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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Updated: Aug 26, 2025

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Diffractive deep neural network based adaptive optics scheme for vortex beam in oceanic turbulence.

Haichao Zhan, Yixiang Peng, Bing Chen

    Optics Express
    |October 13, 2022
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    Summary

    A novel diffractive deep neural network (DDNN) corrects oceanic turbulence distortion in underwater optical communication. This adaptive optics approach significantly improves vortex beam mode purity and communication quality.

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    Area of Science:

    • Optical communication
    • Adaptive optics
    • Machine learning

    Background:

    • Vortex beams carrying orbital angular momentum (OAM) are crucial for underwater wireless optical communication (UWOC).
    • Oceanic turbulence (OT) significantly distorts these beams, degrading UWOC system performance.
    • Adaptive optics (AO) is a key technology for mitigating such distortions.

    Purpose of the Study:

    • To propose and experimentally validate a diffractive deep neural network (DDNN) based AO scheme.
    • To compensate for beam distortion caused by oceanic turbulence in UWOC systems.
    • To improve the performance and communication quality of UWOC systems.

    Main Methods:

    • A DDNN was trained to map the intensity distribution of distorted vortex beams to phase screens representing OT.
    • The trained DDNN, with solidified diffractive layers, was used to process distorted vortex beams.
    • Experimental validation involved inputting distorted beams into the DDNN and recording modulated light field intensity.

    Main Results:

    • The DDNN rapidly extracted characteristics of the distorted vortex beam's intensity pattern.
    • The predicted compensation phase screen effectively corrected OT-induced distortions in real-time.
    • Significant improvement in the mode purity of the compensated vortex beam was achieved, even under strong OT.

    Conclusions:

    • The proposed DDNN-based AO scheme offers a novel approach for distortion compensation in UWOC.
    • This method demonstrates rapid and effective correction of oceanic turbulence effects.
    • The technique is expected to significantly enhance the communication quality of UWOC systems.