Results 51 to 60 of about 1,446,045 (274)
Synthetic aperture sonar imaging using compressive sensing and an ultrasound transducer array [PDF]
, 2013 Includes bibliographical references.Compressive sensing (CS) also known as compressive sampling is a technique used to reconstruct or recover the full-length of a signal with only a few non-adaptive measurements.
Jideani, Josiah Chimnanu
core +1 more source
A Hierarchical Bayesian Model for Frame Representation [PDF]
, 2010 In many signal processing problems, it is fruitful to represent the signal under study in a frame. If a probabilistic approach is adopted, it becomes then necessary to estimate the hyperparameters characterizing the probability distribution of the frame
Amel Benazza-Benyahia +9 more
core +1 more source
Compressed Sensing with uncertainty - the Bayesian estimation perspective
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015 The Compressed Sensing (CS) framework outperforms the sampling rate limits given by Shannon's theory. This gap is possible since it is assumed that the signal of interest admits a linear decomposition of few vectors in a given sparsifying Basis (Fourier, Wavelet, …).
Bernhardt, Stéphanie +3 more
openaire +3 more sources
Defence Technology, 2022 In this paper, we propose a compressive sampling and reconstruction system based on the shift-invariant space associated with the fractional Gabor transform.
Qiang Wang, Chen Meng, Cheng Wang
doaj +1 more source
Bayesian Orthogonal Component Analysis for Sparse Representation [PDF]
, 2010 This paper addresses the problem of identifying a lower dimensional space where observed data can be sparsely represented. This undercomplete dictionary learning task can be formulated as a blind separation problem of sparse sources linearly mixed with ...
Nicolas Dobigeon +3 more
core +1 more source
Compressed sensing MRI with Bayesian dictionary learning [PDF]
2013 IEEE International Conference on Image Processing, 2013 We present an inversion algorithm for magnetic resonance images (MRI) that are highly undersampled in k-space. The proposed method incorporates spatial finite differences (total variation) and patch-wise sparsity through in situ dictionary learning. We use the beta-Bernoulli process as a Bayesian prior for dictionary learning, which adaptively infers ...
Xinghao Ding +5 more
openaire +1 more source
Adaptive Localization in Wireless Sensor Network through Bayesian Compressive Sensing
International Journal of Distributed Sensor Networks, 2015 The estimation of the localization of targets in wireless sensor network is addressed within the Bayesian compressive sensing (BCS) framework. BCS can estimate not only target locations but also noise variance of the environment.
Zuoxin Xiahou, Xiaotong Zhang
doaj +1 more source
Full polarisation ISAR imaging based on joint sparse Bayesian compressive sensing
The Journal of Engineering, 2019 This study proposes a joint sparse algorithm based on Bayesian compressive sensing to improve full polarisation inverse synthetic aperture radar (ISAR) imaging performance.
Yalong Gu +4 more
doaj +1 more source
On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
Applied Sciences, 2021 The compressive sensing (CS) of mechanical signals is an emerging research topic for remote condition monitoring. The signals generated by machines are mostly periodic due to the rotating nature of its components.
Yosra Marnissi +4 more
doaj +1 more source
Multi-signal Compressed Sensing For Polarimetric SAR Tomography [PDF]
, 2011 In recent years, three-dimensional imaging by means of SAR tomography has become a field of intensive research. In SAR tomography, the vertical reflectivity function for every azimuth-range pixel is usually recovered by processing data collected using a ...
Aguilera, Esteban Pedro +6 more
core +1 more source