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Compressive Sensing based Leakage Sampling and Reconstruction: A First Study
compressive sensing matching pursuit OMP
2018/12/3
An important prerequisite for Side-channel Attack (SCA) is leakage sampling where the side-channel measurements (e.g. power traces) of the cryptographic device are collected for further analysis. Howe...
We consider the problem of recovering sparse correlated data on networks. To improve accu-racy and reduce costs, it is strongly desirable to take the potentially useful side-information of network str...
Cost-Aware Compressive Sensing for Networked Sensing Systems
Crowdsensing Compressive Sensing Resource-efficiency
2016/1/22
Compressive Sensing is a technique that can help reduce the sampling rate of sensing tasks. In mobile crowdsensing applications or wireless sensor networks, the resource burden of collecting samples i...
Robust Dequantized Compressive Sensing
compressive sensing signal reconstruction quantization optimization.
2012/9/18
We consider the reconstruction problem in compressed sensing in which the observations are recorded in a finite number of bits. They may thus contain quantization errors (from being rounded to the nea...
Multi-Sparse Signal Recovery for Compressive Sensing
Multi-Sparse Signal Recovery Compressive Sensing Information Theory
2012/6/19
Signal recovery is one of the key techniques of Compressive sensing (CS). It reconstructs the original signal from the linear sub-Nyquist measurements. Classical methods exploit the sparsity in one do...
In this paper, we demonstrate some applications of compressive sensing over networks. We make a connection between compressive sensing and traditional information theoretic techniques in source coding...
The problem of compressing a real-valued sparse source using compressive sensing techniques is studied. The rate distortion optimality of a coding scheme in which compressively sensed signals are qua...
Restricted p-isometry property and its application for nonconvex compressive sensing
Compressed sensing lp minimization instance optimality stability
2018/4/19
Compressed sensing is a new scheme which shows the ability to recover sparse signal from fewer measurements, using l1 minimization. Recently, Chartrand and Staneva shown in [7] that the lp minimizatio...
Compressive Sensing Using Low Density Frames
Low density frames compressive sensing sumproduct algorithm expectation maximization Gaussian scalemixtures
2010/3/19
We consider the compressive sensing of a sparse or
compressible signal x 2 RM. We explicitly construct a class of
measurement matrices, referred to as the low density frames,
and develop decoding a...