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  1. 1
    An improved kernelization algorithm for r-Set Packing

    An improved kernelization algorithm for r-Set Packing by Abu-Khzam, Faisal N.

    Published 2010
    “…We present a reduction procedure that takes an arbitrary instance of the r -Set Packing problem and produces an equivalent instance whose number of elements is in O(kr−1), where k is the input parameter. …”
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  2. 2
    A Parallel Neural Networks Algorithm for the Clique Partitioning Problem

    A Parallel Neural Networks Algorithm for the Clique Partitioning Problem by Harmanani, Haidar M.

    Published 2002
    “…In this paper we present a parallel algorithm to solve the above problem for arbitrary graphs using a Hopfield Neural Network model of computation. …”
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  3. 3
    A stochastic iterative learning control algorithm with application to an induction motor

    A stochastic iterative learning control algorithm with application to an induction motor by Saab, Samer S.

    Published 2004
    “…A recursive optimal algorithm, based on minimizing the input error covariance matrix, is derived to generate the learning gain matrix of a P-type ILC for linear discrete-time varying systems with arbitrary relative degree. …”
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  4. 4
    A Neural Networks Algorithm for the Minimum Colouring Problem Using FPGAs†

    A Neural Networks Algorithm for the Minimum Colouring Problem Using FPGAs† by Harmanani, Haidar

    Published 2010
    “…This paper presents a hardware implementation to solve the graph colouring problem (chromatic number χ(G)) for arbitrary graphs using the Hopfield neural network (HNN) model of computation. …”
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  5. 5
    Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise

    Selection of the learning gain matrix of an iterative learning control algorithm in presence of measurement noise by Saab, Samer S.

    Published 2005
    “…In particular, this paper presents necessary and sufficient conditions for boundedness of trajectories and uniform tracking in presence of measurement noise and a class of random reinitialization errors for a simple ILC algorithm. The system under consideration is a class of discrete-time affine nonlinear systems with arbitrary relative degree and arbitrary number of system inputs and outputs. …”
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  6. 6
    Shuffled Linear Regression with Erroneous Observations

    Shuffled Linear Regression with Erroneous Observations by Saab, Samer S.

    Published 2019
    “…Although our algorithm is sensitive to initialization errors, to the best of our knowledge, the resulting method is the first working solution for arbitrary large dimensions and arbitrary large observation errors while its computation throughput appears insignificant. …”
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  7. 7
    Learn while tracking. (c2013)

    Learn while tracking. (c2013) by Toukhtarian, Raffi

    Published 2013
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  8. 8
    Morphology for Planar Hexagonal Modular Self-Reconfigurable Robotic Systems

    Morphology for Planar Hexagonal Modular Self-Reconfigurable Robotic Systems by Sadjadi, Hossein

    Published 2009
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  9. 9
    Metaheuristic Optimization‐Based Sliding Mode Control With Modified Perturb and Observe for Controlling MPPT of a PV Interfaced Grid Connected System

    Metaheuristic Optimization‐Based Sliding Mode Control With Modified Perturb and Observe for Controlling MPPT of a PV Interfaced Grid Connected System by Anupama Ganguly (22502969)

    Published 2025
    “…The suggested approach looks for every maximum point across a lengthy number of cycles to find the global maximum point after introducing (MGO‐MPb&O), and the results of the MATLAB/Simulink show that the algorithm performs well under the arbitrary changes of the physical parameters of the proposed system and ambient scenario. …”
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  10. 10
    A quadratic kernel for 3-set packing

    A quadratic kernel for 3-set packing by Abu-Khzam, Faisal N.

    Published 2017
    “…We present a reduction procedure that takes an arbitrary instance of the 3-Set Packing problem and produces an equivalent instance whose number of elements is bounded by a quadratic function of the input parameter. …”
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  11. 11
    Using C++ to Calculate SO(10) Tensor Couplings

    Using C++ to Calculate SO(10) Tensor Couplings by Bhagwagar, Azadan

    Published 2021
    “…The software is capable of accepting invariant couplings involving an arbitrary number of SO(10) Higgs tensors, each having up to five indices. …”
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