Discrete Uncertainty Principles and Sparse Signal Processing [PDF]
Journal of Fourier Analysis and Applications, 2017 We develop new discrete uncertainty principles in terms of numerical sparsity, which is a continuous proxy for the 0-norm. Unlike traditional sparsity, the continuity of numerical sparsity naturally accommodates functions which are nearly sparse. After studying these principles and the functions that achieve exact or near equality in them, we identify ...
Afonso S. Bandeira +2 more
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Method of Range Ambiguity Suppression Combining Sparse Reconstruction and Matched Filtering
Leida xuebao, 2022 To a certain extent, SAR images are affected by range ambiguity due to antenna sidelobe characteristics and pulse operating system. The work of range ambiguity suppression focuses on SAR system design and signal processing.
Meng QI +5 more
doaj +1 more source
Sparse representations in audio & music: from coding to source separation [PDF]
, 2010 —Sparse representations have proved a powerful toolin the analysis and processing of audio signals and already lieat the heart of popular coding standards such as MP3 andDolby AAC.
Davies, ME +15 more
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Data aware sparse non-negative signal processing [PDF]
, 2022 Greedy techniques are a well established framework aiming to reconstruct signals which are sparse in some domain of representations. They are renowned for their relatively low computational cost, that makes them appealing from the perspective of real ...
Voulgaris, Konstantinos
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Audio Source Separation Using Sparse Representations [PDF]
, 2010 This is the author's final version of the article, first published as A. Nesbit, M. G. Jafari, E. Vincent and M. D. Plumbley. Audio Source Separation Using Sparse Representations. In W.
Nesbit, Andrew +7 more
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New Directions In Sparse Sampling and Estimation For Underdetermined Systems [PDF]
, 2013 A central objective in signal processing is to infer meaningful information from a set of measurements or data. While most signal models have an overdetermined structure (the number of unknowns less than the number of equations), traditionally very few ...
Piya Pal, Pal, Piya
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Combinatorial Regression and Improved Basis Pursuit for Sparse Estimation [PDF]
, 2012 Sparse representations accurately model many real-world data sets. Some form of sparsity is conceivable in almost every practical application, from image and video processing, to spectral sensing in radar detection, to bio-computation and genomic signal ...
Khajehnejad, M. Amin
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GRADIENT POLYTOPE FACES PURSUIT FOR LARGE SCALE SPARSE RECOVERY PROBLEMS [PDF]
, 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), Dallas, TX, 14-19 March ...
Plumbley, Mark D. +8 more
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Sparse model construction using coordinate descent optimization [PDF]
, 2013 We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models.
Xia Hong +8 more
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Dictionary learning with large step gradient descent for sparse representations [PDF]
, 2012 This is the accepted version of an article published in Lecture Notes in Computer Science Volume 7191, 2012, pp 231-238.
Boris Mailhé +5 more
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