Showing 1 - 20 results of 38 for search '"National Institute of Standards and Technology"', query time: 0.12s Refine Results
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    Complements to the Theoretical Treatments of the Electron Collision with : An R-Matrix Approach by V. Stancalie

    Published 2013-01-01
    “…The calculated values for fine-structure levels are compared with the available experimental data in Atomic Structure Database of the National Institute for Standards and Technology. The agreement between the calculated and the experimental data is reasonably good, the energy difference average percentage of the low-lying levels usually agreeing to within 3.6% of each other. …”
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  17. 17

    Handwriting Digital Image Generation based on GAN: A Comparative Study of Basic GAN and CGAN Models by Zeng Hongzhi

    Published 2025-01-01
    “…The basic GAN and CGAN models, based on the PyTorch deep learning framework and the Modified National Institute of Standards and Technology (MNIST) dataset, are applied to generate handwritten digital images respectively. …”
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  18. 18

    Analysis of The Role of Deep Learning Models in Image Classification Applications by Li Xiang

    Published 2025-01-01
    “…Training these models involves the use of well-known datasets like Canadian Institute for Advanced Research (CIFAR) and Modified National Institute of Standards and Technology (MNIST), which provide the necessary data for optimization and validation. …”
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  19. 19

    Surdat: Database of physical properties of lead-free solders by Moser Z., Gasior W., Debski A., Pstrus J.

    Published 2007-01-01
    “…The present version of the SURDAT database will be introduced on the website of the National Institute of Standards and Technology (NIST) together with the new version of Lead-Free Solders base elaborated by NIST. …”
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  20. 20

    Ring Oscillators with Additional Phase Detectors as a Random Source in a Random Number Generator by Łukasz Matuszewski, Mieczysław Jessa, Jakub Nikonowicz

    Published 2024-12-01
    “…Our approach employs additional phase detectors to extract more entropy; thus, RNG uses fewer resources to produce bit sequences that pass all statistical tests proposed by National Institute of Standards and Technology (NIST). Generating a specified number of bits is on-demand, eliminating the need for continuous RNG operation. …”
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