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

Receive updates for this collection

Brain Vital Signs: Towards Next Generation Neurotechnologies for Rapid Brain Function Assessments at Point-of-Care

Author: 
Peer reviewed: 
No, item is not peer reviewed.
Date created: 
2019-11-18
Abstract: 

Vital signs such as heart rate, blood pressure and body temperature have revolutionized medical care by providing rapidly assessed, physiology-based, non-invasive and easy-to-understand standardized metrics of different body functions. However, no such vital sign exists for the brain; instead, assessments of the brain are largely reliant on surrogate measures such as observations of behaviour or questionnaire-based measurements, which have been shown to be subjective and unreliable. This research aims to fill this key scientific, clinical, and technological gap by developing a brainwave-based technology platform to evaluate ‘vital sign’ metrics for the brain. A series of studies were undertaken to create and demonstrate a ‘brain vital signs’ platform that is capable of assessing a broad spectrum of functions ranging from the lower-level functions (i.e. sensation) to the highest-level cognition domains (i.e. contextual orientation). In particular, the first study focused on development and initial demonstration of the methods and apparatus for the brain vital signs technology; the next study focused on characterizing the brain vital sign responses to ensure scientific validity; the third study focused on creating a previously non-existant neurophysiology-based neural marker capable of capturing contextual orientation – which is the highest level cognitive domain known to be crucial to frontline clinical assessments; and finally, the last study focused on developing an advanced data analytic technique for maximizing signal capture under noisy environments typical of point-of-care evaluation settings. This research represents the first time that a ‘vital sign’-like metric has been developed for the brain that embodies the key characteristics of existing vital signs, enabling brain function measures that are rapid (~5 minute testing time), easy to use, portable, non-invasive, and standardized with automated analysis. Crucially, these vital sign metrics directly measure the brain’s electrical activity and do not depend on any responses from the test participant, thus providing much more objective information about brain function. The development of portable and objective ‘vital sign’-like metrics for the brain not only advances the scientific understanding of brain function through novel metrics like orientation, but also creates significant opportunities for enhancing clinical diagnosis through improved brain function assessments at the point-of-care.

Document type: 
Thesis
File(s): 
Supervisor(s): 
Ryan D’Arcy
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) Ph.D.

Abstracting OpenCL for Multi-Application Workloads on CPU-FPGA Clusters

Author: 
Date created: 
2019-12-03
Abstract: 

Field-programmable gate arrays (FPGAs) continue to see integration in data centres, where customized hardware accelerators provide improved performance for cloud workloads. However, existing programming models for such environments typically require a manual assignment of application tasks between CPUs and FPGA-based accelerators. Furthermore, coordinating the execution of tasks from multiple applications necessitates the use of a higher-level cluster management system. In this thesis, we present an abstraction model named CFUSE (Cluster Front-end USEr framework), which abstracts the execution target within a heterogeneous cluster. CFUSE allows tasks from multiple applications from unknown workloads to be mapped dynamically to the available CPU and FPGA resources and allows accelerator sharing among applications. We demonstrate CFUSE with an OpenCL-based prototype implementation for a small cluster of Xilinx FPGA development boards. Using this cluster, we execute a variety of multi-application workloads to evaluate three scheduling policies and to determine the relevant scheduling factors for the system.

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

Practical electroencephalography (EEG) applications in stroke rehabilitation: Towards brain-computer interface (BCI) setup and motor function assessment

Author: 
Date created: 
2019-11-04
Abstract: 

Electroencephalography (EEG) records electrical brain activity typically in a non-invasive manner. Recent literature has shown its potential in stroke rehabilitation, to actively engage stroke survivors in rehabilitation. In Chapter 3 of this thesis, the problems of EEG applications in stroke rehabilitation were firstly identified with a pilot study. Two main challenges were identified, hindering further application of EEG in stroke rehabilitation training. One of the challenges is that the BCI involved rehabilitation process is unsatisfying. Three objectives were derived from this challenge. Firstly, at the beginning of all EEG related stroke rehabilitation training, it is both time and effort consuming to go through data collection and model training for every rehabilitation task. Therefore, in Chapter 4 of the thesis, the possibility of using an EEG model from one type of motor imagery (e.g.: elbow extension and flexion) to classify EEG from other types of motor imagery activities (e.g.: open a drawer) was investigated. Secondly, a novel training method was introduced together with a rehabilitation platform in Chapter 5. The results suggested that the proposed methods in this thesis are feasible and potentially effective. Thirdly, the transition of the offline analysis method to an online control method is one of the major factors that affect BCI performance. However, research particularly focused on the method of filtering the prediction of an online classification is scarce. In Chapter 6, two methods of filtering online classification predictions were proposed and evaluated in a pseudo-online classification paradigm, with the EEG data collected from Chapter 5. The other challenge is related to motor function assessments in rehabilitation. Motor function is generally assessed with standard questionnaire-based assessments. In these assessments, the rater requires the ratee to perform pre-defined movements and gives scores based on the quality of the movements. Therefore, those motor function assessments have inevitable subjective influences on the functional scores. In Chapter 7 of the thesis, the author investigated the possibility of using EEG data to assess motor function. As a preliminary investigation, EEG-based motor function assessments were only investigated for upper-extremity among participants with stroke. The results suggested that EEG data can be used to assess motor function accurately.

