Other Computer Engineering Commons^™

Adaptive Solutions For Improving The Quality Of Mobile User Experiences, Meena V

Theses and Dissertations

Nowadays, mobile applications have drawn a lot of attention as they bring computational and storage resources close to consumers globally through high-speed networks. Applications such as the medical microscope, 2D barcode reader, environmental sensor(s), mobile security and authenticator, vehicle remote controller, and IoT-based synchronizer come pre- applications are resource-intensive, as it performs computation(s) by utilizing diverse services like location, app-tracking, networking, camera, calendar, contacts, Bluetooth, etc. for each user activity. Parallel execution of such intense services might utilize the utmost memory and CPU of the mobile device which in turn degrades the overall Quality of Experience (QoE). This ultimately ends …

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

Poster Presentations

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, …

Diegetic Sonification For Low Vision Gamers, Jhané Dawes

Master's Theses

There are not many games designed for all players that provide accommodations for low vision users. This means that low vision users may not get to engage with the gaming community in the same way as their sighted peers. In this thesis, I explore how diegetic sonification can be used as a tool to support these low vision gamers in the typical gaming environment. I asked low vision players to engage with a prototype game level with two diegetic sonification techniques applied, without the use of their corrective lenses. I found that participants had more enjoyment and experienced less difficulty …

Automated Brain Tumor Classifier With Deep Learning, Venkata Sai Krishna Chaitanya Kandula

Electronic Theses, Projects, and Dissertations

Brain Tumors are abnormal growth of cells within the brain that can be categorized as benign (non-cancerous) or malignant (cancerous). Accurate and timely classification of brain tumors is crucial for effective treatment planning and patient care. Medical imaging techniques like Magnetic Resonance Imaging (MRI) provide detailed visualizations of brain structures, aiding in diagnosis and tumor classification^[8].

In this project, we propose a brain tumor classifier applying deep learning methodologies to automatically classify brain tumor images without any manual intervention. The classifier uses deep learning architectures to extract and classify brain MRI images. Specifically, a Convolutional Neural Network (CNN) …

Gamification Of Speech Therapy With Pronunciation Pal, Parker Zbylut

Theses/Capstones/Creative Projects

This capstone report examines the theory and implementation behind applying game design principles to educational applications, and explores their implementation in an educational game through the Pronunciation Pal application. The gamification of learning tools aims to increase learners' engagement and attentiveness with a subject by restructuring content using game design principles of challenges, rewards and feedback. Feedback can be delivered via visuals and/or sounds, as well as regular indicators of player progress and accomplishment. In addition, a successful game implementation establishes challenges to facilitate a player's intrinsic desire to continue playing and improving at the skills presented by the game. …

In-Training Explainability Frameworks To Make Black-Box Machine Learning Models More Explainable., Asuman Cagla Acun

Electronic Theses and Dissertations

Despite ongoing efforts to make black-box machine learning models more explainable, transparent, and trustworthy, there is growing advocacy for using only inherently interpretable models for high-stake decision-making. Post-hoc explanations have recently been criticized for learning surrogate models that may not accurately reflect the actual mechanisms of the original model and for adding computational burden at prediction time. We propose two novel explainability approaches to address these limitations: pre-hoc explainability and co-hoc explainability. These approaches integrate explanations derived from an inherently interpretable white-box model into the learning stage of the black-box model without compromising accuracy. Unlike post-hoc methods, our approach does …

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, …

Geometric Multi-Resolution Analysis Across Signals, Images, And Networks, Felicia Schenkelberg

Dartmouth College Master’s Theses

This research delves into the transformative potential of Geometric Multi-Resolution Analysis (GMRA) as a robust tool for dimensionality reduction and data analysis in the context of high-dimensional graphs. Statistical techniques for classification have historically been tailored for scenarios wherein the number of observations significantly exceeds the number of features, a paradigm characteristic of low-dimensional datasets. However, recent advancements in technologies have ushered in a transformative era in data collection practices across diverse domains, resulting in the acquisition of extensive feature measurements. As a result of this shift, datasets have transitioned into a high-dimensional realm wherein the number of features significantly …

High-Altitude Ballooning And Payload Design, Matthew Smith

Honors College Theses

The Icarus project began in 2017 where a team performs high-altitude balloon (HAB) launches to conduct atmospheric and payload design research. The project involves sending a data-collecting payload containing recording equipment to high altitudes (~100,000 feet). This paper outlines the Icarus project as well as the efforts to design a payload that will remain stable throughout flight while also protecting the equipment housed within the payload shell. SOLIDWORKS computer-aided design (CAD) software was used to design a unique payload system. The payload parts were manufactured with PLA filament using a Prusa MK4 3D printer. The secondary goal of this thesis …

Attention Guided Data Augmentation For Improving The Classification Performance Of Vision Transformers., Nada Baili

Electronic Theses and Dissertations

For over a decade, Deep Neural Networks (DNNs) have been rapidly progressing and achieving great success, forming a robust foundation of state of the art machine learning algorithms that impacted various domains. The advances in data acquisition and processing have undeniably played a major role in these breakthroughs. Data is a crucial component in building successful DNNs, as it enables machine learning models to optimize complex architectures, necessary to perform certain difficult tasks. However, acquiring large-scale data sets is not enough to learn robust models with generalizable features. Instead, an ideal training set should be diverse enough and contain enough …

Defining And Labeling Traversable Space In A Forested Environment, James Nguyen

All Theses

This thesis investigates the problem of identifying traversable terrain in outdoor conditions. We are motivated by research in recent years toward identifying drivable space for the purpose of developing autonomous vehicles. Our motivating application is similar but also different. We envision a “Hiker Helper” that assists humans with dismounted navigation in forested terrain. A common challenge in this type of environment is identifying a viable path for moving through terrain that is congested with trees, bushes, other flora, and natural obstacles that would make navigation difficult. We envision training an artificial intelligence (AI) model to automatically analyze images of this …

Design And Application Of Smart Systems To Address Analytical Problems, Lucas B. Ayres

All Dissertations

This dissertation is a multidisciplinary effort that integrates low-cost analytical instrumentation, redox chemistry, and artificial intelligence to overcome existing limitations in the fields of wearable sensing technology, Deep Eutectic Solvents (DES), and antioxidant chemistry. The overall goal behind each implemented strategy is to enhance the accuracy, efficiency, and accessibility of analytical processes and technologies. A general overview of the thesis, along with the research outcomes is included in Chapter One. The theoretical framework of this dissertation is presented in Chapter Two. Chapter Three describes the development of a wearable platform (sensor and instrumentation) to rapidly detect (~20 minutes) S. aureus …

Building Software At Scale: Understanding Productivity As A Product Of Software Engineering Intrinsic Factors, Gauthier Ingende Wa Boway

Master's Theses

During our education at KSU, we have learned about various factors that affect productivity such as schedule, budget, and risks, but those are often controlled outside of what we could learn as software engineering principles, patterns, or practices. On top of that, other off-work factors such as health conditions, emotional distress, or political climate, just to name a few, could drastically affect the productivity of a software engineering team. We see a demarcation between those factors that affect productivity in software engineering but are not inherent to the discipline itself, which we call resistance factors, and the factors that are …

Secured Blockchain And Fractional Discrete Cosine Transform-Based Framework For Medical Images, Abhay Kumar Yadav, Virendra P. Vishwakarma

Makara Journal of Technology

Images can store large amounts of data and are useful for transmitting large amounts of information across different geographical locations using different cloud services. This data sharing increases the chances of cyber-attacks on digital images. Blockchain has properties that enable it to work as a solution to this problem, providing enhanced security and unchangeable storage. However, image size poses a challenge in image storage, as it increases the related storage cost. Compressing images using fractional discrete cosine transform (fctDCT) reduces the amount of data required to express an image securely. This paper presents a novel framework for securely storing and …

Breast Cancer Classification With Machine Learning, Rahanuma Tarannum

ATU Scholars Symposium

Breast cancer is one of the foremost causes of death amongst women worldwide. Breast tumours are characteristically classified as either benign (non-cancerous) or malignant (cancerous). Benign tumours do not spread external side of the breast and are not fatal, whereas malignant tumours can metastasize and be incurable if untreated. Rapidly and accurate diagnosis of malignant tumours is significant for efficient treatment and advanced outcomes. In 2022, breast cancer claimed 670 000 lives worldwide. Women without any particular risk factors other than age and sex account for half of all cases of breast cancer. In 157 out of 185 nations, breast …

Pyroscan: Wildfire Behavior Prediction System, Derek H. Thompson, Parker A. Padgett, Timothy C. Johnson

ATU Scholars Symposium

During a wildfire, it is of the utmost importance to be updated about all information of the wildfire. Wind speed, wind direction and dry grass often works as fuel for the fire allowing it to spread in multiple directions. These different factors are often issues for any firefighting organization that is trying to help fight the fire. An uncontrolled wildfire is often a threat to wildlife, property, and worse, human and animal lives. In our paper, we propose an artificial intelligence (AI) powered fire tracking and prediction application utilizing Unmanned Aerial Vehicles (UAV) to inform fire fighters regarding the probability …

League Of Learning: Deep Learning For Soccer Action Video Classification, Musfikur Rahaman

ATU Scholars Symposium

The field of sports video analysis using deep learning is rapidly advancing. Proper classification and analysis of sports videos are essential to manage the growing sports media content. It offers numerous benefits for the media, advertising, analytics, and education sectors. Soccer, also known as football, worldwide, is among the most popular sports. This research study used a deep learning-based approach for soccer action detection. Deep learning has become a popular machine learning technique, especially for image and video classification. We have used the SoccerAct dataset, which consists of ten soccer actions like corner, foul, freekick, goal kick, long pass, on …

Development Of Deep Neural Architecture For Continuous Sign Language Video Generation, Natarajan B

Theses and Dissertations

This dissertation presents a deep neural network based sign language video generation framework for translating the multilingual sentences into sign videos. This thesis addresses the challenges persist with the sign language video generation such as (i) Handling longer sequences of input sentences and new words (ii) Pose estimation with higher accuracy (iii) High quality photo realistic sign gesture video generation (iv) Improving realism in sign video generation. Hence, the thesis focuses four contributions to address the above issues.

The first contribution of this thesis automates the translation of multilingual sentences into sign glosses without manual intervention by incorporating Hybrid Neural …

Predictive Ai Applications For Sar Cases In The Us Coast Guard, Joshua Nelson

Cybersecurity Undergraduate Research Showcase

This paper explores the potential integration of predictive analytics AI into the United States Coast Guard's (USCG) Search and Rescue Optimal Planning System (SAROPS) for deep sea and nearshore search and rescue (SAR) operations. It begins by elucidating the concept of predictive analytics AI and its relevance in military applications, particularly in enhancing SAR procedures. The current state of SAROPS and its challenges, including complexity and accuracy issues, are outlined. By integrating predictive analytics AI into SAROPS, the paper argues for streamlined operations, reduced training burdens, and improved accuracy in locating drowning personnel. Drawing on insights from military AI applications …

Human-Machine Communication: Complete Volume. Volume 7 Special Issue: Mediatization

Human-Machine Communication

This is the complete volume of HMC Volume 7. Special Issue on Mediatization