Results 41 to 50 of about 487,780 (314)
Efficient distributed storage strategy based on compressed sensing for space information network
International Journal of Distributed Sensor Networks, 2016 This article investigates the distributed data storage problem with compressed sensing in the space information network. Since there exists a performance-energy trade-off, most existing strategies focus only on improving the compressed sensing ...
Bo Kong +4 more
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Finding needles in noisy haystacks [PDF]
, 2008 The theory of compressed sensing shows that samples in the form of random projections are optimal for recovering sparse signals in high-dimensional spaces (i.e., finding needles in haystacks), provided the measurements are noiseless.
Castro, R.M. +3 more
core +1 more source
Distributed Compressive Sensing: A Deep Learning Approach [PDF]
IEEE Transactions on Signal Processing, 2016 Various studies that address the compressed sensing problem with Multiple Measurement Vectors (MMVs) have been recently carried. These studies assume the vectors of the different channels to be jointly sparse. In this paper, we relax this condition. Instead we assume that these sparse vectors depend on each other but that this dependency is unknown. We
Hamid Palangi, Rabab Ward, Li Deng
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Low-power distributed sparse recovery testbed on wireless sensor networks [PDF]
, 2016 Recently, distributed algorithms have been proposed for the recovery of sparse signals in networked systems, e.g. wire- less sensor networks. Such algorithms allow large networks to operate autonomously without the need of a fusion center, and are ...
DE LUCIA, RICCARDO ROBERTO +2 more
core +1 more source
Distributed Basis Pursuit [PDF]
, 2012 We propose a distributed algorithm for solving the optimization problem Basis Pursuit (BP). BP finds the least L1-norm solution of the underdetermined linear system Ax = b and is used, for example, in compressed sensing for reconstruction.
Aguiar, Pedro M. Q. +3 more
core +3 more sources
A distributed estimation method over network based on compressed sensing
International Journal of Distributed Sensor Networks, 2019 This article presents a distributed estimation method called compressed-combine-reconstruct-adaptive to estimate an unknown sparse parameter of interest from noisy measurement over networks based on compressed sensing.
Lin Li, Donghui Li
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Real Acceleration of Communication Process in Distributed Algorithms with Compression [PDF]
, 2023 Modern applied optimization problems become more and more complex every day. Due to this fact, distributed algorithms that can speed up the process of solving an optimization problem through parallelization are of great importance. The main bottleneck of distributed algorithms is communications, which can slow down the method dramatically.
arxiv +1 more source
International Journal of Distributed Sensor Networks, 2016 Energy-efficient data aggregation is important for underwater acoustic sensor networks due to its energy constrained character. In this paper, we propose a kind of energy-efficient data aggregation scheme to reduce communication cost and to prolong ...
Deqing Wang, Ru Xu, Xiaoyi Hu, Wei Su
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Distributed Compressed Hyperspectral Sensing Imaging Incorporated Spectral Unmixing and Learning
Journal of Spectroscopy, 2022 Compressed hyperspectral imaging is a powerful technique for satellite-borne and airborne sensors that can effectively shift the complex computational burden from the resource-constrained encoding side to a presumably more capable base-station decoder ...
Hua Xiao +5 more
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Variational Bayesian algorithm for distributed compressive sensing [PDF]
2015 IEEE International Conference on Communications (ICC), 2015 Distributed compressive sensing (DCS) concerns the reconstruction of multiple sensor signals with reduced numbers of measurements, which exploits both intra- and inter-signal correlations. In this paper, we propose a novel Bayesian DCS algorithm based on variational Bayesian inference.
Chen, W, Wassell, IJ
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