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

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Non-parametric modeling in non-intrusive load monitoring

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

Non-intrusive Load Monitoring (NILM) is an approach to the increasingly important task of residential energy analytics. Transparency of energy resources and consumption habits presents opportunities and benefits at all ends of the energy supply-chain, including the end-user. At present, there is no feasible infrastructure available to monitor individual appliances at a large scale. The goal of NILM is to provide appliance monitoring using only the available aggregate data, side-stepping the need for expensive and intrusive monitoring equipment. The present work showcases two self-contained, fully unsupervised NILM solutions: the first featuring non-parametric mixture models, and the second featuring non-parametric factorial Hidden Markov Models with explicit duration distributions. The present implementation makes use of traditional and novel constraints during inference, showing marked improvement in disaggregation accuracy with very little effect on computational cost, relative to the motivating work. To constitute a complete unsupervised solution, labels are applied to the inferred components using a Res-Net-based deep learning architecture. Although this preliminary approach to labelling proves less than satisfactory, it is well-founded and several opportunities for improvement are discussed. Both methods, along with the labelling network, make use of block-filtered data: a steady-state representation that removes transient behaviour and signal noise. A novel filter to achieve this steady-state representation that is both fast and reliable is developed and discussed at length. Finally, an approach to monitor the aggregate for novel events during deployment is developed under the framework of Bayesian surprise. The same non-parametric modelling can be leveraged to examine how the predictive and transitional distributions change given new windows of observations. This framework is also shown to have potential elsewhere, such as in regularizing models against over-fitting, which is an important problem in existing supervised NILM.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Stephen Makonin
Ivan Bajic
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Addressing the challenges posed by human machine interfaces based on force sensitive resistors for powered prostheses

Date created: 
2020-10-09
Abstract: 

Despite the advancements in the mechatronics aspect of prosthetic devices, prostheses control still lacks an interface that satisfies the needs of the majority of users. The research community has put great effort into the advancements of prostheses control techniques to address users’ needs. However, most of these efforts are focused on the development and assessment of technologies in the controlled environments of laboratories. Such findings do not fully transfer to the daily application of prosthetic systems. The objectives of this thesis focus on factors that affect the use of Force Myography (FMG) controlled prostheses in practical scenarios. The first objective of this thesis assessed the use of FMG as an alternative or synergist Human Machine Interface (HMI) to the more traditional HMI, i.e. surface Electromyography (sEMG). The assessment for this study was conducted in conditions that are relatively close to the real use case of prosthetic applications. The HMI was embedded in the custom prosthetic prototype that was developed for the pilot participant of the study using an off-the-shelf prosthetic end effector. Moreover, prostheses control was assessed as the user moved their limb in a dynamic protocol.The results of the aforementioned study motivated the second objective of this thesis: to investigate the possibility of reducing the complexity of high density FMG systems without sacrificing classification accuracies. This was achieved through a design method that uses a high density FMG apparatus and feature selection to determine the number and location of sensors that can be eliminated without significantly sacrificing the system’s performance. The third objective of this thesis investigated two of the factors that contribute to increased errors in force sensitive resistor (FSR) signals used in FMG controlled prostheses: bending of force sensors and variations in the volume of the residual limb. Two studies were conducted that proposed solutions to mitigate the negative impact of these factors. The incorporation of these solutions into prosthetic devices is discussed in these studies.It was demonstrated that FMG is a promising HMI for prostheses control. The facilitation of pattern recognition with FMG showed potential for intuitive prosthetic control. Moreover, a method for the design of a system that can determine the required number of sensors and their locations on each individual to achieve a simpler system with comparable performance to high density FMG systems was proposed and tested. The effects of the two factors considered in the third objective were determined. It was also demonstrated that the proposed solutions in the studies conducted for this objective can be used to increase the accuracy of signals that are commonly used in FMG controlled prostheses.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Carlo Menon
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Automating assessment of human embryo images and time-lapse sequences for IVF treatment

Author: 
Date created: 
2021-05-21
Abstract: 

As the number of couples using In Vitro Fertilization (IVF) treatment to give birth increases, so too does the need for robust tools to assist embryologists in selecting the highest quality embryos for implantation. Quality scores assigned to embryonic structures are critical markers for predicting implantation potential of human blastocyst-stage embryos. Timing at which embryos reach certain cell and development stages in vitro also provides valuable information about their development progress and potential to become a positive pregnancy. The current workflow of grading blastocysts by visual assessment is susceptible to subjectivity between embryologists. Visually verifying when embryo cell stage increases is tedious and confirming onset of later development stages is also prone to subjective assessment. This thesis proposes methods to automate embryo image and time-lapse sequence assessment to provide objective evaluation of blastocyst structure quality, cell counting, and timing of development stages.

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

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.