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Electronic Distance Measuring Instruments01:30

Electronic Distance Measuring Instruments

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 distances...

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Related Experiment Video

Updated: May 10, 2026

Label-free Single Molecule Detection Using Microtoroid Optical Resonators
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Novel Approach to Phase-Sensitive Optical Time-Domain Reflectometry Response Analysis with Machine Learning Methods.

Vasily A Yatseev1, Oleg V Butov1,2, Alexey B Pnev2

  • 1Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Science, 125009 Moscow, Russia.

Sensors (Basel, Switzerland)
|March 13, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to assess non-uniform responses in optical fiber systems. Machine learning significantly improves accuracy in measuring phase differences, enhancing reflectometric system performance.

Keywords:
chirped-OTDRdistributed fiber optic sensingmachine learningphase demodulation

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Area of Science:

  • Metrology
  • Optical Fiber Sensing
  • Machine Learning Applications

Background:

  • Phase-sensitive reflectometric systems face challenges with non-uniform responses along optical fibers.
  • The stochastic nature of Rayleigh reflectors complicates accurate measurements in fiber optic sensing.

Purpose of the Study:

  • To investigate the metrological properties of phase-sensitive reflectometric systems.
  • To develop and evaluate a methodology for assessing response non-uniformity in optical fibers.
  • To compare the efficacy of cross-correlation and machine learning algorithms for this assessment.

Main Methods:

  • Utilized chirped-reflectometry as an example system for analysis.
  • Simulated deformation impacts by altering light source wavelength.
  • Employed cross-correlation algorithms and neural network-based machine learning approaches.
  • Experimentally estimated response non-uniformity.

Main Results:

  • Neural network algorithms demonstrated over 34% improvement in phase difference measurement accuracy compared to cross-correlation.
  • The proposed methodology effectively evaluates response non-uniformity across different fiber sections.
  • Chirped-reflectometry was used to quantify these non-uniformities.

Conclusions:

  • The developed methodology provides an effective means to evaluate response non-uniformity in optical fibers.
  • Machine learning offers a significant advantage in improving the accuracy of phase-sensitive reflectometric measurements.
  • This research contributes to enhanced metrological properties of fiber optic sensing systems.