Engineering Commons^™

Effect Of Levodopa On Eeg Connectivity In Parkinson's Patients, Sepehr Torab Parhiz

Electronic Thesis and Dissertation Repository

Levodopa is a dopamine replacement medication administered to patients with Parkinson’s disease (PD) to alleviate their motor symptoms. However, its long-term use can cause adverse side effects, including involuntary motor movements. We studied 16 PD patients before and after taking Levodopa based on resting-state electroencephalography (EEG) recordings to determine how Levodopa affects the functional connectivity of their brain networks. We used several metrics from graph theory, in particular the minimum spanning tree (MST) metric, and analyzed how they change after subjects take Levodopa. We observed significant changes in the lower alpha band toward a more path-like and less globally efficient …

Situation-Aware Quality Of Service Enhancement For Heterogeneous Ultra-Dense Wireless Iot Networks, Sabin Bhandari

Electronic Thesis and Dissertation Repository

By engaging a massive number of heterogeneous devices, future Internet of Things (IoT) systems are expected to support diverse applications ranging from eHealthcare to industrial control. In highly-dense deployment scenarios such as Industrial IoT (IIoT) systems, meeting the stringent Quality of Service (QoS) requirements such as low-latency and high reliability becomes challenging due to the uncertainty and dynamics within the IoT networks. To enhance the overall QoS performance, this thesis aims to address the technical challenges of IoT networks. Firstly, to enhance the network reliability, a cloud-assisted priority-based channel access and data aggregation scheme is proposed to minimize the network …

Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho

Electrical and Computer Engineering Publications

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this paper, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …

Smart Chatbot For User Authentication, Peter Voege

Electronic Thesis and Dissertation Repository

The field of authentication has a lot of room to develop in the age of big data and machine learning. Conventional high-accessibility authentication mechanisms including passwords or security questions struggle with critical vulnerabilities, creating a need for alternative authentication mechanisms able to cover said weaknesses.

We sought to create an authentication mechanism that creates dynamic, ever-changing security questions only the user can answer while remaining intuitive to use and as accessible as typical security questions by creating an authentication chatbot that leverages big data and natural language processing to pose dynamic authentication challenges.

We tested the components of our design …

Edge Intelligence Enabled Distributed And Collaborative Authentication In Uav Swarms, Huanchi Wang

Electronic Thesis and Dissertation Repository

Unmanned Aerial Vehicles (UAVs) have been widely deployed in various fields with many benefits such as cost reduction, safety improvement and service coverage enhancement. Unlike the other mobile ad hoc networks, the UAV swarm, which is a flying ad hoc network, may operate in a hostile environment or experience rapid network topology change which brings high vulnerability by using cloud-based centralized security provisioning techniques. Hence, securing the UAV networks with the on-site authentication resources becomes a vital aspect to accomplish the mission. The on-site authentication resources, such as the cross-layer attributes, can be utilized to form a unique characteristic of …

Reliability Models For Smartphone Applications, Sonia Meskini, Ali Bou Nassif, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

Smartphones have become the most used electronic devices. They carry out most of the functionalities of desktops, offering various useful applications that suit the user’s needs. Therefore, instead of the operator, the user has been the main controller of the device and its applications, therefore its reliability has become an emergent requirement. As a first step, based on collected smartphone applications failure data, we investigated and evaluated the efficacy of Software Reliability Growth Models (SRGMs) when applied to these smartphone data in order to check whether they achieve the same accuracy as in the desktop/laptop area. None of the selected …

Precision Grasp Using An Arm-Hand System As A Hybrid Parallel-Serial System: A Novel Inverse Kinematics Solution, Shuwei Qiu, Shuwei Qiu Ph.D., P.Eng.

Electrical and Computer Engineering Publications

In this letter, we present a novel inverse kinematics (IK) solution for a robotic arm-hand system to achieve precision grasp. This problem is kinematically over-constrained and to address the issue and to solve the problem, we propose a new approach with three key insights. First, we propose a human-inspired thumb-first strategy and consider one finger of the robotic hand as the “thumb” to narrow down the search space and increase the success rate of our algorithm. Second, we formulate the arm-thumb serial chain as a closed chain such that the entire arm-hand system is controlled as a hybrid parallel-serial system. …

Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger

Electrical and Computer Engineering Publications

The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms’ limitations plays a vital role in selecting the …

Data And Sensor Fusion Using Fmg, Semg And Imu Sensors For Upper Limb Prosthesis Control, Jason S. Gharibo

Electronic Thesis and Dissertation Repository

Whether someone is born with a missing limb or an amputation occurs later in life, living with this disability can be extremely challenging. The robotic prosthetic devices available today are capable of giving users more functionality, but the methods available to control these prostheses restrict their use to simple actions, and are part of the reason why users often reject prosthetic technologies. Using multiple myography modalities has been a promising approach to address these control limitations; however, only two myography modalities have been rigorously tested so far, and while the results have shown improvements, they have not been robust enough …

Conversion Bridge Of Sony Sublvds To Mipi Csi-2, Oladayo R. Ogunjimi

Undergraduate Student Research Internships Conference

Undergraduate Summer Research Output

Liam Briggs 2021 Usri - Audio Testing Device, Liam Briggs

Undergraduate Student Research Internships Conference

Portable and cost-effective hearing testing device with a UI that can be accessed on a remote web server.

Evaluating Algorithms Used For Fetal Brain Scan Segmentation, Connor Stewart Burgess

Undergraduate Student Research Internships Conference

The goal for this project was to successfully segment a fetal brain scan (fetal scan) using the algorithms provided by the program Slicer3D. To better understand the hurdles that arose when segmenting a fetal scan, we first look at the segmentation of an adult brain scan. This will allow us to see the straightforward nature of a brain segmentation when a high quality, high resolution volume with distinct structures is available. After examining the adult brain scan, attention will be moved to the segmentation of the fetal scan, where we’ll first look at the algorithms used and methods followed. Finally …

Web Application – Utilizing A Pose Estimation And Augmented Reality Api For Hand Telerehabilitation, Herbert Shin

Undergraduate Student Research Internships Conference

No abstract provided.

Real-Time Parkinsonian Tremor Signal Identifier Based On Internal Model Principle, Jian Dong

Electronic Thesis and Dissertation Repository

Parkinsonian tremor is one of the clinical hallmarks of Parkinson's disease. Since the traditional medical treatments are not effective, many wearable devices are developed to help suppress the tremor. In order to suppress the tremor, a well-designed tremor estimator is needed. Previous tremor estimators treat a 3-D tremor signal as three independent 1-D signals. Moreover, they did not consider the real-life characteristics of tremor signals. For instance, the tremor does not always exist in the postural tremor signal, and the patient's voluntary motion can be included in the kinetic tremor signal. This paper presents a real-time adaptive parkinsonian tremor signal …

Leveraging Machine Learning Techniques Towards Intelligent Networking Automation, Cesar A. Gomez

Electronic Thesis and Dissertation Repository

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the …

Novel Zcs Pwm Methods For Industrial Applications, Ramtin Rasoulinezhad

Electronic Thesis and Dissertation Repository

Pulse width modulation (PWM) converters that consist of two or more interleaved boost/buck converter modules are used widely in industry. Soft-switching approaches for these converters can either be zero-voltage switching (ZVS) if implemented with MOSFETs or zero-current switching (ZCS) if implemented with IGBTs. The main idea of this thesis is to implement ZCS for IGBT turn-on and turn-off. Most converters use an auxiliary circuit that is activated whenever a main converter switch is about to be turned off, gradually diverting current away from the switch so that it can turn off with ZCS.

