Results 131 to 140 of about 63,884 (167)

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).
Idit Keidar, Stacy Patterson, Yonina C. Eldar   +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
Yanliang Jin, Zongxin Yu, Yixing Fu, Haiyan Zhang, Rui Wang   +4 more
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Distributed compressive sensing of light field

SPIE Proceedings, 2015
The 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, Ying Zhou, Zhi-jiang Zhang, Rui Lei   +3 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, Bulent Sankur, Can Altay
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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 compressive sensing vs. dynamic compressive sensing: improving the compressive line sensing imaging system through their integration

SPIE Proceedings, 2015
In recent years, a compressive sensing based underwater imaging system has been under investigation: the Compressive Line Sensing (CLS) imaging system. In the CLS system, each line segment is sensed independently; with regard to signal reconstruction, the correlation among the adjacent lines is exploited via the joint sparsity in the distributed ...
Anni K. Vuorenkoski, Weilin Hou, Frank M. Caimi, Sue Gong, Fraser Dalgleish, Bing Ouyang   +5 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, Heung-No Lee
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Distributed compressed sensing for despeckling of SAR images

Digital Signal Processing, 2018
Abstract Speckle noise is one of the critical disturbances that present in the radar imagery. This noise degrades the quality of synthetic aperture radar (SAR) images and needs to be reduced before using SAR images. This paper investigates a novel method for despeckling of SAR images in the distributed compressed sensing (DCS) framework.
Ahmad Shafiei, Mojtaba Beheshti, Ehsan Yazdian   +2 more
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Distributed compressive sensing in heterogeneous sensor network

Signal Processing, 2016
In this paper, we apply distributed compressive sensing (DCS) in heterogeneous sensor network (HSN). Combining different types of measurement matrices and different numbers of measurements, we firstly investigate three different scenarios in which HSN is used for signal acquisition.
Jing Liang, Chengchen Mao
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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, Wenbo Wang, Wenbin Guo, Yang Lu   +3 more
openaire   +2 more sources