Results 251 to 260 of about 7,796 (295)

Distributed Compressed Estimation Based on Compressive Sensing

IEEE Signal Processing Letters, 2015
This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation.
Songcen Xu, Rodrigo C. de Lamare, H. Vincent Poor   +2 more
openaire   +1 more source

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
openaire   +1 more source

Mobile distributed compressive sensing for spectrum sensing

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
This paper studies the effect of mobility on the sensing performance of a cognitive radio network with mobile nodes. The secondary nodes sense the spectrum using a distributed compressive sensing approach to detect the available channels. Distributed compressive sensing is suggested to reduce the number of samples by exploiting correlation between the ...
Veria Havary-Nassab, Shahrokh Valaee, Shahram Shahbazpanahi   +2 more
openaire   +1 more source

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
openaire   +1 more source

Compressed Sensing for Distributed Systems

2015
This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. It has to be noted that, while compressed sensing has been studied for some time now, its distributed applications are relatively new.
COLUCCIA, GIULIO, RAVAZZI, CHIARA, MAGLI, ENRICO   +2 more
openaire   +4 more sources

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, Song Xiao, Chengke Wu   +3 more
openaire   +1 more source

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