Results 31 to 40 of about 3,267,859 (298)
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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PLoS ONE, 2022 In the compressed processing of hyperspectral images, orthogonal matching pursuit algorithm (OMP) can be used to obtain sparse decomposition results.
Li Wang, Wei Wang
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Remote Sensing, 2021 In this paper we consider the tracking problem of a moving target competing against noise and clutter in a surveillance radar scenario. For a single array-antenna multiple-target tracking system and according to the Track-Before-Detect paradigm, we ...
Nicomino Fiscante +5 more
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, 2012 (c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or ...
Emiya, Valentin +18 more
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Comparative Analysis of Sparse Matrix Algorithms For Information Retrieval [PDF]
Journal of Systemics, Cybernetics and Informatics, 2003 We evaluate and compare the storage efficiency of different sparse matrix storage formats as index structure for text collection and their corresponding sparse matrixvector multiplication algorithm to perform query processing in information retrieval (IR)
Nazli Goharian, Ankit Jain, Qian Sun
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Sparse Deconvolution Using Support Vector Machines
EURASIP Journal on Advances in Signal Processing, 2008 Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them ...
Aníbal R. Figueiras-Vidal +5 more
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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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PCGen: A Fully Parallelizable Point Cloud Generative Model
SensorsGenerative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing.
Nicolas Vercheval +3 more
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International Journal of Distributed Sensor Networks, 2020 Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing ...
Hongchao Wang, Wenliao Du
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3D SAR Imaging Method Based on Learned Sparse Prior
Leida xuebao, 2023 The development of 3D Synthetic Aperture Radar (SAR) imaging is currently hampered by issues such as high data dimension, high system complexity, and low imaging processing efficiency.
Mou WANG +5 more
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