Results 21 to 30 of about 1,758,706 (251)
Lower bounds for sparse recovery problems [PDF]
, 2020 Sparse recovery or compressed sensing is the problem of estimating a signal from noisy linear measurements of that signal. Sparse recovery has traditionally been used in areas like image acquisition, streaming algorithms, genetic testing, and, more ...
Kamath, Akshay Devdas
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High-resolution Sparse Self-calibration Imaging for Vortex Radar with Phase Error
Leida xuebao, 2021 The Orbital Angular Momentum (OAM)-based vortex radar has drawn increasing attention because of its potential for high-resolution imaging. The vortex radar high resolution imaging with limited OAM modes is commonly solved by sparse recovery, in which the
Haiyou QU +3 more
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Identification of Matrices Having a Sparse Representation [PDF]
, 2008 We consider the problem of recovering a matrix from its action on a known vector in the setting where the matrix can be represented efficiently in a known matrix dictionary.
Pfander, Goetz E. +6 more
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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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Compressed Sensing of Extracellular Neurophysiology Signals: A Review
Frontiers in Neuroscience, 2021 This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless ...
Biao Sun, Wenfeng Zhao
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Mathematics, 2019 Compressed sensing theory is widely used in the field of fault signal diagnosis and image processing. Sparse recovery is one of the core concepts of this theory.
Dingfei Jin +3 more
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Low-Complexity DCD-Based Sparse Recovery Algorithms
IEEE Access, 2017 Sparse recovery techniques find applications in many areas. Real-time implementation of such techniques has been recently an important area for research.
Yuriy V. Zakharov +3 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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Sparse Signal Recovery via Exponential Metric Approximation
Tsinghua Science and Technology, 2017 Sparse signal recovery problems are common in parameter estimation, image processing, pattern recognition, and so on. The problem of recovering a sparse signal representation from a signal dictionary might be classified as a linear constraint ℓ0 ...
Jian Pan, Jun Tang, Wei Zhu
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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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