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Showing 1 - 20 results of 24 for search '(( binary data share optimization algorithm ) OR ( binary either process optimization algorithm ))', query time: 1.67s Refine Results
  1. 1
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
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  2. 2
    Comparisons between ADAM and NADAM optimizers.

    Comparisons between ADAM and NADAM optimizers. by Hend Bayoumi (22693738)

    Published 2025
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  3. 3
    Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30.

    Comparisons of computation rate performance for different offloading algorithms.for N = 10, 20, 30. by Hend Bayoumi (22693738)

    Published 2025
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  4. 4
    Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30.

    Comparison of total time consumed for different offloading algorithms.for N = 10, 20, 30. by Hend Bayoumi (22693738)

    Published 2025
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  5. 5
    Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations.

    Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. by Tristan Mary-Huard (3864)

    Published 2023
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  6. 6
    An Example of a WPT-MEC Network.

    An Example of a WPT-MEC Network. by Hend Bayoumi (22693738)

    Published 2025
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  7. 7
    Related Work Summary.

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
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  8. 8
    Simulation parameters.

    Simulation parameters. by Hend Bayoumi (22693738)

    Published 2025
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  9. 9
    Training losses for N = 10.

    Training losses for N = 10. by Hend Bayoumi (22693738)

    Published 2025
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  10. 10
    Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods.

    Performance Comparison of State-of-the-Art WPT-MEC Offloading Methods. by Hend Bayoumi (22693738)

    Published 2025
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  11. 11
    The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames.

    The evolution of the Wireless Power Transfer (WPT) time fraction β over simulation frames. by Hend Bayoumi (22693738)

    Published 2025
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  12. 12
    Normalized computation rate for N = 10.

    Normalized computation rate for N = 10. by Hend Bayoumi (22693738)

    Published 2025
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  13. 13
    CDF of task latency, approximated as the inverse of the achieved computation rate.

    CDF of task latency, approximated as the inverse of the achieved computation rate. by Hend Bayoumi (22693738)

    Published 2025
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  14. 14
    Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate.

    Comparison between Bisection and Newton-Raphson in terms of (A) total time consumed, and (B) maximum computation rate. by Hend Bayoumi (22693738)

    Published 2025
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  15. 15
    Summary of Notations Used in this paper.

    Summary of Notations Used in this paper. by Hend Bayoumi (22693738)

    Published 2025
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  16. 16
    Image processing workflow.

    Image processing workflow. by Denis Tamiev (7404980)

    Published 2020
    “…<p>Raw fluorescent microscope images (a) were processed with a binary segmentation algorithm, and clusters of bacterial cells were manually annotated. …”
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  17. 17
    Surround Suppression mechanism.

    Surround Suppression mechanism. by Elnaz Nemati (21402730)

    Published 2025
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  18. 18
    Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging

    Bayesian Spatial Binary Regression for Label Fusion in Structural Neuroimaging by D. Andrew Brown (6584837)

    Published 2021
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  19. 19
    Segmentation, trace denoising and spike extraction framework.

    Segmentation, trace denoising and spike extraction framework. by Changjia Cai (10647521)

    Published 2021
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  20. 20
    Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF

    Image1_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF by Sizhuo Yu (11429743)

    Published 2021
    “…Its current hardware implementation relies on D-Wave’s Quantum Processing Units, which are limited in terms of number of qubits and architecture while being restricted to solving quadratic unconstrained binary optimization (QUBO) problems. …”
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