Engineering Commons^™

Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark

Honors Theses

Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …

Ensuring Non-Repudiation In Long-Distance Constrained Devices, Ethan Blum

Honors Theses

Satellite communication is essential for the exploration and study of space. Satellites allow communications with many devices and systems residing in space and on the surface of celestial bodies from ground stations on Earth. However, with the rise of Ground Station as a Service (GsaaS), the ability to efficiently send action commands to distant satellites must ensure non-repudiation such that an attacker is unable to send malicious commands to distant satellites. Distant satellites are also constrained devices and rely on limited power, meaning security on these devices is minimal. Therefore, this study attempted to propose a novel algorithm to allow …

The Effects Of Social Media On Mental Health And Career Planning, Spencer A. Rowan

Honors Theses

Social media use is prevalent and necessary in society—nearly anything can be accomplished with a mobile device or smartphone. Among the US population, two thirds of American adults admit to using social media (Perrin, 2015) and in 2022, Georgiev (2023) found Americans spent an average of two and a half hours daily on social media. Furthermore, social media use is tied to mental well-being, work confidence levels, and feelings of being an imposter (Johnson et al., 2020; Uram & Skalski, 2022; Hernandez & Chalk, 2021; Myers, 2021; Ramm, 2019).

This project examined the role of social media use among college …

The Effects Of Virtual Reality On Mental Health Software User Satisfaction And Retention, William Hooten

Honors Theses

Mental health issues have become increasingly important in today's society. With that being said, researchers and consumers are looking for new ways to manage and treat mental health using new technologies in labs and the consumer space. This innovation has led to the presence of mobile self-help mental health applications, applications for peoples’ phones that are used to manage symptoms of mental health problems, such as depression and anxiety, track goals, meditate, and more. However, mobile mental health applications, and mobile applications in general, have a problem concerning user satisfaction and overall user retention – studies have shown that 95% …

An Ios Application For Visually Impaired Individuals To Assist With Crossing Roads, Ali Khan

Honors Theses

In day-to-day life, visually impaired individuals face the problem of crossing roads by themselves. This project was designed and built to solve this key issue. The system is supposed to give the user a warning before approaching a crosswalk for their safety and also give information about when it is safe to cross the road. An iOS application was developed to address the problem since recent studies have discovered that a vast number of visually impaired individuals are using smartphones (iPhones in particular) due to the ease and convenience it brings to their daily life. The application should be able …

Examining The Wording Of Digital Synthesizer Presets To Help Novice Producers, Nicholas O'Toole

Honors Theses

My research looks into the use of ”presets” in digital synthesizers, which alter the timbre (quality) of the synthesizer’s sound by loading in pre-selected configurations of settings. My study compares imagery- based and feelings-based preset names – for example, Cloud City Keys and Mellow lead, respectively – in an attempt to see which better predicts the sound it represents. Through my results, I will then explain how the wording of these preset names affected my subjects’ perception of certain sounds. The results I found lead me to believe that imagery-based preset names could be representative of their respective presets’ sounds.

Investigation Of Python Variable Privacy, Joshua Bartholomew

Honors Theses

This study looks at the relative security of Python regarding private variables and functions used in most other programming languages. Python has only grown in popularity due to its simple syntax and developing capabilities. However, little research has been published about how secure Python code and programs compiled from Python code actually are. This research seeks to expose vulnerabilities in Python code and determine what must be done for these vulnerabilities to be exploited by hackers to abuse potentially sensitive information contained within the program.

The proposed methodology includes examining the private variable concept in other programming languages and conducting …

Comparative Analysis Of Imputation Methods In Real Estate Data, Connor Donlen

Honors Theses

This project involves comparing different methods of missing data imputation in the context of predicting real estate listing prices. These methods are compared against each other in both their ability to recreate the original data and their effects on a final predictive model. In order to evaluate their effectiveness, first, a predictive model is made using the complete dataset to use as a benchmark for the imputed datasets. Then, a complete dataset is split into 80% training and 20% testing datasets, and missing values are created in the training data using two different missing data mechanisms, missing completely at random …

Privacy-Preserving Blockchain-Based Registration Scheme For Av Parking System, Alexander Haastrup

Honors Theses

Autonomous Vehicles (AV) are a prime example of how innovation and automation are at the forefront of growing technology trends. The concern of parking systems is becoming apparent as research into ways to increase the efficiency and cost-effectiveness of AV continues. To ward against various internet attackers and secure users' sensitive information, an efficient AV parking system must have powerful user privacy and cyber security capabilities. In my work, I present a blockchain-based privacy registration system for AV parking systems that meets the following criteria. The proposed scheme incorporates k-Nearest Neighbor (kNN) - an efficient and lightweight algorithm - for …

Self-Efficacy Development In Elementary-Aged Learners Through Dance As An Algorithmic Thinking Tool, Niva Shrestha

Honors Theses

The purpose of this research is to demonstrate the effectiveness of a transdisciplinary approach in teaching computational thinking through dance to elementary-aged learners, with primary attention to females. With limited literature available on how pre-adolescents begin to construct conceptions of computer science and other engineering domains, including potential career pathways, the incentive of this project was to leverage a day camp for about 20 rising 3rd - 5th-grade learners to assess their identity development in computer science. Modules that teach computational thinking through dance paired with Unruly splats (block-based programmable electronic gadgets) were implemented. By conducting pre-and post-surveys and a …

Computational Analysis Of The Synthesis Of Hydrogels Using The Diels Alder Reaction, Avery Boley

Honors Theses

No abstract provided.

