Asynchronous decentralized algorithms for the noisy 20 questions problem

2016 ◽  
Author(s):  
Theodoros Tsiligkaridis
Keyword(s):  
Decentralized Algorithms

scholarly journals Decentralized Algorithms for Multi-Robot Manipulation via Caging

2004 ◽  
Vol 23 (7-8) ◽  
pp. 783-795 ◽  
Author(s):  
Guilherme A. S. Pereira ◽  
Mario F. M. Campos ◽  
Vijay Kumar
Keyword(s):  
Robot Manipulation ◽  
Decentralized Algorithms ◽  
Multi Robot

scholarly journals COINSTAC: Decentralizing the future of brain imaging analysis

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1512 ◽  
Author(s):  
Jing Ming ◽  
Eric Verner ◽  
Anand Sarwate ◽  
Ross Kelly ◽  
Cory Reed ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Data Sharing ◽  
Large Scale ◽  
Data Transfer ◽  
Imaging Data ◽  
Sensitive Data ◽  
Large Scale Data ◽  
Decentralized Algorithms ◽  
Neuroimaging Data ◽  
Brain Imaging Data

In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.

scholarly journals Decentralized algorithms for consensus-based power packet distribution

2021 ◽  
Vol 12 (2) ◽  
pp. 181-193
Author(s):  
Seongcheol Baek ◽  
Hiroyasu Ando ◽  
Takashi Hikihara
Keyword(s):  
Decentralized Algorithms

scholarly journals Decentralized State Estimation Algorithm of Centralized Equivalent Precision for Formation Flying Spacecrafts Based on Junction Tree

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Mengyuan Dai ◽  
Hua Mu ◽  
Meiping Wu ◽  
Zhiwen Xian
Keyword(s):  
State Estimation ◽  
Formation Flying ◽  
Dynamic Bayesian Network ◽  
Estimation Algorithm ◽  
Junction Tree ◽  
Estimation Algorithms ◽  
Distributed Structure ◽  
Decentralized Algorithms ◽  
Formation Flying Spacecraft ◽  
Decentralized State Estimation

As centralized state estimation algorithms for formation flying spacecraft would suffer from high computational burdens when the scale of the formation increases, it is necessary to develop decentralized algorithms. To the state of the art, most decentralized algorithms for formation flying are derived from centralized EKF by simplification and decoupling, rendering suboptimal estimations. In this paper, typical decentralized state estimation algorithms are reviewed, and a new scheme for decentralized algorithms is proposed. In the new solution, the system is modeled as a dynamic Bayesian network (DBN). A probabilistic graphical method named junction tree (JT) is used to analyze the hidden distributed structure of the DBNs. Inference on JT is a decentralized form of centralized Bayesian estimation (BE), which is a modularized three-step procedure of receiving messages, collecting evidences, and generating messages. As KF is a special case of BE, the new solution based on JT is equivalent in precision to centralized KF in theory. A cooperative navigation example of a three-satellite formation is used to test the decentralized algorithms. Simulation results indicate that JT has the best precision among all current decentralized algorithms.

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