Engineering Commons^™

Data Cleaning With Excel, Rubab Shahzad

Day Family Research Lab Workshop Series

This workshop will delve into the importance of cleaning research data and why clean data is an important part of the larger research data lifecycle. This session will also provide an overview of data cleaning and preprocessing using Microsoft Excel including a hands-on demonstration.

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Tableau Part Ii - October 2025, Rubab Shahzad

Day Family Research Lab Workshop Series

Part Two of Introduction to Tableau. Learn to make cool visualizations using Tableau. A hands-on opportunity where we will go over calculated fields, hierarchies, unions, dashboards, and stories.

Prior experience with Tableau is recommended

Advancing Mobileclip Through Hybrid Quantization: A Cpu-Focused Approach, Atiqur Rahman

2025 Fall Honors Capstones Projects - Archive

MobileCLIP is a compact model that connects images and text, enabling it to perform tasks like image identification and question answering without needing to be retrained for each new task. Although it’s designed to be lightweight, it still runs slowly on regular computers without a powerful graphics card (GPU). This research focuses on making MobileCLIP run faster and smaller by using post-training quantization, which reduces the model’s precision after training without hurting performance. We combined several strategies: analyzing which parts of the model are more sensitive to changes, applying targeted adjustments to its structure, and running everything using CPU-only tools. …

Improved Context For Llm Queries On Knowledge Graphs Using The Model Context Protocol, Talha Tahmid

2025 Fall Honors Capstones Projects - Archive

Large Language Models (LLMs) struggle on factual, relation-heavy questions without structured external knowledge. This thesis implements a standards-based pipeline that connects LLMs to Resource Description Framework (RDF) knowledge graphs through the Model Context Protocol (MCP). The system comprises an MCP tool that executes SPARQL against approved endpoints, an Natural Language (NL) to SPARQL step in which the LLM (ChatGPT) generates the SPARQL query using prompt-based templates, followed by a generation stage conditioned on the retrieved graph facts. Final experiments focus on the Rhea knowledge graph. We evaluated the same base model with and without MCP on a 50-question benchmark spanning …

Design And Implementation Of A Notification System For The Purpose Of Improving Communication Between Nurses And Patients, Aindrila Bhattacharya

2025 Spring Honors Capstone Projects - Archive

Communication is an important part of how nurses administer treatment to their patients. Without proper communication, it is difficult for the nurses to connect with their patients and vice versa. This paper will be going into some previous literature about communication between patients and nurses in general, communication with respect to pediatric patients and how notification systems have been implemented in the past. It will then discuss the methodology and implementation of a notification system for a medical app. The medical app is meant to facilitate communication from the side of the patient to the nurse, while the notification system …

Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta

2025 Spring Honors Capstone Projects - Archive

Reinforcement Learning is a Machine Learning paradigm that involves simulating learning through rewards and penalties in intelligent systems. This technique is often employed in robotics when traditional control methods are insufficient or when human intuition does not provide a good solution on how to control robot systems, This project involves training a Segway-style Mobile Inverted Pendulum (MIP) robot to balance and push a box forward. The BeagleBone Blue board is used that includes a built-in Inertial Measurement Unit (IMU) and encoder ports. These sensors enable the system to measure its current state. The goal is to find the optimal leaning …

Integration Of Notes Section With Access Management, Inshaad Merchant

2025 Spring Honors Capstone Projects - Archive

This research explores the development of a notes section with access management, specifically designed to assist Computer Science and Engineering students to revisit and revise all the notes and key points highlighted in their tutoring sessions. While the senior design project focuses on the CSE Student Success Center application that allows students to schedule tutoring sessions, manage appointments, and manage their profiles within this application, the Honors capstone project adds on a specific section for students to save all their notes and important video links and attachments to continue their learning outside of the tutoring sessions. A centralized platform for …

Tutortech: A Web App For A Smarter And More Efficient Tutoring System, Smarika Pathak

