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Quantization and Compressive Sensing [PDF]
, 2014 Quantization is an essential step in digitizing signals, and, therefore, an indispensable component of any modern acquisition system. This book chapter explores the interaction of quantization and compressive sensing and examines practical quantization ...
A. Ai +65 more
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Measure What Should be Measured: Progress and Challenges in Compressive Sensing [PDF]
, 2012 Is compressive sensing overrated? Or can it live up to our expectations? What will come after compressive sensing and sparsity? And what has Galileo Galilei got to do with it? Compressive sensing has taken the signal processing community by storm.
Strohmer, Thomas
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Nonlinear Compressive Particle Filtering [PDF]
, 2013 Many systems for which compressive sensing is used today are dynamical. The common approach is to neglect the dynamics and see the problem as a sequence of independent problems. This approach has two disadvantages. Firstly, the temporal dependency in the
Ohlsson, Henrik +2 more
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Efficient Compressive Sensing with Deterministic Guarantees Using Expander Graphs [PDF]
, 2007 Compressive sensing is an emerging technology which can recover a sparse signal vector of dimension n via a much smaller number of measurements than n.
Hassibi, Babak, Xu, Weiyu
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Compressive Sensing DNA Microarrays [PDF]
, 2008 Compressive sensing microarrays (CSMs) are DNA-based sensors that operate using group testing and compressive sensing (CS) principles. In contrast to conventional DNA microarrays, in which each genetic sensor is designed to respond to a single target, in
Baraniuk, RG +3 more
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Further Results on Performance Analysis for Compressive Sensing Using Expander Graphs [PDF]
, 2007 Compressive sensing is an emerging technology which can recover a sparse signal vector of dimension n via a much smaller number of measurements than n. In this paper, we will give further results on the performance bounds of compressive sensing.
Hassibi, Babak, Xu, Weiyu
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Photonics-enabled sub-Nyquist radio frequency sensing based on temporal channelization and compressive sensing [PDF]
, 2014 A novel approach to sensing broadband radio frequency (RF) spectrum beyond the Nyquist limit based on photonic temporal channelization and compressive sensing is proposed.
Gomes, Nathan J., Wang, Chao
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How well can we estimate a sparse vector? [PDF]
, 2013 The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on the mean-squared error, which holds regardless of the sensing/design matrix ...
Candès, Emmanuel J., Davenport, Mark A.
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Sublinear-Time Algorithms for Compressive Phase Retrieval
, 2020 In the compressive phase retrieval problem, or phaseless compressed sensing, or compressed sensing from intensity only measurements, the goal is to reconstruct a sparse or approximately $k$-sparse vector $x \in \mathbb{R}^n$ given access to $y= |\Phi x|$,
Li, Yi, Nakos, Vasileios
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Improved Bounds for Universal One-Bit Compressive Sensing
, 2017 Unlike compressive sensing where the measurement outputs are assumed to be real-valued and have infinite precision, in "one-bit compressive sensing", measurements are quantized to one bit, their signs.
Acharya, Jayadev +2 more
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