Results 81 to 90 of about 17,602 (258)
Consensus-based sparse signal reconstruction algorithm for wireless sensor networks
International Journal of Distributed Sensor Networks, 2016 This article presents a distributed Bayesian reconstruction algorithm for wireless sensor networks to reconstruct the sparse signals based on variational sparse Bayesian learning and consensus filter.
Bao Peng +3 more
doaj +1 more source
Cramér-Rao Bounds for DoA Estimation of Sparse Bayesian Learning with the Laplace Prior. [PDF]
Sensors (Basel), 2022 Bai H, Duarte MF, Janaswamy R.
europepmc +1 more source
Sparse Bayesian Learning for EEG Source Localization [PDF]
, 2015 Sajib Saha +5 more
openalex +1 more source
Structure-aware Sparse Bayesian Learning-based Channel Estimation for Intelligent Reflecting Surface-aided MIMO [PDF]
, 2022 Yanbin He, Geethu Joseph
openalex +1 more source
Advanced Intelligent Systems, EarlyView.This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He +2 more
wiley +1 more source
Sparse Bayesian learning-based massive multi-user detection algorithm
Tongxin xuebao, 2023 Aiming at the problem that most existing algorithms were based on the Gaussian inverse gamma prior model (GIG-SBL), which ignored the sparsity of the support set vector within the sparse solution, a sparse Bayesian learning framework based on the ...
Pingping CHEN +4 more
doaj
Reliable and Efficient Inference of Bayesian Networks from Sparse Data by Statistical Learning Theory [PDF]
, 2003 Dominik Janzing, Daniel Herrmann
openalex +1 more source
Inversion-Free Sparse Bayesian Learning for Temporally Correlated Signal Recovery [PDF]
, 2023 Yuhui Song
openalex +1 more source
Enabling Stochastic Dynamic Games for Robotic Swarms
Advanced Intelligent Systems, EarlyView.This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley +1 more source
SensorsPartially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter ...
Xiaoyu Lan +5 more
doaj +1 more source