Results 51 to 60 of about 2,932,031 (355)
Deterministic Sensing Matrices in Compressive Sensing: A Survey
The Scientific World Journal, 2013 Compressive sensing is a sampling method which provides a new approach to efficient signal compression and recovery by exploiting the fact that a sparse signal can be suitably reconstructed from very few measurements.
Thu L. N. Nguyen, Yoan Shin
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Multi-Channel Deep Networks for Block-Based Image Compressive Sensing [PDF]
IEEE transactions on multimedia, 2019 Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently.
Siwang Zhou +4 more
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Perceptual Compressive Sensing [PDF]
, 2018 Accepted by The First Chinese Conference on Pattern Recognition and Computer Vision (PRCV 2018). This is a pre-print version (not final version)
Jiang Du +3 more
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Blind Compressed Sensing [PDF]
IEEE Transactions on Information Theory, 2011 The fundamental principle underlying compressed sensing is that a signal, which is sparse under some basis representation, can be recovered from a small number of linear measurements. However, prior knowledge of the sparsity basis is essential for the recovery process.
Yonina C. Eldar, Sivan Gleichman
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Model-Based Compressive Sensing [PDF]
IEEE Transactions on Information Theory, 2008 Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for the acquisition of sparse or compressible signals that can be well approximated by just K ¿ N elements from an N -dimensional basis.
Richard Baraniuk +3 more
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Compressive-sensing data reconstruction for structural health monitoring: a machine-learning approach [PDF]
Structural Health Monitoring, 2019 Compressive sensing has been studied and applied in structural health monitoring for data acquisition and reconstruction, wireless data transmission, structural modal identification, and spare damage identification.
Y. Bao, Zhiyi Tang, Hui Li
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Sparse Representation for Wireless Communications: A Compressive Sensing Approach [PDF]
IEEE Signal Processing Magazine, 2018 Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with a focus on the most recent compressive
Zhijin Qin +4 more
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(Compressed) sensing and sensibility [PDF]
Proceedings of the National Academy of Sciences, 2011 For decades, researchers have built computer models of molecular interactions to predict properties of new molecules (1). These models take the form of potential functions, equations that can be used predict the molecular energy of interaction. Potential functions have very broad applications. Other than ab initio quantum mechanics-based approaches (2),
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A New Method for EEG Compressive Sensing
Advances in Electrical and Computer Engineering, 2012 The paper investigates the possibility of using compressive sensing techniques for the acquisition and reconstruction of EEG signals containing the evoked potential P300. A method of EEG compressive sensing based on the physiological correlation of EEG
FIRA, M., GORAS, L.
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Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach [PDF]
IEEE Transactions on Communications, 2018 A key challenge of massive MTC (mMTC), is the joint detection of device activity and decoding of data. The sparse characteristics of mMTC makes compressed sensing (CS) approaches a promising solution to the device detection problem. However, utilizing CS-
Kamil Senel, E. Larsson
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