Computer Engineering Commons^™

Wake-On-Anomaly Federated Architecture For Privacy-Preserving Poultry Health Early Warning, Mahmoud Aziz Louati

Masters Theses

Highly pathogenic avian influenza outbreaks, respiratory disease, heat stress, and silent equipment failures share one operational reality: they are detected too late because today’s poultry-health workflow is reactive, manual, and dependent on producers volunteering commercially sensitive data. This thesis presents a wake-on-anomaly federated architecture that addresses both the detection-latency problem and the privacy–adoption deadlock that has so far prevented cross-farm collaboration. The architecture is organized in two tiers. Tier 1 is a lightweight LSTM autoencoder that continuously screens four routine telemetry channels (water, feed, house temperature, activity proxy) and emits a per-window reconstruction-error score. A debounced k-of-m trigger with cooldown …

Native Wayland Compositing On Apple Ecosystems: Assessing The Feasibility Of “Wawona” Compositor, Alex Spaulding

2026 Symposium

The Wayland display protocol is the modern standard for Linux window management, emphasizing security, performance, and simplicity. Expanding this ecosystem to macOS, iOS, and Android introduces technical hurdles due to proprietary windowing systems and divergent hardware APIs. This research evaluates the feasibility of developing a native Wayland Compositor for Apple and Android, given the closed nature of these ecosystems.

“Wawona” bridges this gap by architecting a native Wayland Compositor capable of executing unmodified Linux applications. The methodology involves implementing the Wayland protocol stack into native abstractions leveraging Metal, Android’s graphics pipeline, and CoreAnimation.

Investigating Creative Possibility Using Automated Composition, Kian Drees

Undergraduate Honors Theses

As a constructive method, Johann Joseph Fux’s theory of counterpoint defines a space of musical possibility for contrapuntal composition. I developed a computational method for generating melodies to systematically investigate selected properties of this space. My program recursively generates a tree of all possible cantus firmus melodies of a specified length or all possible first species counterpoints on a given cantus firmus, starting with an empty root node and adding child nodes representing possible musical notes at each step until the specified length is reached. I then investigated several properties of the generated melodies, such as the approximate relationship between …

Improving Human Visual Search: Enhancing Lung Cancer Nodule Detection In Medical Images, Christopher Khajira

Masters Theses

Human visual search involves the identification of relevant signals within information-rich environments, which is a fundamental problem in visual perception. While detection accuracy and response time are commonly used to evaluate performance in visual search, these measures do not reveal the underlying cognitive and computational structure that produces observable behavior. A key challenge lies in distinguishing between competing processing architectures, particularly in complex visual domains where different models can produce similar behavioral outcomes. This study addresses this challenge by developing a computational experimental framework for analyzing visual search behavior using System Factorial Technology (SFT). The experimental framework integrates naturalistic medical …

Exploration Of Real-Time Power Electronic Simulation On Amd Npu, Shouyu Du

All Theses

The rapid electrification of the automotive and data center sectors has created a critical demand for high-fidelity, real-time simulation of complex power electronic systems. While Hardware- in-the-Loop (HIL) simulation on Field-Programmable Gate Arrays (FPGAs) is the current industry standard, it presents significant challenges regarding development complexity and memory limita- tions. This dissertation investigates the feasibility of utilizing Neural Processing Units (NPUs)— specifically the AMD AI Engine (AIE) XDNA2 architecture—as a novel platform for real-time, deterministic circuit simulation. This study establishes a comprehensive automated modeling framework based on graph theory and Massarini’s method to derive linear state-space representations for arbitrary circuit …

A Full System Co-Simulation Platform For Evaluating Edge Machine Learning Inference Using Compute-In-Memory, Belsen Lee

Electrical Engineering and Computer Science (MS) Theses

We present a full-system co-simulation platform for evaluating embedded machine learning (ML) inference using compute-in-memory (CIM). CIM architectures aim to reduce data movement overhead by performing matrix operations in memory, but end-to-end benefits depend on system-level integration costs that are difficult to assess with isolated hardware models alone. To address this gap, we develop an integrated RISC-V QEMU-SystemC co-simulation environment that allows standard embedded Linux to interact with a transaction-level CIM accelerator model via memory-mapped I/O, direct memory access (DMA), and interrupts. To evaluate performance, we benchmark an MNIST image inference workload and a synthetic fully connected neural network, comparing …

Design And Verification Of The Multi-Slit Solar Explorer Camera Field Programmable Gate Arrays, Jordan M. Johnson

All Graduate Reports and Creative Projects, Fall 2023 to Present

The Multi-Slit Solar Explorer, or MUSE, is a NASA mission that will take images of the Sun to study solar flares and the solar corona. The mission will provide insight into the mechanisms behind space weather. The mission consists of two cameras: the Spectrograph (SG), and the Context Imager (CI). The Utah State University Space Dynamics Laboratory is providing both cameras for the mission.

