Engineering Commons^™

Enhancing Non-Player Character Dialogue In Video Gages: An Evaluation Of Large Language Model-Generated Responses, Lam P. Quach

Master's Theses

As video games increasingly emphasize narrative depth and player immersion, the quality of Non-Player Character (NPC) dialogue has become crucial for creating engaging gaming experiences. This thesis investigates the potential of Large Language Models (LLMs) to generate high-quality NPC dialogue by comprehensively evaluating four state-of-the-art models: Gemma 3 27B, Mistral 7B, QWEN 2.5, and LLAMA 3.1. The study employs a mixed-methods approach, combining human evaluation (N=50 participants) with AI-based assessment across five key benchmarks: coherence, personality expression, engagement, style/tone appropriateness, and overall quality. Participants evaluated 32 dialogue samples (8 per model) generated for a fantasy game context featuring two distinct …

Designing The Protocol For An Experimental Flight Simulation Study Encouraging Fuel Efficient Behavior, Thomas S. Reardon

Theses and Dissertations

This study designed and tested an experimental instrument to examine how pilots respond to fuel efficiency feedback in a flight simulator. A standardized flight plan, script, and hardware setup were created using X-Plane 12 software and physical flight simulator hardware. The chosen sortie guided participants from Monterey Regional Airport to Moffett Federal Airfield using instrument flight rules (IFR). The flight script provided step-by-step guidance to ensure consistent behavior across participants. The simulator was mapped to match real cockpit controls and allowed for precise data collection including flight time, altitude, heading, and fuel use. The goal was to support a larger …

Implementing Lean Principles To Enhance Warehouse Operations At King Abdulaziz Air Base(Kaab): A Case Study Of Royal Saudi Air Force (Rsaf), Saleh A. Alghamdi

Theses and Dissertations

The Royal Saudi Air Force (RSAF) relies on efficient logistics to sustain readiness. At King Abdulaziz Air Base, warehouse receiving inefficiencies caused delays and waste. This study used Lean principles and a six-month time–motion analysis, with Pareto and Fishbone tools, to identify 55% waste in dead pile and 75% in palletized shipments. Standard times of 5.98 and 6.55 minutes were set. Key recommendations include SOPs, cross-training, forklift certification, layout redesign, and RFID. Lean adoption could save 100+ labor hours and $4,000 annually, improving safety, accuracy, and mission readiness.

Sainik Vol 1 Issue 1, Sastra Deemed To Be University

SAINIK

No abstract provided.

Enhancing Network Security: Dynamical Intrusion Detection Systems Leveraging Zero Trust Architecture, Ekramul Haque

Tennessee State University Alumni Theses and Dissertations

This thesis discussed two original methodologies for proposing security frameworks incorporating machine learning (ML) and Zero Trust Architecture (ZTA) principles to manage advanced persistent threats faced by Unmanned Aerial Vehicles (UAVs) and Network intrusion detection systems (IDS). The first methodology examined the use of RF signals and deep learning to identify and classify UAVs. The RF signal characteristics used in the method for detecting UAVs improved the ability to determine RF drone protocols. Although the models led to promising findings, their lack of ability to generalize to new drone types identified the need to improve both the data set and …

Hybrid Forecasting Of University Electricity Demand Using Time Series And Deep Learning, Minsoo Baek, Youngguk Seo

Faculty Articles

University buildings are energy-intensive and operate on complex schedules, making electricity demand forecasting particularly challenging. This study develops and evaluates monthly forecasting models for a public university campus in Georgia using six years of data (January 2019–December 2024) that integrate weather variables and academic calendar indicators. Three modeling approaches are compared: Seasonal Autoregressive Integrated Moving Average (SARIMA), SARIMA with exogenous variables (SARIMAX), and a hybrid SARIMAX–Long Short-Term Memory (LSTM) model. Feature selection methods, correlation analysis, Granger causality, Random Forest importance, Recursive Feature Elimination (RFE), and Least Absolute Shrinkage and Selection Operator (LASSO) regression, were applied to optimize input relevance. The …

