Search Results - (( parallel estimation learning algorithm ) OR ( variable detection window algorithm ))

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    Damage visualization based on frequency shift of single-mode ultrasound-guided wavefield by Shahrim, Muhamad Azim, Harmin, Mohammad Yazdi, Romli, Fairuz Izzuddin, Chia, Chen Ciang, Jung-Ryul, Lee

    Published 2022
    “…Last but not least, the comparison of accuracy between frequency analysis algorithm and variable time window amplitude mapping (VTWAM) algorithm is performed. …”
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  2. 2

    Analyzing land surface temperature in response to massive urbanization by using single window algorithm in Penang Island / Ainna Naeemah Zainal Abidin by Zainal Abidin, Ainna Naeemah

    Published 2019
    “…Next will be the extraction of Land Surface Temperature (LST) by using Spectral Radiance Model and Single Window Algorithm through the satellite imagery. Last objectives will be the analysis on the relationship between the dependent and independent variables that be made through the correlation coefficient analysis. …”
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    Thesis
  3. 3

    Super resolution imaging using modified lanr based on separable filtering by Somadina, Ike Chidiebere

    Published 2019
    “…The underlying idea is to process and reconstruct information in low and high frequency sub-bands based on separable property of neighbourhood filtering to achieve fast parallel and vectorized operation, while enhancing algorithmic performance by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. …”
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    Thesis
  4. 4

    Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model by Megat Syahirul Amin, Megat Ali, Azlee, Zabidi, Nooritawati, Md Tahir, Ihsan, Mohd Yassin, Eskandari, Farzad, Azlinda, Saadon, Mohd Nasir, Taib, Abdul Rahim, Ridzuan

    Published 2024
    “…System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. …”
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