Digital Commons Network^™

Protecting Health Data In A Pandemic: A Systematic Adversarial Threat Analysis Of Contact Tracing Apps, Leah Krehling

Electronic Thesis and Dissertation Repository

In this thesis centralized, decentralized, Bluetooth, and GPS based applications of digital contact tracing were reviewed and assessed. Using privacy principles created by a contingent of security and privacy experts from across Canada, a metric of assessing an application’s privacy was created. An attack tree was built to assess the security of the contact tracing applications. Eighteen attacks were theorized against contact tracing applications currently in use. An application’s vulnerability to the attacks was measured using a scoring system developed for this purpose. The results of the security scores were used to create a metric for assessing the security of …

Efficient Hardware Architectures For Public-Key Cryptosystems, Mohammadamin Saburruhmonfared

Electronic Thesis and Dissertation Repository

Finite field arithmetic plays an essential role in public-key cryptography as all the underlying operations are performed in these fields. The finite fields are either prime fields or binary fields. Binary field elements can mainly be represented on a polynomial basis or a normal basis (NB). NB representation offers a simple squaring operation, especially in hardware. However, multiplication is typically complex, and a particular subset of NB called Gaussian Normal Basis (GNB) features an efficient multiplication operation used in this work. The first part of this thesis has focused on improving finite field arithmetic architectures over GNB. Among different arithmetic …

Towards Efficient And Secure Iiot: Solutions For The Sensing Domain, Elena Uchiteleva

Electronic Thesis and Dissertation Repository

This work explores reduced complexity solutions for the increased efficiency and safety of the industrial Internet of Things (IIoT) sensing domain. Resource virtualization, security, and predictive modeling are the main subjects of these studies.

The first solution is a joint throughput and time-resource allocation scheme for virtualization of IEEE 802.15.4-based wireless sensor networks. Virtualization is realized through the utilization of the guaranteed time slot mechanism for scheduling on the medium access control (MAC) layer. The solution abstracts resources into logical units that are allocated to segregated applications with different service requirements. The problem is formulated in a linear optimization framework …

Predicting Residential Energy Consumption Using Wavelet Decomposition With Deep Neural Network, Dagimawi Eneyew, Miriam A M Capretz, Girma Bitsuamlak, London Hydro

Electrical and Computer Engineering Publications

Electricity consumption is accelerating due to economic and population growth. Hence, energy consumption prediction is becoming vital for overall consumption management and infrastructure planning. Recent advances in smart electric meter technology are making high-resolution energy consumption data available. However, many parameters influencing energy consumption are not typically monitored for residential buildings. Therefore, this study’s main objective is to develop a data-driven energy consumption forecasting model (next-hour consumption) for residential houses solely based on analyzing electricity consumption data. This research proposes a deep neural network architecture that combines stationary wavelet transform features and convolutional neural networks. The proposed approach utilizes automatically …

Deep Neural Network For Load Forecasting Centred On Architecture Evolution, Santiago Gomez-Rosero, Miriam A M Capretz, London Hydro

Electrical and Computer Engineering Publications

Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate residential load forecasting plays an essential role as an individual component for integrated areas such as neighborhood load consumption. Short-term load forecasting can help electric utility companies reduce waste because electric power is expensive to store. This paper proposes a novel method to evolve deep neural networks for time series forecasting applied to residential load forecasting. The approach centres its efforts on the neural network architecture during the evolution. Then, the model weights are adjusted using an evolutionary optimization technique to tune the model performance automatically. Experimental results on …

Deep Reinforcement Learning In Medical Object Detection And Segmentation, Dong Zhang

Electronic Thesis and Dissertation Repository

Medical object detection and segmentation are crucial pre-processing steps in the clinical workflow for diagnosis and therapy planning. Although deep learning methods have achieved considerable performance in this field, they impose several shortcomings, such as computational limitations, sub-optimal parameter optimization, and weak generalization. Deep reinforcement learning as the newest artificial intelligence algorithm has great potential to address the limitation of traditional deep learning methods, as well as obtaining accurate detection and segmentation results. Deep reinforcement learning has a cognitive-like process to propose the area of desirable objects, thereby facilitating accurate object detection and segmentation. In this thesis, we deploy deep …

Material Evaluation And Structural Monitoring Of Early-Age Masonry Structures, Kyle Dunphy

Electronic Thesis and Dissertation Repository

During the initial construction period, “early-age” masonry walls are susceptible to lateral loads induced by wind or earthquake, which may result in damages or catastrophic failures. To mitigate such consequences at construction sites, temporary bracings are adopted to provide lateral support to masonry walls until they are matured enough to serve as the inherent lateral system of the structure. However, current temporary bracing guidelines provide oversimplified design due to the lack of available information on the material properties of early-age masonry. Moreover, there are no existing techniques for monitoring masonry walls to detect cracks due to construction activities. …

Network Resource And Performance Optimization In Autonomous Systems: A Connected Vehicles And Autonomous Networks Perspective, Ibrahim Shaer

Electronic Thesis and Dissertation Repository

This thesis covers two topics that optimize a network-related problem subject to environment-specific constraints; placing vehicular applications and executing network traffic assignment changes. The first topic introduces an optimization model, Resource and Delay-aware V2X service Placement (RDP), and a baseline approach that only considers the resource requirements of vehicular services. Both are responsible for placing vehicular services used by vehicular applications in an edge computing environment. Under different simulation scenarios, the results obtained by RDP satisfy the delay requirements of vehicular applications as opposed to the baseline approach. The second topic examines the efficient execution of inter-domain traffic changes under …

Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat

Electronic Thesis and Dissertation Repository

The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …

Vehicle Networks: Statistical And Game Theoretic Approaches To Their Evaluation And Design, Gleb Dubosarskii

Electronic Thesis and Dissertation Repository

Vehicle ad hoc networks (VANETs) have become a popular topic in modern research. The main advantages of these networks include: improved security, traffic optimization, and infotainment. However, deploying such networks in practice requires extensive infrastructure. To estimate the network load, one needs to have information about the network, such as the number of clusters, cluster size, etc. Since VANETs are formed by vehicles that rapidly change their location, the network topology is constantly changing, making its analysis by deterministic methods impossible. Therefore, in this dissertation, we use probability theory methods to obtain probability distributions of such fundamental network properties, such …

A Blockchain Approach To Social Responsibility, Augusto Bedin, Wander Queiroz, Miriam A M Capretz, London Hydro

Electrical and Computer Engineering Publications

As blockchain technology matures, more sophisticated solutions arise regarding complex problems. Blockchain continues to spread towards various niches such as government, IoT, energy, and environmental industries. One often overlooked opportunity for blockchain is the social responsibility sector. Presented in this paper is a permissioned blockchain model that enables enterprises to come together and cooperate to optimize their environmental and societal impacts. This is made possible through a private or permissioned blockchain. Permissioned blockchains are blockchain networks where all the participants are known and trust relationships among them can be fostered more smoothly. An example of what a permissioned blockchain would …

Advanced Hardware And Software Approach To Seismic Site Response Investigations, Aleksandar Dimitrov Mihaylov

Electronic Thesis and Dissertation Repository

Vibration measurement is an essential aspect of modern geotechnical engineering. It is particularly vital task for measuring the dynamic soil parameters, estimating seismic hazards and evaluating influence of industrial, traffic and construction vibrations on the surrounding buildings, structures and their elements. Meanwhile, commercial exploration seismic stations and data acquisition systems require significant professional knowledge and training in geophysics or vibration measurement, as well as practical skills and experience in adjusting data acquisition parameters. Furthermore, available seismological investigation and vibrometry sensors are not universally suitable for field applications in geophysical studies, soil-structure interaction investigations or structural vibrations. The frequency range suitable …

A Lightweight Magnetorheological Actuator Using Hybrid Magnetization, Masoud Moghani, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

Copyright © 2020, IEEE

This paper presents the design and validation of a lightweight Magneto-Rheological (MR) clutch, called Hybrid Magneto-Rheological (HMR) clutch. The clutch utilizes a hybrid magnetization using an electromagnetic coil and a permanent magnet. The electromagnetic coil can adjust the magnetic field
generated by the permanent magnet to a desired value, and fully control the transmitted torque. To achieve the maximum torque to mass ratio, the design of HMR clutch is formulated as a multiobjective optimization problem with three design objectives, namely the transmitted torque, the mass of the clutch, and the
magnetic field strength within the clutch …

Geometric State Observers For Autonomous Navigation Systems, Miaomiao Wang

Electronic Thesis and Dissertation Repository

The development of reliable state estimation algorithms for autonomous navigation systems is of great interest in the control and robotics communities. This thesis studies the state estimation problem for autonomous navigation systems. The first part of this thesis is devoted to the pose estimation on the Special Euclidean group $\SE(3)$. A generic globally exponentially stable hybrid estimation scheme for pose (orientation and position) and velocity-bias estimation on $\SE(3)\times \mathbb{R}^6$ is proposed. Moreover, an explicit hybrid observer, using inertial and landmark position measurements, is provided.

The second part of this thesis is devoted to the problem of simultaneous estimation of the …

Water Conservation Potential Of Self-Funded Foam-Based Flexible Surface-Mounted Floatovoltaics, Koami Soulemane Hayibo, Pierce Mayville, Ravneet Kaur Kailey, Joshua M. Pearce

Electrical and Computer Engineering Publications

A potential solution to the coupled water–energy–food challenges in land use is the concept of floating photovoltaics or floatovoltaics (FPV). In this study, a new approach to FPV is investigated using a flexible crystalline silicon-based photovoltaic (PV) module backed with foam, which is less expensive than conventional pontoon-based FPV. This novel form of FPV is tested experimentally for operating temperature and performance and is analyzed for water-savings using an evaporation calculation adapted from the Penman–Monteith model. The results show that the foam-backed FPV had a lower operating temperature than conventional pontoon-based FPV, and thus a 3.5% higher energy output per …

Intrinsic Measures And Shape Analysis Of The Intratemporal Facial Nerve, Thomas Hudson, Bradley Gare, Daniel Allen, Hanif Ladak, Sumit Agrawal

Electrical and Computer Engineering Publications

Hypothesis: To characterize anatomical measurements and shape variation of the facial nerve within the temporal bone, and to create statistical shape models (SSMs) to enhance knowledge of temporal bone anatomy and aid in automated segmentation.

Background: The facial nerve is a fundamental structure in otologic surgery, and detailed anatomic knowledge with surgical experience are needed to avoid its iatrogenic injury. Trainees can use simulators to practice surgical techniques, however manual segmentation required to develop simulations can be time consuming. Consequently, automated segmentation algorithms have been developed that use atlas registration, SSMs, and deep learning.

Methods: Forty cadaveric temporal bones were …

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …