\"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
","abstract_html":"<p>"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).</p> <p>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.</p> <p>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</p>","abstract_has_math":false,"creators":["Naku, Wassana"],"institution":"Missouri University of Science and Technology","degree_name":"Ph. D. in Electrical Engineering","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T03:18:26Z","subjects":["Electrical and Computer Engineering","Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsmine.mst.edu/doctoral_dissertations/3257","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Naku, Wassana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph. D. in Electrical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Missouri University of Science and Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical and Computer Engineering","Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsmine.mst.edu/doctoral_dissertations/3257"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["\"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
"]},{"key":"dc:title","label":"Title","values":["Fiber Optic Sensors for Liquid Identifications"]}]}],"canonical_facts":{"dc:creator":["Naku, Wassana"],"dc:description.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
"],"dc:identifier":["https://scholarsmine.mst.edu/doctoral_dissertations/3257"],"dc:subject":["Electrical and Computer Engineering","Engineering"],"dc:title":["Fiber Optic Sensors for Liquid Identifications"],"dc:type":["Dissertation - Open Access"],"thesis:degree_name":["Ph. D. in Electrical Engineering"],"thesis:institution_name":["Missouri University of Science and Technology"]},"updated_at":"2026-07-24T03:18:26Z"}