2025 Spring Honors Capstone Projects - Archive

The Computer Science and Engineering (CSE) department faces challenges with managing its tutoring services, especially tracking attendance, booking sessions, and overall management of the tutoring system - all of which severely limits the ability for tutors to connect and engage with students. To help overcome these issues, TutorTech, a web-based application that provides improved management of the tutoring system and supports more engaging learning experiences between students and tutors was designed. Through this project, the aim was to optimize the TutorTech search capabilities - assisting students to find tutors based on skills, while also considering the effect of user interface …

Beyond The Hard Hat: Examining Barriers Of Women In Construction, Allison Pham

2025 Spring Honors Capstone Projects - Archive

The construction industry is falling behind in female representation, with women only making up 11.2% of the industry. The existing labor shortage is further exacerbated by the limited interest among women to pursue a career in construction. The purpose of this research is to investigate the factors that contribute to the lack of women in the construction industry and explore how HR strategies can attract and retain women in construction careers. Environmental, Social, and Governance (ESG) reports from three different industries were used to analyze the percentage of men and women employees. Multivariate regression analyses were conducted to examine whether …

Development Of A Cost-Effective Daq For Measuring Brake Performance In Race Cars, Adrin Alias

2025 Spring Honors Capstone Projects - Archive

This project explores the feasibility of creating a cost-effective data acquisition (DAQ) system for high-speed, real-time brake performance testing of Formula SAE racecars. The research addresses the limitations of the current MoTeC DAQ system currently employed by the team, which is costly and time-consuming to set up for on-car testing. The team will use a brake dynamometer for steady-state comparisons of different brake pad compounds (senior design project), but evaluating real-world performance requires on-car testing. By systematically comparing various hardware platforms, sensors, communication protocols, and storage solutions, this project aims to balance cost-efficiency with reliability and performance. The research evaluates …

Computational Modeling Of Torque Arm And Load Cell Interactions In Uta Racing Brake Dynamometer, Anthony A. Aiyedun

2025 Spring Honors Capstone Projects - Archive

The UTA Racing Team is developing a brake dynamometer to measure the coefficients of friction of various brake pads and calipers under controlled conditions. The dynamometer simulates on-track braking scenarios, testing brake pads at temperatures up to 1300ºF and pressures similar to racing environments. Its primary goal is to generate accurate friction vs. temperature graphs for various brake pad and rotor combinations, addressing the need for dedicated testing equipment. This project creates an ANSYS simulation of the torque arm and load cell interaction. The computational model accurately represents the relationship between applied torque and measured load cell force, incorporating precise …

A Data Driven Approach To Student Success: Visualizing Engagement And Performance Metrics, Araohat Kokate

2025 Spring Honors Capstone Projects - Archive

Many tutoring centers lack tools to analyze and visualize key performance metrics, limiting data driven decision making. This study develops a data visualization feature for the CSE Student Success Center App at the University of Texas at Arlington, enabling administrators to track student engagement, tutor performance and session trends. Using the data of students and tutors, the feature provides interactive dashboards for real-time insights. Administrators can monitor attendance patterns, tutor workloads and booking trends, optimizing resource allocation. Findings indicate that real-time data visualization enhances decision-making, reducing manual effort while improving operational efficiency. This can further help improve student support services. …

Design Of A Helical-Type Rotor For Enhanced Energy Harvesting In Smallscale Vertical Axis Wind Turbines, Aadee M. Mitee

2025 Spring Honors Capstone Projects - Archive

This research investigates the design optimization of a small-scale Vertical Axis Wind Turbine (VAWT) utilizing helical rotor blades fabricated from graphene-reinforced polyethylene terephthalate. While previous studies have largely concentrated on largescale turbines for rural environments or on high-performance composite materials that often compromise recyclability, this research addresses the specific challenges associated with small-scale VAWTs intended for urban and populated areas. The study identified tip speed ratio, helical angle, and blade length as critical design parameters. Optimization of these parameters was achieved through systematic evaluation of their impact on the turbine’s aerodynamic performance, quantified by the coefficient of performance. Structural and …

Uta Datathon 2025 Challenges (Archive), Rubab Shahzad, Srinivasa Sai Abhijit Challapalli, Ghanbarian Behzad, Mei Yang, Clivin Geju, Zecil Yogeshkumar Jain, Jay Mahavirkumar Shah, Sai Nikhitha Chandana

