Showing 1 - 20 results of 118 for search '(((( algorithm rate function ) OR ( algorithm cell function ))) OR ( algorithm both function ))', query time: 0.15s Refine Results
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    New enumeration algorithm for regular boolean functions by Nasrallah, Walid F.

    Published 2018
    “…After proving this equivalence, this paper introduces a novel data structure that may, with further tweaking, enable faster enumeration algorithms for both regular Boolean functions and all-capacities knapsack problem instances.…”
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    New fault models and efficient BIST algorithms for dual-portmemories by Amin, A.A.

    Published 1997
    “…New fault models are proposed, and efficient O(n) test algorithms are described for both the memory array and the address decoders. …”
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    A parallel tabu search algorithm for VLSI standard-cell placement by Suit, S.M.

    Published 2000
    “…VLSI standard-cell placement is an NP-hard problem to which various heuristics have been applied. …”
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    A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks by Saad, Walid

    Published 2012
    “…While most existing literature focuses on downlink-only or uplink-only scheduling algorithms, the proposed algorithm aims at ensuring a utility function that jointly captures the quality of service in terms of delay and channel quality on both links. …”
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    GATS: A Novel Hybrid Algorithm for Multiobjective Cell Placement in VLSI Circuit Design by Sait, Sadiq M.

    Published 2020
    “…This paper addresses the optimization of cell placement step in VLSI circuit design [1]. A novel hybrid algorithm is proposed for performance and low power driven VLSI standard cell placement. …”
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    Consensus-Based Distributed Formation Control of Multi-Quadcopter Systems: Barrier Lyapunov Function Approach by Nargess Sadeghzadeh-Nokhodberiz (16904952)

    Published 2023
    “…The method is firstly developed in a centralized scheme and then extended to a distributed framework using appropriate asymptotically convergent consensus algorithms. Therefore, the asymptotic convergence of the designed distributed algorithm to the centralized one is guaranteed. …”
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    Distributed optimal coverage control in multi-agent systems: Known and unknown environments by Mohammadhasan Faghihi (22303057)

    Published 2024
    “…The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The proposed approach leverages a novel cost function for optimizing the agents’ coverage and the cost function eventually aligns with the conventional Voronoi-based cost function. …”
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    Modelling Exchange Rates during Currency Crisis using Neural Networks by Nasr, G. E.

    Published 2006
    “…The models are built using the feedforward ANN structure trained by the backpropagation algorithm. Exchange rate data from the Lebanese currency crisis period of 1985-1992 is used for training, testing and evaluation of the models. …”
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    Evaluation of transfer functions for punctured turbo codes by Mugaibel, A.H.

    Published 2000
    “…A modified algorithm for evaluating transfer functions of turbo codes with punctured systematic bits is presented. …”
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    High-Accurate Parameter Identification of PEMFC Using Advanced Multi-Trial Vector-Based Sine Cosine Meta-Heuristic Algorithm by Badreddine Kanouni (23073244)

    Published 2025
    “…The trigonometric function-based sine cosine algorithm (SCA) may solve such problems, but it traps in local optima, making it inappropriate for larger optimization tasks. …”
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    Iterative Least Squares Functional Networks Classifier by Faisal, Kanaan A

    Published 2007
    “…Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. …”
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    Cross entropy error function in neural networks by Nasr, G.E.

    Published 2002
    “…The ANN is implemented using the cross entropy error function in the training stage. The cross entropy function is proven to accelerate the backpropagation algorithm and to provide good overall network performance with relatively short stagnation periods. …”
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