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Related Experiment Video

Updated: Jun 27, 2026

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements
14:18

Automation of Mode Locking in a Nonlinear Polarization Rotation Fiber Laser through Output Polarization Measurements

Published on: February 28, 2016

Salinity Sensor Using a Tapered Polarization-Maintaining Fiber-Based Sagnac Loop in a Fiber Ring Laser with Support

Weihao Lin1, Zihan Huang1, Keyu Cai1

  • 1The Higher Educational Key Laboratory for Flexible Manufacturing Equipment Integration of Fujian Province, Xiamen Institute of Technology, Xiamen 361021, China.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel fiber ring laser (FRL) salinity sensor using a Sagnac loop (SL) for precise measurements. The system achieves high accuracy by combining laser advantages with AI algorithms, improving salinity detection.

Keywords:
Sagnac loopfiber ring lasersalinity sensingsupport vector regression

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Last Updated: Jun 27, 2026

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Published on: February 28, 2016

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Area of Science:

  • Optical Sensing
  • Fiber Optics
  • Metrology

Background:

  • Salinity measurement is crucial in various environmental and industrial applications.
  • Traditional optical sensing methods often face limitations in sensitivity and signal quality.
  • Fiber optic sensors offer advantages like remote sensing and immunity to electromagnetic interference.

Purpose of the Study:

  • To propose and demonstrate a fiber ring laser (FRL) based salinity sensing system.
  • To enhance the accuracy and signal-to-noise ratio (SNR) of salinity measurements.
  • To integrate artificial intelligence for improved spectral data analysis and salinity determination.

Main Methods:

  • Constructed a Sagnac loop (SL) using a tapered polarization-maintaining fiber (TPMF).
  • Utilized the FRL to narrow the optical spectrum and boost the SNR.
  • Employed support vector regression (SVR) for advanced linear regression modeling of spectral data.

Main Results:

  • Achieved a high sensitivity of 97 pm/‱ for salinity detection over a 0–30‱ range.
  • Demonstrated a significant SNR improvement from 22 dB to approximately 54 dB.
  • Reduced mean absolute error (MAE) to 0.04‱ and root mean square error (RMSE) to 0.11‱ using SVR.

Conclusions:

  • The FRL-SL system offers a highly sensitive and accurate method for salinity measurement.
  • The integration of SVR algorithm significantly enhances the precision of salinity determination.
  • This work presents the first combination of laser-based salinity sensing with AI for superior performance.