Computer Engineering Commons^™

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 …

Open Multi-Agent Systems: The Free-Range-Zoo Framework And Moasei Competition, Ceferino J. Patino Iv

School of Computing: Dissertations, Theses, and Student Research

The field of multi-agent reinforcement learning (MARL) has made significant strides in addressing sequential decision-making problems under uncertainty. However, traditional MARL frameworks assume closed-world settings with fixed agent sets, static task distributions, and unchanging environment dynamics. This thesis presents two complementary contributions that advance the state of open-world multi-agent systems research: (1) the free-range-zoo framework, an open-source environment suite for MARL in open environments featuring dynamic agent populations, evolving task sets, and changing operational frames; and (2) the MOASEI Competition, an international benchmarking event that leverages free-range-zoo to evaluate how artificial agents handle openness in complex, partially observable domains. The …

Learn To Fly: Enabling Deep Learning Based Perception And Control In Aerial Robotics, Krishna Muvva

School of Computing: Dissertations, Theses, and Student Research

Uncrewed Aerial Vehicles (UAVs) are increasingly deployed in dynamic, GPS degraded, and cluttered environments, yet their autonomy remains fundamentally constrained by limitations in onboard perception and real-time control. This dissertation addresses these challenges by proposing a unified framework that co-designs deep learning-based perception and model-based control, organized around three core thrusts: Learn to Track, Learn to Localize, and Learn to Evade.

Learn to Track develops dynamic and adaptive perception control mechanisms that optimize CNN inference for target tracking. A control-aware CNN framework dynamically adjusts inference frequency based on UAV motion, reducing latency while maintaining visual lock. An adaptive CNN with …

Traffic Prediction For Research And Education Networks: Anomaly-Aware Deep Learning And Benchmarking, Mohammad Arafath Uddin Shariff

School of Computing: Dissertations, Theses, and Student Research

Research and Education Networks (RENs) and High-Performance Computing (HPC) environments are critical infrastructures for modern scientific discovery, demanding sustained high-throughput and low-latency data transfers. Unlike commercial networks, RENs exhibit unique traffic characteristics, including predominant “elephant flows,” inherent burstiness, and complex temporal-spatial dynamics often decoupled from human-driven cycles. Traditional traffic forecasting methods, tailored for commercial Wide Area Networks (WANs), consistently fail to capture these distinct REN dynamics, leading to inefficient resource management and potential impediments to scientific progress.

This thesis addresses this critical gap by developing and validating a robust, scalable, and anomaly-aware traffic forecasting framework specifically tailored for REN/HPC networks. …

How Developers Use Type-System Related Programming Language Features, Samuel W. Flint

School of Computing: Dissertations, Theses, and Student Research

Optional type annotations are a popular feature of programming languages that allow developers to omit explicit type information in code while, in some cases, retaining many of the benefits of static typing, such as in-code documentation, improved detection of type errors, or enforcement of code properties. However, how developers use and understand optional type annotations is not clear. The focus of this dissertation is to understand the use and comprehension of optional type annotations.

Optional type annotations are examined through four lenses: first, by examining the evolution of usage in a statically typed programming language (Kotlin, the default language for …

Investigating The Impact Of Agent Openness On Planning In Multi-Agent Systems, Bala Subramanyam Duggirala

School of Computing: Dissertations, Theses, and Student Research

Multi-agent systems (MAS) possess significant potential for modeling real-world scenarios requiring coordinated actions (like wildfire fighting or ridesharing) among autonomous entities or agents (e.g., wildfire fighting agents) in complex, dynamic environments. Effective decision-theoretic planning (where each agent must carefully consider both the immediate and the future situations or states, and coordinate with the other agents (neighbors) to evaluate what needs to be done at present) within MAS, especially multiagent planning, where the planning agent directly models its neighbors in order to estimate their optimal actions, is critical, yet challenged by factors like partial observability, openness, and diverse agent types with …

Biocomputing Approach To Modeling And Modulating Calcium Signaling, Sehee Sun

School of Computing: Dissertations, Theses, and Student Research

Biocomputing is an emerging field that seeks to perform computational tasks using biological substrates and processes. Unlike conventional computing systems based on silicon hardware, biocomputing leverages the parallelism, energy efficiency, and complex dynamics of living systems. Among various cellular mechanisms, calcium (Ca2+) signaling stands out as a central regulator of diverse biological functions, offering a promising basis for programmable logic and control in living cells.

