Search Results - (( parallel visualization matching algorithm ) OR ( using codification using algorithm ))

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  1. 1
    Parallel batch self-organizing map on graphics processing unit using CUDA

    Parallel batch self-organizing map on graphics processing unit using CUDA by Daneshpajouh, H., Delisle, P., Boisson, J.-C., Krajecki, M., Zakaria, N.

    Published 2018
    “…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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  2. 2
    Parallel batch self-organizing map on graphics processing unit using CUDA

    Parallel batch self-organizing map on graphics processing unit using CUDA by Daneshpajouh, H., Delisle, P., Boisson, J.-C., Krajecki, M., Zakaria, N.

    Published 2018
    “…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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  3. 3
    Adopting morphology method in camera based EAN-13 1D barcode recognition using Simulink: article / Siti Nor Safinaz Ozaimidi

    Adopting morphology method in camera based EAN-13 1D barcode recognition using Simulink: article / Siti Nor Safinaz Ozaimidi by Ozaimidi, Siti Nor Safinaz

    Published 2012
    “…A result of barcode numbers saved in file.mat is used for product matching code. The percentage of success is 94.44% and failure of the system is 5.56%.…”
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  4. 4
    Adopting morphology method in camera based EAN-13 1D barcode recognition using Simulink / Siti Nor Safinaz Ozaimidi

    Adopting morphology method in camera based EAN-13 1D barcode recognition using Simulink / Siti Nor Safinaz Ozaimidi by Ozaimidi, Siti Nor Safinaz

    Published 2012
    “…A result of barcode numbers saved in file.mat is used for product matching code. The percentage of success is 94.44% and failure of the system is 5.56%.…”
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  5. 5
    The Determination of Pile Capacity Using Artificial Neural-net: An Optimization Approach

    The Determination of Pile Capacity Using Artificial Neural-net: An Optimization Approach by Ab. Malik, Rosely, Jamil S., Mohamed

    Published 2001
    “…Using the developed algorithm, the safety measures involved are such as reliability index and the probability of failure; instead of only factor of safety if conventional deterministic approach is used. …”
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  6. 6
    Discriminative feature representation for Malay children’s speech recognition / Seyedmostafa Mirhassani

    Discriminative feature representation for Malay children’s speech recognition / Seyedmostafa Mirhassani by Mirhassani, Seyedmostafa

    Published 2015
    “…In case of multiple filterbanks the cepstral features are used in different experts for performing classification based on different representation of speeches. …”
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