Computer Engineering Commons^™

Fourier-Feature Mlp Toolkit For Gpu-Accelerated Cardiac-Mri 4dcmr Strain Analysis, Aarnav T. Sabale, Marco A. Prado, Craig J. Goergen

Discovery Undergraduate Interdisciplinary Research Internship

This paper explores the embedding of a Fourier-Feature—enhanced multiplayer perceptron(MLP-FEE) at the heart of a newly refactored python workflow for four-dimensional cardiac-MRI strain quantification demonstrating how a single, compact network can outperform traditional convolution and spline-based methods. The original code, capable of orientation normalization, displacement tracking, and finite-difference strain computation, has been translated and consolidated into pytorch. By injecting sinusoidal positional encodings at the network’s input layer supplied a rich set of high-frequency basis functions hence enabling multilayer MLP to resolve gradients that cubic splines and conventional CNNs typically blur or struggle with. Profiling on an Apple-silicon GPU shows interactive …

Low-Level Memory Attacks On Edge Assisted Robotic Applications, William Arnold

Master of Engineering Theses

This thesis investigates how low-level memory faults can undermine edge-assisted robotic systems that rely on memory optimization. As robots are utilized in real world applications, the ability to operate safely and successfully in mission critical deployment becomes important. To help achieve these goals, developers are increasingly starting to place computation nodes at network edges to meet latency and reliability requirements. Edge nodes, however, are resource-constrained and resources conservation techniques such as Kernel Same-page Merging (KSM) are enabled to deduplicate identical pages across processes or virtual machines. This thesis shows that this optimization technique quietly widens the attack surface and can …

Visor-Zt: A Visibility, Simulation, And Operational Resilience Framework For Zero Trust Security In Ros 2, Noah Tinker

All Theses

Robotic systems are becoming more and more prevalent in modern society, with Robot Operating System 2 (ROS 2) being the dominant operating system for these implementations. Its popularity can be attributed to its design, which is purpose-built for distributed systems and asynchronous communications. However, ROS 2 security is static and therefore less capable of responding to contemporary threats and network behavior. This becomes a greater issue when considering its applications in the military and defense sectors, where security is of the highest importance. In recent years, the U.S. Department of Defense (DoD) has implemented zero trust (ZT) security based on …

Multiple View Neural Regression Of A Facial Shape Model, Xiang Li

All Dissertations

Creating re-topologized 3D facial meshes is a critical step in high-quality facial animation pipelines, yet it remains a labor-intensive and time-consuming task. Traditional approaches typically rely on multiview stereo reconstruction and specialized photometric environments to acquire accurate geometric and reflectance data under controlled conditions. This dissertation presents work toward more efficient capture of production-ready meshes including (1) developmental aspects of VarIS, a custom-designed light sphere capable of capturing high-resolution stereo geometry and reflectance maps—including diffuse, specular, and normal components under programmable illumination; (2) a study of the effects of camera parameters on automatic 2D and 3D landmarking methods, (3) methods …

Effects Of Lossy Compression Data On Machine Learning Models, Max H. Faykus Iii

All Dissertations

Machine learning is a fundamental tool that is incorporated in every field across academia and other industries. Due to the large amount of data needed for training machine learning models, lossy compression plays a crucial role in storing data. Machine learning involves the use of algorithms and models to learn patterns in data. This allows the AI to make decisions without specific programming. On the other hand, compression utilizes encoding and decoding techniques to reduce the size of files. Compression is either lossy or lossless, lossy causes a loss of data while lossless preserves the data. This dissertation will explore …

Advancing Real-World Implementation Of The Well Optimized Linear Finder (Wolf) High-Speed Atmospheric Turbulence Compensation Method, Timothy Evan Coon

Theses and Dissertations

This dissertation advances the real-world implementation of the Well Optimized Linear Finder (WOLF) method for high-speed Atmospheric Turbulence Compensation (ATC). Atmospheric turbulence introduces phase aberrations into optical wavefronts and degrades image quality in terrestrial imaging systems. Traditional phase diversity methods are computationally intensive and poorly suited to real-time operation. The WOLF method addresses these limitations through a novel, point-wise formulation of the optical transfer function (OTF) as a structured autocorrelation of the generalized pupil function (GPF). This formulation enables the estimation of phase aberrations at individual spatial coordinates with distributed computational complexity.