This thesis introduces a novel framework for modeling and modulating Ca2+ dynamics using biologically inspired Boolean logic circuits. Specifically, we propose the Ca2+ Boolean Logic (CaBL) model, in which Ca2+ fluxes and interactions are abstracted …

A Machine Learning Framework For Packet Anomaly Detection In Smartgrid Substation Networks, Sowmya Bandari

School of Computing: Dissertations, Theses, and Student Research

The increasing reliance on Smart Grid Substation Networks for efficient electricity distribution has amplified cybersecurity vulnerabilities, particularly within Supervisory Control and Data Acquisition (SCADA) systems. The IEC 60870-5-104 (IEC-104) protocol, widely adopted for communication between Remote Terminal Units (RTUs) and Human-Machine Interfaces (HMIs), lacks inherent encryption and authentication mechanisms, rendering it susceptible to sophisticated cyberattacks. Threats such as False Data Injection Attacks (FDIAs), command injection, covert attacks and replay attacks pose significant risks by manipulating grid control signals, potentially leading to undetected operational disruptions, cascading failures, or system-wide instability. Conventional signature-based Intrusion Detection Systems (IDS) often fail to identify zero-day …

Enhancing Remote Sensing Imagery Temporal Resolution Using Starfm Data Fusion Approach For Improved Land Surface Monitoring, Ahmadreza Pourghodrat

School of Computing: Dissertations, Theses, and Student Research

High-resolution remote sensing imagery plays a critical role in various domains, such as farm-level agricultural operations, environmental monitoring, and natural resource management. However, data with high spatial resolution typically have low temporal resolution, and those with high temporal resolution often lack spatial detail. For example, Landsat 8 and 9 satellites deliver high spatial resolution images with a 30-meter pixel size but suffer from low temporal resolution, with a 16-day revisit cycle. In contrast, satellites like MODIS and VIIRS provide daily images but with a much coarser spatial resolution (375 meters or more), reducing spatial details. Additionally, there is a lack …

Using Symbolic Execution To Analyze The Hardware Tcp Protocol, Nianhang Hu

School of Computing: Dissertations, Theses, and Student Research

As the demand for high performance and flexible networking capabilities increases, the shift from software to hardware implementations of stateful networking functions (such as TCP) is becoming increasingly important. This transition not only enhances processing efficiency in modern networking environments where data transmission rates are rising, but it also reduces the inherent CPU overhead found in software implementations, allowing hardware devices to handle network traffic more efficiently. However, validating the correctness of these hardware designs poses significant challenges due to the complex timing requirements and the vast input space associated with packet-level properties.

The verification of packet-level properties requires coverage …

Not All Samples Are Created Equal: Task-Aware Informative Sampling And Adaptive Inference For Efficient Edge Ai, Rebati Gaire

School of Computing: Dissertations, Theses, and Student Research

The rapid proliferation of Internet of Things (IoT) devices has resulted in an unprecedented influx of data generated at the edge by billions of sensors. Traditional approaches relying on cloud-based processing are increasingly inadequate due to constraints in bandwidth, latency, and privacy. Edge computing has emerged as a transformative paradigm, enabling real-time data processing and decision-making by decentralizing computation to the edge. While the integration of deep learning into edge environments—termed edge intelligence—promises autonomous and personalized operations, it is hindered by challenges such as limited computational resources, energy constraints, and data redundancies.

This thesis addresses these challenges by presenting three …

Prevalence Of Autism Spectrum Characteristics In Students Taking Undergraduate Computing Courses, Rachel Michaela Mettenbrink

School of Computing: Dissertations, Theses, and Student Research

The incidence rate of autism spectrum condition (ASC) has increased significantly in recent decades, as awareness of the condition and its impacts increases amongst clinicians, parents, and the general population. Medical literature has proposed that there may be a relationship between ASC and participation in the computing field. This study tests for the prevalence of autism spectrum condition traits measured by delivering the Autism Spectrum Quotient (AQ) to a population of undergraduate computer science students. We examine the relationships between AQ scores and students taking undergraduate computer science classes, sex, socioeconomic status, and parents in the computing industry. Additionally, we …

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

School of Computing: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

Sc-Fuse: A Feature Fusion Approach For Unpaved Road Detection From Remotely Sensed Images, Aniruddh Saxena

School of Computing: Dissertations, Theses, and Student Research

Road network extraction from remote sensing imagery is crucial for numerous applications, ranging from autonomous navigation to urban and rural planning. A particularly challenging aspect is the detection of unpaved roads, often underrepresented in research and data. These roads display variability in texture, width, shape, and surroundings, making their detection quite complex. This thesis addresses these challenges by creating a specialized dataset and introducing the SC-Fuse model.

