Computer Engineering Commons^™

Design And Evaluation Of A Single-Gate Multi-Threshold Null Convention Logic Architecture For Area Efficiency, John Edward Swaim

Graduate Theses and Dissertations

Asynchronous circuit design paradigms, such as NULL convention logic (NCL) and Multi-Threshold NCL (MTNCL), offer increased energy efficiency over synchronous equivalents and robust pipelines with minimal timing analysis. However, their dependency on dual-rail signal encoding causes significant overhead compared to single-rail equivalents. Previous single-gate and single-rail NCL paradigms have sought to reduce circuit area, but most compromise their quasi-delay-insensitivity and correct-by-construction nature with logic gate and system-level design choices. This thesis presents Single-Gate MTNCL (SG-MTNCL), a register controlled, single-gate, and dual-rail asynchronous architecture to improve the area efficiency of MTNCL without compromising the reliability of previous paradigms. The benefits of …

Stretch Web Teleoperation Keyboard Final Report, Aaron Chou, Francisco Irazaba

Computer Engineering

This project added keyboard controls to the Hello Robot Stretch Web Teleop interface. The goal was to make the robot easier to control from a web browser without needing a gamepad or separate terminal-based keyboard program. The new interface allows users to control different parts of the robot with customizable key bindings.

Parallelism In Java, Matt Vierow

Departmental Honors & Graduate Capstone Projects

Parallelism and multithreading have become an important facet of programming in recent years. Much work has been done in programming languages such as C and C++ with libraries such as OpenMP. However, less effort has been made to create parallelism tools in higher level languages such as Java. This is important because most programmers learn the basics in languages such as Python and Java that lack parallel tools like OpenMP. This means they must directly manage threads which requires knowledge of object-oriented programming and design.

This project sought to incorporate some elements of OpenMP into Java to allow newer programmers …

Learning Adaptive Control For Safe Collaborative Autonomous Driving, Fabian Alexis Hernandez

Theses and Dissertations

Connected autonomous vehicles improve urban driving through collaborative perception via Vehicle-to-Everything (V2X) communication. Frameworks such as V2Xverse leverage this collaboration for planning and perception, yet their controllers rely on fixed parameters that cannot adapt to varying traffic. Control Barrier Functions (CBFs) enforce safety by constraining actions within a safe set, but a fixed barrier gain imposes a single operating point: conservative settings reduce throughput while permissive settings under-react to hazards.

This research proposes an adaptive CBF framework that learns state-dependent, class-specific barrier gains via constrained Reinforcement Learning (RL). A compact policy outputs separate gains for vehicles, pedestrians, and bicycles, parameterizing …

Faster Than The Speed Of Bram: In-Memory Computing For Next Generation Fpgas On The Edge, Nathaniel Joseph Fredricks

Graduate Theses and Dissertations

Traditional computer systems are hitting the Memory Wall as machine learning applications are bottlenecked by the bandwidth between separate memory and compute units. Current FPGA architectures are able to bypass this bottleneck with block RAMs (BRAMs) that provide on-chip, in-fabric storage. However, their potential as the foundation of computing components is often overlooked; machine learning accelerators implemented on FPGAs face additional delays when transferring data between memory and compute units. These penalties arise from BRAM bandwidth limitations and movement of data through the reconfigurable fabric. To support FPGA-based accelerators on the edge and break the Memory Wall, reconfigurable architectures must …

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 …

Multimodal Aerial Image-Based Ground Object Detection And Classification Using Yolo, Erik Nielsen

All Theses

Object detection in unmanned aerial vehicles (UAVs) present a unique challenge due to small object sizes, varying viewpoints, and changing environmental conditions. These challenges are exacerbated when operating during daytime and nighttime scenarios where illumination differences can heavily impact detection performance. This work is motivated by military object detection applications where the ability to reliably identify small objects such as landmines or unexploded ordnance from aerial imagery presents a critical safety need and a significant technical challenge. In this paper, we investigate object detection using both visible (RGB) and infrared (IR) imagery to improve robustness and reliability across diverse operating …

