Microfluidic olfaction detector for outdoor applications : a pattern recognition approach
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Aly, Mohamed Tarek
issued: 2022-01-31T08:00:00Z
issued: 2021
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: There exists a growing demand for efficient gas sensing devices in applications involving indoor and outdoor air monitoring, food quality assessment, law enforcement, medical diagnoses and others. Advances in the field of replicating mammalian olfaction are still at infancy as compared to technologies developed to function close to the human senses of vision, hearing, and touch. Microfluidic olfaction detectors (MFODs) are an emerging class of detectors that are low-cost, compact, and highly selective, having a great potential for a wide range of gas monitoring applications. MFODs are, however, susceptible to fluctuations in environmental conditions, and suffer from prolonged sampling times, which hinder their introduction to outdoor applications. This thesis aims at overcoming these two obstacles by developing robust pattern recognition systems using machine learning techniques. To take into account the effect of temperature on MFOD, we perform a machine learning comparison of MFOD performance at three temperature ranges (5, 15, and 25°C) using a limited dataset (as it is challenging to collect substantial amount of data in many applications). By transforming the raw responses using dimensionality reduction techniques and inputting temperature as features, an efficient classifier was able to learn from the small datasets and reached a 95.5% classification accuracy for methane vs. methane-ethane mixure. A unique pattern recognition system was then designed to reduce the recognition time of a MFOD. This system involved: (i) the ensemble of four heterogeneous base machine learning models, (ii) a Long-Short Term Memory Neural Network (LSTM) to construct a portion of the transient response, and (iii) features from an additional fast response sensor. The system was applied to different transient response times to further identify the minimal transient time needed for the proposed system. This effort resulted in facilitating gas discrimination and quantification with less…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/77210
note: MICROFLUIDIC OLFACTION DETECTOR FOR OUTDOOR APPLICATIONS: A PATTERN RECOGNITION APPROACH by Mohamed Tarek Aly B.Sc., United Arab Emirates University, UAEU 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) January 2021 © Mohamed Tarek Aly, 2021 ii The following individuals certify that they have read, and recommend to College of Graduate Studies for acceptance, the dissertation entitled: Microfluidic Olfaction Detector for Outdoor Applications: A Pattern Recognition Approach submitted by Mohamed Tarek Aly in partial fulfillment of the requirements for the degree of Master of Applied Science in Mechanical Engineering Examining Committee: Dr. Mina Hoorfar, School of Engineering Supervisor Dr. Homayoun Najjaran, School of Engineering Co- Supervisor Dr. Abbas Milani, School of Engineering Supervisory Committee Member Dr. Anas Chaaban, School of Engineering Supervisory Committee Member Dr. Rehan Sadiq, School of Engineering University Examiner iii Abstract There exists a growing demand for efficient gas sensing devices in applications involving indoor and outdoor air monitoring, food quality assessment, law enforcement, medical diagnoses and others. Advances in the field of replicating mammalian olfaction are still at infancy as compared to technologies developed to function close to the human senses of vision, hearing, and touch. Microfluidic olfaction detectors (MFODs) are an emerging class of detectors that are low-cost, compact, and highly selective, having a great potential for a wide range of gas monitoring applications. MFODs are, however, susceptible to fluctuations in environmental conditions, and suffer from prolonged sampling times, which hinder their introduction to outdoor applications. This thesis aims at overcoming these two obstacles by developing robust pattern recognition systems using mach…
has type: Thesis/Dissertation
date issued: 2021-02
is shown at: 10.14288/1.0395770
language: eng
degree discipline: Mechanical Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/77210