Our custom dataset comprises high resolution remote sensing imagery which primarily targets unpaved roads of the American Midwest. To capture the diverse seasonal variation and their impact, the dataset includes images from different …

Seek And Classify: End-To-End Joint Multi-Signal Detection And Classification Using Deep Learning, Prashant Subedi

School of Computing: Dissertations, Theses, and Student Research

The rise in the use of wireless communication has led to the problem of spectrum scarcity in licensed bands. The popularity of Internet of Things (IoT) requires innovative solutions that maximize the use of available spectrum to support the increasing number of connected devices. This thesis tackles two significant problems in wireless communication: the need for efficient spectrum sensing techniques and the scarcity of large, diverse raw in-phase (I) and quadrature (Q) datasets.

The ability to detect and classify modulation of the signals efficiently can enable a cognitive radio to monitor the spectrum activity in real time and utilize unused …

Experimental Study Of Linux Flightsize Estimation, Mingrui Zhang

School of Computing: Dissertations, Theses, and Student Research

Transmission Control Protocol (TCP) is a fundamental Internet protocol responsible for controlling and coordinating the Internet traffic. As a result, TCP significantly influences the overall performance and stability of the Internet. One critical information required by a TCP connection to make decisions is FlightSize, which is the total amount of outstanding data contributed by the connection to the Internet. The FlightSize information is used by a TCP connection to determine its future sending rate and also avoid traffic congestion and collapse in the Internet. Consequently, an inaccurate estimation of FlightSize can result in degraded performance and instability of the Internet. …

Leveraging Aruco Fiducial Marker System For Bridge Displacement Estimation Using Unmanned Aerial Vehicles, Mohamed Aly

School of Computing: Dissertations, Theses, and Student Research

The use of unmanned aerial vehicles (UAVs) in construction sites has been widely growing for surveying and inspection purposes. Their mobility and agility have enabled engineers to use UAVs in Structural Health Monitoring (SHM) applications to overcome the limitations of traditional approaches that require labor-intensive installation, extended time, and long-term maintenance. One of the critical applications of SHM is measuring bridge deflections during the bridge operation period. Due to the complex remote sites of bridges, remote sensing techniques, such as camera-equipped drones, can facilitate measuring bridge deflections. This work takes a step to build a pipeline using the state-of-the-art computer …

Sim-To-Real Reinforcement Learning Framework For Autonomous Aerial Leaf Sampling, Ashraful Islam

School of Computing: Dissertations, Theses, and Student Research

Using unmanned aerial systems (UAS) for leaf sampling is contributing to a better understanding of the influence of climate change on plant species, and the dynamics of forest ecology by studying hard-to-reach tree canopies. Currently, multiple skilled operators are required for UAS maneuvering and using the leaf sampling tool. This often limits sampling to only the canopy top or periphery. Sim-to-real reinforcement learning (RL) can be leveraged to tackle challenges in the autonomous operation of aerial leaf sampling in the changing environment of a tree canopy. However, trans- ferring an RL controller that is learned in simulation to real UAS …

An Empirical Study On The Classification Of Python Language Features Using Eye-Tracking, Jigyasa Chauhan

School of Computing: Dissertations, Theses, and Student Research

Python, currently one of the most popular programming languages, is an object-
oriented language that also provides language feature support for other programming
paradigms, such as functional and procedural. It is not currently understood how
support for multiple paradigms affects the ability of developers to comprehend that
code. Understanding the predominant paradigm in code, and how developers classify
the predominant paradigm, can benefit future research in program comprehension as
the paradigm may factor into how people comprehend that code. Other researchers
may want to look at how the paradigms in the code interact with various code smells.
To investigate how …

Bevers: A General, Simple, And Performant Framework For Automatic Fact Verification, Mitchell Dehaven

School of Computing: Dissertations, Theses, and Student Research

Fact verification has become an important process, primarily done manually by humans, to verify the authenticity of claims and statements made online. Increasingly, social media companies have utilized human effort to debunk false claims on their platforms, opting to either tag the content as misleading or false, or removing it entirely to combat misinformation on their sites. In tandem, the field of automatic fact verification has become a subject of focus among the natural language processing (NLP) community, spawning new datasets and research. The most popular dataset is the Fact Extraction and VERification (FEVER) dataset. In this thesis an end-to-end …

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

School of Computing: Dissertations, Theses, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide …

Sequence-Based Bioinformatics Approaches To Predict Virus–Host Relationships In Archaea And Eukaryotes, Yingshan Li

