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    This study introduces an improved ResNet for accurately identifying elliptical vortex beam properties, crucial for advanced optical communication. The method achieves high accuracy in detecting topological charge and ellipticity, enhancing data transmission capabilities.

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    Area of Science:

    • Optical physics
    • Information optics
    • Machine learning applications

    Background:

    • Elliptical vortex beams possess two degrees of freedom: topological charge and ellipticity.
    • These properties enhance information transmission capacity and processing in optical communication.
    • Accurate detection of these properties is vital for realizing the full potential of elliptical vortex beams.

    Purpose of the Study:

    • To develop an accurate method for identifying the topological charge and ellipticity of elliptical vortex beams.
    • To demonstrate the effectiveness of the proposed method in a practical optical communication system.

    Main Methods:

    • An improved ResNet architecture based on convolutional neural networks was developed.
    • The method was trained to identify high-resolution interference patterns of elliptical vortex beams.
    • An elliptical vortex beam multiplexing encryption system was utilized for data transmission tests.

    Main Results:

    • The ResNet achieved 88.12% recognition accuracy for topological charge (resolution 0.01) and 99.85% for ellipticity (resolution 0.1).
    • Image transmission using the elliptical vortex beam system demonstrated high accuracy (99.64% for a cat image at 0.1 topological charge resolution).

    Conclusions:

    • The proposed ResNet-based method enables accurate detection of elliptical vortex beam degrees of freedom.
    • This approach has significant implications for free-space optical communication utilizing orbital angular momentum.
    • The findings offer new perspectives for the manipulation and application of elliptical vortex beams.