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Quantized compressive sensing (QCS) deals with the problem of representing compressive signal measurements with finite precision representation, i.e., a mandatory process in any practical sensor design.
“Decoding by linear programming,”
E. J. Candès and T. Tao, · 2005
Earlier work this paper cites.
“Empirical processes and random projections,”
B. Klartag and S. Mendelson, · 2005
Earlier work this paper cites.
Nonadaptive lossy encoding of sparse signals
R. J. Pai, · 2006
Earlier work this paper cites.
“Near-optimal signal recovery from random projections: Universal encoding strategies?,”
E. J. Candès and T. Tao, · 2006
Earlier work this paper cites.
“Signal recovery from random measurements via orthogonal matching pursuit,”
J. A. Tropp and A. C. Gilbert, · 2007
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“A simple proof of the restricted isometry property for random matrices,”
R. Baraniuk, M. Davenport, R. DeVore, and M. Wakin, · 2008
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“Uniform uncertainty principle for Bernoulli and subgaussian ensembles,”
S. Mendelson, A. Pajor, and N. Tomczak-Jaegermann, · 2008
Earlier work this paper cites.
“1-bit compressive sensing,”
P. T. Boufounos and R. G. Baraniuk, · 2008
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“Compressive sensing and structured random matrices,”
H. Rauhut, · 2010
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“Compressive sensing with quantized measurements,”
A, Zymnis, S. Boyd and E. J. Candès, · 2010
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“Frame permutation quantization,”
H. Q. Nguyen, V. K. Goyal, and L. R. Varshney, · 2011
Cited alongside, same era.
“Universal rate-efficient scalar quantization,”
P. T. Boufounos, · 2012
Cited alongside, same era.
“Message-passing de-quantization with applications to compressed sensing,”
U. S. Kamilov, V. K. Goyal, and S. Rangan, · 2012
Cited alongside, same era.
A mathematical introduction to compressive sensing
S. Foucart and H. Rauhut, · 2013
Cited alongside, same era.
“Sobolev duals for random frames and
C. S. Güntürk, M. Lammers, A. M. Powell, R. Saab, and Ö. Yılmaz, · 2013
Cited alongside, same era.
“Probability in Banach Spaces: isoperimetry and processes”
M. Ledoux and M. Talagrand, · 2013
Cited alongside, same era.
“Near-optimal bounds for binary embeddings of arbitrary sets,”
S. Oymak and B. Recht, · 2015
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“Isometric sketching of any set via the Restricted Isometry Property,”
S. Oymak, B. Recht, and M. Soltanolkotabi, · 2015
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“Optimizing quantization for Lasso recovery,”
X. Gu, S. Tu, H.-J. Michael Shi, M. Case, D. Needell, and Y. Plan, · 2016
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“The generalized Lasso with non-linear observations,”
Y. Plan and R. Vershynin, · 2016
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“Exponential decay of reconstruction error from binary measurements of sparse signals,”
R. G. Baraniuk, S. Foucart, D. Needell, Y. Plan, and M. Wootters, · 2017
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“Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach,”
Y. Plan and R. Vershynin, · 2013
Cited alongside, same era.
“Quantized Iterative Hard Thresholding: Bridging 1-bit and High-Resolution Quantized Compressive Sensing,”
L. Jacques, K. Degraux, and C. De Vleeschouwer, · 2013
Cited alongside, same era.
“Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors,”
L. Jacques, J. N. Laska, P. T. Boufounos, and R. G. Baraniuk, · 2013
Cited alongside, same era.
“Small width, low distortions: quantized random embeddings of low-complexity sets,”
L. Jacques, · 2015
Cited alongside, same era.
“One-bit compressed sensing with partial Gaussian circulant matrices,”
S. Dirksen, H. C. Jung, and H. Rauhut, · 2017
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“Time for dithering: fast and quantized random embeddings via the restricted isometry property,”
L. Jacques and V. Cambareri, · 2017
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“High-dimensional estimation with geometric constraints,”
Y. Plan, R. Vershynin, and E. Yudovina, · 2017
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“Fast binary embeddings, and quantized compressive sensing with structured matrices,”
T. Huynh and R. Saab, · 2018
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“Quantized compressive sensing with rip matrices: The benefit of dithering,”
C. Xu and L. Jacques, · 2018
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