Engineering Commons^™

Intelligent Cost-Optimized Mix Design Prediction And Engineered Strength System For Geopolymer Concrete: A Machine Learning-Based Recommender System, Yuvaraj Natarajan, K.R. Sri Preethaa, V. Danushkumar, Syed Muhammad Oan Naqvi, M. Shyamala Devi, Karen Lozano, Bubryur Kim, Jinwoo An

Civil Engineering Faculty Publications

Geopolymer concrete is a promising low-carbon alternative to ordinary Portland cement concrete, but its practical use is limited by complex mix-design requirements and limited cost-aware decision-support tools. This study developed the Intelligent Cost-Optimized Mix Design Prediction and Engineered Strength System (iCOMPRESS), a machine learning-based recommender system that integrates 28-day compressive strength prediction, cost optimization, and compositionally diverse mixture recommendation. A database of 443 literature-derived mixtures was used to train a hyperparameter-optimized Random Forest model with domain-informed features related to binder chemistry, alkaline activation, water content, aggregates, and curing conditions. The model achieved a five-fold cross-validation mean absolute error (MAE) of …

A Software-Defined Radio Testbed For Mimo-Ofdm Communications, Chaz G. Maschman

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

In this thesis, we implement a testbed for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems via GNU Radio. Specifically, we implement a configurable framework for the construction of MIMO-OFDM software-defined radio (SDR) systems as a GNU Radio module. The GNU Radio MIMO-OFDM module consists of multiple algorithmic blocks necessary for implementation of a MIMO-OFDM system. This includes a library for the generation of orthogonal or pseudo-random pilot sequences, amendments to the Schmidl-Cox protocol for MIMO synchronization, and the creation of click-and-drag GNU Radio blocks implementing the conversion of arbitrary data sent via external programs to MIMO-OFDM frames, the initial …

X-Ray Tomography Of Damage Dynamics In Advanced Materials Using A Laser Wakefield Accelerator, Vigneshvar Senthilkumaran, Nicholas F. Beier, Sylvain Fourmaux, Peter Kenesei, Sean Dobson, Joël Maltais, Alvaro R. Arce-Borkent, Tait Richards, Michael G. Lipsett, Le Zhou, John A. Moore, Amina E. Hussein

Mechanical Engineering Faculty Research and Publications

Additively manufactured (AM) metals offer the potential for customizable, cost-effective components, but qualification and certification are crucial. Key to this process is understanding pore dynamics under stress, typically analyzed using micro-computed tomography. This study introduces laboratory-scale “betatron” x-rays from laser wakefield acceleration as a high-throughput alternative for x-ray tomography of advanced materials, such as AM AlSi10Mg alloys. Coupled with 3D finite element modeling, this method provides detailed insights into stress-porosity interactions. The approach delivers high-resolution scans, revealing that pore shape and local triaxiality significantly influence fracture dynamics, supporting advanced material characterization. This work also demonstrates the potential and versatility of …

Cure Sound Project: Study In Silence?, Quezia Abrao, Deeya Bhadresa, Elisa Castro, Fiona Coulbourne, Carlos Flores, Lizzeth Holguin, Lazarus Maldonado, Jeremy Mares, Andrew Martini, Nicole Matthews, Katherine Montero, Mariana Reyes, Jacob Rodriguez, Julieta Ruiz, Eliana Sanchez, Gabriela Varela

Posters - 2026

Difference in sound levels in study areas across campus can affect students’ ability to work and learn efficiently 1.

According to the WHO, safe sound levels are measured to be around 70dB 2. Some study areas on campus experience sound levels exceeding the safe threshold. Unsafe sound levels are considered to be at or above 85dB 3. These unsafe and excessive noise levels negatively affect a student’s ability to concentrate and work efficiently which correlates to a decrease in class performance, attention deficits, and stress 4.