The research begins by developing a MATLAB-based simulation …

Exploring Immune System Through Computational Modeling: A Comprehensive Study Of Lymph Nodes And Immune Response Scaling, Vaccine Efficacy, And Large-Scale Extreme First Passage Time, Jannatul Ferdous

Computer Science ETDs

The adaptive immune response is a complex defense mechanism that develops over time to recognize and eliminate pathogens with remarkable precision and durability. This dissertation investigates the dynamics, scaling, and efficiency of the adaptive immune response through a synthesis of computational modeling, mathematical analysis, and agent-based simulations. First, we analyze the topology of the lymphatic network and investigate the T cell search time to find the lymph node that is containing the matching dendritic cell. Second we show how the scaling of lymph node number and volume with body mass, leads to scale-invariant search times for T cells locating antigen-bearing …

Adaptive Multi-Agent Reinforcement Learning For Electric Vehicle Charging Optimization Under Dynamic Traffic Conditions, Shaghayegh Rabbanian

LSU Master's Theses

Electric vehicle (EV) charging optimization is a critical challenge in sustainable transportation. This study focuses on three fundamental questions: (1) when is the best time to charge an EV, (2) where is the optimal charging location, and (3) how should charging be planned considering navigation and routing decisions. Our primary objective is to determine the optimal time and location for EV charging while accounting for key factors such as real-time traffic conditions, spatial distribution of charging stations, and EV-specific attributes such as state of charge (SOC), driving range, and efficiency. To develop a robust and adaptive EV charging recommendation system, …

Multi-Scale Color Correction And Contrast Enhancement Via Leaf In Wind Optimization For Improved Weld Defect Detection In Non- Destructive Testing, Senthil Anand N Mr

Theses and Dissertations

Image enhancement is an essential process in numerous fields, including industrial inspection, medical imaging, remote sensing, and photography, as it improves image quality for accurate analysis and interpretation. Among the advanced image enhancement techniques, Focused Super Resolution (FSR) with Self-Attention Single Candidate Optimizer-based Generative Adversarial Networks (GANs) is specifically designed for weld defect detection, while Advanced Image Enhancement through Multi-scale Color Correction and Contrast Stretching using Leaf in Wind Optimization focuses on enhancing the overall visual quality of images. Although both approaches aim to improve image quality, they differ significantly in their objectives and application areas. The FSR method concentrates …

From Devices To The Cloud: Digital Forensics In The Changing Social Media Landscape, Joseph Brown

LSU Master's Theses

This thesis presents a comprehensive digital forensic analysis of emerging and alternative social media platforms, including Truth Social, Threads, Bluesky, Nextdoor, and Neighbors. These platforms, which range from politically aligned alt-tech networks to hyperlocal neighborhood apps, present unique forensic challenges and security vulnerabilities. Across all case studies, established forensic techniques were applied using a hybrid methodology combining mobile device analysis, network traffic monitoring, and API interrogation. Findings include the discovery of plaintext credentials, session tokens, and other sensitive artifacts, particularly in platforms with weaker security postures such as Truth Social, Bluesky, Nextdoor, and Neighbors. Threads, by contrast, demonstrated greater resilience …

A Predictive Framework For Early Detection And Personalised Monitoring Of Parkinson’S Disease Using Artificial Intelligence And Large Language Models, Priyadharshini S

Theses and Dissertations

Parkinson’s Disease (PD) is a multifaceted and progressive neurodegenerative disorder that presents a spectrum of motor and non-motor symptoms. Early and accurate diagnosis is essential for effective disease management and improved patient outcomes, yet remains clinically challenging due to symptom overlap and diagnostic limitations. This thesis proposes a comprehensive and interpretable artificial intelligence (AI)-driven diagnostic framework that aims to transform the early detection, personalised monitoring, and treatment recommendation process for PD. The proposed solution integrates deep learning, radiomics, evolutionary optimisation, and large language models (LLMs), ensuring a highly accurate and clinically adaptable system.

