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Updated: Jul 1, 2025

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Structured light enhanced machine learning for fiber bend sensing.

Sara Angelucci, Zhaozhong Chen, Ľubomír Škvarenina

    Optics Express
    |March 5, 2024
    PubMed
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    Orbital angular momentum (OAM) feature extraction effectively mitigates optical distortions in fiber sensing. This structured light approach significantly improves accuracy and reduces data requirements compared to traditional methods.

    Area of Science:

    • Optics and Photonics
    • Machine Learning
    • Fiber Optic Sensing

    Background:

    • Optical distortions in complex media like optical fibers cause errors in communication and sensing.
    • Traditional methods struggle to accurately interpret these distortions, limiting sensing capabilities.

    Purpose of the Study:

    • To propose and demonstrate orbital angular momentum (OAM) feature extraction for mitigating phase-noise in fiber sensing.
    • To leverage intermodal coupling as an effective tool for fiber sensing using OAM features.

    Main Methods:

    • Passive all-optical orbital angular momentum (OAM) demultiplexing for feature extraction.
    • Demonstration of fiber bend tracking using OAM feature extraction.
    • Comparison with convolutional neural network (CNN) trained on intensity measurements.

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    Related Experiment Videos

    Last Updated: Jul 1, 2025

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    Main Results:

    • Achieved 94.1% accuracy in fiber bend tracking using OAM feature extraction.
    • Conventional CNN intensity-based methods yielded only 14% accuracy for the same task.
    • OAM feature extraction required 120 times less training information than intensity-based measurements.

    Conclusions:

    • Orbital angular momentum (OAM) feature extraction offers a robust solution for mitigating optical distortions in fiber sensing.
    • Structured light enhanced machine learning presents a promising avenue for advanced sensing technologies.
    • This method significantly enhances accuracy and data efficiency in fiber optic sensing applications.