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Enhancing Multichannel Fiber Optic Sensing Systems with IFFT-DNN for Remote Water Level Monitoring.

Erfan Dejband1, Tan-Hsu Tan1,2, Cheng-Kai Yao3

  • 1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

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|August 10, 2024
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Summary
This summary is machine-generated.

A novel Inverse Fast Fourier Transform-based Deep Neural Network (IFFT-DNN) accurately predicts multichannel sensor responses, overcoming signal overlap and crosstalk. This technology offers a reliable and cost-effective solution for remote water level sensing.

Keywords:
FSOIFFT-DNNfiber opticremote sensingwater level sensor

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Area of Science:

  • Optoelectronics and Sensor Technology
  • Artificial Intelligence in Engineering
  • Signal Processing

Background:

  • Multichannel fiber optic sensing systems face challenges with signal overlapping and crosstalk, hindering accurate response prediction.
  • Accurate remote sensing of environmental parameters like water level is crucial for various industrial applications.

Purpose of the Study:

  • To introduce a novel Inverse Fast Fourier Transform-based Deep Neural Network (IFFT-DNN) for enhanced multichannel fiber optic sensing.
  • To develop and validate a multichannel water level sensing system using Free Space Optics (FSO) integrated with the IFFT-DNN.

Main Methods:

  • Integration of an IFFT-DNN to process sensor signals, leveraging both frequency and time domain information for feature extraction.
  • Development of a multichannel water level sensing system utilizing Free Space Optics (FSO) for remote measurements.
  • Experimental validation of the IFFT-DNN's performance in predicting sensor responses under complex conditions.

Main Results:

  • The IFFT-DNN demonstrated high accuracy in predicting sensor responses, effectively mitigating issues of signal overlap and crosstalk.
  • The proposed FSO-based water level sensing system achieved a Mean Absolute Error (MAE) of 0.07 cm.
  • The system proved reliable and precise even in challenging environmental conditions.

Conclusions:

  • The IFFT-DNN significantly enhances the accuracy and reliability of multichannel fiber optic sensing systems.
  • The developed FSO-based system offers a cost-effective and practical solution for remote water level monitoring.
  • This approach holds significant potential for various industrial applications requiring precise remote sensing.