This study aims to get a base-line measurement of dB levels in common study …

Using Ai To Predict Energy Expenditure In Lower Limb Prosthesis Users, Nelly Diaz, Siem Hadish

Posters - 2026

• Computer vision has evolved from simple image classification and object detection to analyzing human motion and biomechanics (1). • CNN’s are usually focused on image classification, but, in this case, we are not asking the model if a person is walking. • Many real-world problems require regression: Predicting a continuous number like energy expenditure of walking is a complex task. • It is essential for Prosthetists to understand energy expenditure of their prosthetic patients (2). • An amputee may use 20-30% more energy to walk. • In this project, we developed an AI model to analyze human motion and …

Analysis Of Live Load Distribution Factors In Prestressed Concrete Girder-Bridges With Asymmetric Losses In Prestressing Strands, Mohamed T. Elshazli, Mohamed Elgawady, Ahmed Ibrahim

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Asymmetric loss of prestressing strands in concrete bridge girders presents a critical yet underrecognized threat to structural integrity, particularly in exterior girders subjected to impact or localized deterioration. This form of localized and unbalanced damage leads to eccentric reductions in prestressing force, rotation of the principal axis, and diminished flexural capacity, effects not explicitly addressed in current design standards such as the AASHTO LRFD Bridge Design Specifications. This study presents a comprehensive numerical investigation into the structural implications of such asymmetric damage, focusing on its effect on live load distribution factors (LLDFs) across prestressed girder bridges. A parametric study of …

Comparative Numerical Analysis Of Lead-Free Perovskite Solar Cells Using Scaps-1d, Md Shazarul Islam, Md Abdul Kuddus Sheikh, Hasina Huq

Electrical and Computer Engineering Faculty Publications

A comprehensive numerical study of lead-free perovskite solar cells was conducted using the SCAPS-1D simulation framework with the device architecture ITO/SnO2/Perovskites/NiOx/Au. The work investigates the replacement of the central Pb cation with Sn, Ge, and Bi, followed by absorber-layer thickness optimization to enhance device performance. The impact of systematic Pb substitution on key photovoltaic parameters was first evaluated. Among the candidates, FASnI3-based devices exhibited the most promising performance, achieving a power conversion efficiency (PCE) of 26.48%, with a short-circuit current density (Jsc) of 19.31 mAcm-2, an open -circuit voltage (Voc) of 1.57 V, and a fill factor (FF) of 87.29%. …

Bio-Payload Senior Design Report, Connor Bishop, Oliver Goodall, Marcus Kirk, Gabby Robles, Anish Shanmuganathan

Interdisciplinary Design Senior Theses

This project focuses on the design and development of an autonomous experimental platform capable of conducting cellular biology experiments in a well plate within a CubeSat environment. The system integrates microfluidics, robotics, and onboard sensors to remotely initiate experiments, monitor them, and collect data without human intervention. The objective is to create a platform for automated biological experimentation in microgravity, while reducing reliance on ground-based control and increasing mission efficiency and reproducibility. The team used Saccharomyces cerevisiae to assess the biocompatibility of the well plate and monitor changes in cell culture, including optical density and cell viability.

Aiw26s: Machine Learning Of Structured Data, Moumita Saha

Paul English Applied Artificial Intelligence (AI) Institute Publications

This workshop introduces the fundamentals of machine learning for structured data, focusing on tabular datasets and real-world applications. Participants explore key concepts such as data types, data preprocessing, feature engineering, and supervised learning methods. The session covers commonly used models, including linear regression, logistic regression, decision trees, and neural networks, along with evaluation metrics such as RMSE, accuracy, and confusion matrices. By the end of the workshop, participants will have gained a practical understanding of how to build, interpret, and evaluate machine learning models for structured data.

Active Listening And Reassurance In Text-Based Virtual Health Coaches, Ghulam Hussain

Dissertations

Conversational agents (CAs) have strong potential to support health and physical wellbeing through text-based coaching, but they remain limited in their ability to demonstrate supportive social behaviours that are important in human coaching interactions. In particular, relatively little is known about how Active Listening and Reassurance are perceived, modelled, and evaluated in text-based virtual healthcare coaching, or how such behaviours should be adapted to individual users.

This dissertation investigates how supportive interaction behaviours can enhance text based virtual health coaching, with an initial focus on Active Listening and Reassurance and a later theoretical emphasis on Active Listening. Across five unique …

The Psychology Behind Ai-Generated Phishing And Social Engineering Attacks, A’Shya Reynolds

School of Cybersecurity Master's Level Projects and Papers

Cybercrime has evolved significantly with the integration of artificial intelligence (AI), transforming traditional phishing and social engineering attacks into highly sophisticated and personalized threats. While early phishing attempts relied on generic messaging and low success rates, modern AI-driven attacks leverage advanced data analytics, natural language processing, and behavioral prediction to manipulate victims more effectively.

