Electrical and Computer Engineering Commons^™

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 …

Virtual Sensor Middleware: Managing Iot Data For The Fog-Cloud Platform, Fadi Almahamid, Hanan Lutfiyya, Katarina Grolinger

Electrical and Computer Engineering Publications

This paper introduces the Virtual Sensor Middleware (VSM), which facilitates distributed sensor data processing on multiple fog nodes. VSM uses a Virtual Sensor as the core component of the middleware. The virtual sensor concept is redesigned to support functionality beyond sensor/device virtualization, such as deploying a set of virtual sensors to represent an IoT application and distributed sensor data processing across multiple fog nodes. Furthermore, the virtual sensor deals with the heterogeneous nature of IoT devices and the various communication protocols using different adapters to communicate with the IoT devices and the underlying protocol. VSM uses the publish-subscribe design pattern …

Agglomerative Hierarchical Clustering With Dynamic Time Warping For Household Load Curve Clustering, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy companies often implement various demand response (DR) programs to better match electricity demand and supply by offering the consumers incentives to reduce their demand during critical periods. Classifying clients according to their consumption patterns enables targeting specific groups of consumers for DR. Traditional clustering algorithms use standard distance measurement to find the distance between two points. The results produced by clustering algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on the clustering parameters or initial clusters. In contrast, our methodology uses a shape-based approach that combines Agglomerative Hierarchical Clustering (AHC) with Dynamic Time Warping (DTW) to classify …

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 …

Optimal Inverter And Wire Selection For Solar Photovoltaic Fencing Applications, Koami Soulemane Hayibo, Joshua M. Pearce

Electrical and Computer Engineering Publications

Despite the benefits and the economic advantages of agrivoltaics, capital costs limit deployment velocity. One recent potential solution to this challenge is to radically reduce the cost of racking materials by using existing farm fencing as vertical photovoltaic (PV) racking. This type of fenced-based PV system is inherently electrically challenging because of the relatively long distances between individual modules that are not present in more densely packed conventional solar PV farms. This study provides practical insights for inverter selection and wire sizing optimization for fence-based agrivoltaic systems. Numerical simulation sensitivities on the levelized cost of electricity (LCOE) were performed for …

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 …

Project Khepri: Mining Asteroid Bennu For Water, Erika Frost, Gowtham Boyala, Adam Gremm, Ahmet Gungor, Amirhossein Taghipour, Massimo Biella, Jiawei "Jackson" Qiu, Athip Thirupathi Raj, Arjun Chhabra, Adam Gee, Saanjali Maharaj, Erin Richardson, Julia Empey, Haidar Ali Abdul-Nabi, Lindsay Richards, Ariyaan Talukder, Aaron Groh, Brie Miklaucic, Jd Carlson, Kristina Kim, Maverick Cue

Undergraduate Student Research Internships Conference

Deep space asteroid mining presents the opportunity for the collection of critical resources required to establish a cis-lunar infrastructure. In specific, the Project Khepri team has focused on the collection of water from asteroid Bennu. This water has the potential to provide a source of clean-energy propellant as well as an essential consumable for humans or agriculture on crewed trips to the Moon or Mars. This would avoid the high costs of launching from Earth - making it a highly desirable element for the future of cis-lunar infrastructure. The OSIRIS-REx mission provided a complete survey of asteroid Bennu and is …

Autonomous Unmanned Aerial Vehicle Navigation Using Reinforcement Learning: A Systematic Review, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones, in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously --- without human interaction, perform specific tasks and avoid obstacles. Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles. Understanding the navigation environment and algorithmic limitations plays an essential role in choosing the appropriate RL algorithm to solve the …

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 …

Introduction To Pub/Sub Systems Using Opcua, Mete Isiksalan

Undergraduate Student Research Internships Conference

The purpose of the project was to learn and implement the fundamental basics of OPCUA system architecture using pub/sub systems. The system allows the users to create multiple different publishers and subscribers while accessing data from a local server and a primary HTTP server. The system is designed to be a multi-client and multi-server system to simulate real-life scenarios while having two different sources of generated values to send via sockets in OPCUA protocols, multiple different APIs were used for the clients on how they retrieve data as well.

Triple-Motor Driver For Hand Simulator, Yamaan Bakir

Undergraduate Student Research Internships Conference

No abstract provided.

