Engineering Commons^™

Detecting Wireless Security Threats Through Ieee 802.11 Frame Field Anomalies, Aria Young

Williams Honors College, Honors Research Projects

It is not uncommon for most public spaces to offer Wi-Fi, while it is convenient and affordable, there are significant security risks due to its open nature. Rogue access points pose a security threat to many networks because they have the potential to bypass security measures and intercept traffic containing sensitive information. The attempt to formulate a method that one hundred percent guarantees the detection of a rogue access point has proven to be an intricate and complex problem for many to tackle, as there are numerous ways a rogue access point can be configured. This project aims to demonstrate …

Artificial Intelligence’S Role And Impact On The Engineering Discipline, Andrew Angelini

Williams Honors College, Honors Research Projects

The purpose of this Honors Research project was to explore the impact of Artificial Intelligence (AI) on the Engineering Discipline as a whole. Specifically, civil engineering’s discipline of transportation was used to display the effectiveness of AI in engineering. This discipline was used due to its wide use in engineering and overall designs that can be created within this discipline. These designs include maintenance of traffic plans, engineering plan sets, horizontal and vertical curve design, intersection design, traffic signaling, highway material design, and phasing of intersections and traffic accounting for pedestrians. Through similar prompts that were given to a publicly …

Ultrasonic Sensor-Based Sound Synthesis Using Raspberry Pi Pico W, Niraj Jaishwal

Williams Honors College, Honors Research Projects

At the intersection of Human Computer Interaction and digital art, this project transforms simple motion into musical expression. It explores an interactive real-time sound synthesis system using ultrasonic sensors to generate continuous audio. The objective is to design a system that maps physical distances into musical parameters such as pitch and amplitude, which will create a responsive audio environment. Two ultrasonic sensors are used in combination with the Raspberry Pi Pico W microcontroller running CircuitPython and Adafruit Audio Hat for real-time sound output. One sensor controls the pitch of the generated tone, while the other controls volume. This enables expressive …

Design And Development Of A Rapid Tensile Quench Rig, Tyler Jewell, Justin Naylor

Williams Honors College, Honors Research Projects

This report outlines the design process and implantation of a tensile quenching rig that incorporated forced convection and a frequency generator. When any metal is quenched, a vapor barrier forms around it. When this happens, it limits the heat flux that may occur until the barrier turns into just nucleate boiling. The vapor barrier acts as an insulator and causes the heat flux to fluctuate, causing uneven hardening which would limit the use of some materials. To combat this effect, we are trying to use forced convection, and something new, which is adding high frequency waves into the quenching process. …

Deep Learning-Assisted Diagnostic System: Apices And Odontogenic Sinus Floor Level Analysis In Dental Panoramic Radiographs, Pei Yi Wu, Yuan-Jin Lin, Yu-Jen Chang, Sung-Tsun Wei, Chiung An Chen, Kuo-Chen Li, Wei-Chen Tu, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Odontogenic sinusitis is a type of sinusitis caused by apical lesions of teeth near the maxillary sinus floor. Its clinical symptoms are highly like other types of sinusitis, often leading to misdiagnosis as general sinusitis by dentists in the early stages. This misdiagnosis delays treatment and may be accompanied by toothache. Therefore, using artificial intelligence to assist dentists in accurately diagnosing odontogenic sinusitis is crucial. This study introduces an innovative odontogenic sinusitis image processing technique, which is fused with common contrast limited adaptive histogram equalization, Min-Max normalization, and the RGB mapping method. Moreover, this study combined various deep learning models …

Precision Medicine Assessment Of The Radiographic Defect Angle Of The Intrabony Defect In Periodontal Lesions By Deep Learning Of Bitewing Radiographs, Patricia Angela R. Abu, Yi Cheng Mao, Yuan Jin Lin, Chien Kai Chao, Yi He Lin, Bo Siang Wang, Chiung An Chen, Shih Lun Chen, Tsung Yi Chen, Kuo Chen Li

