Results 211 to 220 of about 7,939 (246)

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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Distributed Compressed Sensing

2015
This 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
Giulio Coluccia, Chiara Ravazzi, Enrico Magli   +2 more
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Distributed compressed sensing for image signals

2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2014
Distributed 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
Zongxin Yu, Rui Wang 0034, Haiyan Zhang, Yanliang Jin, Yixing Fu   +4 more
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Measurement compression in distributed compressive video sensing

2010 3rd IEEE International Conference on Broadband Network and Multimedia Technology (IC-BNMT), 2010
In some application scenarios a video codec with simple encoder and complex decoder is desired. Distributed video coding (DVC) and compressive sensing (CS) theory proposed recently are two techniques suitable to such scenarios, and several video coding schemes that combine CS with DVC have appeared.
null Xiaoran Hao, null Bojin Zhuang, null Anni Cai   +2 more
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Sensing matrix optimization in Distributed Compressed Sensing

2009 IEEE/SP 15th Workshop on Statistical Signal Processing, 2009
Distributed 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.
Pablo Vinuelas-Peris, Antonio Artes-Rodriguez   +1 more
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Number of compressed measurements needed for noisy distributed compressed sensing

2012 IEEE International Symposium on Information Theory Proceedings, 2012
In this paper, we consider a data collection network (DCN) system where sensors take samples and transmit them to a Fusion Center (FC). Signal correlation is modeled with signal sparseness. The number of compressed measurements which allows correct signal recovery at FC is investigated.
Sangjun Park 0002, Heung-No Lee
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Distributed compressed sensing for block-sparse signals

2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, 2011
To address the problems of high sampling rates, shadow fading and additive noise from the receiver, in this paper, a distributed compressed sampling (DCS) and centralized reconstruction approach which utilize the spatial diversity against fading channels is proposed.
Xing Wang, Wenbin Guo, Yang Lu, Wenbo Wang 0007   +3 more
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