This report describes a part of the design and verification process for a central component on these cameras known as the Field Programmable Gate Arrays (FPGAs). These FPGAs are programmed to acquire, handle, and send images …

A Novel Approach To Creativity Assessment: Forced Pairwise Ranking With Large Language Models, Phillip R. Gregory Jr

Master's Theses

Assessing creativity at scale remains a persistent challenge in cognitive science, as human raters are costly, slow, and often inconsistent in their judgments. This thesis introduced a novel framework for automated scientific creativity assessment using forced pairwise ranking, in which fine-tuned large language models compared response pairs and determined which was more creative. Five empirical studies were conducted using Llama-2-7B and Llama-2-13B models adapted via LoRA fine-tuning and benchmarked against human scored responses from the Scientific Creative Thinking Test. A regression baseline achieved Pearson �� = .74 on the test set, matching the human inter-rater ceiling reported in the literature. …

Robust Hardware Trojan Detection Leveraging Dual‑Domain Features And Stacked Ensemble Learning, Sefatun-Noor Puspa

All Theses

In Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under …

Conditioning Hierarchical Diffusion Transformers On Rf Circuit Parameters, Ethan Morton

McKelvey School of Engineering Graduate Student Theses & Dissertations

As modern ML techniques have become increasingly advanced, they have begun to be integrated into wireless RF systems for classification, identification, and spectrum management. Deep Neural Networks (DNNs) enable RF system operators and designers to design more flexible systems with greater robustness to errors and attacks. However, neural networks require significant amounts of properly annotated data to train. Current data labeling methods lack the ability to obtain reliable true labels for circuit properties such as carrier frequency offset (CFO), power amplifier (PA) non-linearity, and in-phase/quadrature (IQ) imbalance. This thesis investigates the efficacy of a novel architecture, RF-Diffusion, for generating high-quality …

Uav-Based Sensor Integration For In-Situ Lake Water Quality Monitoring, Alec R. Campbell

Biological and Agricultural Engineering Undergraduate Honors Theses

Surface water monitoring is often constrained by limited spatial and temporal coverage due to the labor-intensive nature of traditional sampling methods, particularly in environments that are difficult to access or pose safety risks. Unmanned aerial vehicles (UAVs) offer a promising solution by enabling more frequent, spatially distributed, and cost-effective data collection. This study presented the design, development, and field evaluation of a UAV-based system for real-time, in-situ water quality monitoring. The system integrated multiple sensors, including oxidation-reduction potential (ORP), RGB spectrometry, pH, electrical conductivity (EC), dissolved oxygen (DO), and a multispectral spectrometer within a UAV platform.

Field testing was conducted …

Post-Vote Tampering In Nigerian Elections And The Role Of Blockchain-Enabled Electoral Systems, Ransome Chukwubuikem Enechukwu

Electronic Theses and Dissertations

Post-vote tampering during the collation and transmission of election results remains a persistent challenge in Nigerian elections, enabling manipulation of already-cast votes and weakening public trust in electoral outcomes. Existing technological interventions, including biometric voter accreditation and digital result transmission systems, improve voter authentication but do not adequately secure the post-vote result collation process. This thesis proposes a blockchain-enabled framework designed to protect the integrity of election results during the collation and transmission stages. Using a Design Science Research methodology, the study develops a permissioned blockchain framework based on Hyperledger Fabric that records polling-unit results as immutable ledger entries and …

Reward Representation Learning, Gregory M. Hyde

Dartmouth College Ph.D Dissertations

The \emph{Markov decision process} (MDP) has long served as the canonical model for sequential decision-making. However, it assumes that the reward function is Markov with respect to a given state representation---an assumption that often does not hold in practice. Instead, agents typically only perceive streams of observations and actions and must infer the latent structure according to which reward unfolds over time. From this perspective, reward prediction is initially non-Markov, reflecting a mismatch between the agent's current representation and the underlying structure of the environment.

