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Showing 1 - 20 results of 23 for search '(( binary edge _ optimization algorithm ) OR ( binary a pose estimation algorithm ))', query time: 0.55s 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
    An Example of a WPT-MEC Network.

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

    Published 2025
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  6. 6
    Related Work Summary.

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
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  7. 7
    Simulation parameters.

    Simulation parameters. by Hend Bayoumi (22693738)

    Published 2025
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  8. 8
    Training losses for N = 10.

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

    Published 2025
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  9. 9
    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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  10. 10
    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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  11. 11
    Normalized computation rate for N = 10.

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

    Published 2025
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  12. 12
    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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  13. 13
    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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  14. 14
    Summary of Notations Used in this paper.

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

    Published 2025
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  15. 15
    Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model.

    Pre-processing of raw images for pose estimation using physical model-trained convolutional neural network model. by Aniket Ravan (3174171)

    Published 2023
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  16. 16
    Experimental setup and trajectory estimation methodology.

    Experimental setup and trajectory estimation methodology. by Nouman Javed (16624785)

    Published 2023
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  17. 17
    Supplementary file 1_Encodings of the weighted MAX k-CUT problem on qubit systems.pdf

    Supplementary file 1_Encodings of the weighted MAX k-CUT problem on qubit systems.pdf by Franz G. Fuchs (22776248)

    Published 2025
    “…This study explores encoding methods for MAX k-CUT on qubit systems by utilizing quantum approximate optimization algorithms (QAOA) and addressing the challenge of encoding integer values on quantum devices with binary variables. …”
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  18. 18
    Training results.

    Training results. by Jack B. Maguire (5123183)

    Published 2021
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  19. 19
    ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for test data classification results across all folds.

    ROC curves of our out-of-class algorithm (blue) and a threshold decision boundary (red) representing the true positive rates (TPR) compared to the false positive rates (FPR) for te... by G. Christopher Marais (21492084)

    Published 2025
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  20. 20
    Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx

    Table_1_Fusion of fruit image processing and deep learning: a study on identification of citrus ripeness based on R-LBP algorithm and YOLO-CIT model.docx by Chenglin Wang (430151)

    Published 2024
    “…The identification of distinct ripeness stages in citrus fruits plays a crucial role in guiding the planning of harvesting paths for citrus-picking robots and facilitating yield estimations in orchards. …”
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