Results 31 to 40 of about 63,884 (167)
Channel Impulse Response-based Distributed Physical Layer Authentication [PDF]
, 2017 In this preliminary work, we study the problem of {\it distributed} authentication in wireless networks. Specifically, we consider a system where multiple Bob (sensor) nodes listen to a channel and report their {\it correlated} measurements to a Fusion ...
Abbasi, Qammer H. +4 more
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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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Compressive Signal Processing with Circulant Sensing Matrices [PDF]
, 2014 Compressive sensing achieves effective dimensionality reduction of signals, under a sparsity constraint, by means of a small number of random measurements acquired through a sensing matrix.
Magli, Enrico, Valsesia, Diego
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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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Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements [PDF]
, 2010 This paper addresses the problem of distributed coding of images whose correlation is driven by the motion of objects or positioning of the vision sensors. It concentrates on the problem where images are encoded with compressed linear measurements.
Frossard, Pascal +1 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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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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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 Sensing for Static and Time-Varying Networks [PDF]
IEEE Transactions on Signal Processing, 2014 We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal $x$, and the objective is for the agents to recover $x$ from their collective measurements using only communication with neighbors in the network.
Idit Keidar +2 more
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On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis [PDF]
, 2015 Despite the linearity of its encoding, compressed sensing may be used to provide a limited form of data protection when random encoding matrices are used to produce sets of low-dimensional measurements (ciphertexts).
Cambareri, Valerio +4 more
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