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  1. 1
    Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller

    Modeling and control of a Pico-satellite attitude using Fuzzy Logic Controller by Zaridah, Mat Zain

    Published 2010
    “…It is observed that the APFLC showed convincing performance over the entire simulation of the Pico-satellite. Genetic Algorithm (GA) is a computational model inspired by evaluation. …”
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  2. 2
    Simulated Kalman Filter algorithms for solving optimization problems

    Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

    Published 2019
    “…In this research, two novel estimation-based metaheuristic optimization algorithms, named as Simulated Kalman Filter (SKF), and single-solution Simulated Kalman Filter (ssSKF) algorithms are introduced for global optimization problems. …”
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  3. 3
    Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja

    Simulation of shortest path using a-star algorithm / Nurul Hani Nortaja by Nurul Hani , Nortaja

    Published 2004
    “…This means A • algorithm only calculates and consider the next node m path that has the lowest value of G and F, plus searching the shortest route by using heuristic estimation in Manhattan method. …”
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  4. 4
    A Continuous Overlay Path Probing Algorithm For Overlay Networks

    A Continuous Overlay Path Probing Algorithm For Overlay Networks by Feily, Maryam

    Published 2013
    “…Active measurement techniques performed by overlay nodes can provide bandwidth estimations of an end-to-end overlay path. This thesis describes a new algorithm called “COPPA,” which is an in-band path probing algorithm for measuring the end-to-end available bandwidth of an overlay path accurately and continuously. …”
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  5. 5
    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments

    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments by Ali, Mohammed A.H., Radzak, Muhammad S.A., Mailah, Musa, Yusoff, Nukman, Abd Razak, Bushroa, Ab. Karim, Mohd Sayuti, Ameen, Wadea, A. Jabbar, Waheb, Alsewari, AbdulRahman A., Rassem, Taha H., Nasser, Abdullah B., Abdulmolla Abdulghafor, Rawad Abdulkhaleq

    Published 2021
    “…This algorithm, also known as laser simulator logic, has the capability to estimate the inertia moment of the AFC-controller when the robot is moving in a pre-planned path with the presence of noisy measurements. …”
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  6. 6
    GPS boundary navigation of DrosoBots using MATLAB simulation

    GPS boundary navigation of DrosoBots using MATLAB simulation by Zainal Abidin, Zulkifli, Ngah , Vmi Kalthum, Arshad, Mohd Rizal, Kok , Chee Hou

    Published 2010
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  7. 7
    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments

    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments by Ali, Mohammed A. H., M. S. A., Radzak, Musa, Mailah, Nukman, Yusoff, Abd Razak, Bushroa, Mohd Sayuti, Ab. Karim, Ameen, Wadea, Jabbar, Waheb A., Alsewari, Abdulrahman A., Rassem, Taha H., Abdullah Nasser, Mohammed Abdullah, Abdulghafor, Rawad

    Published 2021
    “…This algorithm, also known as laser simulator logic, has the capability to estimate the inertia moment of the AFC-controller when the robot is moving in a pre-planned path with the presence of noisy measurements. …”
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  8. 8
    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments

    A novel inertia moment estimation algorithm collaborated with active force control scheme for wheeled mobile robot control in constrained environments by Ali, Mohammed A. H., Radzak, Muhammad S. A., Mailah, Musa, Yusoff, Nukman, Abd Razak, Bushroa, Ab.Karim, Mohd Sayuti, Ameen, Wadea, Jabbar, Waheb A., Alsewari, Abdulrahman A., Rassem, Taha H., Nasser, Abdullah B., Abdulghafor, Rawad

    Published 2021
    “…This algorithm, also known as laser simulator logic, has the capability to estimate the inertia moment of the AFC-controller when the robot is moving in a pre-planned path with the presence of noisy measurements. …”
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  9. 9
    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control

    Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control by Srazhidinov, Radik

    Published 2016
    “…Using this method, the need for the knowledge of the degree of nonlinearity in advance can be avoided. The proposed algorithm models the Wiener-Hammerstein linear and nonlinear components in the secondary path and applies the estimated degree of nonlinearity of the nonlinear secondary path in the control algorithm design.In previous works, SEF-NLFXLMS and THF-NLFXLMS algorithms for Hammerstein and Wiener structures were developed where the acoustic path is assumed to be a unit gain. …”
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  10. 10
    Development of robust control scheme for wheeled mobile robot in restricted environment