Department of Information Systems & Computer Science Faculty Publications

In dental diagnosis, evaluating the severity of periodontal disease by analyzing the radiographic defect angle of the intrabony defect is essential for effective treatment planning. However, dentists often rely on clinical examinations and manual analysis, which can be time-consuming and labor-intensive. Due to the high recurrence rate of periodontal disease after treatment, accurately evaluating the radiographic defect angle of the intrabony defect is vital for implementing targeted interventions, which can improve treatment outcomes and reduce recurrence. This study aims to streamline clinical practices and enhance patient care in managing periodontal disease by determining its severity based on the analysis of …

A Novel Real-Time Threshold Algorithm For Closed-Loop Epilepsy Detection And Stimulation System, Liang Hung Wang, Zhen Nan Zhang, Chao Xin Xie, Hao Jiang, Tao Yang, Qi Peng Ran, Ming Hui Fan, I. Chun Kuo, Zne Jung Lee, Jian Bo Chen, Tsung Yi Chen, Shih Lun Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Epilepsy, as a common brain disease, causes great pain and stress to patients around the world. At present, the main treatment methods are drug, surgical, and electrical stimulation therapies. Electrical stimulation has recently emerged as an alternative treatment for reducing symptomatic seizures. This study proposes a novel closed-loop epilepsy detection system and stimulation control chip. A time-domain detection algorithm based on amplitude, slope, line length, and signal energy characteristics is introduced. A new threshold calculation method is proposed; that is, the threshold is updated by means of the mean and standard deviation of four consecutive eigenvalues through parameter combination. Once …

Improving The Accuracy Of Neighborhood Median Pixel Method (Nmpm) In Classifying Landsat-8 Oli Images By Optimizing The Scoring System’S Point Values, Abraham T. Magpantay, Proceso L. Fernandez Jr

Department of Information Systems & Computer Science Faculty Publications

The Neighborhood Median Pixel Method has previously been introduced as an image processing technique in remote sensing, developed to classify Landsat-8 OLI satellite image pixels into categories of vegetation, water, and built-up areas. This method relies on a lookup table based on the median pixel values within a pixel’s neighborhood and a scoring system that assigns point values for classification. While a 9x9 neighborhood size was originally proposed, a succeeding study suggested a 13x13 neighborhood for better classification accuracy. This study focuses on refining the scoring system used in the Neighborhood Median Pixel Method, particularly the original set of arbitrary …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange

Physics Dissertations - Archive

Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …

Systematic Co-Culture Of Ipsc-Cm And Ipsc-An Promote Co-Maturation In Vitro, William Gregory Terrell Jr.

Graduate Dissertations and Theses

The cardiac microenvironment is a complex system of multicellular interactions that enables proper heart function. In native heart muscle, sympathetic neurons (SN) and parasympathetic neurons (PSN) modulate the beat rate of cardiomyocytes (CM) to maintain homeostasis. Pluripotent stem cells can differentiate into CM, but differentiated CMs are fetal-like, with a high beat rate, and lack organized sarcomeric structure. Recent reports of co-cultured pluripotent CM/SN pairs have reported electrophysiological changes in the SN (increased upstroke velocity). However, interpretation of these results are obscured by the differences in co-culture parameters between studies (stage of development, co-culture duration, etc.). The goal of this …

Intercellular Stress Generation During 1d Collective Migration Of Cancer Cells, Logan Waddle, Jian Zhang

2025 Research Poster Competition

Physical forces drive many cellular processes such as migration and proliferation. A metastatic tumor will have invasive strands/chains that extend from the main tumor. These chains of cells collectively migrate away from the main tumor to establish new colonies elsewhere in the body. However, how physical forces drive this collective invasion and the required energy for this process is not well understood. Invasion of a metastatic tumor to surrounding tissues leads to a majority of cancer-associated death and is often a collective effort among cells, it is therefore important to fully understand the process behind collective cancer invasion.

