Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks [PDF]
Sensors, 2020 Underwater acoustic sensor networks play an important role in assisting humans to explore information under the sea. In this work, we consider the combination of sensor selection and data routing in three dimensional underwater wireless sensor networks ...
Xuechen Chen, Wenjun Xiong, Sheng Chu
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Variational Bayesian algorithm for quantized compressed sensing [PDF]
IEEE Transactions on Signal Processing, 2013 Compressed sensing (CS) is on recovery of high dimensional signals from their low dimensional linear measurements under a sparsity prior and digital quantization of the measurement data is inevitable in practical implementation of CS algorithms.
Xie, Lihua, Yang, Zai, Zhang, Cishen
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Variational Bayesian Compressive Sensing with Equivalent Source Modeling for Sound Field Reconstruction [PDF]
SensorsWhile conventional Bayesian compressive sensing exploits signal sparsity for accurate sound field reconstruction from under-sampled measurements, its practicality is limited by high computational complexity and slow convergence.
Yue Xiao +3 more
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Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks [PDF]
Sensors, 2018 Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by
Ruisong Wang +5 more
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Bayesian Compressive Sensing Via Belief Propagation [PDF]
IEEE Transactions on Signal Processing, 2010 Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear ...
Baron, Dror +2 more
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Multi-contrast reconstruction with Bayesian compressed sensing [PDF]
Magnetic Resonance in Medicine, 2011 AbstractClinical imaging with structural MRI routinely relies on multiple acquisitions of the same region of interest under several different contrast preparations. This work presents a reconstruction algorithm based on Bayesian compressed sensing to jointly reconstruct a set of images from undersampled k‐space data with higher fidelity than when the ...
Berkin Bilgic̦ +2 more
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Modified complex multitask Bayesian compressive sensing using Laplacian scale mixture prior
IET Signal Processing, 2022 Bayesian compressive sensing (BCS) is an important sub‐class of sparse signal reconstruction algorithms. In this paper, a modified complex multitask Bayesian compressive sensing (MCMBCS) algorithm using the Laplacian scale mixture (LSM) prior is proposed.
Qilei Zhang, Lei Yu, Feng He, Yifei Ji
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Modified Block Sparse Bayesian Learning-Based Compressive Sensing Scheme for EEG Signals [PDF]
International Journal of Electronics and Telecommunications, 2021 Advancement in medical technology creates some issues related to data transmission as well as storage. In real-time processing, it is too tedious to limit the flow of data as it may reduce the meaningful information too.
Vivek Upadhyaya, Mohammad Salim
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Microwave NDT/NDE Through Differential Bayesian Compressive Sensing
IEEE Open Journal of Instrumentation and MeasurementThis article deals with the nondestructive testing and evaluation (NDT/NDE) of dielectric structures through a sparseness-promoting probabilistic microwave imaging (MI) method.
Marco Salucci +5 more
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Anti-noise variational sparse Bayesian estimation ghost imaging based on 3Level factor graph [PDF]
Scientific ReportsIn response to existing compressed sensing ghost imaging (CSGI) schemes, an innovative Bayesian compressed sensing ghost imaging with better anti-noise performance is proposed, by using the sparse representation of K-Singular Value Decomposition (KSVD ...
Siqing Xiang +9 more
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