Low-Resource Ecoacoustic Audio Classification, Enis Berk Coban

Dissertations, Theses, and Capstone Projects

Ecoacoustic monitoring via machine learning enables scalable analysis but is often constrained by labeled data scarcity, particularly in remote regions like the Arctic. This thesis confronts low-resource ecoacoustic audio classification by developing and evaluating complementary machine learning methodologies. We introduce EDANSA, the first publicly available, expert- labeled Arctic dataset of its kind, curated via novel active learning, alongside a baseline CNN. We systematically evaluate transfer learning, showing general audio embeddings effectively bootstrap classifiers for challenging Arctic sounds, significantly outperforming direct label mapping. Optimizing label utility, we investigate standard data augmentation and introduce novel audio data valuation via Shapley values, revealing …

Toward A Generalizable Perceptual Hashing Framework For Image Manipulation Detection, Priyanka Samanta

Dissertations, Theses, and Capstone Projects

This thesis contributes to research in adversarial image manipulation detection. The primary motivation is the increasing need to verify digital images, especially for legal evidence, journalistic proof, or social media content—where manipulated or fabricated images can mislead, defame, or distort reality. A key application and contribution of this work is the development of eWitness, a blockchain application that generates and registers image provenance at capture time to enable independent verification of authenticity. The secret sauce behind the system is SmartHash, a novel and efficient perceptual hashing algorithm designed for real-world deployment in systems like eWitness. Unlike existing algorithms, SmartHash targets …

The Floodnet Community Engagement Guide, Véronëque Ignace, Sofia Mariyamis, Kendra Krueger, Polly Pierone, Hayley Elszasz, Hannah Eisler Burnett

The Science and Resilience Institute at Jamaica Bay, SRIJB

"At the Intersection of Science, Policy, and Community: The FloodNet NYC Community Engagement Strategy” is a public-facing community engagement guide that documents the strategies, tools, and lessons developed through FloodNet NYC, a cross-sector partnership among researchers at NYU and CUNY and New York City agencies. Designed as a practical resource, this guide shares our approach to community engagement and dissemination so that researchers, practitioners, community organizations, and public agencies can adapt these methods to their own urban climate science projects and other community-centered efforts that address climate challenges.

Grounded in community-based participatory research (CBPR) principles, the guide presents community engagement …

Multi-Modal Depth Estimation Using Camera And Mmwave Sensors, Alston D. Devero-Belfon

Student Theses

For accurately estimating the depth of environments with varying lighting conditions, reliable methods are limited. By utilizing wireless sensor technology in conjunction with cameras, a wide range of environments can be visualized, and objects within these environments can be tracked and monitored. Such methods offer cost-effective alternatives and provide a more secure, data-at-rest option for individuals with low vision, while also enhancing machine perception. In this work, we develop such a prototype that utilizes wireless sensors and cameras, which act in sync, enabling us to estimate the depth of objects within varying lighting environments to a level that is recognizable …

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Single-Molecule Orientation And Localization Microscopy, Sophie Brasselet, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

Single-molecule localization microscopy (SMLM) offers enhanced spatial resolution in optical microscopy, providing detailed insights into the spatial organization of proteins in cells at the nanoscale. Over the past decade, SMLM has progressively incorporated the capability to retrieve the orientations of single molecules using their polarized dipolar emission pattern. Here we explore recent advancements in single-molecule orientation and localization microscopy (SMOLM), which yields super-resolved images of molecular three-dimensional (3D) orientations, wobble and 3D positions. This advancement opens possibilities to explore the nanoscale organization and conformation of biological molecules as well as to monitor and design local 3D optical fields in nanophotonics. …

Effect Of Short-Chain Polymer Binders On The Mechanical And Electrochemical Performance Of Silicon Anodes, Fei Sun, L. Zurita-Garcia, Dean R. Wheeler

Faculty Publications

Polymer binders are crucial components in providing both mechanical support and chemical stability to the structure of porous Li-ion electrodes. Particularly in silicon anodes, the active material undergoes substantial volume expansion of up to 275%. Due to the mechanical constraint of the current collector, these silicon materials tend to expand in the normal direction while exhibiting substantial particle rearrangement and plastic deformation. Conventional rigid binders such as polyacrylic acid (PAA) and polyimide (PI), while providing satisfactory initial capacity, do not eliminate diminished long-term performance. Our research attempts to develop binder formulations that can accommodate sufficient flexibility for the substantial volume …

How Does The Sewer Microbiome Impact Wastewater Surveillance For Antibiotic Resistance?, Israel Adedayo Adeoye, Ishi Keenum

Michigan Tech Publications

Antibiotic resistance is a global health threat that is difficult to directly monitor prior to clinical presentation. Wastewater surveillance has emerged as a public health tool that could aid in identifying the community wide circulation of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB), however this is complicated by the potential growth and decay of ARB within the sewer network. Sewer systems have been suggested as a primary place where sub-lethal doses of antibiotics are present with human and environmental adapted bacterial strains, however little is understood about what physiochemical properties might enhance or inhibit ARG propagation. Hence, key …

Large Language Models For Construction Risk Classification: A Comparative Study, Abdolmajid Erfani, Hussein Khanjar

Michigan Tech Publications

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines …

Incorporation Of E-Waste Plastics Into Asphalt: A Review Of The Materials, Methods, And Impacts, Sepehr Mohammadi, Dongzhao Jin, Zhongda Liu, Zhanping You

Michigan Tech Publications

This paper presents a comprehensive review of the environmentally friendly management and reutilization of electronic waste (e-waste) plastics in flexible pavement construction. The discussion begins with an overview of e-waste management challenges and outlines key recycling approaches for converting plastic waste into asphalt-compatible materials. This review then discusses the types of e-waste plastics used for asphalt modification, their incorporation methods, and compatibility challenges. Physical and chemical treatment techniques, including the use of free radical initiators, are then explored for improving dispersion and performance. Additionally, in situations where advanced pretreatment methods are not applicable due to cost, safety, or technical constraints, …

Sparse-Data Orbit Estimation In Low Earth Orbit Using The Markov Chain Monte Carlo Ensemble Gaussian Mixture Filter, Nicholas J. Oden

Master's Theses

The number of space objects (SOs) in low Earth orbit (LEO) continues to increase rapidly, creating challenges for the current ground-based tracking network, which cannot accommodate the projected growth in SOs. Catalog maintenance relies on frequent observations for reliable reacquisition, with Two-Line Element (TLE) sets typically generated daily to mitigate rapid error growth from poor TLE accuracy. This constraint limits the ability to track more objects with existing infrastructure. This work evaluates the Markov Chain Monte Carlo Ensemble Gaussian Mixture Filter (MCMC EnGMF), a nonlinear, non-Gaussian filter well-suited for sparse tracking scenarios where higher post-update accuracy is needed to reduce …

Mitigating Melanin-Induced Bias In Pulse Oximetry: Optical, Algorithmic, Engineering, Hardware And Modeling Tools, Mckenzie Bradley, Sydnee Barrett, Ty Mckelvey, Jeremiah Carpenter, Delphine Dean

Publications

Melanin, the primary determinant of skin pigmentation, absorbs light at wavelengths that can have significant impact on the accuracy of pulse oximetry and other optical biosensing methods. This narrative review examines key factors influencing melanin-dependent pulse oximetry inaccuracies, including optical interference in transmission and reflectance modes. These inaccuracies further highlight the need for use of standardized skin tone metrics in device testing and design such as the Monk Skin Tone scale and Individual Typology Angle for performance stratification. There are several approaches in development that hope to address the errors in pulse oximetry measurements on melanin-rich skin. These include algorithmic …

Communicating Wastewater-Based Surveillance Data To Drive Action, Kata Farkas, Devrim Kaya, Rasha Maal-Bared, Ahmad I. Al-Mustapha, Sarmila Tandukar, Ishi Keenum, Teemu Gunnar, Aaron Bivins, Matthew J Wade, Kyle Bibby, Tarja M. Pitkänen, Ananda Tiwari

Michigan Tech Publications

As exemplified during the COVID-19 pandemic, wastewater-based surveillance (WBS) can deliver near real-time, population-level pathogen data to guide public health action. Its impact, however, hinges on timely, transparent, and context-specific communication to stakeholders, including health authorities, policymakers, scientists, clinicians, and the public. This review examines current WBS communication practices, identifies persistent challenges, and proposes strategies to enhance relevance. Key challenges include data complexity, lack of standardised communication frameworks, ethical and privacy concerns, and variable stakeholder capabilities. The strategic use of digital platforms, such as dashboards, reports, press releases, and social media, alongside traditional media, can broaden reach and aid interpretation. …

Comprehensive Review Of In Vitro Approaches For Environmental Heavy Metal Exposure, Manas Warke, Madeline English, Camila Padilla, Lexie Gasco, Wendy Leisner, Rupali Datta, Smitha Rao

