Results 151 to 160 of about 7,085,552 (171)

Fast Variational Sparse Bayesian Learning With Automatic Relevance Determination for Superimposed Signals

IEEE Transactions on Signal Processing, 2011
—In this work, a new fast variational sparse Bayesian learning (SBL) approach with automatic relevance determination (ARD) is pro-posed. The sparse Bayesian modeling, exemplified by the relevance vector machine (RVM), allows a sparse regression or ...
Vincent Poor, Dmitriy Shutin And Juan Marchal, Sanjeev R. Kulkarni   +2 more
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Sparse Bayesian Learning-Based Kalman Filtering (SBL-KF) for Group-Sparse Channel Estimation in Doubly Selective mmWave Hybrid MIMO Systems

2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019
This work develops a sparse Bayesian learning based Kalman filtering (SBL-KF) framework for channel estimation in frequency and time selective, i.e. doubly selective, millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. Towards this end, the doubly selective mmWave MIMO channel is represented in terms of the beamspace channel vector ...
Suraj Srivastava, Aditya K. Jagannatham
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Improving Quantitative Analysis with Cross Instrument-Sparse Bayesian Learning (CI-SBL) Raman Spectroscopy Analysis Algorithm

Analytical Chemistry
Qualitative and quantitative analysis of Raman spectroscopy is a widely used nondestructive analytical technique in many fields. It utilizes the Raman scattering effect of lasers to obtain molecular vibration information on samples. By comparison with the Raman spectra of standard substances, qualitative and quantitative analyses can be achieved on ...
Jinglei Zhai, Zilong Wang, Xin Chen, Yunfeng Li, Tengyu Wu, Biao Sun, Pei Liang   +6 more
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SBL-LCGL: sparse Bayesian learning based on Laplace distribution for robust cone-beam x-ray luminescence computed tomography

Physics in Medicine & Biology
Abstract Objective. To address the quality and accuracy issues in the distribution of nanophosphors (NPs) using Cone-beam x-ray luminescence computed tomography (CB-XLCT) by proposing a novel reconstruction strategy. Approach. This paper introduces a sparse Bayesian learning reconstruction method termed SBL-LCGL, which
Yifan Wang, Haoyu Wang, Qiuquan Zhu, Yi Chen, Linzhi Su, Huangjian Yi, Chengyi Gao, Xin Cao   +7 more
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Local-Spatiotemporal Change Monitoring in Extracellular Fluid by Time-Variation-Constraint Sparse Bayesian Learning Implemented Into Frequency-Difference Electrical Impedance Tomography (tvcSBL-fdEIT)

IEEE Transactions on Instrumentation and Measurement, 2023
Ryoma Ogawa, Shinsuke Akita, Masahiro Takei   +2 more
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An Efficient Sparse Bayesian Learning Algorithm Based on Gaussian-Scale Mixtures

IEEE Transactions on Neural Networks and Learning Systems, 2022
Jun Wang, Hai-Tao Zhang
exaly  

Data-Drive Site Characterization for Benchmark Examples: Sparse Bayesian Learning Versus Gaussian Process Regression

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 2023
Jianye Ching, Ikumasa Yoshida
exaly  

Sparse Bayesian Learning Based Tensor Dictionary Learning and Signal Recovery With Application to MIMO Channel Estimation

IEEE Journal on Selected Topics in Signal Processing, 2021
Yu T Su, Wen-Che Chang
exaly  

Sparse Signal Recovery With Temporally Correlated Source Vectors Using Sparse Bayesian Learning

IEEE Journal on Selected Topics in Signal Processing, 2011
Bhaskar D. Rao, Zhilin Zhang
exaly  

DNN-Sparse Bayesian Learning (SBL) based Sparse Signal Recovery in Compressed Sensing

Nanotechnology Perceptions
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