Computer Sciences Commons^™

Cybersecurity And Digital Privacy Aspects Of V2x In The Ev Charging Structure, Umit Cali, Murat Kuzlu, Onur Elma, Osman Gazi Gucluturk, Ahmet Kilic, Ferhat Ozgur Catak

Engineering Technology Faculty Publications

With the advancement of green energy technology and rising public and political acceptance, electric vehicles (EVs) have grown in popularity. Electric motors, batteries, and charging systems are considered major components of EVs. The electric power infrastructure has been designed to accommodate the needs of EVs, with an emphasis on bidirectional power flow to facilitate power exchange. Furthermore, the communication infrastructure has been enhanced to enable cars to communicate and exchange information with one another, also known as Vehicle-to-Everything (V2X) technology. V2X is positioned to become a bigger and smarter system in the future of transportation, thanks to upcoming digital technologies …

Teaching Data Acquisition Through The Arduino-Driven Home Weather Station Project, Sheryl Dutton, Kurt Galderisi, Murat Kuzlu, Otilia Popescu, Vukica Jovanovic

Engineering Technology Faculty Publications

The main objective of this paper is to present one possible way to engage undergraduate students in designing a system that uses the Internet of Things (IoT) strategy for data acquisition and management. The MATLAB home weather station project presented here was developed by a team of students for the senior design course in the Electrical Engineering Technology undergraduate program at Old Dominion University (ODU). The main purpose of this project was for undergraduate students to learn how to create a localized, compact, and precise weather station. Utilizing various sensors, both homemade and sourced online, this weather station is capable …

Development Of Sensing And Programming Activities For Engineering Technology Pathways Using A Virtual Arduino Simulation Platform, Murat Kuzlu, Vukica Jovanovic, Otilia Popescu, Salih Sarp

Engineering Technology Faculty Publications

The Arduino platform has long been an efficient tool in teaching electrical engineering technology, electrical engineering, and computer science concepts in schools and universities and introducing new learners to programming and microcontrollers. Numerous Arduino projects are widely available through the open-source community, and they can help students to have hands-on experience in building circuits and programming electronics with a wide variety of topics that can make learning electrical prototyping fun. The educational fields of electrical engineering and electrical engineering technology need continuous updating to keep up with the continuous evolution of the computer system. Although the traditional Arduino platform has …

Development Of A Simevents Model For Printed Circuit Board (Pcb) Assembly Processes, Siqin Dong, Mileta Tomovic, Krishnanand Kaipa

Engineering Technology Faculty Publications

Printed circuit boards (PCBs) are the foundational building blocks of most modern electronic devices. PCB assembly is defined as the process of mounting different electronic components on a PCB. Circuit board assembly utilizes an automated technique with most steps completed by machines for different operations (e.g., pick-and-place components, soldering, etc.). In this paper, details of a student course project, carried out at Old Dominion University, on the design and simulation of PCB assembly processes based on MATLAB discrete-event system are presented. An essential component in the advanced manufacturing technology course is the hands-on experience where students implement multiple software simulation …

Anomaly Based Intrusion Detection System Through Remote Virtual Machine Introspection, Huseyn Huseynov

Dissertations and Theses

Research on identifying malicious applications is an important direction in information security, especially when it comes to detection of evasive malware such as keyloggers, trojans, rootkits and their derivatives. Inspired by a biological immune system and based on negative selection algorithm approach to detect various types of malwares is proposed in this paper.

By deeply studying Linux kernel, understanding links behind different internal system processes, examining, and experimenting with hundreds of various keyloggers we propose a single Artificial Intelligence based solution as a comprehensive protection against wide range of malwares. Developed Intrusion Detection System (IDS) can be deployed in the …

Optimization Of Optical Nanosensor Response For The Detection Of Anthracyclines Using A Binary Machine Learning Classifier, Myesha Thahsin