ZCS-PWM converters that use an auxiliary circuit …

Generative Learning In Smart Grid, Samer M. El Kababji

Electronic Thesis and Dissertation Repository

If a smart grid is to be described in one word, that word would be ’connectivity’. While electricity production and consumption still depend on a limited number of physical connections, exchanging data is growing enormously. Customers, utilities, sensors, and markets are all different sources of data that are exchanged in a ubiquitous digital setup. To deal with data complexity, many researchers recently focused on machine learning (ML) applications in smart grids. Much of the success in ML is attributed to discriminative learning where models define boundaries to categorize data. Generative learning, however, reveals how data is generated by learning the …

Deep Learning For High-Impedance Fault Detection And Classification, Khushwant Rai

Electronic Thesis and Dissertation Repository

High-Impedance Faults (HIFs) are a hazard to public safety but are difficult to detect because of their low current amplitude and diverse characteristics. Supervised machine learning techniques have shown great success in HIF detection; however, these approaches rely on resource-intensive signal processing techniques and fail in presence of non-HIF disturbances and even for scenarios not included in training data. This thesis leverages unsupervised learning and proposes a Convolutional Autoencoder framework for HIF Detection (CAE-HIFD). In CAE-HIFD, Convolutional Autoencoder learns only from HIF signals by employing cross-correlation; consequently, eliminating the need for diverse non-HIF scenarios in training. Furthermore, this thesis proposes …

An Anomaly Detection System For Smart Manufacturing Using Deep Learning, Tareq Tayeh

Electronic Thesis and Dissertation Repository

The smart manufacturing evolution enables financial and operational improvements across the manufacturing industry. However, smart manufacturing encompasses complex, interconnected systems which can fail at any time. To address this challenge, a novel, two-part anomaly detection system for robotic processes, with an application focus on robotic surface finishing, is presented. The first part proposes an unsupervised Attention-based Convolutional Long Short-Term Memory Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series of robotic surface finishing components. The second part proposes a deep residual Convolutional Neural Network-based triplet model for anomaly detection in the produced robotic surface …

Consensus-Enabled And Value-Oriented Collaboration In Distributed Iot Systems: Mechanisms, Design, And Implementation, Ruitao Chen

Electronic Thesis and Dissertation Repository

The ongoing convergence of Internet of Things (IoT), artificial intelligence and big data analytics has inspired many innovative IoT applications. Enabling these new applications requires accurate and reliable capabilities in data sensing, exchange and processing, which can be best fulfilled by collaborative IoT systems. Nevertheless, the dynamic condition of IoT networks may lead to ever-changing demand and objectives among devices, making it difficult for reliable and efficient collaboration. To overcome these challenges, this thesis develops a new framework on consensus-enabled and value-oriented collaboration, which resolves two critical technical challenges, i.e., low latency consensus creation and value-oriented decision-making, to enable collective …

Development Of A Wireless Telemetry Load And Displacement Sensor For Orthopaedic Applications, William Anderson

Electronic Thesis and Dissertation Repository

Due to sensor size and supporting circuitry, in vivo load and deformation measurements are currently restricted to applications within larger orthopaedic implants. The objective of this thesis is to repurpose a commercially available low-power, miniature, wireless, telemetric, tire-pressure sensor (FXTH87) to measure load and deformation for future use in biomechanical applications. The capacitive transducer membrane of the FXTH87 was modified, and a relationship was reported between applied compressive deformation and sensor signal value. The sensor package was embedded within a deformable enclosure to illustrate potential applications of the sensor for monitoring load. Finite element analysis was an effective tool to …

Dynamic Planning Networks, Norman Tasfi, Miriam A M Capretz

Electrical and Computer Engineering Publications

We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, that combines model-based and model-free aspects for online planning. Our architecture learns to dynamically construct plans using a learned state-transition model by selecting and traversing between simulated states and actions to maximize information before acting. DPN learns to efficiently form plans by expanding a single action conditional state transition at a time instead of exhaustively evaluating each action, reducing the number of state-transitions used during planning. We observe emergent planning patterns in our agent, including classical search methods such as breadth-first and depth-first search. DPN shows improved …

Snapshot Three-Dimensional Surface Imaging With Multispectral Fringe Projection Profilometry, Parsa Omidi

Electronic Thesis and Dissertation Repository

Fringe Projection Profilometry (FPP) is a popular method for non-contact optical surface measurements, including motion tracking. The technique derives 3D surface maps from phase maps estimated from the distortions of fringe patterns projected onto the surface of an object. Estimation of phase maps is commonly performed with spatial phase retrieval algorithms that use a series of complex data processing stages. Researchers must have advanced data analysis skills to process FPP data due to a lack of availability of simple research-oriented software tools. Chapter 2 describes a comprehensive FPP software tool called PhaseWare^TM that allows novice to experienced users to …

Arm-Hand Systems As Hybrid Parallel-Serial Systems: A Novel Inverse Kinematics Solution, Shuwei Qiu, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

No abstract provided.

