Engineering Commons^™

Value Of Service-Oriented Multi-Service Provisioning And Resource Allocation In Integrated Localization, Sensing And Communication Systems, Biwei Li

Electronic Thesis and Dissertation Repository

The unprecedented proliferation of wireless infrastructures and their ongoing convergence with diverse industrial Internet of Things (IoT) applications introduce new demands for upcoming wireless networks. In response to such diversity of demands, envisioned future wireless networks must have multiple beyond communication capabilities, such as localization and sensing. To efficiently utilize, allocate, and manage these capabilities, the integration of localization, sensing, and communication (ILSAC) within a unified wireless system structure is of utmost importance. However, the seamless integration of ILSAC into intricate network infrastructures is encumbered by critical challenges, including high-accuracy localization/sensing algorithm, efficient resource management and allocation scheme, and robust …

Multi-Dimensional Qos And Collaborative Mac Layer Design For Dense, Diverse, And Dynamic Iot Network, Jiyang Bai

Electronic Thesis and Dissertation Repository

With the ubiquitous proliferation of Internet of Thing (IoT) devices, Access Points (APs) of future Wireless Fidelity (Wi-Fi) networks are expected to support dense Stations (STAs) with diverse Quality-of-Service (QoS) requirements under dynamic channel conditions. On account of high access collision and optimization problem complexity, the performance degradation brings new challenges to the existing Media Access Control (MAC) layer design in Wi-Fi. This thesis proposes novel technologies to enable low-latency high-performance MAC layer designs, which support dense access of STAs with real-time solutions of resource allocation and link adaptation problems. Both grouping and collaborative architectures are utilized based on game …

A Novel Method For Determining The Inherent Capabilities Of Computer And Robotic-Assisted Total Knee Arthroplasty Devices, Delaney R.G. Stevens

Electronic Thesis and Dissertation Repository

This thesis presents a method for evaluating and comparing assistive total knee arthroplasty (TKA) devices while controlling surgeon landmarking variability. To achieve consistent landmark selection by surgeons during TKA procedures, the method uses identical 3D-printed knees with indented landmarks. This method was used to compare a robotic and computer-assisted TKA device on three metrics: measurement accuracy, alignment accuracy, and cut-surface uniformity. Although both devices had considerable sagittal plane measurement errors, the robotic device had better measurement and alignment accuracy than the computer-assisted device. Furthermore, the robotic device's measuring error compensated for cutting errors, but the computer-assisted device's compounded them. However, …

Evaluating Eeg–Emg Fusion-Based Classification As A Method For Improving Control Of Wearable Robotic Devices For Upper-Limb Rehabilitation, Jacob G. Tryon

Electronic Thesis and Dissertation Repository

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.

One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor …

Nonlinear Adaptive Control Of Drilling Processes, Maksim Faronov

Electronic Thesis and Dissertation Repository

This work deals with the modeling and control of automated drilling operations. Advances in drilling automation are of substantial importance because improvements in drilling control algorithms will result in more efficient drilling, which is beneficial from both economic and environmental points of view. While the primary application of the results is extraction of natural resources, potentially there exists a wide range of applications, including offshore exploration, archaeological research, and automated extraterrestrial mining, where implementation of new methods and control algorithms for drilling processes can bring substantial benefits.

The main contribution of the thesis is development of new methods and algorithms …

Enhancing The Performance Of Nmt Models Using The Data-Based Domain Adaptation Technique For Patent Translation, Maimoonah Ahmed

Electronic Thesis and Dissertation Repository

During today’s age of unparalleled connectivity, language and data have become powerful tools capable of enabling effective communication and cross-cultural collaborations. Neural machine translation (NMT) models are especially capable of leveraging linguistic knowledge and parallel corpora to increase global connectivity and act as a tool for the transmission of knowledge. In this thesis, we apply a data-based domain adaptation technique to fine-tune three pre-existing NMT transformer models with attention mechanisms for the task of patent translation from English to Japanese. Languages, especially in the context of patents, can be very nuanced. A clear understanding of the intended meaning requires comprehensive …