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

The design of soft fluid filled actuators driven by conductive nylon

Author: 
Date created: 
2019-09-17
Abstract: 

Soft robots have become increasingly prevalent due to their distinct advantages over traditional rigid robots such as high deformability and good impact resistance. However, soft robotics are currently limited by bulky, non-portable methods of actuation. In this study, we propose a soft actuator driven by conductive nylon artificial muscles which is able to produce forces up to 1.2N. By utilizing nylon artificial muscles, the system does not require sizable pumps or compressors for actuation. The proposed actuator is made up of two main components, a sealed bladder filled with air and an arrangement of nylon artificial muscles. The quasi static behavior of the actuator is characterized using established hyper elastic models and validated against experimental results (maximum error of 5.3%). Using these models, a set of design considerations are formulated which outline the achievable torques for various actuator dimensions.

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

Audio-visual speech processing using deep learning techniques

Author: 
Date created: 
2019-10-02
Abstract: 

Speech separation is the task of segregating a target speech signal from background interference. To differentiate the separation of multiple speech sources from separating speech from non-speech noise, the terms Speaker Separation and Speech Enhancement (or Denoising) are commonly used, respectively. Speech separation can benefit from exploiting different modalities, i.e., audio and visual, and directional information when multiple microphones are available. A new approach is developed for subject-independent speaker separation by incorporating spectral, spatial and visual features. The audio signals have their magnitude and phase modified in the frequency domain for the speaker separation. The key idea is to estimate the target magnitudes from the audio with the Permutation Invariant Training (PIT) technique and then refine these estimates using both visual and spatial audio features. Specifically, visual features are matched to the corresponding audio, and spatial audio features are used as side information and shown to provide drastic improvement for magnitude and phase estimation in terms of output speech intelligibility, quality, and the separation performance. Visual information also provides improvements. Hence, both visual and spatial features are shown to be useful for speaker separation. A monaural speech enhancement model is also developed which incorporates both audio and visual information. In contrast to the audio-visual speaker separation model, the audio-visual speech enhancement model operates in time-domain. Hence, there is no need for a transformation and separate models for estimation of magnitude and phase spectra. According to the results of the objective evaluations, exploiting visual information for enhancement applications improves the performance in terms of both output quality and intelligibility.

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

Developing a machine learning framework for 24-hour data analysis aimed at early detection of cardiac arrhythmias as a guiding tool for physicians

Author: 
Date created: 
2019-05-20
Abstract: 

Cardiovascular diseases (CVD), defined as a spectrum of disorders primarily impacting the heart and the circulatory system, account for a substantial fraction of worldwide morbidity and mortality. Electrocardiograms (ECGs) are routinely implemented in a patient’s diagnosis, both in hospitals and outpatient settings. They serve as one of the primary diagnostic tools as patients encounter medical personnel, particularly in suspected CVD. A cardiac holter monitor is a medical diagnostic device, connected to the patient via several conductive leads placed across the chest, and "worn" on a strap across the shoulder. A holter is applied to record continuous ECG data (typically 24 hours). With recently emerging applications of Machine Learning (ML) in data analysis techniques, the need for human expertise and potential human error could be minimized, and prediction accuracy optimized considerably. Hence, the objective of this research is to develop a machine learning approach to eventually aid physicians with their decisions as a powerful guiding/assisting tool to analyse the ECG information reported by holter monitors. Furthermore, we aim to develop a computer aided diagnostic system that can assist expert cardiologists by providing intelligent, cost effective, and time-saving diagnosis. In this thesis, we implement a deep learning-based solution to analyse readings of 24-holter monitors. In our proposed solution, we train neural networks to extract high-level features from temporal signal recordings of holter monitors. We present a supervised neural network framework to predict the physician’s final interpretations based on the holter recorded signals. The outputs of the network contain the likelihood of four possible scenarios of Normal and three types of arrhythmias. The high classification performance of the proposed methodology emphasizes the capability of this framework to be used as an assisting tool alongside the physicians to interpret holter reports.

Document type: 
Thesis
Supervisor(s): 
Andrew Rawicz
Department: 
Applied Sciences: School of Engineering Science
Thesis type: 
(Thesis) M.A.Sc.