The research begins by analysing T2-weighted 3D …

Towards Leveraging Social Media Data For Fostering Collaborations Among Non-Profits, Monazil Chowdhury

LSU Doctoral Dissertations

Nonprofit organizations serve a crucial role in tackling a wide range of significant social, environmental, and economic issues. But it is often hard to get a clear picture of their work because their information is spread out and it is difficult to see how they are collaborating. To address this issue we developed a web-based tool to collect scattered data—from a variety of sources, such as the IRS, social media, and the Census, into one easy-to-use resource. The tool begins by taking IRS records and geocoding each nonprofit’s physical address With its coordinates. It then retrieves census tract information from …

Multi-Modal Covid-19 Detection Using Cough Sounds And Medical Information With Attention-Enhanced Deep Learning, Mohamed Talaat Saidahmed, Reda Elbasiony, Marwa Reda Bastwesy, Asmaa Aly Hagar

Journal of Engineering Research

The COVID-19 pandemic has highlighted the need for fast, non-invasive, and cost-effective diagnostic tools. Cough sounds, as a prominent symptom of respiratory diseases, present a promising modality for automated COVID-19 detection. In this study, we propose a novel multi-modal deep learning framework for COVID-19 detection that leverages cough sounds and patient-specific medical information. Our approach extracts two types of acoustic features—Mel-Frequency Cepstral Coefficients (MFCCs) and Mel spectrograms—and integrates them with clinical metadata to improve diagnostic ac-curacy. The MFCC branch employs 1D convolutional layers followed by Efficient Channel Attention mechanism. The Mel spectrogram branch utilizes ResNet-50 combined with ECA to capture …

Evaluation Of Machine And Deep Learning Models For Predicting Water Distillate Rate, Ghada Hamisa

Journal of Engineering Research

Freshwater scarcity has become a critical global challenge due to rapid population growth and environmental pollution caused by industrial and urban expansion. Solar stills offer a sustainable solution by desalinating impure water using solar energy, making them valuable for domestic, industrial, and academic applications. However, traditional methods for optimizing solar still performance face significant limitations, including time-consuming experimental data collection, computational inaccuracies, and high development costs. To address these challenges, this study leverages machine learning (ML) and deep learning (DL) techniques to predict the distilled water production rate of solar stills before physical construction or modification. A heat pump solar …

Director, Military Cyber Institute, Joseph Schafer

Military Cyber Affairs

No abstract provided.

Throughput Of Ascon Compared With Popular Iot Encryption Algorithms, Mitchel R. Harvey (Ryan), Andrew M. Kaiser, Garrett W. Hoiness

Military Cyber Affairs

No abstract provided.

Anomaly Detection Of Network Layer Attacks Against Cyber Physical Systems Using Machine Learning And Deep Learning Techniques, James Alger, Michael Tu

Military Cyber Affairs

This research paper presents the analysis of using machine learning and deep learning algorithms on detecting anomalous network traffic in Cyber-Physical Systems (CPS). Using a real PLC CPS-based system, normal and anomalous network traffic will be captured using Wireshark. The research analyzes a DDoS attack. The focus of the research is to identify the most effective feature combinations and evaluate them on ML and DL models. The emphasis is on enhancing detection strategies rather than exploiting device vulnerabilities. The detection of network attacks often involves handling a vast array of high-level features. Previous studies (Li & Chasaki, 2022) apply machine …

