LAUSR: sparse signal

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Arbitrary Block-Sparse Signal Reconstruction Based on Incomplete Single Measurement Vector

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0 Published in 2017 at "Circuits, Systems, and Signal Processing"

DOI: 10.1007/s00034-017-0528-3

Abstract: Within the compressive sensing framework, reconstruction algorithms of block-sparse signal (BSS) often have special requirements on sparsity patterns. As a result, only some particular BSSs can be reconstructed. In this paper, we present a new… read more here.

Keywords: reconstruction; block; sparse signal; sparsity ... See more keywords

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Block Sparse Signal Recovery in Compressed Sensing: Optimum Active Block Selection and Within-Block Sparsity Order Estimation

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1 Published in 2018 at "Circuits, Systems, and Signal Processing"

DOI: 10.1007/s00034-017-0617-3

Abstract: In this paper, we develop a new algorithm for recovery of block sparse signals in compressed sensing framework based on orthogonal matching pursuit. Furthermore, we point out that a major issue in conventional sparse signal… read more here.

Keywords: recovery; order; block; sparse signal ... See more keywords

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A Tutorial on Sparse Signal Reconstruction and Its Applications in Signal Processing

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1 Published in 2019 at "Circuits, Systems, and Signal Processing"

DOI: 10.1007/s00034-018-0909-2

Abstract: Sparse signals are characterized by a few nonzero coefficients in one of their transformation domains. This was the main premise in designing signal compression algorithms. Compressive sensing as a new approach employs the sparsity property… read more here.

Keywords: reconstruction; sparse signal; signal processing; sparse signals ... See more keywords

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A linearly convergent algorithm for sparse signal reconstruction

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0 Published in 2018 at "Journal of Fixed Point Theory and Applications"

DOI: 10.1007/s11784-018-0635-1

Abstract: For the sparse signal reconstruction problem in compressive sensing, we propose a projection-type algorithm without any backtracking line search based on a new formulation of the problem. Under suitable conditions, global convergence and its linear… read more here.

Keywords: sparse signal; algorithm sparse; signal reconstruction; linearly convergent ... See more keywords

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Sparse signal recovery for WIM measurements from undersampled data through compressed sensing

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0 Published in 2020 at "Measurement"

DOI: 10.1016/j.measurement.2019.107181

Abstract: Abstract This study presents a method to recover the signal components critical for weigh-in-motion (WIM) measurements using compressed sensing. Through a comparative study, the wavelet basis ‘bior2.4’ is selected to sparsely represent the measured signals.… read more here.

Keywords: sparse signal; wim measurements; compressed sensing; wim ... See more keywords

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Recovery of Block-Structured Sparse Signal Using Block-Sparse Adaptive Algorithms via Dynamic Grouping

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0 Published in 2018 at "IEEE Access"

DOI: 10.1109/access.2018.2872671

Abstract: A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal. It has been shown recently that grouping the signal,… read more here.

Keywords: recovery; block; sparse signal; tex math ... See more keywords

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Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks

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1 Published in 2022 at "IEEE Access"

DOI: 10.1109/access.2022.3197594

Abstract: Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a… read more here.

Keywords: application areas; sensing; sparse signal; sensing frameworks ... See more keywords

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Successive Hypothesis Testing Based Sparse Signal Recovery and Its Application to MUD in Random Access

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1 Published in 2017 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2017.2648798

Abstract: Based on successive hypothesis testing, we propose an approach for sparse signal recovery and apply it to random access to detect multiple block-sparse signals over frequency-selective fading channels. By introducing the sparsity variable, the proposed… read more here.

Keywords: successive hypothesis; sparse signal; random access; hypothesis testing ... See more keywords

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Positive Sparse Signal Denoising: What Does a CNN Learn?

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1 Published in 2022 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2022.3160372

Abstract: Convolutional neural networks (CNNs) provide impressive empirical success in various tasks; however, their inner workings generally lack interpretability. In this paper, we interpret shallow CNNs that we have trained for the task of positive sparse… read more here.

Keywords: sparse signal; signal denoising; denoising cnn; positive sparse ... See more keywords

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Efficient and Robust Recovery of Sparse Signal and Image Using Generalized Nonconvex Regularization

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0 Published in 2017 at "IEEE Transactions on Computational Imaging"

DOI: 10.1109/tci.2017.2744626

Abstract: This paper addresses the robust reconstruction problem of a sparse signal from compressed measurements. We propose a robust formulation for sparse reconstruction that employs the $\ell _1$ -norm as the loss function for the residual… read more here.

Keywords: tex math; generalized nonconvex; sparse signal; inline formula ... See more keywords