Results 291 to 300 of about 70,734 (340)

Variational Bayesian dynamic compressive sensing

2016 IEEE International Symposium on Information Theory (ISIT), 2016
Dynamic compressed sensing (DCS) has recently gained popularity as a successful approach to recovering dynamic sparse signals. In this paper, we attack the problem from a Bayesian perspective. The proposed model imposes sparse constraints on both the unknown sparse signal and its temporal innovation via t priors. Due to the conjugacy between the priors
Hongwei Wang, Hang Yu, Michael Hoy, Justin Dauwels, Heping Wang   +4 more
openaire   +1 more source

Through-the-Wall Radar Imaging Based on Bayesian Compressive Sensing Exploiting Multipath and Target Structure

IEEE Transactions on Computational Imaging, 2021
Compressive sensing (CS) applied to through-the-wall radar imaging (TWRI) exploits the group sparsity of a target scene in the presence of wall clutter and multipath from enclosed structures towards achieving high-resolution imaging with limited ...
Qisong Wu, Z. Lai, M. Amin
semanticscholar   +1 more source

A block bayesian compressive sensing method based on the equivalent source method for near-field acoustic holography

Journal of Vibration and Control, 2021
The conventional equivalent source method for near-field acoustic holography is an effective noise diagnosis method using microphone array. However, its performance is limited by microphone spacing, so the effect is unsatisfied when the wave number is ...
Ming Zan, Zhongming Xu, Linsen Huang, Zhonghua Tang, Zhifei Zhang, Yansong He   +5 more
semanticscholar   +1 more source

Compressive Sensing via Variational Bayesian Inference

2020 Intermountain Engineering, Technology and Computing (IETC), 2020
The sparse signal recovery problem from a set of compressively sensed noisy measurements using sparse Bayesian learning (SBL) modeling and variational Bayesian (VB) inference technique is considered. In the context of SBL, two main approaches are considered here. In the first approach, each component of the sparse signal is modeled via a Gaussian prior
Mohammad Shekaramiz, Todd K. Moon
openaire   +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 clustered sparse signals

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
In traditional framework of Compressive Sensing (CS), only sparse prior on the property of signals in time or frequency domain is adopted to guarantee the exact inverse recovery. Besides sparse prior, cluster prior is introduced in this paper in order to investigate a class of structural sparse signals, called clustered sparse signals.
Yu, Lei, Sun, Hong, Barbot, Jean-Pierre, Zheng, Gang   +3 more
openaire   +2 more sources

Nonparametric and Continuous Variable-Based Stratigraphic Modelling from Sparse Boreholes using Signed Distance Function and Bayesian Compressive Sensing

Canadian geotechnical journal (Print)
An accurate stochastic interpretation of subsurface stratigraphy with quantified uncertainty can benefit the subsequent risk management of geotechnical infrastructure.
Zehang Qian, Chao Shi, Yu Wang, Zijun Cao   +3 more
semanticscholar   +1 more source

Synthesis of planar arrays through Bayesian Compressive Sensing

Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation, 2012
The synthesis of sparse planar arrays matching a desired pattern is addressed by means of an innovative compressive sensing technique, namely the Bayesian Compressive Sensing (BCS). A relevance vector machine (RVM) is employed for computing the solution of the design problem formulated in a probabilistic fashion. A set of preliminary synthesis examples
Oliveri, Giacomo, Carlin, Matteo, Massa, Andrea   +2 more
openaire   +2 more sources

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

Preliminary Investigations on Hybridization of Bayesian Compressive Sensing and Array Dilation Technique

2021 IEEE Indian Conference on Antennas and Propagation (InCAP), 2021
This paper investigates the hybridization between the Bayesian Compressive Sensing (BCS) algorithm and Array Dilation Technique (ADT) in order to synthesize thinned, isophoric sparse arrays while allowing complete control over the thinning process and ...
A. Kedar, Pratap Vangol, Rahul Chikkodi, M. A.   +3 more
semanticscholar   +1 more source