A Maintenance-Aware Machine Learning Framework For Network-Level Highway Pavement Condition Prediction, Jin Hwan Kim, Guk Gon Song, Youngguk Seo

Faculty Articles

This study develops and validates maintenance-aware machine learning models for predicting the Highway Pavement Condition Index (HPCI) on the Korean expressway network. Multiple regression and tree-based models were trained and tested using the pavement condition surveys archived in the Highway Pavement Management System (HPMS). A stacking regressor that integrates random forest, gradient boosting, and extreme gradient boosting as base learners exhibited the most robust predictions. Performance metrics indicated that the stacking ensemble achieved a mean absolute error of 0.21, a root mean square error of 0.31, and a coefficient of determination exceeding 0.73 on the testing dataset. Also, the residuals …

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 …

Hdra-Fusion: Hybrid Detection With Routed Architecture For Manipulation-Aware Ai Face Forgery Detection, Omar Ebeid

Theses and Dissertations

With the rapid advancements in artificial intelligence-based image generation and manipulation tools, it is extremely difficult to detect if an image is genuine or artificially crafted. Despite extensive research in this area, existing image detection systems suffer from three major problems: suboptimal cross-dataset generalization due to shortcut learning of dataset-specific patterns, unreliable probability estimates due to domain shift, particularly in cross-manipulation evaluation settings, and an inability to detect images manipulated by multiple types of manipulations within a single detection framework. To address these limitations, we propose HDRA-Fusion (Hybrid Detection with Routed Architecture), a framework built on the conclusion that different …

A Machine Learning-Based Apogee Prediction Methodology For Experimental Student Rockets, Price Hamilton Drawdy

Senior Honors Theses

The ability to predict the maximum altitude of a rocket (apogee) in real-time is incredibly useful for collegiate-level competition rockets. This project creates a machine learning-based real-time apogee prediction methodology. Three model types were tested: linear regression, random forest, and a 3-layer multi-layer perceptron (MLP) neural network. These models were trained on a large dataset of simulated flights. All models performed well on simulated test flights, with the linear regression model showing most promise for use on edge compute. More development and real-world testing are necessary to determine how applicable this method is for real-time operation. Nevertheless, this methodology provides …

Analyzing The Writing Style Of Generative Ai When Prompted With Writing Samples, Samuel Mcdowell

Senior Honors Theses

Authorship attribution is an important topic in today’s world of Large Language Models (LLMs). It is the technology that helps to verify the author of a written work. This study explores whether LLMs can successfully mimic an individual’s writing style if they are given a text sample. A dataset of human-written texts was collected and used to prompt several LLMs to generate new texts that attempt to replicate the original author’s stylistic characteristics. The generated texts were then tested with modern authorship attribution models to determine whether they would be identified as being written by the original author. The results …

Introduction To The Special Issue On Computer Modeling For Future Communications And Networks, Wenbing Zhao, Pan Wang

Electrical and Computer Engineering Faculty Publications

No abstract provided.

Generative Ai Of Breast Cancer Progression In Gene Expression Space, Xusheng Ai

All Dissertations

Breast cancer is one of the most common and deadly cancers in the world. Although doctors can use gene activity data to better understand different types of breast cancer, it is still difficult to identify the most important genes and to track how the disease changes over time. This is partly because gene data are very large and complex.

This dissertation develops a computer-based framework to study breast cancer using gene expression data. The work focuses on three goals: creating realistic synthetic gene data, identifying important genes linked to cancer, and modeling how breast cancer changes from normal tissue to …

Safe Control Design For Quadruped Locomotion In Unstructured Environments Using Linear Transfer Operators, Sriram Sundar Krishnamoorthy Shankara Narayanan

