Results 251 to 260 of about 73,229 (279)

Distributed Outlier Detection using Compressive Sensing

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 2015
Computing outliers and related statistical aggregation functions from large-scale big data sources is a critical operation in many cloud computing scenarios, e.g. service quality assurance, fraud detection, or novelty discovery. Such problems commonly have to be solved in a distributed environment where each node only has a local slice of the entirety ...
Ying Yan, Jiaxing Zhang, Bojun Huang, Xuzhan Sun, Jiaqi Mu, Zheng Zhang, Thomas Moscibroda   +6 more
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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, Bulent Sankur
openaire   +1 more source

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   +1 more source

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, Chengchen Mao
openaire   +1 more source

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   +3 more
openaire   +1 more source

Distributed Bayesian Compressive Sensing using Gibbs sampler

2012 International Conference on Wireless Communications and Signal Processing (WCSP), 2012
Bayesian Compressive Sensing (BCS) observes s-parse signal from the statistics viewpoint. In BCS, a Bayesian hierarchy is established utilizing Bayesian inference, thus gives the reconstruction algorithm plenty of robust and flexibility. When dealing with distributed scenario, Bayesian hierarchy is also an effective method. Not only can statistic model
Hua Ai, Yang Lu, Wenbin Guo
openaire   +1 more source

Compressive sensing in distributed applications

2010
The theory of compressive sensing (CS) has recently been proposed as a framework for joint signal acquisition and compression by replacing the standard sample by sample measurement approach with the idea of collecting a set of random projections of the signal; it has already been successfully employed in a number of signal processing applications, e.g.,
GAETA, Rossano, GRANGETTO, Marco, SERENO, Matteo   +2 more
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Rate-Distortion Theory of Distributed Compressed Sensing

2015
In this chapter, correlated and distributed sources without cooperation at the encoder are considered. For these sources, the best achievable performance in the rate-distortion sense of any distributed compressed sensing scheme is derived, under the constraint of high-rate quantization.
Giulio Coluccia, Chiara Ravazzi, Enrico Magli   +2 more
openaire   +1 more source

Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey

IEEE Communications Surveys and Tutorials, 2023
Sijing Duan, Dan Wang, Ju Ren
exaly  

A Comprehensive Survey on Coded Distributed Computing: Fundamentals, Challenges, and Networking Applications

IEEE Communications Surveys and Tutorials, 2021
Jer Shyuan Ng, Bryan Wei Yang Lim, Cong Luong Nguyen   +2 more
exaly