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Mobile distributed compressive sensing for spectrum sensing
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014This paper studies the effect of mobility on the sensing performance of a cognitive radio network with mobile nodes. The secondary nodes sense the spectrum using a distributed compressive sensing approach to detect the available channels. Distributed compressive sensing is suggested to reduce the number of samples by exploiting correlation between the ...
Shahram Shahbazpanahi +2 more
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Sensing matrix optimization in Distributed Compressed Sensing
2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009Distributed Compressed Sensing (DCS) seeks to simultaneously measure signals that are each individually sparse in some domain(s) and also mutually correlated. In this paper we consider the scenario in which the (overcomplete) bases for common component and innovations are different.
Antonio Artés-Rodríguez +1 more
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Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction
Journal of Information Processing Systems, 2019Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently.
Xingyi Chen, Yujie Zhang, Rui Qi
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Distributed Compressed Sensing
2015This chapter first introduces CS in the conventional setting where one device acquires one signal and sends it to a receiver, and then extends it to the distributed framework in which multiple devices acquire multiple signals. In particular, we focus on two key problems related to the distributed setting. The former is the definition of sparsity models
Enrico Magli +2 more
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Distributed Compressed Sensing for biomedical signals
2011 3rd International Conference on Awareness Science and Technology (iCAST), 2011This paper presents a novel iterative greedy algorithm for Distributed Compressed Sensing (DCS) scenario based on backtracking technique, which is denoted by DCS-SAMP. The algorithm can reconstruct several input signals simultaneously, even when the measurements are contaminated with noise and without any prior information of their sparseness.
Zhiwen Liu, Qun Wang
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Distributed Compressive Hyperspectral Image Sensing
2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2010A novel compression framework called distributed compressed hyper spectral image sensing (DCHIS) is proposed in this paper. In our framework, the random measurements of each spectral band are obtained using compressed sensing (CS) encoding independently at the encoder.
Haiying Liu +3 more
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Distributed compressed sensing in dynamic networks
2013 IEEE Global Conference on Signal and Information Processing, 2013We consider the problem of in-network compressed sensing, where the goal is to recover a global, sparse signal from local measurements using only local computation and communication. Our approach to this distributed compressed sensing problem is based on the centralized Iterative Hard Thresholding algorithm (IHT).
Idit Keidar +2 more
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Distributed sparse signal sensing based on compressive sensing OFDR
Optics Letters, 2020The maximum detectable vibration frequency of an optical frequency domain reflectometry (OFDR) system is limited by the tunable rate of the laser source. Unlike uniform sampling with the time-resolved method, the sampling frequency is randomly modulated so that the vibration signal applied on the interrogation fiber is sampled by a multi-frequency sub ...
Zengguang Qin +6 more
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Distributed compressed sensing for image signals
2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2014Distributed compressed sensing (DCS) is able to exploit both intra-and inter-signal correlation structures of multi-signal ensemble. This paper proposes a DCS scheme for image signal compression and reconstruction. The key idea is to exploit the inter-correlation of the blocks that split from the image. Significantly, joint sparse model was employed to
Yanliang Jin +4 more
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Distributed compressive sensing of light field
SPIE Proceedings, 2015The light field camera array can be regarded as distributed source. The image sequence captured by a camera array contains the inter-correlation and intra-correlation. In order to utilize the correlation, a joint sparsity model was established to combine the light field with distributed compressive sensing, and a recovery algorithm was proposed for the
Wei Shen +3 more
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