Michigan Tech Publications

Heavy metals are ubiquitous environmental pollutants, contaminating air, soil, and water via the erosion of natural deposits, as well as originating from anthropogenic sources, such as agriculture, industries, transportation, and landfills. The increasing utilization of heavy metals over the years, combined with the persistent nature of metals in the environment poses a direct threat to human and environment health. Although regulatory limits have been established for toxic metals, assessing the associated health risks using real-life exposure scenarios remains challenging. In this review, we summarize the development and use of in vitro models based two- and three-dimensional cell culture systems, focusing …

Distributed Coherent Beamforming At 60 Ghz Enabled By Optically-Established Coherence, Drake Silbernagel, Yu Rong, Isabella Lenz, Prithvi Hemanth, Carl Morgenstern, Owen Ma, Nolan Matthews, Nadar Zaki, Kyle W. Martin, John D. Elgin, Jacob Holtom, Daniel W. Bliss, Kimberly Frey

Space Dynamics Laboratory Publications

We implement and experimentally demonstrate a 60 GHz distributed system leveraging an optical time synchronization system that provides precise time and frequency alignment between independent elements of the distributed mesh. Utilizing such accurate coherence, we perform receive beamforming with interference rejection and transmit nulling. In these configurations, the system achieves a coherent gain over an incoherent network of N nodes, significantly improving the relevant signal power ratios. Our system demonstrates extended array phase coherence times, enabling advanced techniques. Results from over-the-air experiments demonstrate a 14.3 dB signal-to-interference-plus-noise improvement in interference-laden scenarios with a contributing 13.5 dB null towards interference in …

Mesospheric Gravity Waves Observed By Nasa Atmospheric Waves Experiment (Awe), Yucheng Zhao, Jiarong Zhang, Pierre-Dominique Pautet, Jun Ma, Joe Mcinerney, Hanli Liu, Steve Eckermann, Ludger Scherliess, Michael John Taylor, Burt Lamborn, Russ Kirkham

Space Dynamics Laboratory Publications

Although smaller scale gravity waves (GWs) (horizontal wavelengths ~30–300 km) are thought to account for the dominant energy and momentum inputs at the Ionosphere-Thermosphere-Mesosphere (ITM) altitudes, the sources, variability, and influences of these smaller-scale GWs are still major unknowns. The NASA Atmospheric Waves Experiment (AWE) is designed to measure these GWs in the mesopause region. In November 2023, AWE was successfully launched and deployed on the International Space Station (ISS) and science data collection was started. The AWE instrument maps the nighttime hydroxyl (OH) layer (~87 km), providing 2D GW fields in mesospheric temperature and OH band intensity over a …

Orographic Wave Activity At Mesospheric Altitude Over The Southern Ocean Islands Observed By The Atmospheric Waves Experiment, P.-D. Pautet, S. D. Eckermann, L. Scherliess, J. Ma, Y. Zhao

Space Dynamics Laboratory Publications

A major source for the gravity waves (GW) measured in the stratosphere and higher is the orographic forcing produced by the tropospheric wind blowing over mountainous regions. Recent studies have shown that even small islands can generate waves which, under appropriate conditions, are able to penetrate above 80 km altitude, transporting large amount of momentum into the upper atmosphere. Investigating the exact effects of those islands is challenging because ground-based and even airborne measurements are limited above those generally isolated places.

The Utah State University (USU) Atmospheric Waves Experiment (AWE) was designed and built to study mesospheric GW globally, even …

Storage Location Optimization In Automated Storage And Retrieval Systems: A Deep Reinforcement Learning Approach, Lingjun Wang, Aldy Gunawan, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

This study investigates the optimization of storage location in automated storage and retrieval systems (AS/RS). We introduce an optimization approach based on the Deep Q-Network (DQN) algorithm to enhance warehouse task efficiency and minimize stacker travel during storage and retrieval. To accelerate the algorithm training process, we integrate a prioritized experience replay mechanism. Furthermore, we decouple action selection from value estimation within the DQN framework to address the issue of value overestimation. The proposed model is evaluated against three heuristic methods. The experimental results demonstrate that our approach significantly outperforms these baselines.

