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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short...
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Related Experiment Video

Updated: Jul 31, 2025

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings
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Machine learning for a Vernier-effect-based optical fiber sensor.

Chen Zhu, Osamah Alsalman, Wassana Naku

    Optics Letters
    |May 1, 2023
    PubMed
    Summary

    Machine learning (ML) offers a novel solution for demodulating optical Vernier-effect fiber sensors. This approach enables fast, reliable measurand readout, overcoming limitations of traditional methods for enhanced sensing systems.

    Area of Science:

    • Photonics and Optical Sensing
    • Machine Learning Applications in Science
    • Fiber Optic Interferometry

    Background:

    • The optical Vernier effect enhances sensitivity in fiber optic interferometer sensors.
    • Existing research prioritizes physical sensor design over signal demodulation.
    • Effective signal demodulation is crucial for overall sensing system performance.

    Purpose of the Study:

    • To introduce and validate machine learning (ML) for demodulating optical Vernier-effect fiber sensors.
    • To demonstrate ML's capability for direct and reliable measurand extraction.
    • To address the neglected signal demodulation aspect in Vernier-effect sensor development.

    Main Methods:

    • Application of machine learning algorithms for signal processing.

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  • Direct readout of measurand from optical spectra using ML analysis.
  • Comparison with conventional, complex data processing techniques.

    • Main Results:

    • Successful demonstration of ML for demodulating optical Vernier-effect fiber sensors.
    • ML enables fast, direct, and reliable extraction of sensing information.
    • Elimination of cumbersome data processing associated with traditional methods.

    Conclusions:

    • Machine learning provides an efficient and effective solution for Vernier-effect sensor demodulation.
    • This ML-based approach significantly simplifies data processing and improves reliability.
    • Opens new possibilities for developing next-generation, high-sensitivity optical fiber sensing systems.