Characterizing Caldera’S Cyber Attack Emulation Capabilities, Caleb Chang, Matthew Cao, Kenyou Teoh, Ekzhin Ear, Shouhuai Xu

Military Cyber Affairs

Autonomous cyber attack emulation can aid cyber defenders to identify and remediate cyber risks. MITRE’s Caldera software is the state-of-the-practice for automated attack emulation. Yet, it has not been systematically analyzed, putting its performance and effectiveness into question. This paper systematically characterizes Caldera’s architecture, abilities and use cases, and assesses its strengths and weaknesses. It draws useful insights, such as: Caldera excels in stealthy access and execution tactics to pilfer data against Windows operating systems. It also discusses two directions for Caldera improvement: module-level automation and end-to-end attack emulation.

The Digital Battlefield: Safeguarding Military Drones Against Cyberattacks, Jason Ashong, Arun Venkitanarayanan, Benjamin Yankson

Military Cyber Affairs

The Internet of Battlefield Things (IoBT) is an advanced network of interconnected devices that significantly enhance military operations through real-time data exchange and situational awareness. While IoBT offers tactical advantages like improved surveillance, reconnaissance, and operational effectiveness, it also introduces substantial cybersecurity risks. Adversaries can exploit vulnerabilities within these networks, potentially compromising mission integrity and national security. This research examines the cybersecurity measures of commercial drone controllers and their correlation with military devices. It aims to enhance future vulnerability assessments with advanced tools and approaches to better secure critical military operations. The study highlights the need for robust security architectures …

Using Blockchain Technology To Help Secure America's Defense Critical Infrastructure, Vimal Buck, Aerin Krebs, Brynn Hillard, Jakob Gerha, Joseph Lutma, Srikar Maduposu, Ted Allen

Military Cyber Affairs

Critical water infrastructure in the United States faces increasing cybersecurity threats from state-sponsored actors, with potentially devastating consequences for national security, economic stability, and public health. (Cybersecurity and Infrastructure Security Agency, 2025). This infrastructure supports defense critical assets and is actively being targeted by various state-sponsored hacking groups, which poses a major concern for civilians and military alike. K. Herath (personal communication, February 24, 2025) reported being aware of two attacks on Ohio water systems during his tenure as Cybersecurity Strategic Advisor to Ohio Governor Mike DeWine.

Water is essential to everyday life and defense and presents as a high-value …

Network And Multipath Traceroute Visualization, Cameron Makowski

Military Cyber Affairs

TraceCam introduces a new paradigm in network path analysis, leveraging GPU-accelerated WebGL visualization, advanced traceroute integrations, and AI-driven insights to transform complex routing data into actionable intelligence. Early prototypes have demonstrated significant improvements in performance, clarity, and multi-path discovery, overcoming traditional limitations in traceroute analysis. By incorporating retrieval-augmented language models and enriched metadata sources like IPinfo.io, TraceCam enables automated anomaly detection, contextual explanations, and rapid root-cause analysis, enhancing operational efficiency. The platform’s architecture ensures scalability and adaptability, supporting deeper investigations and real-time situational awareness. Future development will focus on clustering-based anomaly detection, expanded geographic visualizations, and enhanced AI-generated analysis to …

Quantifying Adversary Military Forces’ Susceptibility To Cognitive Attacks, Bonnie Rushing, Cole Nelson, Shouhuai Xu, Christofer “Raven” O’Keefe, Olga Karpoyan

Military Cyber Affairs

This paper introduces a method to quantify international populations’ susceptibility to cyber cognitive attacks using press freedom and media trust metrics. We present the Cognitive Influence Calculator, a tool that estimates susceptibility (𝑆) based on Press Freedom Scores (PFS) and media trust levels. Findings show that while authoritarian regimes are harder to reach, successful cognitive attacks have greater impacts due to higher trust in state-controlled narratives. Using U.S. wargaming data and international trust metrics, we compute susceptibility scores for the U.S., Russia, China, Iran, and North Korea. Results show an inverse relationship between PFS and media susceptibility, with local/allied …

Forward, Amy Hamilton

Military Cyber Affairs

No abstract provided.

