Search alternatives:
limitations algorithm » maximization algorithm (Expand Search), location algorithm (Expand Search)
resource limitations » resource utilization (Expand Search)
process optimization » model optimization (Expand Search)
task resource » a resource (Expand Search)
wave process » same process (Expand Search), whole process (Expand Search), phase process (Expand Search)
binary task » binary mask (Expand Search)
binary wave » binary image (Expand Search)
Showing 1 - 20 results of 21 for search '(( binary task resource limitations algorithm ) OR ( binary wave process optimization algorithm ))', query time: 1.60s Refine Results
  1. 1
    Proposed Algorithm.

    Proposed Algorithm. by Hend Bayoumi (22693738)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  2. 2
    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
    Subjects:
    Save to List
    Saved in:
  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
    Subjects:
    Save to List
    Saved in:
  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
    Subjects:
    Save to List
    Saved in:
  5. 5
    An Example of a WPT-MEC Network.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  6. 6
    Related Work Summary.

    Related Work Summary. by Hend Bayoumi (22693738)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  7. 7
    Simulation parameters.

    Simulation parameters. by Hend Bayoumi (22693738)

    Published 2025
    Subjects:
    Save to List
    Saved in:
  8. 8
    Training losses for N = 10.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  9. 9
    Comparisons between ADAM and NADAM optimizers.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  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
    Subjects:
    Save to List
    Saved in:
  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
    Subjects:
    Save to List
    Saved in:
  12. 12
    Normalized computation rate for N = 10.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  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
    Subjects:
    Save to List
    Saved in:
  14. 14
    Summary of Notations Used in this paper.

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

    Published 2025
    Subjects:
    Save to List
    Saved in:
  15. 15
    Parameters of the experiments.

    Parameters of the experiments. by Enrico Zardini (17382523)

    Published 2023
    “…<div><p>In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. …”
    Save to List
    Saved in:
  16. 16
    Quantum pipeline workflow overview.

    Quantum pipeline workflow overview. by Enrico Zardini (17382523)

    Published 2023
    “…<div><p>In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. …”
    Save to List
    Saved in:
  17. 17
    MCLP_quantum_annealer_V0.5

    MCLP_quantum_annealer_V0.5 by Anonymous Anonymous (4854526)

    Published 2025
    “…Theoretical and applied experiments are conducted using four solvers: QBSolv, D-Wave Hybrid binary quadratic model 2, D-Wave Advantage system 4.1, and Gurobi. …”
    Save to List
    Saved in:
  18. 18
    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. …”
    Save to List
    Saved in:
  19. 19
    Image3_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF

    Image3_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. …”
    Save to List
    Saved in:
  20. 20
    Image2_Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing.TIF

    Image2_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. …”
    Save to List
    Saved in:

Search Tools: