Engineering Science - Theses, Dissertations, and other Required Graduate Degree Essays

Receive updates for this collection

Development of wearable, screen-printable conductive polymer biosensors on flexible and textile substrates

Author: 
Date created: 
2021-06-25
Abstract: 

Wearable biosensors have great potential for real-time diagnostics, but have been encumbered by costly fabrication processes, rigid materials, and inadequate sensitivity for physiological ranges. Sweat has hitherto been an understudied sample for measurement of components like pH and lactate, which can provide meaningful guidance for wound healing, eczema, and sports medicine applications. This thesis presents the development of a flexible, textile-based, screen-printed electrode system for biosensing applications. Furthermore, a flexible, pH-sensitive composite for textile substrates is developed by mixing polyaniline with dodecylbenzene sulfonic acid and textile screen-printing ink. The optimized composite’s pH response is compared to electropolymerized and drop-cast polyaniline sensors via open circuit potential measurements. A linear response is observed for all sensors between pH 3-10, with the composite demonstrating sufficient response time and a sensitivity better than -20 mV/pH, exceeding existing flexible screen-printed pH sensors. Investigations into a potentiometric, non-enzymatic lactate sensor using polyaminophenylboronic acid are also discussed.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Bonnie Gray
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Utilization and experimental evaluation of occlusion aware kernel correlation filter tracker using RGB-D

Author: 
Date created: 
2021-02-12
Abstract: 

Unlike deep-learning which requires large training datasets, correlation filter-based trackers like Kernelized Correlation Filter (KCF) uses implicit properties of tracked images (circulant matrices) for training in real-time. Despite their practical application in tracking, a need for a better understanding of the fundamentals associated with KCF in terms of theoretically, mathematically, and experimentally exists. This thesis first details the workings prototype of the tracker and investigates its effectiveness in real-time applications and supporting visualizations. We further address some of the drawbacks of the tracker in cases of occlusions, scale changes, object rotation, out-of-view and model drift with our novel RGB-D Kernel Correlation tracker. We also study the use of particle filter to improve trackers' accuracy. Our results are experimentally evaluated using a) standard dataset and b) real-time using Microsoft Kinect V2 sensor. We believe this work will set the basis for better understanding the effectiveness of kernel-based correlation filter trackers and to further define some of its possible advantages in tracking.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Shahram Payandeh
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Estimation of soil moisture and earth resistivity using Wenner’s method and machine learning

Author: 
Date created: 
2020-12-16
Abstract: 

The present research consists of using Wenner’s four electrodes method to measure the electrical resistivity of soil (e.g., clayey silt and clay), applying two machine-learning algorithms (k Nearest Neighbor (KNN) and Support Vector Machine (SVM)) to predict the type of soil. Such predictions may be leveraged, e.g., to extract parameters to help choose materials to withstand electrochemical corrosion in a hybrid environment (soil and moisture). A dataset of 162 sample points was obtained from the literature (151 training, 11 testing points). From laboratory experiments, 26 sample points (corresponding to 130 measurements) were obtained; 6 points were added to the literature training dataset, and 20 were used as testing points for final validation. The results show that given the electrical resistivity of soil and its moisture, the KNN model is capable of predicting the type of soil with accuracy, error rate, sensitivity, specificity, and precision of 70%, 30%, 64%, 83%, and 90% respectively. In contrast, the SVM presented an error rate and accuracy of 44.1% and 55.9 % respectively.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Bonnie Gray
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Nano-optical devices and their fabrication for data storage and system integration

Author: 
Date created: 
2020-05-08
Abstract: 

Quick response (QR) codes enable optical machine-readable data storage in an image format, and are at the forefront of prevalent security systems with widespread applications in document and packaging authentication. Despite their popularity, they have been subject to ongoing investigations to boost their capacity and security without compromising their readability. This can be caused by various physical (e.g., cross-module interference in ink-based QR codes) and optical sources of interference in high-capacity multi-colour information storage. QR codes which function by employing different properties of light such as wavelength, polarization, amplitude, and phase, provide an unparalleled level of data protection. However, various constraints such as spectral overlap, multisource illumination, photobleaching, photoblinking, autofluorescence, fluorescence quenching, and prolonged read out processes limit their applicability. In this thesis, a new diffractive structural colour QR code with enhanced security, scalability, lifetime, readability, and capacity is developed to address these issues. The angle-dependent recovery, unique regional intensity signatures, and the technological difficulty of physical duplication provides strong security for protecting important products and documents. Colour is used as a means of pushing the limit of the information density to three times the maximum value obtained using conventional monochrome QR codes. However, leveraging colours for embedding higher volumes of data tends to elevate the noise level due to the presence of cross-module and cross-channel interference which may occur in both pigment-based and structural colours. As a result, various image processing techniques such as histogram equalization and decorrelation stretching are used to retrieve structural colour QR code images with different lighting conditions. The manufacturing technique is based on nanoimprinting, selective UV laser activation, and thermal treatment. For boosting its throughput, thermoplastic flow and crosslinking of exposed/unexposed nanostructured SU-8 as a long-lasting medium with high thermal, mechanical, and chemical stability is standardized. To enable mass-production of the developed QR code, a new method for origination of Ni stamps from the SU-8 master is developed. Also, contributions for the development of a new optical layer-by-layer alignment ruler for guiding 3D integration as a potential method for increasing the capacity of data storage devices are made. The optical ruler can function based on the two concepts of intensity transmission blocking and induced EOT resonance.