2025 Datathon Challenges-Archive

Timed and Model Challenges from the UTA Datathon 2025

Virtual Fixtures For Teleoperated Robots For The Visually Impaired, Vishwaak Chandran Thamaraiselvan

Computer Science and Engineering Theses - Archive

This paper presents our preliminary study on enabling individuals with visual impairments to safely operate mobile robots and vehicles. To achieve this, we developed a teleoperation with accessibility at its core. The system incorporates features that enhance usability and situational awareness, including assistive control based on artificial potential fields to prevent collisions and ensure smooth navigation. It also provides multimodal feedback through (a) haptic vibrations on the gamepad controller, which convey the proximity of nearby objects detected by the robot’s laser sensor, and (b) color-coded overlays that differentiate paths, obstacles, and people through semantic segmentation performed by a deep neural …

Event-Based Histogram Of Gradients For Lane Detection, Ganesh Gupta

Computer Science and Engineering Theses - Archive

In the rapidly evolving landscape of autonomous driving technology, lane detection systems stand as fundamental guardians of vehicular safety. The National Highway Traffic Safety Administration identifies unintentional lane departures as responsible for approximately one-third of all road accidents—a sobering statistic that underscores the critical importance of robust lane detection methodologies. This thesis embarks on an academic exploration at the intersection of neuromorphic engineering and computer vision, examining how the distinctive properties of event-based cameras might be harnessed to enhance lane detection capabilities under challenging environmental conditions. Unlike conventional frame-based imaging sensors that capture entire scenes at fixed intervals, event-based cameras …

Exploring Instruction Generation For Uavs: Dataset Adaptation, Model Behavior, And Diagnostic Insights, Seyedarman Vaziri Bozorg

Computer Science and Engineering Theses - Archive

This thesis explores the development of an answering agent capable of generating natural language instructions for unmanned aerial vehicles (UAVs), grounded in a limited, real-world dialogue dataset. The objective is to adapt a static dataset into a training pipeline that can support instruction generation and serve as a foundation for future interactive systems involving question-asking agents and internal dialogue. A hybrid architecture is implemented using a semantic teacher model (MPNet) and a T5-base encoder-decoder trained with contrastive and supervised objectives. The adapted training process yields statistically acceptable performance across standard evaluation metrics. However, qualitative analysis reveals a mismatch between metric …

Training Data Privacy In Machine Learning: A Systematization Of Attacks And Defenses, Mohammad Sufyaan Saeed

Computer Science and Engineering Theses - Archive

Training and deploying Machine Learning (ML) models introduce significant data confidentiality risks, as modern models can inadvertently memorize and leak information about their training data. While attacks such as membership inference and model inversion are well studied, the literature remains fragmented, with inconsistent threat models and unclear relationships across attack classes and defenses. This work presents a Systematization of Knowledge (SoK) that unifies the landscape of training-data privacy attacks and defenses, aligning them with the NIST Adversarial Machine Learning (AML) taxonomy to enable standardized threat modeling and comparison. Our analysis shows that, despite significant progress in characterizing attack vectors, defenses …

Methods Of Optimizing Storage And Retrieval Of Structured Data, Neelim Haider

Computer Science and Engineering Theses - Archive

Storing and retrieving large amounts of data reliably is becoming more and more important as time goes on. There are high demands to store highly personal information such as social security numbers, bank account information, and residence information to rapidly changing data such as employee information, inventory information, and stock information. Therefore, the ability of a system to store, remove, and update such information efficiently and correctly is critical. There are different types of data that database systems can potentially hold: structured, unstructured, and semistructured data. Various database models have been developed to provide a framework that allows designers to …

Transformer And Recurrent Architectures For Dynamics Prediction And Policy Learning On Long-Horizon Tasks, Vinal Jitendrabhai Gadhiya

Computer Science and Engineering Theses - Archive

Model-based reinforcement learning promises improved sample efficiency by learning environment dynamics and using them for planning or policy improvement. However, the choice of neural architecture for dynamics prediction significantly impacts the model's ability to capture temporal dependencies and maintain long-term context, capabilities crucial for complex, open-world environments.