Investigation Of Sensorimotor Integration And Control In Parkinson’S Disease Using Haptics-Enabled Robotics And Machine Learning, Yokhesh Krishnasamy Tamilselvam

Electronic Thesis and Dissertation Repository

Non-motor symptoms such as perceptual deficits and cognitive impairments, i.e., deficits in executive functions, presented at an early stage of Parkinson’s Disease (PD) substantially affect a PD patient’s quality of life and may contribute to motor impairments. Studies have emphasized the need to better understand these impairments and the abnormalities contributing to them as it provides a means to efficiently manage the disease. Further, due to the early onset of these deficits, the contributing abnormalities may be considered a potential biomarker for early diagnosis of PD. However, the impairments and the contributing abnormalities are not yet fully understood, leading to …

A Novel Two-Stage Ac-Dc Power Converter With Partial Power Processing, Mina Fakhri

Electronic Thesis and Dissertation Repository

A two-stage power converter with an AC-DC boost converter and a soft-switched DC-DC full-bridge converter is proposed in this thesis. The first stage has two interleaved modules that perform power factor correction; the second stage converts the output of the first stage to the desired output. An auxiliary circuit with a switch, a small transformer, and passive components is used to turn off the AC-DC converter switches with soft-switching; the auxiliary switch can also be turned on and off softly. The secondary of the auxiliary transformer is connected to the output of the overall converter so that some power can …

Exploration Of Force In Movement And Perception In Parkinson’S Disease, Caroline Stefanie Aitken

Electronic Thesis and Dissertation Repository

Parkinson’s Disease (PD) causes force control deficits in upper and lower limbs. About 50% of patients with advanced PD develop freezing of gait (FOG). There is limited research comparing force control in PD with and without FOG, especially in upper limbs. It has been suggested that motor control deficits in PD are related to deficits in kinesthesia, but there is conflicting evidence whether levodopa alleviates kinesthetic deficits. In this thesis, force control was explored using an upper-and-lower-limb haptics-enabled robot in a reaching task, and kinesthesia was investigated using a haptic device in a force discrimination task while participants were on …

Cluster-Based Station Reporting And Multi-Ap Coordination In Wi-Fi Networks, Zeyad Abdelmageid

Electronic Thesis and Dissertation Repository

Due to the few available Wi-Fi channels and the existence of other technologies, the channel selected by an access point (AP) to be assigned to the network is of extreme importance for network performance due to different interference conditions at different channels. While channel selection algorithms have been proposed, very few of them are user-centric, which could incur a large signaling overhead. As a result, in this thesis, a channel selection algorithm with a low-overhead station (STA) reporting mechanism is proposed, which utilizes the spatial correlation of interference by clustering close-by STAs in order to reduce the feedback overhead by …

Identifying Sources Of Error In Computer Navigated Total Knee Arthroplasties Using A Metric On Se(3) And Sensitivity Analyses, Nicole E. Martensson

Electronic Thesis and Dissertation Repository

Throughout the procedure of a computer-navigated total knee arthroplasty (TKA), there are many opportunities for sources of error to be introduced. Identifying these errors can improve surgical outcomes. There is also a lack of accessible methods in available literature for clinicians to perform research in this area using engineering analysis techniques. This thesis aims to provide a greater understanding of the sources of error that can occur pre-bone cut. Possible sources of error include the bony landmark selections and the placement of the cut guide. Using artificial bone models and a 3D point capture system concurrently with a computer-navigation system, …

Anomaly Detection On Partial Point Clouds For The Purpose Of Identifying Damage On The Exterior Of Spacecrafts, Kaitlin T. Hutton

Electronic Thesis and Dissertation Repository

The Canadarm3 is going to operate autonomously aboard the Lunar Gateway space station for the purpose of inspections and repairs. To make the repairs, damage to the spacecraft needs to be detected accurately and automatically. This research investigates methods for training Machine Learning models on 3D point clouds to identify anomalous structural damage. The PointNet algorithm was used to train models on point clouds without affecting their structure. The optimal training data style was found by comparing how well the different styles of data performed at classifying the point cloud testing data. Two different methods of anomaly detection were tested …

Computer Vision-Based Hand Tracking And 3d Reconstruction As A Human-Computer Input Modality With Clinical Application, Tania Banerjee

