Results 241 to 250 of about 141,696 (278)

Sparse signal recovery via generalized gaussian function

Journal of Global Optimization, 2022
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Li, Haiyang, Zhang, Qian, Lin, Shoujin, Peng, Jigen   +3 more
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Sparse Signal Recovery with Exponential-Family Noise

2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2009
The problem of sparse signal recovery from a relatively small number of noisy measurements has been studied extensively in the recent literature on compressed sensing. However, the focus of those studies appears to be limited to the case of linear projections disturbed by Gaussian noise, and the sparse signal reconstruction problem is treated as linear
Irina Rish, Genady Grabarnik
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Streaming signal recovery using sparse Bayesian learning

2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
We consider the progressive reconstruction of a streaming signal from compressive measurements. We reconstruct the streaming signal over shifting intervals using an algorithm based on sparse Bayesian learning (SBL). Although computationally expensive, compared to other recovery algorithms, SBL provide the full posterior distribution of the sparse ...
Codreanu Marian, Wijewardhana Uditha Lakmal   +1 more
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Bootstrapped sparse Bayesian learning for sparse signal recovery

2014 48th Asilomar Conference on Signals, Systems and Computers, 2014
In this article we study the sparse signal recovery problem in a Bayesian framework using a novel Bootstrapped Sparse Bayesian Learning method. Sparse Bayesian Learning (SBL) framework is an effective tool for pruning out the irrelevant features and ending up with a sparse representation.
Ritwik Giri, Bhaskar D. Rao
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Sublinear Recovery of Sparse Wavelet Signals

Data Compression Conference (dcc 2008), 2008
There are two main classes of decoding algorithms for "compressed sensing," those which run in time polynomial in the signal length and those which use sublinear resources. Most of the sublinear algorithms focus on signals which are compressible in either the Euclidean domain or the Fourier domain.
R. Maleh, A. C. Gilbert
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FAST L0-based sparse signal recovery

2010 IEEE International Workshop on Machine Learning for Signal Processing, 2010
This paper develops an algorithm for finding sparse signals from limited observations of a linear system. We assume an adaptive Gaussian model for sparse signals. This model results in a least square problem with an iteratively reweighted L2 penalty that approximates the L0-norm.
Yingsong Zhang, Nick Kingsbury
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Sparse signal recovery from nonlinear measurements

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
We treat the problem of minimizing a general continuously differentiable function subject to sparsity constraints. We present and analyze several different optimality criteria which are based on the notions of stationarity and coordinate-wise optimality.
Amir Beck, Yonina C. Eldar
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On uniqueness of sparse signal recovery

Signal Processing, 2018
Abstract A basic issue of sparse signal recovery (SSR) is to explore the condition of the uniqueness with regard to the solution of the relevant optimization framework. However, the standard uniqueness conditions, such as spark condition, NSP (null space property), RIP (restricted isometry property) and mutual coherence condition, are with respect to
Xiao-Li Hu, Jiajun Wen, Wai Keung Wong, Le Tong, Jinrong Cui   +4 more
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Sparse Signal Recovery Using MPDR Estimation

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Utilizing the array processing minimum power distortionless response (MPDR) beamformer framework, we present a new perspective on the sparse Bayesian learning (SBL) algorithm used in sparse signal recovery. In addition to providing more insight into the SBL algorithm, this new perspective allows us to extend the algorithm to more general non-Gaussian ...
Maher Al-Shoukairi, Bhaskar D. Rao
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Group testing and sparse signal recovery

2008 42nd Asilomar Conference on Signals, Systems and Computers, 2008
Traditionally, group testing is a design problem. The goal is to design an optimally efficient set of tests of items such that the test results contain enough information to determine a small subset of items of interest. It has its roots in the statistics community and was originally designed for the selective service during World War II to remove men ...
Anna C. Gilbert, Mark A. Iwen, Martin J. Strauss   +2 more
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