This thesis investigates three neural architectures for learning world models: Transformer-based, GRU-based, and a hybrid Transformer+GRU approach. We evaluate these architectures on Crafter, a 2D open-world survival environment that requires long-horizon planning and sequential task completion. In Crafter, agents must perform hierarchical sequences of actions, such as collecting wood, placing a table, and …

Diversity-Driven Xor Secret Sharing: Reliable And Secure Multi-Path Transmission, Richard M. Olu Jordan

Computer Science and Engineering Theses - Archive

The growing reliance on distributed storage and multipath communication sys- tems has intensified the need for security mechanisms that remain robust even when individual nodes or channels are compromised. Secret sharing provides an information- theoretic approach to achieving both confidentiality and availability, and XOR-based constructions in particular offer lightweight and highly structured designs. This thesis develops a unified analytical framework for understanding and evalu- ating XOR-based secret sharing schemes across multiple operational settings, includ- ing plaintext storage, encrypted-data scenarios, and noisy binary symmetric chan- nels (BSCs). Building on a general (t, n) system model, we examine five threshold configurations—(2, 3), …

Crime Theory Informed Agent-Based Modeling For Crime Prediction And Patrolling Route Optimization, Shohreh Moradi

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Crime reduction remains a global priority, demanding both accurate modeling of criminal dynamics and efficient allocation of scarce policing resources. To address these needs, this study presents a two‐fold framework that (1) simulates street‐level crime patterns using an agent‐based model (ABM) grounded in Routine Activity Theory (RAT), Rational Choice Theory (RCT), and Crime Pattern Theory (CPT), and (2) optimizes patrol routing through a time-dependent, multi‐visit mixed‐integer linear programming (MILP) formulation.

In the first component, we integrate real‐world crime, environmental, and census data to reproduce realistic offender, citizen, and Police behaviors, capturing where and when robbery, burglary, and larceny occur across …

Guided-Mode Resonant Nanopatterns For Raman Generation And Photonic Devices, Renjie Chen

Electrical Engineering Dissertations - Archive

This dissertation explores advanced strategies for enhancing Raman amplification in silicon photonic devices, focusing on guided-mode resonance engineering and resonant mode manipulation. Silicon, despite its indirect bandgap, exhibits a strong Raman scattering coefficient, enabling it to function as a viable gain medium for integrated photonic systems. However, the realization of efficient, compact, and low-threshold silicon Raman amplifiers and lasers necessitates innovative design approaches that overcome inherent material and structural limitations.

The first chapter provides a fundamental overview of optics, including physical principles, spectral characteristics, guided-mode resonance, simulation methods, and nanopattern fabrication methods.

The second chapter delves into silicon-based Raman amplification …

Data-Driven Forecasting Of Power Demand Via Convex Optimization, Mersedeh Ashraphijuo

Electrical Engineering Dissertations - Archive

This dissertation develops interpretable, data-driven frameworks for short-term power demand forecasting using convex optimization and advanced feature engineering. The models combine historical load data, calendar structures, and meteorological variables to deliver accurate point, quantile, and probabilistic forecasts. By leveraging multi-periodic Fourier features, temperature-based regressors, and autoregressive memory, the proposed approach balances predictive performance with interpretability and scalability. Evaluations on multi-year datasets across US regions show consistent accuracy gains over benchmarks, while preserving transparency critical for real-world deployment. Beyond power systems, the framework generalizes to other time series applications in data science and AI, offering a robust, explainable alternative to black-box …

Characterization And Modeling Of Polyphase Machines And Power Conditioning Components, Nathan Lockhart

Electrical Engineering Dissertations - Archive

Microgrid power configurations have become increasingly prevalent in recent power systems due to the rise of power electronic energy generation, energy storage, and the many diverse electrical demands. Microgrids offer numerous advantages over traditional power electronic networks, which rely on large rotating generators to supply power over extensive distances to multiple users. Remote power grids are particularly beneficial for smaller networks that may be isolated or have unique power requirements, often incorporating energy storage to enhance operational flexibility. Advances in power electronics, such as medium voltage DC distribution, are enhancing the reliability, redundancy, and integration capabilities of isolated microgrids.

