Results 291 to 300 of about 555,264 (327)

Block Sparse Signals Recovery Algorithm for Distributed Compressed Sensing Reconstruction

Journal of Information Processing Systems, 2019
Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently.
Xingyi Chen, Yujie Zhang, Rui Qi
semanticscholar   +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.
Antonio Artés-Rodríguez, Pablo Vinuelas-Peris   +1 more
openaire   +2 more sources

Massive MIMO-OFDM Channel Estimation via Distributed Compressed Sensing

IEEE Wireless Communications Letters, 2019
Massive multiple-input multiple-output orthogonal frequency division multiplexing (mMIMO-OFDM) channel estimation is considered recently utilizing compressed sensing (CS) based methods.
Abbas Akbarpour-Kasgari, M. Ardebilipour
semanticscholar   +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
Yanliang Jin, Zongxin Yu, Yixing Fu, Haiyan Zhang, Rui Wang   +4 more
openaire   +2 more sources

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.
Zhiwen Liu, Qun Wang
openaire   +2 more sources

Pilot Allocation for Distributed-Compressed-Sensing-Based Sparse Channel Estimation in MIMO-OFDM Systems

IEEE Transactions on Vehicular Technology, 2016
This paper addresses the sparse channel estimation problem in multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems from the perspective of distributed compressed sensing (DCS). It is focused on deterministic pilot
Xueyun He, Rong-fang Song, Weiping Zhu
semanticscholar   +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, Song Xiao, Chengke Wu   +3 more
openaire   +2 more sources

Distributed sparse signal sensing based on compressive sensing OFDR

Optics Letters, 2020
The maximum detectable vibration frequency of an optical frequency domain reflectometry (OFDR) system is limited by the tunable rate of the laser source. Unlike uniform sampling with the time-resolved method, the sampling frequency is randomly modulated so that the vibration signal applied on the interrogation fiber is sampled by a multi-frequency sub ...
Zengguang Qin, Zhaojun Liu, Yanping Xu, Li Zhao, Shuai Qu, Zhenhua Cong, Wang Heng   +6 more
openaire   +3 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).
Idit Keidar, Stacy Patterson, Yonina C. Eldar   +2 more
openaire   +2 more sources

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, Bulent Sankur, Can Altay
openaire   +2 more sources