Results 251 to 260 of about 2,616,250 (281)

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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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 ...
Yanping Chen 0007, Yulong Gao 0002, Yongkui Ma   +2 more
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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 0002, Chengchen Mao
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A survey on distributed compressed sensing: theory and applications

Frontiers of Computer Science, 2014
The compressed sensing (CS) theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account.
Hongpeng Yin, Jinxing Li, Yi Chai 0003, Simon X. Yang   +3 more
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A greedy pursuit algorithm for distributed compressed sensing

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012
We develop a greedy pursuit algorithm for solving the distributed compressed sensing problem in a connected network. This algorithm is based on subspace pursuit and uses the mixed support-set signal model. Through experimental evaluation, we show that the distributed algorithm performs significantly better than the standalone (disconnected) solution ...
Dennis Sundman, Saikat Chatterjee, Mikael Skoglund   +2 more
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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.
Wenbo Xu 0003, Yupeng Cui, Zhilin Li 0003, Jiaru Lin   +3 more
openaire   +1 more source