Results 241 to 250 of about 2,616,250 (281)

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, Vincent Poor, Rodrigo C. de Lamare   +2 more
exaly   +2 more sources

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
openaire   +2 more sources

Distributed compressive video sensing

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the order of that for still image encoding. In addition, compressive sensing (CS) has been applicable to directly capture compressed image data efficiently.
Li-Wei Kang, Chun-Shien Lu
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

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
Zongxin Yu, Rui Wang 0034, Haiyan Zhang, Yanliang Jin, 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.
Qun Wang, Zhiwen Liu
openaire   +2 more sources

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

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 0001, Song Xiao 0001, Chengke Wu 0001   +3 more
openaire   +1 more source

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, Can Altay, Bülent Sankur
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