Results 261 to 270 of about 7,447 (293)

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 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 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 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
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Distributed Compressive Sensing Based Spectrum Sensing Method

2018
For multi-antenna system, the difficulties of preforming spectrum sensing are high sampling rate and hardware cost. To alleviate these problems, we propose a novel utilization of distributed compressive sensing for the multi-antenna case. The multi-antenna signals first are sampled in terms of distributed compressive sensing, and then the time-domain ...
Yulong Gao, Yongkui Ma, Yanping Chen
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Perceptual-Based Distributed Compressed Video Sensing

2015 Data Compression Conference, 2015
This paper proposes an approach of compressed sensing (CS) of video in which distributed video coding DVC and CS are integrated as in [1], and the sensing matrix is modulated in suit of [2] but with proposed fixed weighting strategy to certain DCT coefficients in an effort to improve the visual quality of reconstruction.
Sawsan Elsayed, Maha Elsabrouty
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A Decentralized Reconstruction Algorithm for Distributed Compressed Sensing

Wireless Personal Communications, 2017
This paper considers the distributed compressed sensing (DCS), where each node has a common component and an innovation component. Most existing reconstruction methods for this DCS model are actually centralized, where the measurements of each signal are utilized together at a certain node.
Yupeng Cui, Wenbo Xu, Jiaru Lin, Zhilin Li   +3 more
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Simple and efficient algorithm for distributed compressed sensing

2008 IEEE Workshop on Machine Learning for Signal Processing, 2008
In this paper we propose a new iterative thresholding algorithm for distributed compressed sensing (CS) based on a set of local cost functions referred as HALS-CS algorithm (compare with). This algorithm allows reconstructing all sources simultaneously by processing row by row of the compressed signals. Moreover, with an adaptive nonlinearly decreasing
Andrzej Cichocki, K.S. Nguyen, Anh Huy Phan   +2 more
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