In this thesis, we advance the view that reward is not simply a signal to …

Using Ali-4 Z-Implication In Controller Design, Shamil Azer Ahmadov

Chemical Technology, Control and Management

One of the widely used theories in the information processing is Professor Zadeh's fuzzy logic theory. Fuzzy implications form the basis of this theory. When processing information using fuzzy implication, the chosen judgment method and the type of implication affect the result. Referring to the review of the relevant literature on fuzzy implications, it can be noted that there are still unresolved problems and issues. For example, fuzzy implications cannot be used in processing imperfect information or information based on probabilistic and fuzzy uncertainty. Existing fuzzy implications face limitations in practical applications. Fuzzy implications only take into account inaccuracy, but …

Shelter Portal: Qr-Based Service Tracking For Low-Barrier Shelters, Kate Steele, Colton Knopik, Michael Fischer

2026 Symposium

Shelter Portal is a web-based service tracking application developed for low-barrier shelters, including Catholic Charities’ House of Charity and Rising Strong programs. Many shelters still rely on manual headcounts and estimated meal totals, which are labor-intensive, error-prone, and insufficient for tracking individual service use over time. This limits operational visibility and makes it difficult to generate reliable reports, identify usage trends, and support external reporting requirements. Shelter Portal addresses this problem by providing a more accurate and privacy-conscious way to document shelter services.

The system was designed as a kiosk and web-based platform that uses scannable QR code cards to …

Escaping Isolation: An Analysis Of Virtual Machine And Container Breakout Vulnerabilities, Felix Iov

Cybersecurity Undergraduate Research Showcase

Cloud computing providers rely on multi-tenant architectures to maximize resource efficiency. This infrastructure depends on virtualization, which provides isolation between clients. This comes primarily in the form of Virtual Machines (VMs) and Containers. However, “breakout attacks” or “escapes” are a critical threat where attackers bypass these isolation layers to gain unauthorized access to the host system and neighboring environments. This paper surveys virtualization escape threats and analyzes three case studies: a runc container escape (Leaky Vessels), a VMware ESXi VM escape (VSOCKPuppet), and an NVIDIA GPU container escape (NVIDIAScape). Each demonstrates different attack surfaces, including file descriptor misuse, kernel driver …

Reducing Systemic Bias In Behavioral Targeting Using Explainable Ai: The Harmonia Complex Systems Approach, Ruchira Deokar, Preethi Nanjundan, Jossy P. George, Carlos Gershenson

Northeast Journal of Complex Systems (NEJCS)

Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

A Data-Driven Framework For Modeling Car-Following Behavior Using Conditional Transfer Entropy And Dynamic Mode Decomposition, Poorendra Ramlall

Student Research Symposium (SRS)

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of …

Move Fast And Don’T Break Things: Collaborative Privacy Governance In Higher Education, Tyler Schroder, Chad Fenner

Research & Publications

Student-developed applications increasingly replicate or replace official university platforms, often prioritizing speed over security and privacy. This “shadow IT” ecosystem emerges from gaps in institutional tools and is amplified by AI-assisted development, which can introduce insecure defaults. These informal systems risk exposing FERPA‑protected or sensitive institutional data, as seen in student‑built directory and club‑information apps that redistributed restricted information more permissively than intended. While most universities lack clear governance mechanisms for student developers, Yale’s structured, student‑specific data‑use policy offers a notable model. This paper examines these risks and proposes a collaborative, API‑first framework that supports innovation while enforcing privacy, security, …

Demystifying Hardware Formal Verification For Undergraduate Education: A Risc-V Processor Case Study With Coursework Implementation, Riley A. Peters

Master's Theses

Hardware verification engineers apply formal methods to prove that a digital device always behaves according to its specification. This differs from traditional functional verification, in which engineers establish correctness by repeatedly sending test inputs to the device and comparing the outputs against a reference model. With the growing complexity of integrated circuits, the demand for digital verification engineers with formal methods experience has continued to increase. However, California Polytechnic State University: San Luis Obispo's current curriculum lacks dedicated material to prepare students for these roles.

This thesis seeks to address the lack of formal methods material through two efforts. First, …

Evaluating Regularized Logistic Regression And K-Nn On Mnist Under Increasing Random Missingness, Daniel Markwei

Data Science and Data Mining

This paper investigates the effect of random missingness on the performance of regularized multinomial logistic regression and the k-nearest neighbors (k-NN) classifier for handwritten digit recognition on the MNIST dataset. In particular, we study L1-regularized (LASSO) logistic regression and L2-regularized (Ridge) logistic regression alongside k-NN. Varying percentages of random missingness were introduced into the original dataset, and each model was evaluated in terms of its classification performance. The results show that random missingness degrades the performance of all three classifiers. Overall, k-NN consistently achieves higher accuracy than both L1- and L2-regularized logistic regression across all missingness levels; however, its performance …

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Toward Transparent Bureaucracy: Nlp-Based Document Classification And Power Dynamics In The Srikandi System, Zulfatun Sofiyani, Suprayitno Suprayitno, Faisal Fahmi, Mega Putri Mahadewi

