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|October 14, 2022

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A novel dual-core fiber sensor offers sensitive detection of illumination, temperature, and humidity. This multi-parameter sensor utilizes a deep learning algorithm for accurate data demodulation, showing promise for environmental monitoring.

Area of Science:

Optoelectronics
Fiber Optics Sensing
Environmental Monitoring

Background:

Accurate multi-parameter sensing is crucial for environmental monitoring and industrial applications.
Existing fiber optic sensors often face limitations in sensitivity and simultaneous detection capabilities.
Developing integrated sensors for simultaneous measurement of diverse environmental parameters remains a challenge.

Purpose of the Study:

To propose and demonstrate a multi-parameter dual-core fiber sensor for simultaneous detection of illumination, temperature, and humidity.
To enhance sensing performance through partial grating etching and Mach-Zehnder interference.
To leverage deep learning for improved sensing data demodulation and accuracy.

Main Methods:

Fabrication of a dual-core fiber sensor with partial grating etching on one core.

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Last Updated: Aug 25, 2025

Writing Bragg Gratings in Multicore Fibers

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Published on: April 20, 2016

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

A Random-displacement Measurement by Combining a Magnetic Scale and Two Fiber Bragg Gratings

Published on: September 30, 2019

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response

A Silicon-tipped Fiber-optic Sensing Platform with High Resolution and Fast Response

Published on: January 7, 2019

Utilizing the Mach-Zehnder interference effect within the dual-core structure.

Employing a one-dimensional convolutional neural network (1D-CNN) for data processing and parameter demodulation.

Main Results:

Achieved high temperature sensitivity exceeding 6.1952 nm/°C.
Demonstrated humidity resolution of 0.041 relative humidity (RH) and illumination resolution of 0.025 light units.
Obtained a 99.05% accuracy with a 2.00 root mean square error (RMSE) using the 1D-CNN algorithm.

Conclusions:

The proposed dual-core fiber sensor enables highly sensitive and simultaneous detection of temperature, humidity, and illumination.
The integration of deep learning significantly improves the accuracy and reliability of multi-parameter sensing.
This advanced sensor technology holds significant potential for applications in environmental monitoring and intelligent manufacturing.