Power Management And Control To Balance Residential Microgrids With Individual Phase-Wise Generation And Storage, Syed Ahmed Raza Naqvi

Electronic Thesis and Dissertation Repository

The past decade has seen a significant rise in proliferation of roof-top photovoltaic (PV) systems with storage units at residential sites. This has affected the way power system engineers and researchers have previously studied distribution systems as passive networks. With the introduction of these local distributed energy resources, a distribution system has become part of an active network. This modernization of the power distribution network, brings along with itself a number of key issues that need to be pro-actively tackled by the local utilities.

In North America, with family-owned roof-top PV systems, storage devices and electric vehicles, the concept of …

Development Of A Wearable Haptic Feedback Device For Upper Limb Prosthetics Through Sensory Substitution, Marco B.S. Gallone

Electronic Thesis and Dissertation Repository

Haptics can enable a direct communication pipeline between the artificial limb and the brain; adding haptic sensory feedback for prosthesis wearers is believed to improve operation without drawing too much of the user's attention. Through neuroplasticity, the brain can become more cognizant of the information delivered through the skin and may eventually interpret it as inherently as other natural senses. In this thesis, a wearable haptic feedback device (WHFD) is developed to communicate prosthesis sensory information. A 14-week, 6-stage, between subjects study was created to investigate the learning trajectory as participants were stimulated with haptic patterns conveying joint proprioception. 37 …

Design, Development, And Evaluation Of Customized Electronics For Controlling A 5-Dof Magneto-Rheological Actuator Collaborative Robot, Ziqi Yang

Electronic Thesis and Dissertation Repository

In recent years, Magneto-Rheological (MR) fluids has been used in various fields such as robotics, automotive, aerospace, etc. The most common use of the MR fluids is within a clutch-like mechanism, namely an MR clutch. When mechanical input is coupled to the input part of the MR clutch, the MR clutch provides a means of delivering this mechanical input to its output, through the MR fluids. The combination of the mechanical input device and the MR clutch is called an MR actuator. The MR actuator features inherently compliance owing to the characteristic of the MR fluids while also offering higher …

Transfer Learning By Similarity Centred Architecture Evolution For Multiple Residential Load Forecasting, Santiago Gomez-Rosero, Miriam A M Capretz, Syed Mir

Electrical and Computer Engineering Publications

The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could lead to a better understanding of low voltage consumption behaviour. Nevertheless, users with similar characteristics can present diversity in consumption patterns. Consequently, transfer learning methods have become a useful tool to tackle differences among residential time series. This paper proposes a method combining evolutionary algorithms for neural architecture search with transfer learning to perform short term load …

‘Digits’ App - Smartphone Augmented Reality For Hand Telerehabilitation, Hongdao Dong, Edward Ho, Herbert Shin, Tania Banerjee, Geoffrey Masschelein, Jacob Davidson, Sandrine De Ribaupierre, Roy Eagleson, Caitlin Symonette

Electrical and Computer Engineering Publications

Hand telerehabilitation currently has limitations for accurate and remote assessment of range of motion (ROM) in small finger joints. ‘DIGITS’ application utilises the front smartphone camera to measure finger ROM in a reliable and rapid assessment protocol. Our initial beta-phase testing examined the consistency of our software measurements to in-person goniometry. 6 to 9 degrees of difference existed between the smartphone application recorded data versus the in-person measurements. This range is within acceptable 7 to 9 degree tolerance for interrater goniometry measurements. The effect of environmental factors such as hand distance, lightings and hand orientation was evaluated. The intraclass correlation …

A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz

Electrical and Computer Engineering Publications

With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent …