Results 321 to 330 of about 204,120 (332)
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Accelerating SENSE using compressed sensing
Magnetic Resonance in Medicine, 2009AbstractBoth parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for ...
Bo Liu +4 more
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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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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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Compressive Sensing in Acoustic Imaging
2015Acoustic sensing is at the heart of many applications, ranging from underwater sonar and nondestructive testing to the analysis of noise and their sources, medical imaging, and musical recording. This chapter discusses a palette of acoustic imaging scenarios where sparse regularization can be leveraged to design compressive acoustic imaging techniques.
Bertin, Nancy +3 more
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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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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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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 ...
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Research on Compressed Sensing
Chinese Journal of Computers, 2011Chang-Jun Fu +2 more
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