Doctoral Dissertations

Abstract

"The fiber optic Fabry-Perot interferometer (FPI) is a widely utilized sensing configuration, offering numerous advantages over conventional electronic sensors, including high accuracy, distributed sensing capabilities, immunity to electromagnetic interference, and compact size. In this study, we propose a remarkably simple fiber optic-tip sensor system combined with machine learning techniques for the identification of pure and volatile organic liquids (VOLs).

A liquid droplet forms an extrinsic FPI (EFPI), with its effective reflectance being a function of the droplet's length. As the droplet evaporates, its length decreases. We conducted immersion tests using optical fiber tip sensors and monitored the time-transient responses of the evaporating droplets. Inspired by the evaporation dynamics of liquids, we employed machine learning techniques to efficiently extract valuable information from the evaporation time-transient signals of liquid pendant droplets. The time-transient signal was converted into image data using a continuous wavelet transform, and convolutional neural network (CNN) models were then applied to predict the liquid being tested based on the image data. Consequently, we developed a sensing system utilizing advanced data-driven techniques, such as machine learning, for liquid identification.

This innovative and intelligent sensor system has the potential to serve as a foundation for a new generation of powerful sensor networks"--Abstract, p. iv

Advisor(s)

Huang, Jie

Committee Member(s)

Gerald, Rex
Bo, Rui
Zhang, Jiangfan
Kaur, Amardeep
Ma, Hongyan

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2023

Pagination

xii, 102 pages

Note about bibliography

Includes_bibliographical_references_(pages 100-101)

Rights

© 2023 Wassana Naku, All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12255

Electronic OCLC #

1426305747

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