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2017
We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert +2 more
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We consider the problem of sparse signal recovery in dynamic sensing scenarios. Specifically, we study the recovery of a sparse time-varying signal from linear measurements of a single static sensor that are taken at two different points in time. This setup can be modelled as observing a single signal using two different sensors – a real one and a ...
Dalitz, Robert +2 more
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The Geometry of Off-the-Grid Compressed Sensing
Foundations of Computational Mathematics, 2018Compressed sensing (CS) ensures the recovery of sparse vectors from a number of randomized measurements proportional to their sparsity. The initial theory considers discretized domains, and the randomness makes the physical positions of the grid nodes ...
C. Poon, N. Keriven, G. Peyr'e
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Proceedings of the fifth international conference on Information processing in sensor networks - IPSN '06, 2006
Compressive sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of compressive wireless sensing for sensor networks in which a fusion center retrieves signal field information from an ensemble of ...
Waheed U. Bajwa +3 more
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Compressive sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of compressive wireless sensing for sensor networks in which a fusion center retrieves signal field information from an ensemble of ...
Waheed U. Bajwa +3 more
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An image encryption scheme based on multi-objective optimization and block compressed sensing
Nonlinear dynamics, 2022Xiu-li Chai +4 more
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On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices
IEEE Transactions on Biomedical Circuits and Systems, 2018On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data.
Wenfeng Zhao +3 more
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Compressive Sensing and Compressive Holography
2015It is well known from communication theory that for a sampled signal, the sampling rate must be greater than twice the signal bandwidth for faithful reproduction of the original signal. The concept of sampling at the Nyquist rate was postulated by Shannon in 1949; in the same year, Golay introduced the idea of artificial discrete multiplex coding in ...
Rola Aylo +2 more
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Rethinking Compressive Sensing
2018 26th European Signal Processing Conference (EUSIPCO), 2018In this paper we show that Compressive Sensing (CS) can be casted as an impulse response estimation problem. Using this interpretation we re-obtain some theoretical results of CS in a simple manner. Moreover, we prove that in the case of a randomly generated sensing matrix, reconstruction probability depends on the kurtosis of the distribution used for
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Image encryption based on compressed sensing and DNA encoding
Signal processing. Image communication, 2021Xing-yuan Wang, Yining Su
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Sensing Matrices in Compressed Sensing
2019One of the most important aspects of compressed sensing (CS) theory is an efficient design of sensing matrices. These sensing matrices are accountable for the required signal compression at the encoder end and its exact or approximate reconstruction at the decoder end.
Sanjay L. Nalbalwar, Yuvraj V. Parkale
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Circuits, Systems, and Signal Processing, 2011
Combining the principles behind Compressed Sensing theory with a wavelet-based implementation of a foveation operator inspired by the human visual system yields significant compression performances on both 1D and 2D signals. The solution provides spatially variable quality of the reconstructed information, enabling better approximation on specific ...
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Combining the principles behind Compressed Sensing theory with a wavelet-based implementation of a foveation operator inspired by the human visual system yields significant compression performances on both 1D and 2D signals. The solution provides spatially variable quality of the reconstructed information, enabling better approximation on specific ...
openaire +3 more sources