Results 31 to 40 of about 17,602 (258)
Process monitoring plays an important role in ensuring the safety and stable operation of equipment in a large-scale process. This paper proposes a novel data-driven process monitoring framework, termed the ensemble adaptive sparse Bayesian transfer ...
Hongchao Cheng +4 more
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Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.
Yu Xie +5 more
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Relevance Vector Machines for Enhanced BER Probability in DMT-Based Systems
A new channel estimation method for discrete multitone (DMT) communication system based on sparse Bayesian learning relevance vector machine (RVM) method is presented.
Ashraf A. Tahat, Nikolaos P. Galatsanos
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Variational Bayesian Sparse Signal Recovery With LSM Prior
This paper presents a new sparse signal recovery algorithm using variational Bayesian inference based on the Laplace approximation. The sparse signal is modeled as the Laplacian scale mixture (LSM) prior.
Shuanghui Zhang +3 more
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Micro Doppler analysis of spin stabilized objects is of a great significance for attitude estimation and recognition of space targets. In practice, the radar cannot dwell on one target in a long interval continuously.
Ling Hong, Fengzhou Dai, Xili Wang
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The shallow water noise shows obvious impulsive property, which greatly degrades the direction of arrival (DOA) performance due to the conventional design concept based on the Gaussian assumption.
Xiao Feng +5 more
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EM-based parameter iterative approach for sparse Bayesian channel estimation of massive MIMO system
One of the main challenges for a massive multi-input multi-output (MIMO) system is to obtain accurate channel state information despite the increasing number of antennas at the base station.
Sulin Mei, Yong Fang
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Nowadays, high-speed sampling and transmission is a foremost challenge of radar system. In order to solve this problem, a compressive sensing approach is proposed for radar target signals in this study.
Zhong Jinrong, Wen Gongjian
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Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records
One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions.
Yikuan Li +8 more
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The direct position determination method based on compressed sensing depends on the accurate signal propagation model. With partially unknown propagation model parameters, its location performance will decline significantly.
Hongzhen YE +4 more
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