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ICUMT
2009
13 years 6 months ago
A Bayesian analysis of Compressive Sensing data recovery in Wireless Sensor Networks
Abstract--In this paper we address the task of accurately reconstructing a distributed signal through the collection of a small number of samples at a data gathering point using Co...
Riccardo Masiero, Giorgio Quer, Michele Rossi, Mic...
ICASSP
2011
IEEE
13 years 9 days ago
Combined compressed sensing and parallel mri compared for uniform and random cartesian undersampling of K-space
Both compressed sensing (CS) and parallel imaging effectively reconstruct magnetic resonance images from undersampled data. Combining both methods enables imaging with greater und...
Daniel S. Weller, Jonathan R. Polimeni, Leo Grady,...
ACISICIS
2008
IEEE
13 years 10 months ago
Efficient Projection for Compressed Sensing
Compressed sensing (CS), a joint compression and sensing process, is a emerging field of activity in which the signal is sampled and simultaneously compressed at a greatly reduced...
Vo Dinh Minh Nhat, Duc Vo, Subhash Challa, Sungyou...
CORR
2011
Springer
259views Education» more  CORR 2011»
13 years 3 months ago
The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding vs. Dynamic Range
Compressive sensing (CS) exploits the sparsity present in many common signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, ...
Mark A. Davenport, Jason N. Laska, John R. Treichl...
CORR
2011
Springer
155views Education» more  CORR 2011»
13 years 3 months ago
Reconciling Compressive Sampling Systems for Spectrally-sparse Continuous-time Signals
The Random Demodulator (RD) and the Modulated Wideband Converter (MWC) are two recently proposed compressed sensing (CS) techniques for the acquisition of continuous-time spectral...
Michael A. Lexa, Mike E. Davies, John S. Thompson