Document type: 
Thesis
File(s): 
Decoding process of the red, green, and blue channels of a typical image of a developed structural colour QR code
Supervisor(s): 
Bonnie Gray
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Autonomous task-based grasping for mobile manipulators

Author: 
Date created: 
2021-02-12
Abstract: 

A fully integrated grasping system for a mobile manipulator to grasp an unknown object of interest (OI) in an unknown environment is presented. The system autonomously scans its environment, models the OI, plans and executes a grasp, while taking into account base pose uncertainty and obstacles in its way to reach the object. Due to inherent line of sight limitations in sensing, a single scan of the OI often does not reveal enough information to complete grasp analysis; as a result, our system autonomously builds a model of an object via multiple scans from different locations until a grasp can be performed. A volumetric next-best-view (NBV) algorithm is used to model an arbitrary object and terminates modelling when grasp poses are discovered on a partially observed object. Two key sets of experiments are presented: i) modelling and registration error in the OI point cloud model is reduced by selecting viewpoints with more scan overlap, and ii) model construction and grasps are successfully achieved while experiencing base pose uncertainty. A generalized algorithm is presented to discover grasp pose solutions for multiple grasp types for a multi-fingered mechanical gripper using sensed point clouds. The algorithm introduces two key ideas: 1) a histogram of finger contact normals is used to represent a grasp “shape” to guide a gripper orientation search in a histogram of object(s) surface normals, and 2) voxel grid representations of gripper and object(s) are cross-correlated to match finger contact points, i.e. grasp “size”, to discover a grasp pose. Constraints, such as collisions with neighbouring objects, are incorporated in the cross-correlation computation. Simulations and preliminary experiments show that 1) grasp poses for three grasp types are found in near real-time, 2) grasp pose solutions are consistent with respect to voxel resolution changes for both partial and complete point cloud scans, 3) a planned grasp pose is executed with a mechanical gripper, and 4) grasp overlap is presented as a feature to identify regions on a partial object model ideal for object transfer or securing an object.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Kamal Gupta
Mehran Mehrandezh
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Deep learning for optical coherence tomography angiography: Quantifying microvascular changes in diabetic retinopathy

Author: 
Date created: 
2020-12-18
Abstract: 

Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. Machine learning applications have directly benefited ophthalmology, leveraging large amounts of data to create frameworks to aid clinical decision-making. In this thesis, several techniques to quantify the retinal microvasculature are explored. First, high-quality, averaged, 6x6mm OCT-A enface images are used to produce manual segmentations for the corresponding lower-quality, single-frame images to produce more training data. Using transfer learning, the resulting convolutional neural network (CNN) segmented the superficial capillary plexus and deep vascular complex with performance exceeding inter-rater comparisons. Next, a federated learning framework was designed to allow for collaborative training by multiple participants on a de-centralized data corpus. When trained for microvasculature segmentation, the framework achieved comparable performance to a CNN trained on a fully-centralized dataset.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Marinko Sarunic
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Downsized flexible capacitive strain sensing filament and its application in knee angle tracking during walking

Author: 
Date created: 
2019-12-18
Abstract: 

Assistive Rehabilitation technologies have been a prominent research topic in recent years for patients who suffer from impaired lower extremity motor abilities. Continuous tracking of knee movement can provide valuable insight into the effectiveness of therapeutic or surgical interventions such as improvements in range of motion or stability over the course of the treatments. Video-based motion tracking is an industry standard for motion tracking, but its usage is limited in a clinical setting due to its prohibitive cost and space requirement. This study proposes a downsized flexible capacitive strain sensing filament that can be weaved into textiles to achieve in situ motion tracking. Its effectiveness is shown in a knee joint angle tracking with video-based motion capture as reference. Sensor-predicted knee angle is 99% accurate when compared to the reference with root mean square error of 1.79 degrees. An improved sensor is also fabricated and characterized to show enhanced performance.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Super learner implementation in corrosion rate prediction