This research examines how cybercriminals utilize AI to enhance psychological manipulation techniques in phishing and social engineering attacks, increasing victim susceptibility. Drawing from interdisciplinary literature in cybersecurity and psychology, this study explores key psychological mechanisms, including cognitive biases, emotional triggers, and decision-making processes that influence victim …

Workforce Resilience In Public Transportation Agencies, Kristal D. Metro

Civil Engineering ETDs

A strong, resilient public workforce is necessary to keep the United States’ infrastructure system running smoothly. Across the public sector, hiring is lagging behind increasing retirement rates, leaving public agencies facing the loss of institutional knowledge at unprecedented levels. Combined with above-average retirement rates, low unemployment rates, and below-average salaries, workforce shortages are expected to become critical, putting the resilience of the public transportation workforce, and by extension, the United States public infrastructure and services at risk. The purpose of this research is to determine the best practices for recruiting and retaining employees at public transportation agencies, thereby positively influencing …

From Our Stories, To Our Streets (Osos), Jennifer Lopez

Student Publications

Youth traffic safety persists as a critical planning challenge and public health concern in New Mexico, where pedestrian fatality rates consistently rank among the highest in the United States (National Highway Traffic Safety Administration, NTSA, Governors Highway Safety Association, GHSA, 2023; Baca, 2025). Students (children), due to their daily interactions with roadways, whether when they walk, bike, ride transit, or drive/get driven to and from school, are among the most vulnerable groups within the transportation system (Ferenchak, N. Wesley, M., 2017. For many students in Albuquerque, the commute to or from school is one of the most dangerous parts of …

Graphene Synthesis: A Reactor-Oriented Review Of Conventional And Emerging Production Methods, Paul C. Ani, Zeyad Zeitoun, Hasan J. Al-Abedi, Joseph D. Smith

Chemical and Biochemical Engineering Faculty Research & Creative Works

Graphene's unparalleled electrical, mechanical, and thermal properties have positioned it as a transformative material across diverse sectors, including electronics, energy storage, biomedicine, and environmental remediation. However, scalable, cost-effective, and high-quality production remains a critical challenge. This review presents a comprehensive, reactor-oriented analysis of both conventional and emerging graphene synthesis methods, categorized into top-down and bottom-up approaches. Special emphasis is placed on the role of reactor configurations in determining product quality, layer control, scalability, and economic viability. Key synthesis techniques explored include chemical vapor deposition (CVD), epitaxial growth, electrochemical exfoliation, ultrasonic-assisted methods, microwave reactors, combustion synthesis, and plasma-enhanced processes. By evaluating …

Life Cycle Costing Of Modular Timber Residential Construction, Logan Micah Hammel

Construction Management

This study examines the economic feasibility of modular residential construction using life-cycle costing (LCC) and life-cycle assessment (LCA) approaches to assess the sustainability and maintenance costs of modular construction. Using the structures and documents from the Gateway Decathlon project as the subject, we applied LCC formulas and programs to assess their economic performance. These calculations were conducted manually in Excel and modeled using the OpenLCA platform to compare traditional and software-based approaches. The study aims to identify cost drivers across building scopes, such as building materials and electrical and mechanical systems, and to evaluate how modular construction performs over its …

Drones Detection Via Skeletonization And Small-Object-Aware Detr, Gissell Torres, Delio Rincon

Center for Cybersecurity

Detecting drones in video streaming environments remains challenging in computer vision due to scale variation and background complexity. Building up from prior work in real-time detection and skeletonization for streaming environments, this study aims to improve small object detection through Skeletonization and a Small-Object-Aware Detection Transformer framework, which uses DETR technology as a foundational step toward reliable motion prediction in dynamic aerial scenes. A transformer-based detection model was trained on a drone dataset converted to COCO format and evaluated using standard COCO metrics, including AP, AP50, and AP_small. Initial testing revealed low-confidence predictions, suggesting limitations in backbone freezing and training …

Corrigendum To “Advanced Techniques In Quartz Wafer Precision Processing: Stealth Dicing Based On Filament-Induced Laser Machining” [Opt. Laser Technol. 171 (2024) 110474] (Optics And Laser Technology (2024) 171, (S0030399223013671), (10.1016/J.Optlastec.2023.110474)), Yun Wang, Yutang Dai, Farhan Mumtaz, Kaiyan Luo

Electrical and Computer Engineering Faculty Research & Creative Works

The authors regret, that the affiliation for author Yun Wang was incomplete. To accurately reflect both the author's academic affiliation and the research platform where the work was conducted. The correct affiliation for Yun Wang is updated as above. The authors would like to apologize for any inconvenience caused.