Investigation on compressive devices based on dielectric elastomer actuators

Author: 
Date created: 
2019-11-20
Abstract: 

Dielectric elastomer actuators (DEAs) are an emerging technology from the larger class of artificial muscle actuators, showing interesting properties such as softness and large actuation strains. A DEA can be used to create various types of motions (such as planar, rotary, and bending) and contact forces (such as tension, biaxial compression, and bending moment). Due to its interesting properties, there are a growing number of studies on improving DEA applicability, reliability, and characteristics. Additionally, despite limited commercial use, there is a growing global push toward commercializing DEA in various sectors. In this thesis, in an effort to investigate practical applications of DEAs, their functionality is studied in applications where compressive forces are exerted by the actuator. Two different modalities in which DEAs could apply compressive forces to an encompassed object are identified. To narrow the focus of the thesis, a practical application is introduced and investigated for each modality. The first practical application, based on the first modality, introduces the use of DEAs as a compression bandage to improve blood circulation in the human leg. The proposed compression bandage could potentially enable a controlled variable compression around the lower leg. The second practical application, based on the second modality, proposes a novel gripper that uses DEAs as a mean to apply a soft touch on objects. The proposed gripper can apply up to 2N of grasping force to select objects. The gripper may be adopted as an end effectors for collaborative robotic arms, where a human operator is collaborating with the robotic arm to handle a delicate object. Typically, DEAs are actuated using high electrical voltages of several kilovolts. This operating voltage gives rise to a safety concern for practical applications where DEAs are sought to be operated in proximity to the human body. Since the two aforementioned applications require, or may require the operation of DEAs close to the human body, it is very important to study the electrical safety of DEAs and investigate methods to manage the risk of their high operating voltages. The last part of this thesis discusses the electrical safety of DEAs in scenarios where DEA circuitry is in contact with the human body, and shows that they can be safely used. Critical parameters for identification of the electrical safety of DEAs are also introduced to assist in designing safer DEAs for such applications.

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

Differences in neuroimaging, clinical, cognitive score measures between males and females in Alzheimer's Dementia and Healthy Aging

Author: 
Date created: 
2019-07-18
Abstract: 

In this thesis, differences in the brain structure, clinical and cognitive scores as a function of sex (male/female) were studied for the control (healthy aging) and Alzheimer's Dementia (AD) group. Analysis of brain structure volumes, cortical thickness, CSF measures and cognitive scores were performed on the entire ADNI dataset. In healthy aging, the male brain structure shows greater atrophy with age. However, in case of AD, differences in brain structure that could be explained due to sex differences are found to be reduced. This suggests that female brains showed greater atrophy in AD as compared to male brains. Also, the role of APOE4 gene status on brains of control and AD group was studied. APOE4 gene status did not have any significant effect on baseline measurements in the control group, but showed significant difference for left hippocampus volume in the AD group. In addition, APOE4 carriers having a thicker cortex than non carriers for regions in right temporal lobe for AD patients. Role of APOE4 towards AD has been well studied, but the role played by the absence of even a single APOE4 allele in Alzheimer's Dementia must be explored next.

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

Enhanced fireworks algorithm to optimize extended Kalman filter speed estimation of an induction motor drive system

Author: 
Date created: 
2019-08-15
Abstract: 

To maximize efficiency, the speed of an induction motor (IM) is controlled to match the load. Often an extended Kalman filter (EKF) estimates the speed of the IM, eliminating the speed sensor. The EKF requires a mathematical model of the IM and system and noise covariances, typically determined by optimization using trial-and-error or a genetic algorithm (GA). My research objective was to investigate a relatively new algorithm, the enhanced fireworks algorithm (EFWA) and its ability to determine covariances compared to current methods. I used a Simulink model of a system comprised of an IM controlled by a variable frequency drive (VFD) to experiment with the EKF using trial and error, genetic algorithm and EFWA optimization methods. My results indicated that in this application, using selected parameters, the EFWA provides a good solution in fewer iterations than the GA, which may be required for online adaptive tuning of the EKF.

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

Towards the Development of an Adaptive Compression System

Author: 
Date created: 
2016-11-16
Abstract: 

Regarded as the mainstay for treatment of venous insufficiency and the associated complications, compression therapy aims at assisting with venous return through the exertion of external pressure on the limbs. Compression is achieved by medical bandages and stockings, which hold promise only during supine and walking conditions, or mechanical pumps, which are usually bulky and limited to non-ambulatory use. Hence, the purpose of this study was to develop an improved compression system that eliminates the flaws of the existing products. To attain this goal, a motorized compression bandage was designed that takes advantage of force-sensing resistors (FSRs®) to exert reproducible, controlled pressure on the lower extremities. The performance of the device in enhancing venous return was explored in a pilot experiment, wherein graded lower body negative pressure (LBNP) was employed as a surrogate of standing erect. The results revealed a significant reduction in the mean cardiovascular changes to LBNP.

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