The goal …

Llm-Driven Fmea For Safe Human-Robot Collaboration In Disassembly, Morteza Jalali Alenjareghi, Samira Keivanpour, Yuvin Adnarain Chinniah, Sabrina Jocelyn

Articles dans des actes de congrès

Disassembly operations often present unstructured and unpredictable scenarios, such as handling hazardous materials, addressing ergonomic strain, and managing dynamic robot interactions that pose safety risks. To tackle these challenges, we propose an innovative use of large language models (LLMs) to enhance failure mode and effect analysis (FMEA) in the context of human-robot collaboration (HRC) for disassembly tasks. We developed an LLM system leveraging retrieval-augmented generation (RAG) for real-time risk analysis and recommendation generation. RAG retrieves domain-specific information from the FMEA knowledge database, enabling accurate risk analysis, contextual understanding, and relevant recommendations based on user input and operational data. Evaluation of …

Stakeholder Perspective-Taking In Engineering Design; A Neurocognitive Approach, Megan Taylor, Yakhoub Ndiaye, Jenn Campbell

2025 Research Poster Competition

During the engineering design process, the ‘black box’ of a designer's mind determines who the stakeholders are, which stakeholder needs they are addressing and how they will address them. This process can build bias into engineering designs, leading to less equitable, accessible, and inclusive engineered artifacts. Design neurocognition, a new field that aims to determine the unknowns of a working designer’s mind, has arisen because of this uncertainty. The fNIRS (Functional Near-Infrared Spectroscopy) allows insight into the prefrontal cortex during decision making processes by analyzing the change in oxygenated blood in the surface of the prefrontal cortex, about 3 cm …

The Bacteriostatic, Regenerative, And Immunomodulatory Properties Of Extracellular Matrix Particles For Lung Injury, Keera P. Rhoads

Theses and Dissertations

Acute respiratory distress syndrome (ARDS) is a prevalent, life-threatening lung condition, affecting nearly 200,000 Americans annually, with a 40% international mortality rate. There is no cure for ARDS, and current pharmacological treatments have limited effectiveness. Symptoms can be mitigated with mechanical ventilation, though this often leads to ventilator-induced lung injuries (VILI) and puts critically ill patients at risk of infections, including ventilator-associated pneumonia (VAP). A promising therapeutic is the extracellular matrix (ECM), a complex network of structural proteins and bioactive molecules that has been shown to have anti-inflammatory properties and prevent fibrosis. We aim to utilize the regenerative and immunomodulatory …

Exploring User Sentiment On Social Issues Via Neural Network Transfer Learning In Digital Communities, Ananya R. Vangoor

2025 Research Poster Competition

Understanding public sentiment on social issues is crucial for gauging the stance of the general population. Traditionally, surveys have been a common approach for this. However, to capture more candid opinions, social media provides a rich source of unadulterated opinions. By analyzing social media statements, we can gain insights into the perspectives of specific groups. More specifically, we will investigate the attitudes of the public into the relationship between hard work and success in the workplace.

To begin, I will be training a neural network on X, formerly known as Twitter, tweets to categorize each tweet as either pro-luck or …

Analyzing Unmanned Aircraft System (Uas) Incidents From Nasa Asrs Data Using Unsupervised Machine Learning, Kacey Haws, Neelakshi Majumdar

2025 Research Poster Competition

The NASA Aviation Safety Reporting System (ASRS) assembles voluntarily submitted aviation safety incident reports in their database to act on the information provided. This database allows the government, companies, and citizens to submit incident or situational reports to its database to discern recurring issues in the National Aviation System (NAS) so that the proper officials can act [1]. The narratives provided in these reports are text-based, resulting in large amounts of data to process. Previous work in the University of Arkansas Aerospace Systems Engineering and Transportation Laboratory (ASYST) lab involved parsing unmanned aircraft system (UAS) incident reports manually. While these …

The Towering Inferno: Art, Policy, And Legacy, Charles R. Jennings

Publications and Research

The Christian Regenhard Center for Emergency Response Studies, leveraging the expertise of its faculty, staff, Advisory Board and colleagues, has consistently programmed activities focused on high-rise fire safety. The upcoming 50^th anniversary of the film The Towering Inferno was an inspiration.

We decided this would be more lighthearted and less purely technical than many of our events. We wanted to consider both the film, and use it to reflect on the state of fire safety in tall buildings in New York City.