Optimizing Auv Perception For Competitive Underwater Robotics: Analyzing Performance And Simplicity Of Machine Vision Hardware, Joseph King

Honors Theses

Underwater robotics is a growing field with many practical applications, such as pipeline and offshore structure monitoring, deep sea mining, mine reconnaissance/removal, and marine environmental monitoring. USM’s Robotics Club will participate in the international RoboSub competition, where student-led teams build autonomous underwater vehicles (AUVs) that perform a variety of tasks within a pool requiring camera-based object detection. This project analyzes affordable hardware and software options for providing machine vision to USM’s future RoboSub AUV. The performance of an ESP32-CAM microcontroller, Raspberry Pi 5, and Jetson Orin Nano single-board computers running FOMO and YOLO object detection models was compared. These models …

2026 (Spring) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the spring of 2026.

Traditional And Machine-Learning Equalization Techniques For Bandwidth-Limited Short-Reach Optical Communication Channels, Abdullah Khawatmi

Theses

This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods. The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally …

Efficient Fpga Implementation Of A 1 Million-Point Fft, Jwaher Abdulqader Al Tamimi

Theses

The one-million-point Fast Fourier Transform is implemented using a radix-2 single-path delay feedback pipeline architecture. To minimize the computational overhead, twiddle factors were pre-computed and stored in memory. The design uses a fixed-point representation with two integer bits and seven fractional bits, achieving a measured signal-to-noise ratio of 37.98. Given the substantial memory requirements, a memory partitioning approach was used. It mapped the delay buffers in each stage lookup table memory, block random-access memory, or ultra random-access memory, based on word width and memory depth.

The implementation operates successfully at 100 megahertz on a mid-scale field-programmable gate array. Post-implementation reported …

Modeling And Autonomous Control Systems For A Delivery Drone Application, Dana Helal Alnuaimi

Theses

This thesis focuses on the development of a multi-drone navigation and control system, aiming to enhance payload capacities beyond the limits of single-drone systems. By integrating multiple drones to work collaboratively as a unit, this study addresses the challenges associated with lifting and transporting heavier payloads.

The primary objective is to design and evaluate a multi-drone system capable of working in tandem to transport larger payloads efficiently. The research aims to develop robust control algorithms, supported by system identification for dynamic modeling, and navigation strategies to enable effective coordination between drones.

The study employs a combination of simulation and real-world …

Breath-Based Detection Of Liver Cancer Biomarkers Using An Swcnt-Fet Nano-Biosensor: Quantumatk, Mohamed Mohieb Rashdan

Theses

Early detection of liver cancer remains limited by the slow pace and invasiveness of current testing methods. This study proposes a single-walled carbon nanotube field-effect transistor (SWCNT-FET) designed to detect hexanal—a volatile organic compound (VOC) elevated in liver cancer—directly from exhaled breath. The device is modeled in QuantumATK using a semi-empirical Extended Hückel Hamiltonian within the non-equilibrium Green's function (NEGF) framework, emphasizing realistic contact physics by employing metallic SWCNT electrodes instead of conventional metal films. Zigzag channels with (11,0) and (12,0) chiralities are examined to analyze how geometry and contact matching influence charge transport. Simulations include current–voltage (I–V) characteristics and …

Development Of A Novel Cfd Approach For Predicting Multiphase Flow Enhanced By Machine Learning, Abdulrahman Elazazi Salem

Theses

Accurate estimation of interface orientation is crucial for ensuring both the accuracy and robustness of Volume of Fluid (VOF) schemes in multiphase flow simulations, especially when using non-uniform Cartesian meshes. Conventional gradient reconstruction approaches, such as least-squares (LSQ) methods, often suffer from significant errors and strong oscillations on highly stretched grids. This work proposes a grid-transferable, learning-based methodology that predicts interface unit normal vectors directly from the local volume-fraction field on nonuniform structured Cartesian grids using a feedforward neural network. The methodology extends earlier work originally developed for uniform grids, which is first reproduced to assess how performance deteriorates in …