All Dissertations

Deploying quadruped robots in unstructured, obstacle-rich environments requires control and planning methods that remain safe and reliable despite complex terrain geometry, limited sensing, and inevitable modeling errors. This thesis develops operator-theoretic tools for safe control design of robotic systems using linear transfer operators, with a focus on quadruped locomotion in unstructured environments. The central goal is to develop a unified operator-theoretic framework for safe control design based on the Perron–Frobenius (P–F) and Koopman operators. In particular, the thesis leverages \emph{density functions} to develop safe navigation frameworks in the dual space of densities. In the operator-theoretic perspective, the P–F operator governs …

Learning Global Context For Sparse Activity Recognition In Lengthy Recordings With Limited Dataset Size, Zeyu Tang

All Dissertations

This dissertation describes methods to analyze lengthy recordings of data in order to detect sparsely occurring activities. The narrative below describes the progression of research that led to the development of these methods and their generalization into a unified framework. My research started with designing models for dietary monitoring, including detecting meals from day-long recordings and detecting intake gestures from meal-length recordings. Both tasks share some common characteristics: (a) the target event takes only a small portion of data recordings, and (b) there is global context within full-length data recordings that can help a model make better decisions. After finishing …

Asynchronous Polymorphic Logic Locking, Kelby Haulmark

Graduate Theses and Dissertations

This work presents Asynchronous Polymorphic Logic Locking (APLL), a logic locking methodology that integrates polymorphic logic within the Multi-Threshold NULL Convention Logic (MTNCL) paradigm. APLL achieves Boolean satisfiability-attack resilience through a fault-based logic stripping approach followed by logic restoration, while leveraging the analog, dual-functionality of polymorphic gates to impede reverse engineering and removal attacks. In contrast to comparable SAT-resistant logic locking techniques, APLL provides inherent resistance to reverse engineering, reducing the viability of a broad class of attacks that rely on access to the locked netlist. A complete automated design flow is developed, enabling the transformation of combinational circuits into …

Developing A Framework For Microchip Design Recovery, Eric Diep

Graduate Theses and Dissertations (2019 - present)

Due to the increase in diverse chip production over the past decade, reverse engineering has become a difficult and daunting task. This research develops a methodology for microchip design recovery, seeking to validate and reproduce prior approaches to physical reverse engineering using low-cost tools and techniques. We used mechanical hardware abrasion tools and techniques to delayer and capture silicon integrated chip (IC) layout. We focused on the Mifare Classic EVl microchip, commonly implemented in public transit/transportation cards, to extract information for design recovery. The research explores limitations and advantages of mechanical abrasion and optical microscopy in context to modem chip …

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 …

Culturally Inclusive Usability Engineering (Ciue): A Framework For Evaluating Cultural Inclusion In Study Abroad Platforms, Louis Muhammad

Master's Theses

Study abroad programs provide students with opportunities to develop global com- petence and intercultural understanding. However, the digital platforms that support these programs often fail to communicate information that reflects the cultural and faith-based needs of diverse student populations. While prior research in Human-Computer Interac- tion (HCI) and usability engineering has explored cross-cultural and accessible design, cultural and religious considerations are rarely integrated into usability engineering pro- cesses. This thesis introduces the Culturally Inclusive Usability Engineering (CIUE) frame- work, which extends traditional usability engineering by integrating culturally informed con- siderations into the system development lifecycle. CIUE begins by identifying the …

Design Of A Low-Power Magneto-Inductive Magnetometer, Rowan Antonuccio

All Graduate Theses and Dissertations, Fall 2023 to Present

Measuring magnetic fields in space helps scientists understand phenomena that can affect satellite communications and navigation systems on Earth. This research develops a new low-power magnetic field sensor for spacecraft that improves upon existing designs by moving the sensitive parts away from electrical interference and using energy-efficient digital electronics for precise measurements. The sensor’s power-efficient design is particularly important for small satellites, where power is limited and must be carefully managed. It will fly on future NASA missions to study disturbances in Earth’s upper atmosphere that can disrupt radio signals and GPS. This work contributes to our ability to better …

Moodify: A Mood-Based Music Recommendation System, Meghana Kagitha

Theses and Dissertations

Music has long been recognised as a powerful tool for emotional regulation, yet existing music streaming platforms often fail to align song recommendations with a user's current emotional state. Moodify is a mood-based music recommendation system designed to bridge this gap by delivering personalised playlists that reflect how a user feels in real time.