Of course it does not escape us that the topic of fire safety is a serious one, …

Multiphysics Modeling Of Material Response To High-Intensity X-Ray And Laser Pulses: Heating, Ablation, And Plasma Expansion, Youssef Abouhussien

Theses and Dissertations

This dissertation presents a computational framework to investigate material response under high-intensity X-ray fluxes produced by an exo-atmospheric nuclear detonation and laser-material irradiations, with a focus on heating, ablation, and plasma expansion phenomena relevant to satellite vulnerability and high-energy-density environments. A hybrid Monte Carlo and Two-Temperature Model (MC-TTM) was developed to simulate X-ray and laser energy deposition and thermal relaxation in metals and semiconductors across a range of X-ray and laser pulse durations from femtoseconds to nanoseconds. Results demonstrate distinct thermal behavior between materials, with ablation thresholds and phase transitions captured in good agreement with experimental data.

In parallel, a …

Exploring The Stem Career Identity Development Of Black Women Across Their Lifespan, Veronica Hurd

Theses and Dissertations

Although STEM is the fastest-growing career sector, Black women are grossly underrepresented as they account for 2.5% of the workforce. Research highlights that this underrepresentation is due to racialized structures in K-12, postsecondary, and career settings that restrict Black girls’ and women’s STEM opportunities. While macrosystems such as hegemonic ideologies, attitudes, and social conditions shape Black girls’ and women’s opportunities in STEM, they continue to persist and achieve their career goals. To explore these barriers and Black women’s persistence in this industry, this study draws from the autobiographical memories of 10 Black women in the field or formerly in the …

Pla Polymer Binder In Core Production - Influence On Final Casting Dimensions, Artur Soroczyński, Krzysztof Rechowicz

Virginia Digital Maritime Center (VDMC) Faculty Publications

The foundry industry is seeking an ecological alternative to synthetic molding resins. This study evaluates the technological properties of core sands bonded with biodegradable polylactide (PLA). Cores prepared on a 2% quartz sand matrix were subjected to casting processes using two alloys with extremely different pouring temperatures: gray cast iron (approx. 1200 °C) and AK11 silumin (approx. 710 °C). The research methodology included macroscopic assessment, dimensional analysis using 3D scanning (GOM Inspect), and qualitative knock-out assessment supported by numerical temperature field simulation. The results showed that the high crystallization temperature of cast iron leads to complete thermal degradation of the …

Optical Study Of Small Jet Engine Combustion Ignition, Bryce Anthony Ullman

Browse all Theses and Dissertations

Improving the ignition reliability in small-scale gas turbine engines is critical for safety aspects of auxiliary power units (APUs). To better understand the ignition characteristics of these small-scale combustors, an optically accessible combustor is designed and tested. The combustor accommodates twelve prevaporizer tubes (PVTs) in accordance with the commercial-off-the-shelf (COTS) rendition and allows for interchangeable materials (quartz and Inconel) and igniter positions. Another notable design feature introduces a quartz outer combustor liner to allow visualization into key regions of the combustor. The study aims to replicate a COTS ignition sequence using glow plug igniters and examine the effects of different …

Transient Power And Thermal Management Of A Hypersonic Vehicle, Jacob H. Jadischke

Browse all Theses and Dissertations

Design of high speed vehicles necessitates incorporating power generation and thermal management systems. Power generation is required as traditional high-speed propulsion sources do not contain rotating components to extract power, and the harsh external thermal environment calls for thermal management. To size these systems, the transient power requirements and the heat generated inside the vehicle must be understood. Sizing these systems at the earliest stages of the vehicle design allows for a more optimized geometry and a trajectory to design the most favorable vehicle. Characterization of these low-quality power and thermal loads from the actuation and fuel pump subsystems has …

Enhanced Diagnostics And Surveillance Of Enteroviruses Including Serotypes Associated With Acute Flaccid Myelitis, Denise Lynette Kramer

