Showing 1 - 20 results of 10,274 for search '(((( data using algorithm ) OR ( image sampling algorithm ))) OR ( element data algorithm ))', query time: 0.49s Refine Results
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    Comparison of different optimization algorithms. by Hang Zhao (143592)

    Published 2025
    Subjects: “…crayfish optimization algorithm…”
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    Algorithm for MFISTA-VA [30]. by Faisal Najeeb (20542714)

    Published 2025
    “…GRASP uses Temporal Total Variation (TV) norm as a sparsity transform to promote sparsity among multi-coil MRI data and Nonlinear Conjugate Gradient (NL-CG) algorithm to obtain an optimal solution. …”
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    Algorithmic experimental parameter design. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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    Dual deconvolution algorithm: analysis code with experimental data by Wonshik Choi (18877660)

    Published 2025
    “…The dual-deconvolution algorithm is an advanced deep-tissue imaging technique designed to measure fluorescence signals from thick, aberrated samples. …”
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    Spatial spectrum estimation for three algorithms. by Chuanxi Xing (20141665)

    Published 2024
    “…The results of numerical simulations and sea trial experimental data indicate that the use of subarrays comprising 5 and 3 array elements, respectively, is sufficient to effectively estimate 12 source angles. …”
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    Image_1_Fast and flexible spatial sampling methods based on the Quadtree algorithm for ocean monitoring.tif by Yanzhi Zhou (18285742)

    Published 2024
    “…In second-phase sampling, QT decomposition and the greedy algorithm are combined (the BG algorithm). QT decomposition is used to divide the region into small blocks first, and then within the small blocks, the greedy algorithm is applied to sampling simultaneously. …”
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    The run time for each algorithm in seconds. by Edward Antonian (21453161)

    Published 2025
    “…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”
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