing the same resources in a given geographical area …
coexistence of wireless systems sharing the same resources in a given geographical area …
Enhancing low-rank solutions in semidefinite relaxations of Boolean quadratic problems
Boolean quadratic optimization problems occur in a number of applications. Their mixed
integer-continuous nature is challenging, since it is inherently NP-hard. For this motivation …
integer-continuous nature is challenging, since it is inherently NP-hard. For this motivation …