Dissertations and Theses

Pharmacokinetic variables such as interindividual variation in metabolizing and eliminating drugs makes dose selection of chemotherapeutic anthracyclines difficult. One potential solution to determining dosing levels of an anthracycline is the development of non-invasive sensors to monitor their pharmacology in vivo. Single-walled carbon nanotubes (SWCNT) have substantial potential for in vivo sensor development, as they exhibit near-infrared fluorescence in the tissue-transparent window and a robust response to their local environment. An emerging method for evaluating and optimizing SWCNT sensor response is through machine learning. In this study, anthracyclines Daunorubicin, Doxorubicin, Epirubicin, Mitoxantrone and Idarubicin, were used to interrogate 12 SWCNT preparations …

Blockchain Securities Issues: Decentralized Identity System With Key Management Perspective, Olalekan O. Adaramola

College of Graduate Studies: Theses & Dissertations

Blockchain was created many years ago to solve the problems of data transfer Integrity, several years later the issues persist. Blockchain securities are one of the most important considerations to be investigated, and data integrity is about ensuring the accuracy and validity of messages such that when they are read, they are the same as when they were first written. It is of the opinion that passing information across from one person to another cannot be the same as it was first said at the onset. Our work investigated Blockchain security issues, studying Integrity emanating from transactions across the blocks …

Comparative Analysis Of Fullstack Development Technologies: Frontend, Backend And Database, Qozeem Odeniran

College of Graduate Studies: Theses & Dissertations

Accessing websites with various devices has brought changes in the field of application development. The choice of cross-platform, reusable frameworks is very crucial in this era. This thesis embarks in the evaluation of front-end, back-end, and database technologies to address the status quo. Study-a explores front-end development, focusing on angular.js and react.js. Using these frameworks, comparative web applications were created and evaluated locally. Important insights were obtained through benchmark tests, lighthouse metrics, and architectural evaluations. React.js proves to be a performance leader in spite of the possible influence of a virtual machine, opening the door for additional research. Study b …

Artificial Intelligence-Based Medical Device Technologies Implementation Strategies In The Nigerian Health Care Industry, Oliver Chikaodinaka Iheme

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI)-based medical device technologies can aid medical professionals in delivering faster and more accurate treatment, but health care leaders are concerned with eliminating challenges that impede implementation. Grounded in the technology-organization-environment and technology acceptance models, the purpose of this qualitative multi-case study was to explore strategies health care leaders in Nigeria use to obtain, adopt, and implement AI-based medical device technologies. The participants were 11 health care leaders in Nigeria who successfully implemented AI-based medical device technologies in their hospitals. Data were collected using semi-structured interviews and the review of organizational documents. Through thematic analysis, five themes were …

Strategies Information Technology Managers Use To Retain Qualified Information Technology Employees, Wayne Arnold Reu

Walden Dissertations and Doctoral Studies

Retaining qualified information technology (IT) personnel can take time and effort, given the high demand for skilled positions. Business leaders are concerned with the high turnover of IT employees because of the cost of recruiting and training personnel and the disruption to organizational processes and performance. Grounded in job characteristics theory, the purpose of this qualitative pragmatic inquiry was to explore IT managers' strategies to retain qualified IT employees in organizations across the southwestern United States. Eight IT leaders participated because of their years of experience implementing strategies to retain qualified IT professionals. Data were collected using semistructured interviews and …

How It Professionals Acquire Soft Skills, Paul Majett

Walden Dissertations and Doctoral Studies

AbstractIn this study, I investigated how information technology (IT) professionals learn/acquire soft skills. Little is understood about how IT professionals describe and apply the soft skills that they consider necessary for their own employability. Exploring how IT professionals describe and apply soft skills is important to their future work and career advancement. The purpose of this basic qualitative study was to better understand how IT professionals learn/acquire soft skills. Bandura’s 1986, social learning theory served as the organizational conceptual framework of this study and guided the research question, which asked how IT professionals acquire learn/acquire soft skills. This research question …

Effective Strategies For Using Telecommuting By Owners Of Small Businesses, Thomas Law

Walden Dissertations and Doctoral Studies

Small business owners who lack effective strategies to incorporate telecommuting may be unable to retain teleworking employees, create a flexible working environment, or improve workforce morale, negatively impacting company productivity and profitability. Grounded in transformational leadership theory and sociotechnical systems theory, the purpose of this qualitative multiple case study was to explore strategies small business owners use to incorporate telecommuting to retain teleworking employees. Data were collected from five small business owners in Texas with at least 1 year of management experience and created and maintained remote working strategies. Data collection included semistructured interviews and company documents. Three themes emerged …

Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei

Walden Dissertations and Doctoral Studies

Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and …

Ai Usage In Development, Security, And Operations, Maurice Ayidiya

Walden Dissertations and Doctoral Studies

Artificial intelligence (AI) has become a growing field in information technology (IT). Cybersecurity managers are concerned that the lack of strategies to incorporate AI technologies in developing secure software for IT operations may inhibit the effectiveness of security risk mitigation. Grounded in the technology acceptance model, the purpose of this qualitative exploratory multiple case study was to explore strategies cybersecurity professionals use to incorporate AI technologies in developing secure software for IT operations. The participants were 10 IT professionals in the United States with at least 5 years of professional experience working in DevSecOps and managing teams of at least …

Making Sense Of Big (Kinematic) Data: A Comprehensive Analysis Of Movement Parameters In A Diverse Population, Naomi Wilma Nunis

University of the Pacific Theses and Dissertations

OBJECTIVE

The purpose of this study was to determine how kinematic, big data can be evaluated using computational, comprehensive analysis of movement parameters in a diverse population.

METHODS

Retrospective data was collected, cleaned, and reviewed for further analysis of biomechanical movement in an active population using 3D collinear resistance loads. The active sample of the population involved in the study ranged from age 7 to 82 years old and respectively identified as active in 13 different sports. Moreover, a series of exercises were conducted by each participant across multiple sessions. Exercises were measured and recorded based on 6 distinct biometric …

Developing Consensus On The Use Of Emotional Intelligence Training In Small Utility Companies, Nathaniel E. Holloway

Walden Dissertations and Doctoral Studies

Park and Shaw shared the impact on organizations from unmotivated and unsatisfied employees link to higher turnover ratios. The use of emotional intelligence in manager training lowered employee turnover by 13%. The problem address in this Delphi study was that small utility companies do not have an emotional intelligence plan in place for managerial training. Goleman and Mayer’s framework was used as the theorical lens for examining response of participants to the Delphi study. A panel of experts submitted data in the form of responses to three rounds of questions regarding the use of emotional intelligence training in small businesses. …

Generalized Sparse Bayesian Learning And Application To Image Reconstruction, Jan Glaubitz, Anne Gelb, Guohui Song

Mathematics & Statistics Faculty Publications

Image reconstruction based on indirect, noisy, or incomplete data remains an important yet challenging task. While methods such as compressive sensing have demonstrated high-resolution image recovery in various settings, there remain issues of robustness due to parameter tuning. Moreover, since the recovery is limited to a point estimate, it is impossible to quantify the uncertainty, which is often desirable. Due to these inherent limitations, a sparse Bayesian learning approach is sometimes adopted to recover a posterior distribution of the unknown. Sparse Bayesian learning assumes that some linear transformation of the unknown is sparse. However, most of the methods developed are …

Algorithms And Variations On The Positional Burrows-Wheeler Transform And Their Applications, Ahsan Sanaullah

Electronic Theses and Dissertations, 2020-2023

In this dissertation, we develop algorithms and variations on the Positional Burrows-Wheeler Transform (PBWT). The PBWT is a data structure that stores M binary strings of length N while allowing efficient search. We develop the dynamic-PBWT (d-PBWT). The d-PBWT is a variation of the PBWT that allows its relevant algorithms to run with unchanged time complexity, but also allows efficient insertion and deletion of haplotypes. We provide insertion and deletion algorithms on the PBWT with average case O(N) time complexity. We also improve upon the query algorithms for the PBWT. Durbin described a set maximal match query algorithm on the …

Reconstructing 3d Humans From Visual Data, Ce Zheng

Graduate Thesis and Dissertation 2023-2024

Understanding humans in visual content is fundamental for numerous computer vision applications. Extensive research has been conducted in the field of human pose estimation (HPE) to accurately locate joints and construct body representations from images and videos. Expanding on HPE, human mesh recovery (HMR) addresses the more complex task of estimating the 3D pose and shape of the entire human body. HPE and HMR have gained significant attention due to their applications in areas such as digital human avatar modeling, AI coaching, and virtual reality [135]. However, HPE and HMR come with notable challenges, including intricate body articulation, occlusion, depth …