Understanding Russia’S Cyber Policies, Strategies, And Doctrines, Bryan Hancock, Hanh Nguyen, Olga Karpoyan, Ekzhin Ear, Shouhuai Xu

Military Cyber Affairs

This study analyzes the strengths and weaknesses of Russia’s cyber policies, strategies, and doctrines through a systematic set of attributes, leading to key insights: (i) Russia has proactively adapted its cyber policies, strategies, and doctrines to its evolving environment; (ii) Russia actively conducts cognitive warfare, but remains equally vulnerable to it; and (iii) Russia’s cyber posture faces significant challenges, including a limited technological base, shortage of skilled personnel, and restrictive approach to information control, all of which undermine the effectiveness of its strategies. These insights offer valuable implications for US Cyber Command and the Department of Defense.

Characterizing Cyberattacks Against Operational Technology Infrastructures Through The Lens Of Attack Flows, Sherman Kettner, Caleb Chang, Ekzhin Ear, Shouhuai Xu

Military Cyber Affairs

Operational Technology (OT) infrastructures play a critical role in modern society and economy. However, their increasing connectivity with public networks such as the Internet has made them vulnerable to cyberattacks, much like traditional Information Technology (IT) systems. In particular, cyberattacks against OT infrastructures remain relatively underexplored and little understood. In this paper, we aim to deepen our understanding of cyberattacks against OT infrastructures. For this purpose, we propose a methodology, including novel cybersecurity metrics to analyze the attack flows of these attacks in an end-to-end fashion, which allows us to draw useful insights. We demonstrate the utility of the methodology …

Cst110.1 Analysing Everyday Interfaces Example 1, Sae University College

Exemplars

A Case Study on MyTime Interface analysing the usability of it.

Cst110.1 Analysing Everyday Interfaces Example 2, Sae University College

Exemplars

A Case Study on usability analysis of the Apple iOS fitness app.

Deeppanorf: Deep Prior For Neural 3d Reconstruction From Sparse Panoramas, Edward Du

Master's Theses

Advances in neural field representations have led to a significant improvement in view synthesis quality. However, many current novel view synthesis methods rely on a dense set of input views, which can be impractical and inefficient in real-world applications. We propose DeepPanoRF, a novel method for 360◦ scene reconstruction from a sparse set of input equirectangular panoramas. Built upon K-Planes, a radiance field representation that encodes explicit features on orthogonal feature planes, our method does not directly learn feature grids. Instead, we parameterize the feature grids to enable sparse view reconstruction without pretraining or additional regularization. We implement a custom …

Simulated Live Studio Audience, Theodore David Shellenberger

Computer Engineering

The Simulated Live Studio Audience is a Python based application that utilizes Vosk, Roboflow, and Llama 3.2 to provide a user with auditory feedback based upon both visual and audible input from their device's microphone and camera. This system functions with a custom trained computer vision model to detect a specified object and when individuals walk in and out of the camera frame, outputting sitcom style simulated crowd reaction sounds accordingly. The simulated studio audience program also takes in vocal input from users, converts it to text, and, using a large language model, analyzes it for content that can be …

Analyzing Player Difficulty Perception In Platformers Through Procedural Level Generation, Sasank Madineni

Master's Theses

Games utilizing Procedural Level Generation (PLG), such as Roguelikes, are becoming increasingly popular in today's gaming sphere. In games employing PLG, levels are generated randomly or pseudo-randomly, and aim to retain player attention through variance in levels between playthroughs. However, when generating levels with variance in structure and design, player enjoyment is often a mixed bag. With low enjoyment, player retention for these games can dwindle. This study explores the efficacy of real-time difficulty adjustment in procedurally generated platformers, as a method for maintaining stable player enjoyment without causing frustration. This thesis focuses on creating a short user experience, MIMEVA, …