Data Preprocessing For Machine Learning Modules, Rawan El Moghrabi

Undergraduate Student Research Internships Conference

Data preprocessing is an essential step when building machine learning solutions. It significantly impacts the success of machine learning modules and the output of these algorithms. Typically, data preprocessing is made-up of data sanitization, feature engineering, normalization, and transformation. This paper outlines the data preprocessing methodology implemented for a data-driven predictive maintenance solution. The above-mentioned project entails acquiring historical electrical data from industrial assets and creating a health index indicating each asset's remaining useful life. This solution is built using machine learning algorithms and requires several data processing steps to increase the solution's accuracy and efficiency. In this project, the …

Credit Card Fraud Detection, Charles Wang

Undergraduate Student Research Internships Conference

In recent years, credit card fraud poses a significant threat to banks and customers financially over the world. However, in the banking industry, to counter this issue, machine learning algorithms has become a growing trend to put proactive intervention of credit card fraud in place. In this project, we are going to detect fraudulent credit card transactions with machine learning models. This data set includes 284807 credit card transactions of European cardholders over a period of two days with their personal information kept anonymous. Among all transactions, 492 were fraudulent.

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 …

Monofacial Vs Bifacial Solar Photovoltaic Systems In Snowy Environments, Koami Soulemane Hayibo, Aliaksei Petsiuk, Pierce Mayville, Laura Brown, Joshua M. Pearce

Electrical and Computer Engineering Publications

There has been a recent surge in interest in the more accurate snow loss estimates for solar photovoltaic (PV) systems as large-scale deployments move into northern latitudes. Preliminary results show bifacial modules may clear snow faster than monofacial PV. This study analyzes snow losses on these two types of systems using empirical hourly data including energy, solar irradiation and albedo, and open-source image processing methods from images of the arrays in a northern environment in the winter. Projection transformations based on reference anchor points and snowless ground truth images provide reliable masking and optical distortion correction with fixed surveillance cameras. …

Coexistence Of Wi-Fi And 5g Nr-U In The Unlicensed Band, Sara Saud Zimmo

Electronic Thesis and Dissertation Repository

The communications industry continues to evolve to meet the ever-growing demands of fast connectivity and higher energy-efficiency and has emerged the concept of Internet of Things (IoT) systems. IoT devices can be run on Wi-Fi or cellular network, helping businesses to receive higher return on investments.

As billions of devices on cellular networks operate on the limited licensed spectrum, it is becoming scarcer. Mobile network operators are investigating to access the immense unlicensed spectrum, on which Wi-Fi is prominently operated. Managing this coexistence between the cellular and Wi-Fi networks poses several challenges.

One challenge is the spectrum sharing that affects …

Deep Learning For Load Forecasting With Smart Meter Data: Online And Federated Learning, Mohammad Navid Fekri

Electronic Thesis and Dissertation Repository

Electricity load forecasting has been attracting increasing attention because of its importance for energy management, infrastructure planning, and budgeting. In recent years, the proliferation of smart meters has created new opportunities for forecasting on the building and even individual household levels. Machine learning (ML) has achieved great successes in this domain; however, conventional ML techniques require data transfer to a centralized location for model training, therefore, increasing network traffic and exposing data to privacy and security risks. Also, traditional approaches employ offline learning, which means that they are only trained once and miss out on the possibility to learn from …

Photonic Crystal Edge Couplers For Sensing Applications, Michael A. Zylstra

Electronic Thesis and Dissertation Repository

Microelectromechanical systems (MEMS) have not only enabled the development of inexpensive sensors but have also improved their performance by lowering their mass thus enabling faster sensor response. However, there are limitations regarding the mass-scaling of conventional MEMS sensors that prevent further miniaturization. This makes the measurement of distributed forces with high spatiotemporal resolution challenging. Optical-based sensors provide low-volume confinement of electromagnetic energy and enable further mass-scaling. This thesis investigates the application of an optical deflection sensing mechanism that relies on the position dependent coupling between dielectric-like edge states on nearby photonic crystal slabs for the purposes of acoustic pressure, wall …

Automated Segmentation Of The Inner Ear And Round Window In Computed Tomography Scans Using Convolutional Neural Networks, Kyle A. Rioux

Electronic Thesis and Dissertation Repository

Computed tomography (CT) scans are acquired prior to cochlear implant (CI) surgery. Three-dimensional segmentations of the inner ear (IE) and round window (RW) based on clinical CTs can improve the CI procedure. Software pipelines are presented here which employ convolutional neural networks to automatically segment the IE and RW. The first pipeline produces high resolution segmentations of the IE and RW in tightly cropped CTs. Mean IE Dice score and RW centroid error were 0.88, 0.57mm and 0.93, 0.18mm in implanted and non-implanted samples, respectively. The second pipeline automatically segments the IE in large field of view CTs of any …

Multi-Device Data Analysis For Fault Localization In Electrical Distribution Grids, Jacob D L Hunte

Electronic Thesis and Dissertation Repository

The work presented in this dissertation represents work which addresses some of the main challenges of fault localization methods in electrical distribution grids. The methods developed largely assume access to sophisticated data sources that may not be available and that any data sets recorded by devices are synchronized. These issues have created a barrier to the adoption of many solutions by industry. The goal of the research presented in this dissertation is to address these challenges through the development of three elements. These elements are a synchronization protocol, a fault localization technique, and a sensor placement algorithm.

The synchronization protocol …