    Development of robust control scheme for wheeled mobile robot in restricted environment by Muhammad Sawal, A Radzak

    Published 2021
    “…A novel algorithm so called laser simulator logic (LSL) has been develo ped to estimate the inertia moment when the environment is noisy and cannot use fuzzy logic algorithm. …”
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  11. 11
    Development of the propagation paths and deriving observer of feedforward active noise control system by using state-space formulation

    Development of the propagation paths and deriving observer of feedforward active noise control system by using state-space formulation by Muhssin, Mazin T., Raja Ahmad, Raja Mohd Kamil, Marhaban, Mohammad Hamiruce, Bafti, Payam Shafiei

    Published 2010
    “…This observer will be used to estimate the states along the propagation path which can not be estimated using LMS algorithm because LMS based on the FIR models. …”
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  12. 12
    Delay-based load-balancing routing (DLBR) algorithm for wireless ad-hoc networks

    Delay-based load-balancing routing (DLBR) algorithm for wireless ad-hoc networks by Habib, I., Badruddin, N., Drieberg, M.

    Published 2015
    “…By combining physical layer capacity with MAC layer estimated congestion value, a new delay-based load-balancing routing (DLBR) algorithm is proposed in this paper. …”
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  13. 13
    Delay-based load-balancing routing (DLBR) algorithm for wireless ad-hoc networks

    Delay-based load-balancing routing (DLBR) algorithm for wireless ad-hoc networks by Habib, I., Badruddin, N., Drieberg, M.

    Published 2015
    “…By combining physical layer capacity with MAC layer estimated congestion value, a new delay-based load-balancing routing (DLBR) algorithm is proposed in this paper. …”
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  14. 14
    Autonomous road roundabout detection and navigation system for smart vehicles and cities using laser simulator–fuzzy logic algorithms and sensor fusion

    Autonomous road roundabout detection and navigation system for smart vehicles and cities using laser simulator–fuzzy logic algorithms and sensor fusion by Ali, Mohammed A. H., Musa, Mailah, Jabbar, Waheb A., Moiduddin, Khaja, Ameen, Wadea, Alkhalefah, Hisham

    Published 2020
    “…The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.…”
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  15. 15
    An adaptive relay selection scheme for enhancing network stability in VANETs

    An adaptive relay selection scheme for enhancing network stability in VANETs by Al-Kharasani, Ameen Mohammed Abdulkarem, Ahmad Zukarnain, Zuriati, Subramaniam, Shamala, Mohd Hanapi, Zurina

    Published 2020
    “…In this paper, we propose a new improvement to the mechanism of the Optimized Link State Routing Protocol(OLSR) protocol, named Cluster-based Adept Cooperative Algorithm (CACA), where each vehicle estimate a reliable low-overhead path using the cluster-based QoS algorithm. …”
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  16. 16
    Optimization of turning parameters using genetic algorithm method

    Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

    Published 2008
    “…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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    Undergraduates Project Papers
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  17. 17
    Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis

    Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis by Asrul, Adam

    Published 2018
    “…The efficiency of the three algorithms are evaluated and compared with previous results obtained by other optimization methods on similar studies. …”
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  18. 18
    Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization

    Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization by Nur Iffah, Mohamed Azmi

    Published 2014
    “…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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  19. 19
    Optimization of turning parameters using ant colony optimization

    Optimization of turning parameters using ant colony optimization by Mohamad Nazri, Semoin

    Published 2008
    “…The project objectives are to develop Ant Colony Optimization (ACO) algorithm for CNC turning process and to optimize turning parameters for minimized production cost per unit. …”
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  20. 20
    Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail

    Parameter estimation in double exponential smoothing using genetic algorithm / Foo Fong Yeng, Lau Gee Choon and Zuhaimy Ismail by Foo, Fong Yeng, Lau, Gee Choon, Ismail, Zuhaimy

    Published 2014
    “…Trial and error often serves as the best method to determine the parameter. Therefore, a good optimization technique is required for identify the best parameter in minimizing the forecast errors. …”
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