Proceedings from the Document Academy

As the Indonesian government advances digital document management through the SRIKANDI system, challenges persist regarding fragmented and subjective classification practices. This study proposes the integration of Natural Language Processing (NLP)-based classification within SRIKANDI to enhance consistency, transparency, and accountability in document management. Framed by an interdisciplinary theoretical foundation, the study synthesizes Michael Buckland’s document theory, viewing documents as dynamic social evidence, with Michel Foucault’s theory of power, highlighting classification as an exercise of institutional authority, and NLP methodologies that enable automated, content-driven categorization. The study positions documents as both technological artifacts and political constructs, whose classification practices simultaneously structure meaning …

Ai Applications In Assessing Risk For Periodontal Disease: A Systematic Review, Grant O. Korte, Claudia M. Tellez Freitas

Annual Research Symposium

Objectives:

The goal of this systematic review is to examine the impact of using artificial intelligence (AI) to predict a patient’s risk level for periodontal disease by analyzing proven systemic health diseases linked to periodontitis. This is being done by primarily focusing on prevention and early diagnosis using machine learning programs that have been proven effective in other fields of periodontitis research.

Methods:

To conduct this study, a comprehensive literature review of journals published after 2020 was performed through four databases: PubMed, Scopus, Web of Science and Dentistry and Oral Science Source. The search was completed in adherence …

Artificial Intelligence, Society 5.0 And Smart City Adaptation Initiatives For Businesses: An Integrated Approach, Ines A. M. Gila, Fernando A. F. Ferreira, Neuza C. M. Q. F. Ferreira, Florentin Smarandache, Momtaj Khanam, Tugrul Unsal Daim

Engineering and Technology Management Faculty Publications and Presentations

The mass migration of human populations to urban areas has resulted in unprecedented challenges for city services. To address and find solutions for these emerging issues, decision-makers must embrace the smart city and Society 5.0 paradigms, which comprehensively tackle various dimensions of the problem and ensure adaptability to evolving citizen needs. Central to the success of these paradigms is technology, particularly artificial intelligence (AI). AI’s transformative capabilities enable the expansion of services, automation of tasks, efficient operationalization and processing vast amounts of data to address urban challenges, aligning with several sustainable development goals (SDGs) such as sustainable cities and communities …

Implementation Of Zero Trust Architecture On Local Server Management In Educational Institutions, Joko Purwanto, Safar Dwi Kurniawan

Journal of Strategic and Global Studies

Educational institutions are facing increasingly complex cyber threats, particularly as they continue to rely on on-premises servers with limited cybersecurity resources. Zero Trust Architecture (ZTA) offers a security model that rejects implicit trust and requires strict verification of each access request. This study aims to examine the applicability of ZTA in managing local servers within educational institutions through a qualitative literature review approach. Relevant literature from 2014 to 2025 was analyzed using thematic synthesis to identify recurring concepts, strategies, and gaps. The results show that ZTA, when integrated with Identity and Access Management (IAM), Security Information and Event Management (SIEM), …

A Modular Llm Approach To Argument Extraction In Philosophical Texts, Nate Miller

Masters Theses

Engaging with philosophical works is a rewarding but demanding task that challenges both human readers and computational systems designed to extract arguments from dense philosophical reasoning, and although large language models (LLMs) have made substantial progress in argument extraction, the most advanced models are often costly to run. As a result, there is growing interest in determining if multi-agent pipelines that divide a task into smaller stages can reduce cost while maintaining or improving performance.

This study investigates a modular multi-agent approach for extracting arguments from philosophical texts using LLMs, and compares its performance, cost, and runtime to both single-agent …

The Risc-V Fpga (Rvfpga) Teaching Package, Daniel Chaver, Sarah Harris, Luis Pinuel, Olof Kindgren, Zubair Kakakhel, Chris Owen, Jose I. Gomez-Perez, Fernando Castro, Katzalin Olcoz, Julio Villalba-Moreno, Alexander Grinshpun, Freddy Gabbay, Luke Seed, Rui Duarte, Manuel Lopez, Oscar Alonso, Robert Owen

Electrical & Computer Engineering Faculty Research

RISC-V is a free and open-standard ISA based on RISC principles, allowing anyone to design, manufacture, and sell RISC-V chips and software. Its flexibility and growing ecosystem have made it popular in research, education, and industry, increasing the need for educational materials. This paper provides an in-depth description of the RVfpga course, which offers a solid introduction to computer architecture using the RISC-V instruction set and FPGA technology. It focuses on providing hands-on experience with real-world RISC-V cores, the VeeR EH1 and EL2 cores, developed by Western Digital and hosted by ChipsAlliance. The course targets students and educators in computing-related …