Large Lithium-Ion Battery Model For Secure Shared E-Bike Battery In Smart Cities, Donghui Ding, Zhao Li, Linhao Luo, Ming Jin, Bin Zhu, Yichen Zhong, Junhao Hu, Peng Cai, Huiqi Hu

Research Collection School Of Computing and Information Systems

Electric bikes powered by lithium-ion batteries are increasingly used in smart cities to promote sustainable mobility and efficient delivery services. However, limited battery range and slow plug-in charging remain key challenges. Shared electric bike battery systems, facilitated by battery swapping stations, offer a promising solution by enabling quick and efficient battery replacements. However, their success hinges on accurate anomaly detection, battery health estimation and remain range prediction. These tasks remain challenging due to data scarcity, battery diversity and environmental variability. Here we show that a large-scale lithium-ion battery model trained on over ten million battery time series data enables robust …

Diffusion Mri Biomarkers For Predicting Treatment Outcomes In Infantile Epileptic Spasms Syndrome With Non-Lesional Mri, Daniel Ackom, Scott A. Beardsley, Jennifer Meylor, Kayleigh Butler, Ricardo Vega, Andrew J.D. Crow, Hema Patel, Brian D. Schmit, Pradeep Javarayee

Biomedical Engineering Faculty Research and Publications

Background

Infantile epileptic spasms syndrome (IESS) is a devastating developmental epileptic encephalopathy (DEE) and patients exhibit diffuse white matter alterations and structural remodeling. However, the correlation between these structural changes and brain network properties, or their effect on the efficacy of treatment outcomes in MRI non-lesional IESS patients is not clear.

Method

This retrospective study was conducted on IESS patients using fixel-based analysis (FBA) of diffusion MRI and graph theory analysis of structural connectivity, involving 26 non-lesional IESS patients aged 2 to 12 months and 120 age-matched controls. We further examined the differences between antiseizure medication (ASM) responders and non-responders …

Tissue Engineered Combinatorial Therapeutics For Spinal Cord Injury Repair, Inha Baek

Graduate Theses and Dissertations

Traumatic spinal cord injury (SCI) lead to temporary or permanent sensorimotor deficit due to the complex and multifaceted features of the injury site, making the treatments ineffective. Recent research emphasizes the potential of combinatorial therapeutics approaches combining different therapeutics, such as biomaterials and stem cell transplantation. In this context, nerve composite hydrogels, fabricated from decellularized sciatic nerve (dSN) and spinal cord (dSC) extracellular matrices (ECM), might be promising platforms due to their biocompatibility and ability to mimic native microenvironments. In this study, we developed and characterized nerve mimetic composite hydrogels embedded with human adipose-derived stem cells (hASCs) and investigated their …

Fpga-Based Overlay Accelerators With Massive Parallel Processing Units To Accelerate Deep Neural Networks, Ehsan Kabir

Graduate Theses and Dissertations

Deep neural networks (DNNs) are widely used in applications such as classification, prediction, and regression. Various DNN architectures, such as convolutional neural networks (CNN), multilayer perceptrons (MLP), long short-term memory (LSTM), recurrent neural networks (RNN), and transformers, have become leading machine learning techniques in these applications. They require significant computational resources and have substantial memory demands due to intensive matrix-matrix multiplications and complex data flows. Hence, efficient utilization of on-chip computational and memory resources is essential to maximize parallelism and minimize latency. Designing an optimal tiling scheme that aligns effectively with the architecture is also necessary. Modern FPGAs are equipped …

A Review Of The United States' Long Term War Support Capabilities In The Indo-Pacific Command Region, Brian J. Mullin Jr.

Theses and Dissertations

This study examines U.S. maritime transportation readiness in the Indo-Pacific, highlighting fleet age, mariner shortages, shipyard decline, and port vulnerabilities. It also considers contested logistics and technological threats. Recommendations include fleet recapitalization, mariner pipeline growth, port diversification, and defensive upgrades. The study concludes that secure sea line assumptions are outdated and calls for greater resilience, with follow-on efficiency analysis proposed for ports and ships.

Recent Advances In The Design Principles Of Lithium Selective Membranes, Shayan Abrishami, Huan Xiao, Mohsen Asadnia, Ze Xian Low, Amir Razmjou

Research outputs 2022 to 2026

The growing demand for lithium in energy storage applications has intensified the need for efficient lithium extraction technologies, with membrane processes emerging as a promising approach. Among various membrane technologies, nanostructured membranes with precisely engineered channels have shown exceptional potential for selective lithium extraction due to their ability to control ion transport at the molecular level. This review provides a comprehensive analysis of the fundamental design principles governing lithium-selective membranes, with a specific focus on nanochannel-based systems. We examine the critical parameters that influence lithium selectivity, including surface charge distribution, nanochannel dimensions, morphology, and wettability, while exploring how these factors …