Author: 
Date created: 
2021-02-22
Abstract: 

This thesis proposes a new machine learning model for predicting the corrosion rate of 3C steel in seawater. The corrosion rate of a material depends not just on the nature of the material but also on the material's environmental conditions. The proposed machine learning model comes with a selection framework based on the hyperparameter optimization method and a performance evaluation metric to determine the models that qualify for further implementation in the proposed models’ ensembles architecture. The major aim of the selection framework is to select the least number of models that will fit efficiently (while already hyperparameter-optimized) into the architecture of the proposed model. Subsequently, the proposed predictive model is fitted on some portion of a dataset generated from an experiment on corrosion rate in five different seawater conditions. The remaining portion of this dataset is implemented in estimating the corrosion rate. Furthermore, the performance of the proposed models’ predictions was evaluated using three major performance evaluation metrics. These metrics were also used to evaluate the performance of two hyperparameter-optimized models (Smart Firefly Algorithm and Least Squares Support Vector Regression (SFA-LSSVR) and Support Vector Regression integrating Leave Out One Cross-Validation (SVR-LOOCV)) to facilitate their comparison with the proposed predictive model and its constituent models. The test results show that the proposed model performs slightly below the SFA-LSSVR model and above the SVR-LOOCV model by an RMSE score difference of 0.305 and RMSE score of 0.792. Despite its poor performance against the SFA-LSSVR model, the super learner model outperforms both hyperparameter-optimized models in the utilization of memory and computation time (graphically presented in this thesis).

Document type: 
Thesis
File(s): 
Supervisor(s): 
Bonnie Gray
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

PPM level gaseous ammonia detection using laser Induced fluorescence on vapochromic coordination polymers

Author: 
Date created: 
2020-09-04
Abstract: 

The detection of ammonia in parts per millions range has been challenging in sensors research and is of great importance for industrial applications. This thesis document efforts to develop and test a low-cost optical detection system for ppm level ammonia measurements utilizing a Vapochromic Coordination Polymer (VCP) Zn[Au(CN)2]2 as the sensing material. Upon high concentration ammonia exposure, the polymer’s fluorescent peak under near-UV stimulation undergoes a spectral shift from 470nm to 530nm, while the intensity increases by 3~4X. At ammonia concentrations < 1000ppm, the spectral shift becomes hidden within the overall changing fluorescent spectrum shape so simple detection methods do not work. The key point in this analysis is to note the way the spectrum changes in each wavelength bins varies in different ammonia concentration exposures. We then developed two customized spectral processing techniques named Spectral Region Subtraction (SRS) method and Sum of Integrated Emissions (SIE) method to characterize hidden changes in spectral shape for concentrations < 1000ppm. Both methods give excellent sensitivity between 0 – 50 ppm and > 300 ppm. For wide-range concentration detection, a combination of two metrics have to be used together.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Glenn Chapman
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

A deep learning approach for detecting epileptic spike in magnetoencephalography signals

Author: 
Date created: 
2020-07-16
Abstract: 

Epilepsy is one of the most serious neurological disorders that affects people of all ages. In Canada, an average of 15,500 people discover epilepsy symptoms each year [1]. Numerous scholars have conducted extensive research in automated detection of epilepsy spike for presurgical assessment. However, the study of Magnetoencephalography (MEG) spike detection is limited to under 30 patients’ data. In this thesis, we explore a deep learning approach for detecting spike in interictal MEG recordings of up to 300 epileptic patients in an automated fashion. We evaluate the convolutional neural network architecture and long short-term memory method on both 2D images and 3D spatiotemporal MEG recordings. For 2D images, we tested a simple 3 layer Convolutional Neural Network (CNN/ConvNet) model and a transfer learning model, and achieved an accuracy of 83.12% and 82.73%, sensitivity of 91.66% and 78.52%, and specificity of 74.58% and 86.94%. For 3D spatiotemporal data, we tested the 3 dimensional CNN model and Long short-term memory (LSTM) model to get 86.04% and 83.09% in accuracy, 92.37% and 87.18% in sensitivity, and 79.69% and 78.99% in specificity. The methods show an increasing performance with larger datasets, which provide us confidence on the validity of the proposed automation technique.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Ash Parameswaran
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.