Results 301 to 310 of about 70,734 (340)

Imaging sparse scatterers through Bayesian Compressive Sensing methods

2011 International Conference on Electromagnetics in Advanced Applications, 2011
A review of a set of approaches for electromagnetic imaging that exploit the ‘a-priori’ information on the sparseness of the unknown scatterers to define computationally-efficient inversion procedures is presented. The imaging problem is formulated within the Contrast Source formulation and successively recast into the Bayesian Compressive Sampling ...
Oliveri, Giacomo, Poli, Lorenzo, Massa, Andrea   +2 more
openaire   +2 more sources

Sparse reconstruction of sound field using pattern-coupled Bayesian compressive sensing.

Journal of the Acoustical Society of America
Conventional near-field acoustic holography based on compressive sensing either does not fully exploit the underlying block-sparse structures of the signal or suffers from a mismatch between the actual and predefined block structure due to the lack of ...
Yue Xiao, Lei Yuan, Yuan Liu, Jun-Yu Wang, Wenxin Hu, Ruimin Sun, Yongjin Liu, Peng Ni   +7 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

Clustered Compressed Sensing via Bayesian Framework

2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim), 2015
This paper provides clustered compressive sensing (CCS) based signal processing using Bayesian framework. Images like magnetic resonanse images (MRI) are usually very weak due to the presence of noise and due to the weak nature of the signal itself. Compressed sensing (CS) paradigm can be applied in order to boost such signal recoveries.
Solomon Tesfamicael, Faraz Barzideh
openaire   +1 more source

Contiguously clustered linear arrays through Bayesian compressive sensing

2014 IEEE Conference on Antenna Measurements & Applications (CAMA), 2014
Bayesian Compressive Sensing (BCS) is applied for the synthesis of contiguously clustered linear arrays. The standard sub-array problem is formulated as a probabilistic BCS synthesis problem and the Relevance Vector Machine (RVM) is used to obtain a sparse contiguous non-overlapping subarray configuration which has maximal far-field pattern match with ...
Bekele, Ephrem Teshale, Oliveri, Giacomo, Massa, Andrea   +2 more
openaire   +2 more sources

Localization of Multiple RF Sources Based on Bayesian Compressive Sensing Using a Limited Number of UAVs With Airborne RSS Sensor

IEEE Sensors Journal, 2020
Locating multiple Radio Frequency (RF) sources by Unmanned Aerial Vehicles (UAVs) using Received Signal Strength (RSS) measurements attracts extensive attention for its intrinsic simplicity in hardware and low cost.
Xinhua Jiang, Ning Li, Yan Guo, Dongping Yu, Sixing Yang   +4 more
semanticscholar   +1 more source

Optimized Design for Sparse Arrays in 3-D Imaging Sonar Systems Based on Perturbed Bayesian Compressive Sensing

IEEE Sensors Journal, 2020
Sparse planar array designs significantly reduce the hardware complexity and computational overhead in phased array 3-D imaging sonar systems. Recently, Bayesian compressive sensing (BCS) theory has been applied for synthesizing maximally sparse arrays ...
Zhenwei Lin, Yao-wu Chen, Xuesong Liu, Rongxin Jiang, Binjian Shen, Xinxin Guo   +5 more
semanticscholar   +1 more source

Multifrequency Bayesian compressive sensing methods for microwave imaging

Journal of the Optical Society of America A, 2014
The Bayesian retrieval of sparse scatterers under multifrequency transverse magnetic illuminations is addressed. Two innovative imaging strategies are formulated to process the spectral content of microwave scattering data according to either a frequency-hopping multistep scheme or a multifrequency one-shot scheme.
Poli, Lorenzo, Oliveri, Giacomo, P. P. Ding, T. Moriyama, Massa, Andrea   +4 more
openaire   +4 more sources

Bayesian compressive sensing using generative models

Inverse Problems
Abstract Generative models, used to create new data resembling a given dataset, have achieved notable success in image reconstruction, especially in scenarios involving incomplete or noisy measurements. In Bayesian compressive sensing (CS) and more broadly in Bayesian inverse problems, the posterior inference of unknown quantities plays ...
Ying Zhang, Xiaoqun Zhang, Jiulong Liu
openaire   +2 more sources

Federated Bayesian optimization via compressed sensing

Information Sciences
Federated Bayesian optimization (FBO) has been introduced in recent years to avoid privacy leakage when multiple clients involve in finishing a global optimization task. Parameter-sharing-based FBOs, as one branch of FBOs, however, compromise the optimization efficacy due to the reduced fitting ability of parameterized Gaussian processes (GPs). In this
Liu, Qiqi, Wu, Leming, Jin, Yaochu
openaire   +2 more sources