Near-Wall Void Distribution Characterization In Pebble Bed Reactor Using Gamma-Ray Ct And Dem Simulation, Ahmed Jasim, Mauricio Maestri, Abdullah Al Zubaidi, Omar Farid, Muthanna Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Accurate characterization of void-fraction distributions in pebble-bed reactors (PBRs) is essential for predicting flow, heat transfer, and neutronic behavior. High-fidelity experimental benchmark data for validating such predictions remain scarce, largely due to the challenges of non-invasive measurements. In this study, gamma-ray computed tomography (CT) was employed to measure radial and cross-sectional porosity in a laboratory-scale pebble bed containing 6cm graphite pebbles. A Discrete Element Method (DEM) simulation was implemented and validated against these measurements, then applied to the full-scale geometry of the Xe-100 high-temperature gas-cooled pebble-bed reactor. Analyses included radial and axial void-fraction profiles in the cylindrical section and conical …

Artificial Intelligence In Human Spaceflight Safety-Critical Systems: A Requirements Framework For Ai-Enabled Computer-Based Control Systems, Liz Bosch

Doctoral Dissertations and Master's Theses

Currently, there is no governing standard that addresses the safe integration of AI into computer-based control systems (CBCS) in human spaceflight. The computer-based control expectations of those safety-critical systems on the International Space Station (ISS) are captured in SSP 50038, Computer-Based Control System Safety Requirements, with one caveat: the standard was not designed with AI applications in mind. SSP 50038 does not cover the probabilistic behavior, opacity, and training-data dependency of modern AI/ML systems. The objectives of this work is to develop an AI taxonomy relevant to safety characteristics (determinism, transparency, data dependency, failure predictability), systematically map AI characteristics against …

Construction Of A Deffi Sprayer: Using 3d Printer Methodology And Techniques For Cost-Effective Analysis Using Desi Mass Spectrometry Imaging, Ryan Karst, Savvy Stevens, Luke Amos, Logan Koester, Ethan Newbold, Karson Whitaker

Symposium Projects

Using 3D Printer Methodology and Techniques for Cost-Effective Analysis using DESI Mass Spectrometry Imaging

Visual Pattern Mining With Similarity Metrics For Model-Free Trading In The Korean Futures Market, Juhyeon Jang, Jaeyun Kim, David Enke

Engineering Management and Systems Engineering Faculty Research & Creative Works

The fractal market hypothesis highlights multi-scale dynamics in financial time series and provides a theoretical foundation for pattern-based analysis. This study proposes a model-free visual pattern mining framework that transforms high-frequency market data into image representations to support intelligent decision-making. By converting 1-minute KOSPI200 futures data into candlestick chart and Bollinger band images, the method effectively captures structural patterns and volatility dynamics. The framework applies similarity metrics and Intersection over Union (IoU)-based visual comparison to identify historically similar patterns and generate intelligent trading signals without model training or complex parameter tuning. Experimental results demonstrate that combining visual features of candlestick …

Pegasus: Vfs 2025-2026 Dbvf, A'Zhae Turay, Elisabeth Canjar, Jack Carpenter, Cheng-Ju Wu, Xander Fruin, Sophia Bennett

Mechanical Engineering Senior Theses

Pegasus is an autonomous electric vertical takeoff and landing aircraft developed for the Vertical Flight Society Design-Build-Vertical Flight competition and designed around the operational needs of early-stage wildfire response. The system was developed to provide rapid aerial situational awareness, stable hover capability, modular payload deployment, and autonomous mission execution in environments where conventional reconnaissance methods may be limited by terrain, visibility, response time, or personnel risk. The final aircraft uses a hexacopter configuration with six electric propulsion units, a modular aluminum airframe, independent propulsion and avionics power systems, a Cube Orange+ flight controller, adaptive landing gear, and a bottom-mounted payload …

Plasmoid Vortex System Retrofit A Sustainability And Efficiency Study On Internal Combustion Engines, Walker Hall

Doctoral Dissertations and Master's Theses

The thesis addresses the persistent inefficiency and environmental degradation caused by internal combustion engines in modern vehicles, a major issue as the automotive industry faces increasing pressure to reduce fuel consumption and greenhouse gas emissions. Internal combustion engines, which power most cars today, convert only about 20-30% of fuel energy into useful work, with the remainder lost as heat and exhaust waste, including carbon monoxide (CO), carbon dioxide (CO₂), hydrocarbons (HC), and nitrogen oxides (NOx). This inefficiency contributes to global carbon emissions, with transportation accounting for approximately 29% of U.S. greenhouse gases in 2021 [1]. As regulatory standards tighten (e.g., …