Results 51 to 60 of about 1,531 (279)
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
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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
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Bayesian compressive sensing [PDF]
, 2007 The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M ≪ N of basis-function ...
Ya Xue, Shihao Ji, Lawrence Carin
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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
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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
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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
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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
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Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying
Advanced Engineering Materials, EarlyView.An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara +8 more
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Robust multipath exploitation radar imaging in urban sensing based on Bayesian compressive sensing [PDF]
, 2014 —In through-the-wall radar imaging applications, exploitation of group sparsity of the targets under multipath propagation allows high-resolution ghost-free imaging.
Yimin D. Zhang +3 more
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Autofocus Bayesian compressive sensing for multipath exploitation in urban sensing [PDF]
2015 IEEE International Conference on Digital Signal Processing (DSP), 2015 Exploitation of group sparsity under multipath propagation enables high-resolution ghost-free imaging in urban sensing and through-the-wall radar imaging applications. Multipath exploitation schemes typically require exact prior information of the indoor scene layout and transceiver locations to eliminate ghosts targets.
Qisong Wu +3 more
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