A Tutorial on Sparse Signal Reconstruction and Its Applications in Signal Processing
Sign Up to like & getrecommendations! 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
Expectation maximisation-based approach to recovering multiple sparse signals with common sparsity pattern
Sign Up to like & getrecommendations! Published in 2018 at "Electronics Letters"
DOI: 10.1049/el.2017.1913
Abstract: The problem of simultaneously recovering multiple sparse signals bearing a common sparsity pattern is addressed. Specifically, a common Gaussian prior to all the sparse signals under consideration is assigned. This can make that the signals… read more here.
Keywords: sparse; sparsity pattern; recovering multiple; sparse signals ... See more keywords
Granger Causality for Compressively Sensed Sparse Signals
Sign Up to like & getrecommendations! Published in 2022 at "Physical review. E"
DOI: 10.1103/physreve.107.034308
Abstract: Compressed sensing is a scheme that allows for sparse signals to be acquired, transmitted, and stored using far fewer measurements than done by conventional means employing the Nyquist sampling theorem. Since many naturally occurring signals… read more here.
Keywords: estimation; compressively sensed; causality; compressed sensing ... See more keywords
De-Noising of Sparse Signals Using Mixture Model Shrinkage Function
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3237255
Abstract: In this work a new thresholding function referred to as ’mixture model shrinkage’ (MMS) based on the minimization of a convex cost function is proposed. Normally, thresholding functions underestimate larger signal amplitudes during the de-noising… read more here.
Keywords: function; mixture model; model; sparse signals ... See more keywords
Sampling and Super Resolution of Sparse Signals Beyond the Fourier Domain
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2018.2890064
Abstract: Recovering a sparse signal from its low-pass projections in the Fourier domain is a problem of broad interest in science and engineering and is commonly referred to as super resolution. In many cases, however, Fourier… read more here.
Keywords: saft; sparse signals; low pass; domain ... See more keywords
Sparse Bayesian Learning With Dynamic Filtering for Inference of Time-Varying Sparse Signals
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2019.2961229
Abstract: Many signal processing applications require estimation of time-varying sparse signals, potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an algorithm for dynamic filtering of time-varying sparse signals based on… read more here.
Keywords: time; dynamic filtering; sparse bayesian; sparse signals ... See more keywords
Reconstructing Sparse Signals via Greedy Monte-Carlo Search
Sign Up to like & getrecommendations! Published in 2020 at "Journal of the Physical Society of Japan"
DOI: 10.7566/jpsj.89.124802
Abstract: We propose a Monte-Carlo-based method for reconstructing sparse signals in the formulation of sparse linear regression in a high-dimensional setting. The basic idea of this algorithm is to explicit... read more here.
Keywords: via greedy; monte carlo; signals via; sparse signals ... See more keywords