Search Results - (( parameters variation path algorithm ) OR ( probable distribution sensor algorithm ))

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  1. 1

    A hybrid sampling-based path planning algorithm for mobile robot navigation in unknown environments by Khaksar, Weria

    Published 2013
    “…After optimizing the proposed fuzzy model by means ofgenetic algorithm, the resulted planner produces shorter paths in shorter runtimes with limited variations in results of different runs of the planner. …”
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    Thesis
  2. 2

    Energy Efficient LEACH (EE-LEACH) Routing Algorithm for Wireless Sensor Networks by Pillay, Kosheila Sundram

    Published 2019
    “…Therefore, this research work proposes an energy-efficient LEACH (EE-LEACH) algorithm to elect CHs based on residual energy, RSSI, and random probability to distribute the load evenly among the CHs. …”
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  3. 3

    Energy efficient path reconstruction in wireless sensor network using iPath by Hasan, Sazlinah, Abd, Wamidh Jwdat, Ariffin, Ahmad Alauddin

    Published 2019
    “…This work uses iterative boosting algorithm to find an alternative path with less distance and energy consumption. …”
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    Article
  4. 4
  5. 5

    Energy efficient cluster head distribution in wireless sensor networks by Siew, Zhan Wei

    Published 2013
    “…For network clustering, the distribution of CH selection directly influences the networks lifetime. …”
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    Thesis
  6. 6

    Analysis of robot localisation performance based on extended kalman filter by Che Hassan, Farah Hazwani

    Published 2015
    “…However, there are a number of variations of EKF with different values of variables, which lead to contradicting results in terms of standard deviations of path (distance) and angle. …”
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  7. 7

    An adaptive predictive fuzzy logic for the altitude control of a micro-satellite by Ramachandran, Nagarajan, Pandiyan, Paulraj Murugesa, Prof. Madya Dr., Sazali, Yaacob, Prof. Dr., Zuraidah, Md Zain, Prof. Dr., Rusli, R.

    Published 2010
    “…The adaptive portion of FLC compensates the effect of unknown variations of parameters in the satellite system by using an adaptable gain is connected in the forward path of the FLC. …”
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    Article
  8. 8

    Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

    Published 2018
    “…In comparison, WISDM utilizes an accelerometer sensor embedded in Android smartphone. Meanwhile, PAMAP2 utilizes an accelerometer sensor equipped with three Inertial Measurement Unit (IMU) devices attached to three different placements. …”
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  9. 9

    EFFICIENT DYNAMIC ADDRESSING BASED ROUTING FOR UNDERWATER WIRELESS SENSOR NETWORKS by ARSHAD, MUHAMMAD AYAZ ARSHAD

    Published 2011
    “…Besides long propagation delays and high error probability, continuous node movement also makes it difficult to manage the routing information during the process of data forwarding. …”
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  10. 10

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

    Published 2010
    “…Finally the adaptive portion of FLC is applied in order to compensate the effect of unknown parameter variations in the Pico-satellite system by using an adaptable gain which is connected in the forward path of the FLC. …”
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    Thesis
  11. 11

    DC-based PV-powered home energy system by Sabry, Ahmad H.

    Published 2017
    “…The controller algorithm requires also the variation range of the geographical weather parameters (irradiance and temperature) to specify the MPP which is equivalent to that operating voltage at minimum weather parameters. …”
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    Thesis
  12. 12

    Efficient relay placement algorithm using landscape aware routing (erpalar) by Onabajo, Olawale Olusegun

    Published 2011
    “…ERP ALAR was implemented in Matlab R2009a using Genetic Algorithm (GA) with multi-objectives. GA is an optimization algorithm that simulates natural selection process as found in nature. …”
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  13. 13

    Prediction of rice biomass using machine learning algorithms by Radhwane, Derraz

    Published 2022
    “…The Q-TESI, C-TESI, and L-TESI overcame the LN-TESI in retaining the features’ original probability distribution, minimising the augmentation loss, reducing the VIF, increasing the rs, and decreasing the DNN under- and overfitting. …”
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    Thesis