Electronic Thesis and Dissertation Repository

The recent pandemic has impeded patients with hand injuries from connecting in person with their therapists. To address this challenge and improve hand telerehabilitation, we propose two computer vision-based technologies, photogrammetry and augmented reality as alternative and affordable solutions for visualization and remote monitoring of hand trauma without costly equipment. In this thesis, we extend the application of 3D rendering and virtual reality-based user interface to hand therapy. We compare the performance of four popular photogrammetry software in reconstructing a 3D model of a synthetic human hand from videos captured through a smartphone. The visual quality, reconstruction time and geometric …

Deep Learning For Detection Of Upper And Lower Body Movements, Kyle B. Lacroix

Electronic Thesis and Dissertation Repository

When humans repeat the same motion, the tendons, muscles, and nerves can be damaged, causing repetitive stress injuries (RSI). Symptoms usually begin slowly and become more intense and constant over time. If the motions that lead to RSI are recognized early, these injuries can be prevented. A preventative approach could be implemented in factories to warn workers about possible injuries. By detecting the movements that can cause RSI, the worker can be alerted to stop carrying out those movements. For this purpose, machine learning models can detect human motion with the human activity recognition (HAR) model. HAR models typically require …

Pt-Net: A Multi-Model Machine Learning Approach For Smarter Next-Generation Wearable Tremor Suppression Devices For Parkinson's Disease Tremor, Anas Ibrahim

Electronic Thesis and Dissertation Repository

According to the World Health Organization (WHO), Parkinson's Disease (PD) is the second most common neurodegenerative condition that can cause tremors and other motor and non motor related symptoms. Medication and deep brain stimulation (DBS) are often used to treat tremor; however, medication is not always effective and has adverse effects, and DBS is invasive and carries a significant risk of complications. Wearable tremor suppression devices (WTSDs) have been proposed as a possible alternative, but their effectiveness is limited by the tremor models they use, which introduce a phase delay that decreases the performance of the devices. Additionally, the availability …

Design And Evaluation Of Fabric Cooling Channels For Twisted Coiled Actuators, Alex Lizotte

Electronic Thesis and Dissertation Repository

Twisted coiled actuators (TCAs) are biomimetic and inexpensive artificial muscles. To enable their integration into soft robotics, a novel cooling apparatus was designed, consisting of a fabric channel to house the TCA and a miniature air pump for forced convection. The channel was designed to be lightweight, flexible, and easy to integrate into a soft wearable robotic device. The effect that the channel dimensions had on TCA performance (cooling time, heating time, and stroke) was investigated by testing combinations of three widths (6, 8, and 10 mm) and three heights (4, 6, and 8 mm). In general, as the channel …

A Novel Passive Islanding Detection Method Based On Phase-Locked Loop, Hoda Zamani

Electronic Thesis and Dissertation Repository

The ever-increasing penetration of distributed energy resources in power distribution systems has led to challenges in the detection of islanding. Among different islanding detection methods (IDMs), passive methods are the least intrusive and typically require the lowest investment cost. However, they generally suffer from larger non-detection zones (NDZs) and higher nuisance detection ratios as compared to active, hybrid, and remote IDMs. This study provides an overview of the criteria outlined in the existing technical literature for the performance evaluation of IDMs, a review and comparison of the existing passive IDMs, and an analysis of the phase-locked loop (PLL) behaviour under …

A Framework For Stable Robot-Environment Interaction Based On The Generalized Scattering Transformation, Kanstantsin Pachkouski

Electronic Thesis and Dissertation Repository

This thesis deals with development and experimental evaluation of control algorithms for stabilization of robot-environment interaction based on the conic systems formalism and scattering transformation techniques. A framework for stable robot-environment interaction is presented and evaluated on a real physical system. The proposed algorithm fundamentally generalizes the conventional passivity-based approaches to the coupled stability problem. In particular, it allows for stabilization of not necessarily passive robot-environment interaction. The framework is based on the recently developed non-planar conic systems formalism and generalized scattering-based stabilization methods. A comprehensive theoretical background on the scattering transformation techniques, planar and non-planar conic systems is presented. …

Low Overhead And Application-Oriented Synchronization In Heterogeneous Internet Of Things Systems, Haide Wang

Electronic Thesis and Dissertation Repository

Recent evolution in the Internet of Things (IoT) and Cyber–physical systems (CPS) is expected to change everyday life of its users by enabling low latency and reliable communication, coordinated task execution and real time data processing among pervasive intelligence through the communication network. Precise time synchronization, as a prerequisite for a chronological ordering of information or synchronous execution, has become a vital constituent for many time-sensitive applications.

