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Compressed sensing
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151	This discussion paper is/has been under review for the journal Geoscientific Model Development (GMD). Please refer to the corresponding final paper in GMD if available. Discussion Paper Geosci. Model Dev. Discuss., 7, 5 Add to Reading List Source URL: www.geosci-model-dev-discuss.net Language: English - Date: 2014-11-21 02:38:51 Sparse coding Mathematics Statistics Applied mathematics Inverse problem Compressed sensing Greenhouse gas
152	MRS Sparse-FFT: Reducing Acquisition Time and Artifacts for In Vivo 2D Correlation Spectroscopy Lixin Shi1, Ovidiu C. Andronesi2, Haitham Hassanieh1, Badih Ghazi1, Dina Katabi1, Elfar Adalsteinsson1 1Electrical Engineer Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2013-08-21 18:58:14 Digital signal processing Fourier analysis Signal processing Fast Fourier transform Convolution Compressed sensing Mathematical analysis Algebra Mathematics
153	On sampling optimization based on mutual coherency criterion Md. Mafijul Islam Bhuiyan, University of Alberta, Edmonton, Canada [removed] Summary Add to Reading List Source URL: www.geoconvention.com Language: English - Date: 2014-04-20 15:58:15 Statistics Digital signal processing Sampling techniques Information theory Petroleum Compressed sensing Sampling Aliasing Monte Carlo method Signal processing Mathematics Applied mathematics
154	RICHARD B. DUNN SOLAR TELESCOPE NSO/SP May[removed]SAT SUN Add to Reading List Source URL: nsosp.nso.edu Language: English - Date: 2014-04-10 09:54:00 Mathematical optimization Regression analysis Signal processing Input/output Buffer Information Specification Sun Ibis Electronic engineering Mathematics Physics Compressed sensing Information theory Linear algebra
155	2010 IEEE International Conference on Data Mining Workshops Batch Mode Sparse Active Learning Lixin Shi Institute for Theoretical Computer Science, Tsinghua University Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2012-05-23 01:04:47 Statistical classification Multivariate statistics Machine learning Cybernetics Compressed sensing Support vector machine Submodular set function Sparse matrix K-nearest neighbor algorithm Statistics Mathematics Signal processing
156	Compressive Structured Light for Recovering Inhomogeneous Participating Media Jinwei Gu, Shree Nayar, Eitan Grinspun, Peter Belhumeur, and Ravi Ramamoorthi Columbia University, New York, NY 10027, USA [removed] Add to Reading List Source URL: www1.cs.columbia.edu Language: English - Date: 2009-03-22 20:05:21 Mathematics 3D computer graphics 3D imaging Compressed sensing Linear algebra Regression analysis Video Voxel Light scattering Imaging Information theory Optics
157	Compressive Structured Light for Recovering Inhomogeneous Participating Media Jinwei Gu, Shree Nayar, Eitan Grinspun, Peter Belhumeur, and Ravi Ramamoorthi Columbia University, New York, NY 10027, USA [removed] Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2009-03-22 20:05:21 Mathematics 3D computer graphics 3D imaging Compressed sensing Linear algebra Regression analysis Video Voxel Light scattering Imaging Information theory Optics
158	C o S e R a[removed]nd I n t. Worksh op on Com pre ssi ve Se n si n g a ppl i e d to Ra d a r[removed]Se pte m be r 2 013 , Bon n , G e rm a n y Radar/SAR awaits Compressive Sensing Add to Reading List Source URL: workshops.fhr.fraunhofer.de Language: English - Date: 2013-07-02 08:30:19 Compressed sensing Information theory Linear algebra Regression analysis Synthetic aperture radar Bonn Mathematics Technology Radar Telecommunications engineering Signal processing
159	IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 55, NO. 5, MAY[removed]Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using `1-Constrained Quadratic Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2009-11-08 19:17:59 Mathematical optimization Estimation theory Linear algebra Numerical linear algebra Sparse approximation Least squares Normal distribution Regularization Compressed sensing Statistics Regression analysis Mathematics
160	Background Compressed sensing (CS) techniques offer a framework for the detection and allocation of sparse signals with a reduced number of samples. Today, modern radar systems operate with high bandwidths - demanding Add to Reading List Source URL: workshops.fhr.fraunhofer.de Language: English - Date: 2013-02-14 05:48:26 NATO Abstract management Synthetic aperture radar Military International relations Anti-communism

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