Results 61 to 70 of about 1,492,451 (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 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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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
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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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Compressive Sensing Approaches for Autonomous Object Detection in Video Sequences [PDF]
, 2015 Video analytics requires operating with large amounts of data. Compressive sensing allows to reduce the number of measurements required to represent the video using the prior knowledge of sparsity of the original signal, but it imposes certain conditions
Isupova, O. +5 more
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Compressive Sensing for PAN-Sharpening [PDF]
, 2011 Based on compressive sensing framework and sparse reconstruction technology, a new pan-sharpening method, named Sparse Fusion of Images (SparseFI, pronounced as sparsify), is proposed in [1].
Bamler, Richard +3 more
core
Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy
Advanced Engineering Materials, EarlyView.Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang +6 more
wiley +1 more source
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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