Art To Influence Creativity In Algorithmic Composition, Tyler Braithwaite

Honors Theses

Advances in Recurrent Neural Network (RNN) techniques have caused an explosion of problems posed that revolve around the mass analysis and generation of sequential data, including symbolic music. Building off the work of Nathaniel Patterson’s Musical Autocomplete: An LSTM Approach, we extend this problem of continuing a composition by examining the creative impact that injecting latent-space encoded image data, specifically fine art from the WikiArt Dataset, has on the musical output of RNN architectures designed for autocomplete. For comparison purposes with Patterson, we will also be using a corpus of Erik Satie’s piano music for training, validation, and testing.

Ux/U-Eye: Designing Graphical User Interfaces For Exclusive Eye Gaze Control, Timothy Curol

Honors Theses

No abstract provided.

Generative Adversarial Networks Take On Hand Drawn Sketches: An Application To Louisiana Culture And Mardi Gras Fashion, Stephanie Hines

Honors Theses

No abstract provided.

Conditional Variational Autoencoder (Cvae) For The Augmentation Of Ecl Biosensor Data, Matthew Dulcich

Honors Theses

Machine Learning (ML) is vastly improving the world, from computer vision to fully self-driving cars, we are now able accomplish objectives that were thought to only be dreams. In order to train ML models accurately, they require mountains of information to work with, but sometimes it becomes impossible to collect the data needed, so we turn to data augmentation. In this project we use a conditional variational auto encoder to supplement the original video electrochemiluminescence biosensor dataset, in order to increase the accuracy of a future classification model. In other words, using a cVAE we will create unique realistic videos …

Exploring The Efficiency Of Neural Architecture Search (Nas) Modules, Joshua Dulcich

Honors Theses

Machine learning is obscure and expensive to develop. Neural architecture search (NAS) algorithms automate this process by learning to create premier ML networks, minimizing the bias and necessity of human experts. From this recently emerging field, most research has focused on optimizing a promisingly unique combination of NAS’s three segments. Despite regularly acquiring state of the art results, this practice sacrifices computing time and resources for slight increases in accuracy; this also obstructs performance comparison across papers. To resolve this issue, we use NASLib’s modular library to test the efficiency per module in a unique subset of combinations. Each NAS …

90snet:, Seth Richard

Honors Theses

No abstract provided.

Iot Greenhouse Monitoring System, Raj Basnet

Honors Theses

Our project is a greenhouse monitoring system. The customer states that they need a complete monitoring system for their greenhouse. There are a lot of items within the greenhouse that need to be watered at the right time and kept at a certain temperature. The customer is not always around to check the status of these items due to their busy lifestyle. They would like a system to monitor all these items so they can check it on their smartphone no matter how far away they are from the greenhouse. The customer wants this to be a low-cost and energyefficient …

Iot Garden Frost Alarm, Andrew James

Honors Theses

Home gardeners are faced with yearly challenges due to spring frosts harming young plants. This is frequently mitigated by covering crops with frost blankets, but only on nights when a frost is predicted. In areas with less predictable climate, an unexpected frost can kill vulnerable plants, reducing the amount of food produced. A system is proposed and designed here to use internet of things (IoT) technology to enable a small weather station in the home garden to report current climate data and predict frosts, then alert the gardener in time for them to cover their plants.

The system as designed …

Wearables And Wearable Data In Tele-Health Applications, Jack Mazza

Honors Theses

With the sudden emergence of Covid-19, Tele-Health has been forced into the forefront of healthcare. With no human contact, regular in-person doctor or clinic visits could not be made. Unfortunately, there is a gap in patient data for healthcare professionals when making diagnoses remotely. Fortunately, many users are constantly collecting some primary health data through wearables that have become commonplace in users' homes. Tapping into this unused data could provide healthcare professionals with a better picture of patients' health remotely. In this thesis, I will determine whether this wearable data can be a viable addition to Tele-Health applications, providing additional …

A Study Of Deep Reinforcement Learning In Autonomous Racing Using Deepracer Car, Mukesh Ghimire

Honors Theses

Reinforcement learning is thought to be a promising branch of machine learning that has the potential to help us develop an Artificial General Intelligence (AGI) machine. Among the machine learning algorithms, primarily, supervised, semi supervised, unsupervised and reinforcement learning, reinforcement learning is different in a sense that it explores the environment without prior knowledge, and determines the optimal action. This study attempts to understand the concept behind reinforcement learning, the mathematics behind it and see it in action by deploying the trained model in Amazon's DeepRacer car. DeepRacer, a 1/18^th scaled autonomous car, is the agent which is trained …

Evaluation Of State-Of-The-Art Nlp Deep Learning Architectures On Commonsense Reasoning Task, Guo Rui (Justin) Lee