This project presents the design, development, and evaluation of Moodify, a mobile application that leverages the Circumplex Model of Emotion to capture user mood through an intuitive two-dimensional valence-arousal interface. Rather than relying on text input or manual search, users plot their emotional state directly onto …

An Investigation Of Data Granularity In Rag Pipelines For Personalized Medicine, Paritosh Pandey

Master's Theses

Generative AI, exemplified by large language models like the OpenAI GPT and Meta LLaMA families, can produce diverse content in response to prompts. This capability offers a promising solution to challenges in precision medicine, which seeks to tailor treatments to individual clinical profiles but often struggles with data collection, cost, and privacy concerns. By generating realistic, privacy-preserving patient data, generative AI has the potential to transform patient-centric healthcare. With such motivation, this research develops a comprehensive Generative AI pipeline emphasizing data granularity for accurate prediction of personalized treatments. The pipeline features a central Large Language Model interacting with a Machine …

Application Of Maritime Non-Line-Of-Sight Relay Attack On Wireless Digital Communication, Nathan Meyer

School of Computing: Dissertations, Theses, and Student Research

As wireless communication becomes increasingly prevalent, securing information over wireless channels is an ongoing challenge, especially in maritime environments where communication depends on radio links. While higher layer wireless attacks have been widely studied, lower level physical-layer relay attacks in maritime settings have received less attention. This thesis presents a simulation of a maritime relay attack in a beyond line-of-sight wireless environment for study. A three antenna communication model is developed where a legitimate transmitter sends a digital wireless signal, an attacking antenna intercepts, modifies, and retransmits the signal, and a final receiver observes both the direct and relay transmission. …

A Webcam Eye Tracking Infrastructure For Software Engineering Tasks, Zachary M. Kozak

School of Computing: Dissertations, Theses, and Student Research

Performing eye tracking utilizing commodity webcams has been explored for over a decade, but limited camera quality and sensitivity to head movements have hindered its adoption in research settings. Recent advances in consumer-grade webcams and machine learning methods present an opportunity to improve the accuracy of webcam eye tracking and extend the feasibility of studies beyond controlled laboratory environments.

Current popular webcam eye tracking methods restrict implementations to the browser and rely on continuous user interactions for calibration, limiting the kinds of studies that can be conducted. This thesis presents a feature-based gaze prediction system that incorporates eye geometry and …

Leveraging Code Embeddings To Identify And Address Blind Spots In Benchmark Creation, Charles Moloney

School of Computing: Dissertations, Theses, and Student Research

Formal software verification remains critical for early vulnerability detection, yet benchmarking these tools is costly and often reliant on centralized datasets such as SV-COMP. While such repositories enable standardized evaluation, they introduce risks of overfitting and bias, particularly due to first-party benchmark contributions. To address these limitations, we extend ARG-V, our tool for generating SV-COMP-compatible benchmarks from real-world Java code, with a novel approach of using code embedding techniques to selectively sample from mined code. By leveraging Nomic Embed Code and a cosine-based Minimum Hyperspherical Energy (MHE) objective, we systematically select and transform benchmarks from scraped GitHub code that …

Design Of An Extensible And Scalable Data Acquisition System For Pulse Shape Discrimination, Prince John

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis presents the design and development of a highly scalable, end-to-end data acquisition (DAQ) system for nuclear physics experiments that can be deployed in configurations ranging from a few to thousands of detector channels. The system is built as an extensible platform composed of modular 16-channel chipboards that support a wide range of scintillator and detector types and perform real-time, on-board data sparsification and pulse-shape processing. Three versions of the chipboard have been fabricated to date.

The DAQ architecture is based on a family of analog pulse-shape-processing application- specific integrated circuits (ASICs) developed by the IC Design Laboratory at …