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Updated: Jun 3, 2025

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
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Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment.

Brian Pamukti1, Shofuro Afifah1, Shien-Kuei Liaw1,2

  • 1Graduate Institute of Electro-Optical Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary
This summary is machine-generated.

Distributed fiber optic sensors (DFOSs) offer advanced intrusion detection. A novel Mach-Zehnder interferometer (MZI) and time forest neural network (TFNN) approach improves accuracy and efficiency for smart environments.

Keywords:
deep learningpattern recognitionsmart environment

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Area of Science:

  • Optoelectronics and Sensor Technology
  • Artificial Intelligence and Machine Learning
  • Cybersecurity and Surveillance

Background:

  • Distributed fiber optic sensors (DFOSs) are vital for intrusion detection in sensitive areas.
  • Conventional methods face challenges with complexity, computational load, and signal path inefficiency.
  • Advancements in signal processing and deep learning are needed to enhance DFOS performance.

Purpose of the Study:

  • To introduce an innovative interferometric sensing approach for intrusion detection.
  • To improve the accuracy and efficiency of DFOS systems.
  • To explore the application of a Mach-Zehnder interferometer (MZI) combined with a time forest neural network (TFNN).

Main Methods:

  • Utilized a Mach-Zehnder interferometer (MZI) for signal acquisition.
  • Employed a time forest neural network (TFNN) for pattern recognition and intrusion detection.
  • Compared performance against conventional one-dimensional convolutional neural networks (1D-CNN).

Main Results:

  • The proposed MZI-TFNN approach demonstrated superior performance in intrusion detection.
  • Achieved an 8.43% higher accuracy compared to the 1D-CNN.
  • Indicated enhanced efficiency in real-time signal processing.

Conclusions:

  • The novel interferometric sensing method with TFNN offers a significant improvement over existing DFOS techniques.
  • This approach holds substantial potential for real-time intrusion detection in smart environments.
  • Highlights the effectiveness of integrating advanced optical sensing with deep learning for enhanced security applications.