Honors Theses

The goal of this project was to explore modern neural network technology in the application of discerning and generating statements that are ‘reasonable’, in what is known as commonsense reasoning. We built off of the work of Saeedi et al. In their work on the 2020 SemEval task, Commonsense Validation and Explanation (ComVE). SemEval is a workshop that creates a variety of semantic evaluation tasks to examine the state of the art in the practical application of natural language processing. This particular task involved three sections: task A, Validation, in which a program tries to select which of two statements …

Non-Linear Dimensionality Reduction Using Auto-Encoder For Optimized Malaria Infected Blood Cell Classifier, Aayush Dhakal

Honors Theses

Neural Networks have been widely used in the problem of Medical Image Analysis. However, when dealing with large images, deep networks easily exhaust computer resources, which in turn hinders training. This paper shows the efficacy of using Auto-Encoders as a dimensionality reduction tool to increase the efficiency of a Malaria Infected Blood Cell Image classifier. We show that using an autoencoder, we can reduce the dimensionality of large blood cell images effectively such that the features in the new space retain all the essential information from the original input. Then we show that the new features obtained from the autoencoder …

The Exploration And Analysis Of Mancala From An Ai Perspective, Trevon J. Hunter

Honors Theses

Through the study of popular games such as Chess and Go, countless artificial intelligence (AI) research has been conducted in an attempt to create algorithms equipped for adversarial search problems. However, there are still a plethora of avenues that offer insight into further development. Mancala is traditionally a two-player board game that originated in the East and offers a unique opponent-based playing experience. This thesis not only attempts to create a competitive AI algorithm for mancala games by analyzing the performance of several different algorithms on this classic board game, but it also attempts to extract applications that may have …

Ez-Translate, Mason D. Beattie

Honors Theses

This report describes the EZ-Translate Software that was designed and developed by Mason Beattie for the CSCI495-D1 Honors Capstone Project and Course at Coastal Carolina University. This project is built from Java and is designed for the Windows 10 operating system. While running it displays or hides a translation menu when the designated key binds are pressed. Translations supported include select phrases from English, German, and Russian. The application also provides localization support for these three languages. The overall goal of this project to provide translation services while within another application was successfully achieved. The application utilizes an open-source Java …

Determination Of Hydrogel Degradation By Passive Mechanical Testing, Avery Rosh-Gorsky

Honors Theses

This paper details a new technique to measure the mechanical properties of ETTMP PEGDA hydrogels using Hertz Contact Theory and simultaneously analyze both the model drug release and gel erosion in situ. This method involves curing a drug loaded hydrogel in a standard cuvette and placing a glass bead and phosphate buffer solution (PBS). Over time, the cross-linked network of the hydrogel breaks down, and, as a result, the ball sinks into the hydrogel. This method provides a macroscopic and inexpensive way to continuously and passively measure properties of the hydrogel as the hydrogel degrades. By plotting both the …

Global Privacy Concerns Of Facial Recognition Big Data, Myranda Westbrook

Honors Theses

Facial recognition technology is a system of automatic acknowledgement that recognizes individuals by categorizing specific features of their facial structure to link the scanned information to stored data. Within the past few decades facial recognition technology has been implemented on a large scale to increase the security measures needed to access personal information. This has been specifically used in surveillance systems, social media platforms, and mobile device access control. The extensive use of facial recognition systems has created challenges as it relates to biometric information control and privacy concerns. This concern raises the cost and benefit analysis of an individual’s …

Human Facial Emotion Recognition System In A Real-Time, Mobile Setting, Claire Williamson

Honors Theses

The purpose of this project was to implement a human facial emotion recognition system in a real-time, mobile setting. There are many aspects of daily life that can be improved with a system like this, like security, technology and safety.

There were three main design requirements for this project. The first was to get an accuracy rate of 70%, which must remain consistent for people with various distinguishing facial features. The second goal was to have one execution of the system take no longer than half of a second to keep it as close to real time as possible. Lastly, …

Methods To Detect Forgeries In Static Signatures, Jennifer Lauriello

Honors Theses

Statistical and machine learning approaches to forgery detection in offline sig- natures are attempted and evaluated. Offline signatures are static signatures found on physical media, mainly a piece of paper. A dataset of 330 signatures for 33 people is used, containing five genuine and five forged signatures for each person. The statistical analysis approach proves more successful than a machine learning approach, likely due to the size of the dataset.

Implementation Of A Computer-Vision System As A Supportive Diagnostic Tool For Parkinson’S Disease, Diego Machado Reyes

Honors Theses

Parkinson’s disease is the second most common neurodegenerative disorder, affecting nearly 1 million people in the US and it is predicted that the number will keep increasing. Parkinson’s disease is difficult to diagnose due to its similarity with other diseases that share the parkinsonian symptoms and the subjectivity of its assessment, thus increasing the probabilities of misdiagnosis. Therefore, it is relevant to develop diagnostic tools that are quantitatively based and monitoring tools to improve the patient’s quality of life. Computer-based assessment systems have shown to be successful in this field through diverse approaches that can be classified into two main …