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Compressed Sensing for Distributed Systems

, 2015
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new.
Magli Enrico, Coluccia Giulio, Ravazzi Chiara   +2 more
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Parallel pursuit for distributed compressed sensing

2013 IEEE Global Conference on Signal and Information Processing, 2013
We develop a greedy (pursuit) algorithm for a distributed compressed sensing problem where multiple sensors are connected over a de-centralized network. The algorithm is referred to as distributed parallel pursuit and it solves the distributed compressed sensing problem in two stages; first by a distributed estimation stage and then an information ...
Dennis Sundman, Saikat Chatterjee, Mikael Skoglund   +2 more
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Distributed Compressed Estimation Based on Compressive Sensing

IEEE Signal Processing Letters, 2015
This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation.
Songcen Xu, Rodrigo C De Lamare, H Vincent Poor   +2 more
exaly   +2 more sources

Distributed compressive video sensing

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the order of that for still image encoding. In addition, compressive sensing (CS) has been applicable to directly capture compressed image data efficiently.
Li-Wei Kang, Chun-Shien Lu
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Accelerated MR diffusion tensor imaging using distributed compressed sensing

Magnetic Resonance in Medicine, 2014
Purpose: Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable.
, Chao Zou, Ed X Wu
exaly   +2 more sources

Mobile distributed compressive sensing for spectrum sensing

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
This 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 ...
Veria Havary-Nassab, Shahrokh Valaee, Shahram Shahbazpanahi   +2 more
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Optimal quantization for distributed compressive sensing

2017 25th Signal Processing and Communications Applications Conference (SIU), 2017
In large scale distributed sensing systems such as wireless sensor networks (WSNs), Distributed Source Coding Methods can be difficult to apply, due to lack of signal statistics. Distributed Compressive Sensing (DCS) emerges as a cure to this problem.
Mehmet Yamac, Can Altay, Bülent Sankur
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Distributed Compressed Sensing for biomedical signals

2011 3rd International Conference on Awareness Science and Technology (iCAST), 2011
This 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.
Qun Wang, Zhiwen Liu
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Distributed compressed sensing in dynamic networks

2013 IEEE Global Conference on Signal and Information Processing, 2013
We 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).
Stacy Patterson, Yonina C. Eldar, Idit Keidar   +2 more
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Distributed Compressive Hyperspectral Image Sensing

2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2010
A 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, Yunsong Li 0001, Song Xiao 0001, Chengke Wu 0001   +3 more
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