On one hand, Internet of Things (IoT) systems rely heavily on the temporal coherence among its distributed constituents during data fusion and analysis, however the existing solutions for data synchronization, do not easily …

Numerical Investigations Of The Fluid Flow And Heat Transfer And Construction Of Control System For The Canadian Supercritical Water-Cooled Reactor Power Plant, Huirui Han

Electronic Thesis and Dissertation Repository

Canada participated in the Generation IV nuclear reactors with the Supercritical Water-Cooled Reactor (SCWR) concept. This work focuses on the numerical studies of the fluid flow and heat transfer of the supercritical water in the nuclear reactor fuel bundle, and the construction of the linear dynamic model and the design of the control system for the Canadian SCWR power plant.

Firstly, the fluid flow and heat transfer of the supercritical water in the vertical tube and the rod bundle is numerically investigated to evaluate whether the existing turbulent models could successfully caption the wall temperature variations at supercritical conditions by …

Modelling And Evaluation Of Piezoelectric Actuators For Wearable Neck Rehabilitation Devices, Shaemus D. Tracey

Electronic Thesis and Dissertation Repository

Neck pain is the most common neck musculoskeletal disorder, and the fourth leading cause of healthy years lost due to disability in the world. Due to the need of hands-on physical therapy and Canada’s aging population, access to treatment will become highly constrained. Wearable devices that allow at-home rehabilitation address this future limitation. However, few have emerged from the laboratory setting because they are limited by the use of conventional actuators. An overlooked type of actuation technology is that of piezoelectric actuators, more specifically, travelling wave ultrasonic motors (TWUM).

In this work, a clear procedure that outlines how the required …

Efficient Discovery And Utilization Of Radio Information In Ultra-Dense Heterogeneous 3d Wireless Networks, Mattaka Gamage Samantha Sriyananda

Electronic Thesis and Dissertation Repository

Emergence of new applications, industrial automation and the explosive boost of smart concepts have led to an environment with rapidly increasing device densification and service diversification. This revolutionary upward trend has led the upcoming 6th-Generation (6G) and beyond communication systems to be globally available communication, computing and intelligent systems seamlessly connecting devices, services and infrastructure facilities. In this kind of environment, scarcity of radio resources would be upshot to an unimaginably high level compelling them to be very efficiently utilized. In this case, timely action is taken to deviate from approximate site-specific 2-Dimensional (2D) network concepts in radio resource utilization …

Anonymization & Generation Of Network Packet Datasets Using Deep Learning, Spencer K. Vecile

Electronic Thesis and Dissertation Repository

Corporate networks are constantly bombarded by malicious actors trying to gain access. The current state of the art in protecting networks is deep learning-based intrusion detection systems (IDS). However, for an IDS to be effective it needs to be trained on a good dataset. The best datasets for training an IDS are real data captured from large corporate networks. Unfortunately, companies cannot release their network data due to privacy concerns creating a lack of public cybersecurity data. In this thesis I take a novel approach to network dataset anonymization using character-level LSTM models to learn the characteristics of a dataset; …

Configuration And Sizing Of Small Modular Reactor With Thermal Energy Storage Within A Microgrid For Off-Grid Communities, Michael W. C. Davis

Electronic Thesis and Dissertation Repository

Many off-grid communities in Canada rely on diesel generators for their electricity needs. This is not only expensive but also produces significant greenhouse gas emissions. Small modular reactors (SMRs) have been proposed to replace diesel generators and can be combined with photovoltaic (PV) sources to form a microgrid. However, fluctuations in loads and PV create challenges for SMRs. Integrating a thermal energy storage (TES) system with the SMR can increase the flexibility of the power system to operate more effectively. This thesis first examines methodologies to determine suitable configurations of such a microgrid. Through analysis of the system components and …