Browse all Theses and Dissertations

Prior to 2014, Enterovirus D68 infections typically caused symptoms resembling the common cold. From 2014-2018, D68 was associated with an increase in acute flaccid myelitis. However, since 2020, neurological complications have all but disappeared. We selected 1076 respiratory specimens previously determined to be positive for rhinovirus or enterovirus from Department of Defense members and their beneficiaries collected globally from October 2018 through January 2024 and underwent sequencing. Of these specimens, 93.7% were identified as rhinoviruses, while 6.3% were enteroviruses, including 30 enterovirus D68. We utilized the Nextstrain bioinformatic pipeline to reconstruct the phylogenetic relationship of these 30 D68 viruses. Twenty-two …

Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis

Browse all Theses and Dissertations

Natural-language inference (NLI) asks whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise. Modern transformers reach high raw accuracy on benchmarks such as SNLI, MNLI, and ANLI, yet they often rely on brittle lexical shortcuts and provide little insight into their decision process. This thesis shows that counterfactual-augmented knowledge distillation can simultaneously boost robustness and supply faithful, token-level explanations—without scaling model size. Four T5-v1_1 students (60M, 220M, 770M, 3B parameters) are trained under four curricula: (1) standard fine-tuning, (2) fine-tuning with free-text rationales, (3) multi-task distillation with naive counterfactuals, and (4) multi-task distillation with …

Hardware Trojan Detection In A Segmented Mixed-Signal Circuit Via Leakage Current, Christopher James Otey

Browse all Theses and Dissertations

As computers and integrated circuits become more commonplace, the risk of a Hardware Trojan attack becomes more worrisome. Trojans can exploit design flaws or be inserted between essential components to leak information, change the circuit function, or destroy the circuit altogether. Several methods of trojan detection and prevention have been introduced, however few can handle combined analog and digital circuits, known as mixed-signal circuits. This thesis demonstrates a Hardware Trojan detection method implemented in an Analog-to-Digital Converter (ADC), which is a mixed-signal circuit. The detection method involves splitting the circuit into segments with approximately equal leakage currents (a large part …

Computational Analysis Of A Hafnium-Titanium Alloy Mechanical Properties From First Principles, Abdul Mughni

Browse all Theses and Dissertations

Hafnium and titanium, along with zirconium, are refractory metals with unique properties suitable for extreme-environment applications. Utilizing alloys based on these elements can provide suitable materials with engineered properties. Understanding their mechanical properties is necessary to determine appropriate applications. This thesis research aims at employing quantum-based atomistic simulations to estimate mechanical properties of pristine hafnium, titanium and an alloy based on these elements. The results are compared to available experimental data and the corresponding implications are explored.

Data-Driven Prediction Of Temperature Distribution In Multi-Laser Powder Bed Fusion Using Convolutional Neural Networks, Majid Dousti

Browse all Theses and Dissertations

Additive Manufacturing (AM), particularly Laser Powder Bed Fusion (L-PBF), has gained significant traction in fabricating complex, high-performance metallic components. However, the inherent complexity and computational cost of high-fidelity simulations pose challenges for real-time monitoring and optimization of multi-laser powder bed fusion processes. This study proposes a data-driven surrogate modeling approach using a deep learning architecture to efficiently and accurately predict three-dimensional temperature distributions during ML-PBF. A 3D convolutional neural network (CNN) model, named Decoder-CNN, is developed and trained on a dataset of simulated thermal fields corresponding to various process configurations, including different laser power, scanning speed, and beam arrangements. The …

Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew

Browse all Theses and Dissertations

As AI-driven workloads accelerate the growth of cloud initiatives and spending, resource waste also increases due to persistent inefficiencies in cloud compute and infrastructure management. Overprovisioned resources and suboptimal configurations often lead to operational inefficiencies and unnecessary financial overhead. These challenges arise from the difficulty of anticipating resource demands in dynamic workloads and selecting suitable virtual machines to ensure optimal performance. Our research proposes a holistic, data-driven framework for managing cloud compute resources that reduces costs without compromising application performance. We integrate a predictive, model-driven, threshold-based autoscaling solution for cloud-native applications with an optimized instance right-sizing approach to select cost-effective …