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Showing 161 - 180 results of 486 for search '(( algorithm dual function ) OR ( algorithm python function ))', query time: 0.50s Refine Results
  1. 161
    Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

    Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning by Hao Luo (454719)

    Published 2023
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  2. 162
    Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

    Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning by Hao Luo (454719)

    Published 2023
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  3. 163
    Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

    Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning by Hao Luo (454719)

    Published 2023
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  4. 164
    Modeling with uniform <i>σ</i> values.

    Modeling with uniform <i>σ</i> values. by Giacomo Janson (8138517)

    Published 2019
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  5. 165
    Effect of <i>|Δd</i><sub><i>n</i></sub><i>|</i> perturbation on 3D modeling.

    Effect of <i>|Δd</i><sub><i>n</i></sub><i>|</i> perturbation on 3D modeling. by Giacomo Janson (8138517)

    Published 2019
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  6. 166
    Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling

    Revisiting the “satisfaction of spatial restraints” approach of MODELLER for protein homology modeling by Giacomo Janson (8138517)

    Published 2019
    “…This program implements the “modeling by satisfaction of spatial restraints” strategy and its core algorithm has not been altered significantly since the early 1990s. …”
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  7. 167
    Comparisons among the Dual-Energy (DE) Virtual Monochromatic (VM) with Very-Deep Super-Resolution (VDSR) reconstruction algorithm (DE–VM–VDSR) with and without BF and conventional reconstruction algorithms [DE–VM–VDSR with and without BF (showing window: 0–0.23), FBP (kernel: Ramp; 0–0.4), SART (120 kV; 0–0.02), SART–TV–FISTA (120 kV; 0–0.4), and DE–VM–SART–TV–FISTA (60 keV; 0–0.23)] in the in-focus plane.

    Comparisons among the Dual-Energy (DE) Virtual Monochromatic (VM) with Very-Deep Super-Resolution (VDSR) reconstruction algorithm (DE–VM–VDSR) with and without BF and conventional... by Tsutomu Gomi (7382183)

    Published 2020
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  8. 168
    Example of use of the slightly off-axis strategies.

    Example of use of the slightly off-axis strategies. by Raul Castañeda (13932971)

    Published 2022
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  9. 169
    Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration.

    Example of the fully-compensated phase reconstruction package for off-axis DHM holograms recorded in telecentric configuration. by Raul Castañeda (13932971)

    Published 2022
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  10. 170
    Comparison of edge detector and CHT in detecting droplets.

    Comparison of edge detector and CHT in detecting droplets. by Manibarathi Vaithiyanathan (6655274)

    Published 2019
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  11. 171
    <b>Algorithm Pseudocode</b>

    <b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

    Published 2025
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  12. 172
    Original data for the study "<b>Improving basin-wide flood estimation from a global hydrological model through spatiotemporal-pattern-</b><b>based </b><b>machine learning</b>"

    Original data for the study "<b>Improving basin-wide flood estimation from a global hydrological model through spatiotemporal-pattern-</b><b>based </b><b>machine learning</b>" by Jiaqing Wang (18814630)

    Published 2024
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  13. 173
    Python code, input data, and outputs of Fourier analysis and Dynamic Time Warping from McGlasson et al.

    Python code, input data, and outputs of Fourier analysis and Dynamic Time Warping from McGlasson et al. by Riley McGlasson (13876874)

    Published 2024
    “…</p> <h4><strong>Python Function Files:</strong></h4> <p>radarfuncs.py: Holds three functions used for FFT and DTW analysis.…”
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  14. 174
    Table_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf

    Table_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

    Published 2020
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  15. 175
    Presentation_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf

    Presentation_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

    Published 2020
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  16. 176
    Table_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf

    Table_2_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

    Published 2020
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  17. 177
    Image_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf

    Image_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

    Published 2020
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  18. 178
    Presentation_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf

    Presentation_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

    Published 2020
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  19. 179
    Presentation_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf

    Presentation_1_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

    Published 2020
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  20. 180
    Table_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf

    Table_3_NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data.pdf by Zitong Lu (9913770)

    Published 2020
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