Search Results - parallel computer self algorithm*

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  1. 1

    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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    Article
  2. 2

    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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    Article
  3. 3

    Improving parallel self-organizing map using heterogeneous uniform memory access / Muhammad Firdaus Mustapha by Mustapha, Muhammad Firdaus

    Published 2018
    “…Self-organizing Map (SOM) is a very popular algorithm that has been used as clustering algorithm and data exploration. …”
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    Thesis
  4. 4

    Improving parallel Self-organizing Map using heterogeneous uniform memory access / Muhammad Firdaus Mustapha by Mustapha, Muhammad Firdaus

    Published 2018
    “…Self-organizing Map (SOM) is a very popular algorithm that has been used as clustering algorithm and data exploration. …”
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    Book Section
  5. 5

    Analysing and improving the performance and security of the cryptographically Generated Address (CGA) algorithm for mobile IPv6 networks by Qadir, Sana, Siddiqi, Mohammad Umar, Al-Khateeb, Khalid A. Saeed

    Published 2015
    “…It recommends imposing a minimal computational security of O(280), the use of HAVAL and parallelizing the algorithm with the main process spawning as many threads as one less than the number of cores available on the node. …”
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    Neuro Symbolic Integration and Agent Based Modelling by Sathasivam , Saratha, Velavan, Muraly

    Published 2018
    “…The major domain of neuro-symbolic integration is designed by the theory are usually known as deductive systems which less such elements of human reasoning as adaptation, learning and self-organisation. Meanwhile, neural networks, known as a mathematical model of neurons in the human brain, and have various abilities, and moreover, they also provide parallel computations and therefore can perform some calculations quicker than classical learning algorithms. …”
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  8. 8

    Resource Minimization in a Real-time Depth-map Processing System on FPGA by Ngo, Huy Tan, Hamid, Nor Hisham, Sebastian , Patrick, Yap, Vooi Voon

    Published 2011
    “…Depth-map algorithm allows camera system to estimate depth. It is a computational intensive algorithm, but can be implemented with high speed on hardware due to the parallelism property. …”
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  9. 9

    Online harmonic extraction and synchronization algorithm based control for unified power quality conditioner for microgrid systems by Hasan, K., Othman, M.M., Meraj, S.T., Rahman, N.F.A., Noor, S.Z.M., Musirin, I., Abidin, I.Z.

    Published 2022
    “…This manuscript proposes an Adaptive Linear Neural Network (ADALINE) based control for the unified power quality conditioner to protect the critical loads from voltage related power quality problems and mitigate the current related power quality problems produced by nonlinear loads. Due to parallel computing feature and simple algorithm, ADALINE is utilized widely in parameter estimation. …”
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    MINIMIZATION OF RESOURCE UTILIZATION FOR A REAL-TIME DEPTH-MAP COMPUTATIONAL MODULE ON FPGA by NGO , NGO HUY TAN

    Published 2011
    “…The algorithm is computationally intensive and therefore more effective to be implemented on hardware such as the Field Programmable Gate Array (FPGA). …”
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    Thesis
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    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
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