Results 241 to 250 of about 73,229 (279)

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
openaire   +1 more source

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 ...
Bing Ouyang, Weilin Hou, Frank M. Caimi, Fraser R. Dalgleish, Anni K. Vuorenkoski, Sue Gong   +5 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
null Zongxin Yu, null Rui Wang, null Haiyan Zhang, null Yanliang Jin, null Yixing Fu   +4 more
openaire   +1 more source

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.
null Qun Wang, null ZhiWen Liu
openaire   +1 more source

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
Rui Lei, Wei Shen, Zhi-jiang Zhang, Ying Zhou   +3 more
openaire   +1 more source

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
openaire   +1 more source

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 Abdellatif Abdelsalam Elsayed, Maha Mohamed Elsabrouty   +1 more
openaire   +1 more source

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, Yulong Gao, Yongkui Ma
openaire   +1 more source

Perceptually-aware distributed compressive video sensing

2015 Visual Communications and Image Processing (VCIP), 2015
By combining the advantages of distributed video coding (DVC) and compressive sensing (CS), distributed compressive video sensing (DCVS) poses itself as a very promising low-complexity video coding framework for distributed applications. In order to improve the rate-distortion performance of DCVS, much research efforts have been focused on exploring ...
Jin Xu, Soufiene Djahel, Yuansong Qiao, Zhizhong Fu   +3 more
openaire   +1 more source

Distributed compressive wide-band spectrum sensing

2009 Information Theory and Applications Workshop, 2009
We consider a compressive wide-band spectrum sensing scheme for cognitive radio networks. Each cognitive radio (CR) sensing receiver transforms the received analog signal from the licensed system in to a digital signal using an analog-to-information converter.
null Ying Wang, A. Pandharipande, Y.L. Polo, G. Leus   +3 more
openaire   +1 more source