Optimized And Automated Machine Learning Techniques Towards Iot Data Analytics And Cybersecurity, Li Yang

Electronic Thesis and Dissertation Repository

The Internet-of-Things (IoT) systems have emerged as a prevalent technology in our daily lives. With the wide spread of sensors and smart devices in recent years, the data generation volume and speed of IoT systems have increased dramatically. In most IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges. The first challenge is to process large amounts of dynamic …

The Development Of A Motion Sensing Device For Use In A Home Setting, Jaspreet K. Kalsi

Electronic Thesis and Dissertation Repository

Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, with over 10 million individuals diagnosed with PD world-wide. The most common symptom characterized by PD is tremor. Tremor is an involuntary oscillatory motion that most prominently occurs in upper limb, specifically in the hand and wrist that has a measurable frequency and amplitude. This thesis aims to evaluate the usability and functionality of a tremor sensing device designed to collect quantitative data on individuals with PD. The designed device uses 23 commercially-available inertial measuring units (IMUs) located between 21 joints: distal interphalangeal (DIP) joints, proximal interphalangeal (PIP) joints, Interphalangeal …

Ai-Based Traffic Forecasting In 5g Network, Maryam Mohseni

Electronic Thesis and Dissertation Repository

Forecasting of the telecommunication traffic is the foundation for enabling intelligent management features as cellular technologies evolve toward fifth-generation (5G) technology. Since a significant number of network slices are deployed over a 5G network, it is crucial to evaluate the resource requirements of each network slice and how they evolve over time. Mobile network carriers should investigate strategies for network optimization and resource allocation due to the steadily increasing mobile traffic. Network management and optimization strategies will be improved if mobile operators know the cellular traffic demand at a specific time and location beforehand. The most effective techniques nowadays devote …

Effective Resource Scheduling For Collaborative Computing In Edge-Assisted Internet Of Things Systems, Qianqian Wang

Electronic Thesis and Dissertation Repository

Along with rapidly evolving communications technologies and data analytics, Internet of Things (IoT) systems interconnect billions of smart devices to gather, exchange, analyze data, and perform tasks autonomously, which poses a huge pressure on IoT devices' computing capabilities. Taking advantage of collaborative computing enabled by cloud computing and edge computing technologies, IoT devices can offload computation tasks to idle computing devices and remote servers, thus alleviating their pressure. However, scheduling resources effectively to realize collaborative computing remains a severe challenge due to diverse application objectives, limited distributed resources, and unpredictable environments. To overcome the above challenges, this thesis aims to …

Non-Orthogonal Multi-Dimensional Modulation And Nonlinear Distortion Compensation For Beyond 5g, Thakshanth Uthayakumar

Electronic Thesis and Dissertation Repository

The introduction of new advanced technologies such as higher carrier frequencies, ultra-wide bandwidth, and increased transmission rate in 5G to support ever growing quality-of-service (QoS) demands have brought new challenges such as transmitter-receiver pair specific and domain specific non-orthogonality induced among spatial, time-frequency, and delay-doppler domain radio resource blocks and nonlinear distortions induced among multiple-input multiple-output (MIMO) antennas in spatial domain. In such conditions, current communication systems encounter severe performance degradation and incur higher operational cost. Based on this observation, this thesis aims at creating new multi-dimensional modulation techniques and nonlinear predistortion architectures to achieve higher communication performance with less …

Machine Learning With Big Data For Electrical Load Forecasting, Alexandra L'Heureux

Electronic Thesis and Dissertation Repository

Today, the amount of data collected is exploding at an unprecedented rate due to developments in Web technologies, social media, mobile and sensing devices and the internet of things (IoT). Data is gathered in every aspect of our lives: from financial information to smart home devices and everything in between. The driving force behind these extensive data collections is the promise of increased knowledge. Therefore, the potential of Big Data relies on our ability to extract value from these massive data sets. Machine learning is central to this quest because of its ability to learn from data and provide data-driven …