Sc-Matrl: Semi-Centralized Multi-Agent Transfer Reinforcement Learning, Ayesha Siddika Nipu

Graduate Theses/Dissertations

Distributed decision-making in multi-agent systems (MAS) poses significant challenges for interactive behavior learning in both cooperative and competitive environments. While reinforcement learning (RL) has shown great success in single-agent domains like Checkers, Chess and Go, researchers are motivated to extend RL to MAS. However, as the number of agents increases, effectively dealing with each agent becomes increasingly complex. To mitigate the resulting complexity, a semi-centralized Multi-Agent Influence Dense Reinforcement Learning (MAIDRL) algorithm was previously developed, enhancing agent influence maps to facilitate effective multi-agent control in StarCraft Multi-Agent Challenge (SMAC) scenarios. While MAIDRL shows improved performance in homogeneous multi-agent scenarios, it …

Machine Learning Strategies For Potential Development In High-Entropy Driven Nickel-Based Superalloys, Marium Mostafiz Mou

Graduate Theses/Dissertations

In this study, I developed Deep Learning interatomic potentials to model a multi-phase and multi-component system of Ni-based Superalloys. The system has up to three major phase constituents, namely Gamma, Gamma Prime, and Transition-metal rich Carbide. I utilized invariant scalar-based and/or equivariant, tensor-based neural network (NN) approach as implemented in DEEPMD, NEQUIP/ALLEGRO codes, respectively, and Moment Tensor Potential (MTP). For the training and validation sets, I employed the ab-initio molecular dynamics (AIMD) trajectory results and ground state DFT calculations, including the energy, force, and virial database from highly diverse compositions, temperatures, and pressures following a “High Entropy Strategy.” The Deep …

An Explainable Deep Learning Prediction Model For Severity Of Alzheimer's Disease From Brain Images, Godwin O. Ekuma

Graduate Theses/Dissertations

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. The effectiveness of deep learning models are hampered by limited data sets, skewed class distributions, and the undesirable "black box" of neural networks, which decreases their understandability and usability in precision medicine applications. This thesis addresses the challenge of building an explainable deep learning model for a clinical application: predicting the severity of Alzheimer's disease (AD). AD …

Bimee: Blockchain Based Incentive Mechanism Considering Endowment Effect, Jayanth Madupalli

Graduate Theses/Dissertations

Crowdsensing, a paradigm in modern data collection, harnesses the collective power of mobile users equipped with sensory devices to contribute valuable data based on task-specific criteria. The efficacy of crowdsensing relies on sustained engagement from proficient users over extended periods. Incentivizing long-term participation is crucial, and blockchain technology emerges as a promising framework, providing a decentralized and immutable ledger. However, existing blockchain-based incentive mechanisms for crowdsensing encounter challenges. Firstly, they often overlook users' inherent bias towards loss aversion, a psychological phenomenon where individuals prioritize avoiding losses over acquiring equivalent rewards. Secondly, fairness issues arise, especially concerning newly participating users in …

Sensor Relationship Inference In Single Resident Smart Homes Using Time Series, Samuel Nack

Graduate Theses/Dissertations

Determining sensor relationships in smart environments is complex due to the variety and volume of time series information they provide. Moreover, identifying sensor relationships to connect them with actuators is difficult for smart home users who may not have technical experience. Yet, gathering information on sensor relationships is a crucial intermediate step towards more advanced smart home applications such as advanced policy generation or automatic sensor configuration. Therefore, in this thesis, I propose a novel unsupervised learning approach, named SeReIn, to automatically group sensors by their inherent relationships solely using time series data for single resident smart homes. SeReIn extracts …

Convolutional-Neural-Network-Based Des-Level Aerodynamic Flow Field Generation From Urans Data, John P. Romano, Oktay Baysal, Alec C. Brodeur

