Results 21 to 30 of about 726,041 (339)
Sparse Support Tensor Machine with Scaled Kernel Functions
Mathematics, 2023 As one of the supervised tensor learning methods, the support tensor machine (STM) for tensorial data classification is receiving increasing attention in machine learning and related applications, including remote sensing imaging, video processing, fault
Shuangyue Wang, Ziyan Luo
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Sparsity driven ultrasound imaging [PDF]
The Journal of the Acoustical Society of America, 2012 An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an ...
Tüysüzoğlu, Ahmet +4 more
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Antenna Array Calibration Using a Sparse Scene
IEEE Open Journal of Antennas and Propagation, 2021 In radar systems, antenna arrays acquire direction-dependent information to localize targets or create images of the environment. However, because of unknown complex amplitudes per channel and mutual coupling, a calibration is necessary for good ...
Johanna Geiss +3 more
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Sparsity and incoherence in compressive sampling [PDF]
, 2006 We consider the problem of reconstructing a sparse signal from a limited number of linear measurements. Given m randomly selected samples of Ux0, where U is an orthonormal matrix, we show that ℓ1 minimization recovers x0 exactly when the number of ...
E. Candès, J. Romberg
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Tracking Target Signal Strengths on a Grid using Sparsity [PDF]
, 2011 Multi-target tracking is mainly challenged by the nonlinearity present in the measurement equation, and the difficulty in fast and accurate data association. To overcome these challenges, the present paper introduces a grid-based model in which the state
AK Jain +32 more
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Variational data assimilation via sparse regularisation [PDF]
Tellus: Series A, Dynamic Meteorology and Oceanography, 2014 This paper studies the role of sparse regularisation in a properly chosen basis for variational data assimilation (VDA) problems. Specifically, it focuses on data assimilation of noisy and down-sampled observations while the state variable of interest ...
Ardeshir M. Ebtehaj +3 more
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Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways
Frontiers in Physiology, 2018 Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures.
Sébastien De Landtsheer +2 more
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A New Generalized Projection and Its Application to Acceleration of Audio Declipping
Axioms, 2019 In convex optimization, it is often inevitable to work with projectors onto convex sets composed with a linear operator. Such a need arises from both the theory and applications, with signal processing being a prominent and broad field where convex ...
Pavel Rajmic +3 more
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Journal of Spectral Imaging, 2022 Hyperspectral unmixing (HU) is one of the most active emerging areas in image processing that estimates the hyperspectral image’s endmember and abundance.
K. Priya, K. K. Rajkumar
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Effect of sparsity-aware time–frequency analysis on dynamic hand gesture classification with radar micro-Doppler signatures [PDF]
, 2018 Dynamic hand gesture recognition is of great importance in human-computer interaction. In this study, the authors investigate the effect of sparsity-driven time-frequency analysis on hand gesture classification.
Fioranelli, Francesco +3 more
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