School of Computing: Dissertations, Theses, and Student Research

Viral metagenomics is independent of lab culturing and capable of investigating viromes of virtually any given environmental niches. While numerous sequences of viral genomes have been assembled from metagenomic studies over the past years, the natural hosts for the majority of these viral contigs have not been determined. Different computational approaches have been developed to predict hosts of bacteria phages. Nevertheless, little progress has been made in the virus-host prediction, especially for viruses that infect eukaryotes and archaea. In this study, by analyzing all documented viruses with known eukaryotic and archaeal hosts, we assessed the predictive power of four computational …

Attention In The Faithful Self-Explanatory Nlp Models, Mostafa Rafaiejokandan

School of Computing: Dissertations, Theses, and Student Research

Deep neural networks (DNNs) can perform impressively in many natural language processing (NLP) tasks, but their black-box nature makes them inherently challenging to explain or interpret. Self-Explanatory models are a new approach to overcoming this challenge, generating explanations in human-readable languages besides task objectives like answering questions. The main focus of this thesis is the explainability of NLP tasks, as well as how attention methods can help enhance performance. Three different attention modules are proposed, SimpleAttention, CrossSelfAttention, and CrossModality. It also includes a new dataset transformation method called Two-Documents that converts every dataset into two separate documents required by the …

A Pipeline To Generate Deep Learning Surrogates Of Genome-Scale Metabolic Models, Achilles Rasquinha

School of Computing: Dissertations, Theses, and Student Research

Genome-Scale Metabolic Models (GEMMs) are powerful reconstructions of biological systems that help metabolic engineers understand and predict growth conditions subjected to various environmental factors around the cellular metabolism of an organism in observation, purely in silico. Applications of metabolic engineering range from perturbation analysis and drug-target discovery to predicting growth rates of biotechnologically important metabolites and reaction objectives within dierent single-cell and multi-cellular organism types. GEMMs use mathematical frameworks for quantitative estimations of flux distributions within metabolic networks. The reasons behind why an organism activates, stuns, or fluctuates between alternative pathways for growth and survival, however, remain relatively unknown. GEMMs …

Feed Forward Neural Networks With Asymmetric Training, Archit Srivastava

School of Computing: Dissertations, Theses, and Student Research

Our work presents a new perspective on training feed-forward neural networks(FFNN). We introduce and formally define the notion of symmetry and asymmetry in the context of training of FFNN. We provide a mathematical definition to generalize the idea of sparsification and demonstrate how sparsification can induce asymmetric training in FFNN.

In FFNN, training consists of two phases, forward pass and backward pass. We define symmetric training in FFNN as follows-- If a neural network uses the same parameters for both forward pass and backward pass, then the training is said to be symmetric.

The definition of asymmetric training in artificial …

Simulating Sub-Threshold Communication Channels Through Neurons, Richard Maina

School of Computing: Dissertations, Theses, and Student Research

Molecular Communication is an emerging paradigm with the potential to revolutionize the technology behind wearable and implantable devices and the broad range of functions they support, from tracking physical activity to medical diagnostics. This can be achieved through intra-body communication networks that take advantage of natural biological processes as a means of transmitting, propagating and receiving information. In this thesis we focus particularly on using the neuron as a means to facilitate information transfer for interconnected wearable or implantable devices through a technique known as sub-threshold electrical stimulation. We develop upon a prior work by introducing a linear model of …

Consemblex: A Consensus-Based Transcriptome Assembly Approach That Extends Consemble And Improves Transcriptome Assembly, Richard Mwaba

School of Computing: Dissertations, Theses, and Student Research

An accurate transcriptome is essential to understanding biological systems enabling omics analyses such as gene expression, gene discovery, and gene-regulatory network construction. However, assembling an accurate transcriptome is challenging, especially for organisms without adequate reference genomes or transcriptomes. While several methods for transcriptome assembly with different approaches exist, it is still difficult to establish the most accurate methods. This thesis explores the different transcriptome assembly methods and compares their performances using simulated benchmark transcriptomes with varying complexity. We also introduce ConSemblEX to improve a consensus-based ensemble transcriptome assembler, ConSemble, in three main areas: we provide the ability to use any …

Symbolic Ns-3 For Efficient Exhaustive Testing, Jianfei Shao

School of Computing: Dissertations, Theses, and Student Research

Exhaustive testing is an important type of simulation, where a user exhaustively simulates a protocol for all possible cases with respect to some uncertain factors, such as all possible packet delays or packet headers. It is useful for completely evaluating the protocol performance, finding the worst-case performance, and detecting possible design or implementation bugs of a protocol. It is, however, time consuming to use the brute force method with current NS-3, a widely used network simulator, for exhaustive testing. In this paper, we present our work on Sym-NS-3 for more efficient exhaustive testing, which leverages a powerful program analysis technique …