To …

Multi-Modal Model-Based Optical Flow Estimation For Event-Based Vision, Pritam Karmokar

Computer Science and Engineering Dissertations - Archive

Event cameras offer a fundamentally different sensing paradigm by asynchronously capturing brightness changes at high temporal resolution, directly encoding motion in the scene. However, their sparse and non-traditional data format poses significant challenges for dense motion estimation, particularly in the context of optical flow. Contrast Maximization (CM) has emerged as a powerful model-based framework for estimating optical flow from event data by optimizing the sharpness of motion-compensated event representations. This dissertation builds upon and significantly advances the CM framework through two complementary contributions.

First, we propose Edge-Informed Contrast Maximization (EINCM), a hybrid approach that augments the traditional events-only CM framework …

An Slo-Aware, Multi-Pronged Approach To Enhancing Resource And Energy Efficiency In Server Applications, Ning Li

Computer Science and Engineering Dissertations - Archive

Server applications operating in oversubscribed cloud environments face the dual challenges of meeting strict Quality-of-Service (QoS) requirements and improving resource and energy efficiency. As the number of user connections and workload diversity continue to grow, existing scheduling mechanisms struggle to balance QoS guarantees, fairness, resource efficiency, and power consumption. This dissertation presents a unified, cross-layer framework to address these challenges through three key contributions: AppleS, UTSLO, and REEF.

First, we propose AppleS, a user-space QoS-aware fine-grained I/O scheduling framework that delivers fair and efficient service to concurrent client connections. AppleS introduces a QoS-centric metric that guides admission control and scheduling …

Synthesis And Application Of Cu-Based Ultrasmall Nanoparticles For Targeted Glioblastoma Treatment, Ryan T. Hart

Material Science and Engineering Dissertations - Archive

Glioblastoma (GBM) is a highly malignant form of brain cancer with a bleak prognosis. Current maximal treatment involves surgical resection followed by chemo-or radiotherapy. Two critical barriers make treating GBM a formidable challenge. First, the tumor's tendril-like proliferation throughout healthy brain tissue often makes complete surgical removal and conventional radiation insufficient. Second, the blood-brain barrier (BBB), which protects the brain, unfortunately also shields GBM from life-saving anti-cancer drugs. Here, we develop a radioactive nanoparticle-based therapy that directly confronts these limitations, offering a new strategy for GBM treatment.

Prostate-specific membrane antigen (PSMA) is a promising target for glioblastoma (GBM) treatment because …

Phase-Field Modeling Of A Protective Layer For The Suppression Of Dendrites In Metallic Anode-Based Rechargeable Batteries, Bharat R. Pant

Material Science and Engineering Dissertations - Archive

Metallic anodes, such as Lithium (Li) and Zinc (Zn), offer promise for the development of high-capacity rechargeable batteries. However, metallic anode-based batteries suffer from severe dendrite growth during the plating process, which poses safety hazards and accelerates capacity degradation. Experimental studies suggest that using a protective coating on the anode surface could potentially mitigate dendrite growth and prolong the battery’s life. However, there are limited theoretical studies on the inhibition effect of a protective layer on dendrite growth. Herein, we developed a phase-field model to simulate the impact of a protective layer on the plating and stripping cycle for Li …

Fabrication Of Microscale Oxide Architectures On Silicon Via A Cmos-Compatible, Deposition-Last Process, Jamal A. Brown

Material Science and Engineering Theses - Archive

This work explores the feasibility of integrating complex oxide devices on silicon using a CMOS-compatible, deposition-last approach. While previous demonstrations of this method have succeeded at larger scales, this study focuses on extending the process to microscale features, with lateral dimensions as small as two microns. The fabrication sequence begins with photolithographic patterning and reactive ion etching of the silicon substrate, followed by the deposition of a silicon nitride mask to delineate device regions. We then epitaxially grew SrTiO3 and La-doped SrTiO3 layers on top of the nitride mask via molecular beam epitaxy. Electrical transport measurements of the La:STO layer …