Results 131 to 140 of about 2,383 (165)

Hopping Time Estimation of Frequency-Hopping Signals Based on HMM-Enhanced Bayesian Compressive Sensing With Missing Observations

IEEE Communications Letters, 2022
The hopping time reflects the time-varying characteristics of frequency-hopping (FH) signals, which are essential parameters for the spectrum estimation of FH signals.
Hongbin Wang, Bangning Zhang, Heng Wang, Binbin Wu, D. Guo   +4 more
semanticscholar   +1 more source

Adaptive Beamforming Design of Planar Arrays Based on Bayesian Compressive Sensing

IEEE Sensors Journal, 2021
An adaptive beamformer is effective at suppressing interference and noise. However, when the desired signal component is included in the covariance matrix, the beamformer performance becomes seriously degraded.
Zhenwei Lin, Yao-wu Chen, Xuesong Liu, Rongxin Jiang, Binjian Shen   +4 more
semanticscholar   +1 more source

Inverse lithography source and mask optimization via Bayesian compressive sensing.

Applied Optics, 2022
Source and mask optimization (SMO) is a key technique to guarantee the lithographic fidelity for 14-5 nm technology nodes. The balance between lithography fidelity and computational efficiency is a big issue for SMO.
Yiyu Sun, Yanqiu Li, Lihui Liu
semanticscholar   +1 more source

Uncertainty Quantification of Random Fields Based on Spatially Sparse Data by Synthesizing Bayesian Compressive Sensing and Stochastic Harmonic Function

, 2021
Spatial variation occurs widely in engineering practice and could be quantified by random fields. For instance, the strength of concrete in a large-sized shear wall might be described by a two-dimensional random field in the framework of probability ...
Jingran He, Jianbing Chen, X. Ren, Jie Li   +3 more
semanticscholar   +1 more source

Synthesis of Multiple-Pattern Planar Arrays by the Multitask Bayesian Compressive Sensing

IEEE Antennas and Wireless Propagation Letters, 2021
Recently, the multitask Bayesian compressive sensing has been successfully applied to the synthesis of single-pattern sparse planar arrays. The aim of this letter is to extend MT-BCS to the synthesis of multiple-pattern planar arrays.
Y. Gong, X. Shaoqiu, Yu Zheng, Bingzhong Wang   +3 more
semanticscholar   +1 more source

Bayesian Compressive Sensing for Rough Surface Reconstruction

2021 IEEE 19th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM), 2021
We implement a Bayesian compressive sensing (BCS) inversion for rough surface reconstruction under the small perturbation approximation (SPA). SPA is a linear formulation that relates the scattered fields from a rough surface to the rough surface height ...
A. Fouda, F. Teixeira
semanticscholar   +1 more source

Bayesian Compressive Sensing Approach for Phaseless Microwave Imaging

Symposium, 2021
A novel approach for phaseless data in solving microwave imaging through the Bayesian Compressive Sensing (BCS) formulated within the Born approximation framework is presented in this paper.
Baozhu Li, Zhen Huang, N. Anselmi, L. Poli   +3 more
semanticscholar   +1 more source

Rapid calculation of bistatic scattering problems based on bayesian compressive sensing

Electromagnetics
In this paper, in order to accelerate the solution of the three-dimensional target scattering problems, bayesian compressive sensing (BCS) combined with high-order characteristic basis functions (HCBFs) is proposed.
Zhonggen Wang, Longhui Sun, Wenyan Nie, Yufa Sun, Dai Dong, Yang Liu   +5 more
semanticscholar   +1 more source

A novel data-driven 3D site characterisation method: Tucker decomposition-Bayesian compressive sensing and benchmarking study

Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
Site characterisation plays a pivotal role in geotechnical design and analysis. With advancements in machine learning and other digital technologies, data-driven site characterisation (DDSC) has garnered substantial interest in data-centric geotechnics ...
Menglu Huang, T. Shuku, T. Shibata, Shin-ichi Nishimura   +3 more
semanticscholar   +1 more source

Bayesian Compressive Sensing With Variational Inference and Wavelet Tree Structure for Solving Inverse Scattering Problems

IEEE Transactions on Antennas and Propagation
The inverse scattering problems (ISPs) refer to reconstructing properties of unknown scatterers from measured scattered fields, and their solving process is inherently complex and fraught with various difficulties.
Yang-Yang Li, Huaici Zhao, Peng-fei Liu, Guo-gang Wang   +3 more
semanticscholar   +1 more source