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    This study introduces a new method for recognizing structured light by converting 2D spatial patterns into 1D temporal speckle sequences. This approach achieves high accuracy and robustness for optical communication and sensing applications.

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    Area of Science:

    • Optics and Photonics
    • Information Science

    Background:

    • Conventional structured light recognition methods use spatially resolved imaging, facing limitations like low frame rates and high computational costs.
    • These limitations restrict real-time and scalable applications of structured light recognition.

    Purpose of the Study:

    • To develop a novel, efficient method for structured light recognition by mapping spatial features to temporal speckle sequences.
    • To demonstrate the method's effectiveness and robustness for optical communication and sensing.

    Main Methods:

    • Mapping 2D spatial features onto 1D temporal speckle sequences using a single-pixel detector and a rotating diffuser.
    • Utilizing temporal fluctuations in speckle patterns for feature extraction.
    • Employing a support vector machine classifier for recognizing different types of light beams.

    Main Results:

    • Achieved >99% accuracy in recognizing Laguerre-Gaussian, Hermite-Gaussian, and perfect vortex beams.
    • Demonstrated robustness against atmospheric turbulence, optical alignment sensitivity, and symmetry-breaking optics.
    • Established a free-space optical communication channel with 16 orbital angular momentum states, achieving a bit error rate of 0.001.

    Conclusions:

    • The proposed method offers a scalable, low-latency, and computationally efficient solution for real-time structured light recognition.
    • This technique has significant potential for advancing next-generation optical communication and sensing systems.
    • The spatiotemporal mapping approach overcomes limitations of traditional imaging-based methods.