Search Results - (( probable distribution sensor algorithm ) OR ( rayleigh distribution function algorithm ))

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  1. 1

    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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  2. 2
  3. 3

    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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  4. 4

    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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  5. 5

    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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  6. 6

    Channel Modeling and Direction-of-Arrival Estimation in Mobile Multiple-Antenna Communication Systems by Ravari, Arastoo Rostami

    Published 2005
    “…Properly correlated fading waveforms are obtained by taking into account the angular spread of the scattered signals from a particular distribution of scatterers. By appropriate scaling of the array response vector (ARV) based on non-equal locations for various received signal components as a function of distance from the transmitter, the reflected power from a given scatterer is no longer constant but varies as a function of the distance from the transmitter. …”
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  7. 7

    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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