Mechanical & Aerospace Engineering Faculty Publications

The present paper culminates several investigations into the use of convolutional neural networks (CNNs) as a post-processing step to improve the accuracy of unsteady Reynolds-averaged Navier–Stokes (URANS) simulations for subsonic flows over airfoils at low angles of attack. Time-averaged detached eddy simulation (DES)-generated flow fields serve as the target data for creating and training CNN models. CNN post-processing generates flow-field data comparable to DES resolution, but after using only URANS-level resources and properly training CNN models. This document outlines the underlying theory and progress toward the goal of improving URANS simulations by looking at flow predictions for a class of …

Background Discrimination Of A Neutrino Detector With Dense Neural Networks, Perry Siehien

Dissertations and Theses

Neutrinos are subatomic particles that weakly interact with matter due to their neutral charge and small cross section. Detectors that search for neutrinos require sensitive instrumentation, which makes them susceptible to various background sources such as gamma rays. Additionally, coherent elastic neutrino-nucleus scattering events, or CEvNS, are the weakest neutrino interactions at 1-25 keV, making them exceptionally difficult to observe. To understand the physics of CEvNS events within the detector material, the recoil signatures of relevant interactions must be determined. Traditional analysis methods are effective, but cannot be applied to energies below 50 keV, due to the overlap of discrimination …

Multimodal Learning: Generating Precise Chest X-Ray Report On Thorax Abnormality, Gaurab Subedi

Dissertations and Theses

Chronic respiratory diseases, ranking as the third leading cause of death worldwide according to the 2017 World Health Organization (WHO) report, affect a staggering 544.9 million individuals. Compounding this public health challenge is the fact that over 80% of health systems grapple with shortages in their radiology departments, highlighting an urgent need for accessible and efficient diagnostic solutions. While various image classification models for analyzing thorax abnormalities have been developed, relying solely on one type of dataset (image data, for example) for thorax abnormality analysis is insufficient. Integrating texts with image data could provide more accuracy as well as analysis. …

When Do Data Collection And Use Become A Matter Of Concern? A Cross-Cultural Comparison Of U.S. And Dutch Privacy Attitudes, Jessica Vitak, Yuting Liao, Anouk Mols, Daniel Trottier, Michael Zimmer, Priya C. Kumar, Jason Pridmore

Computer Science Faculty Research and Publications

Around the world, people increasingly generate data through their everyday activities. Much of this happens unwittingly through sensors, cameras, and other surveillance tools on roads, in cities, and at the workplace. However, how individuals and governments think about privacy varies significantly around the world. In this article, we explore differences between people’s attitudes toward privacy and data collection practices in the United States and the Netherlands, two countries with very different regulatory approaches to governing consumer privacy. Through a factorial vignette survey deployed in the two countries, we identify specific contextual factors associated with concerns regarding how personal data are …

Boundary Regulation Processes And Privacy Concerns With (Non-)Use Of Voice-Based Assistants, Jessica Vitak, Priya C. Kumar, Yuting Liao, Michael Zimmer

Computer Science Faculty Research and Publications

An exemplar of human-machine communication, voice-based assistants (VBAs) embedded in smartphones and smart speakers simplify everyday tasks while collecting significant data about users and their environment. In recent years, devices using VBAs have continued to add new features and collect more data—in potentially invasive ways. Using Communication Privacy Management theory as a guiding framework, we analyze data from 11 focus groups with 65 US adult VBA users and nonusers. Findings highlight differences in attitudes and concerns toward VBAs broadly and provide insights into how attitudes are influenced by device features. We conclude with considerations for how to address boundary regulation …

Graph-Based Multi-Task Learning For Fault Detection In Smart Grid, Dibaloke Chanda, Nasim Yahyasoltani

Computer Science Faculty Research and Publications

Timely detection of electrical faults is of paramount importance for efficient operation of the smart grid. To better equip the power grid operators to prevent grid-wide cascading failures, the detection of fault occurrence and its type must be accompanied by accurately locating the fault. In this work, we propose a multi-task learning architecture that encodes the graph structure of the distribution network through a shared graph neural network (GNN) to both classify and detect faults and their locations simultaneously. Deploying GNNs allows for representation learning of the grid structure which can later be used to optimize grid operation. The proposed …