Engineering Commons^™

Effect Of Nozzle Pressure Ratio On Thrust And Flow Behavior In A Supersonic De Laval Nozzle, Esha Jain

Doctoral Dissertations and Master's Theses

Supersonic nozzles operate across a range of flow regimes. While an ideally expanded condition yields optimal thrust, practical propulsion systems rarely operate at this design point due to variations in altitude and engine operating conditions. As a result, nozzles frequently operate in off-design conditions. In overexpanded regime, where the exit pressure is lower than the ambient pressure, shock-induced separation may occur within the divergent section of the nozzle, potentially degrading nozzle performance. Understanding the aerodynamic behavior of nozzles operating under off-design conditions is therefore important for improving propulsion system performance and stability. In particular, direct thrust measurements provide a key …

Intelligent Flight Control Systems Using Adaptive Deep Neural Networks And Concurrent Learning-Based Design Methods, Maddox C. Morrison

Doctoral Dissertations and Master's Theses

This thesis investigates deep neural network (DNN)-based adaptive control strategies for unmanned aerial vehicles (UAVs) operating under aerodynamic uncertainty and complex actuator dynamics.

The first contribution presents a control strategy employing a concurrent learning (CL)-based DNN training algorithm, which combines online adaptive DNN weight adaptation with offline batch-like training updates using a recorded data stack. The analysis focuses on the closed-loop performance improvements resulting from the use of optimum CL data-selection algorithms, which ensure that the recorded data stack maintains sufficient data diversity to provide a statistically meaningful representation of the operating conditions using a reduced data set. Specifically, this …

Study Of Output And Behavior Of Llms Using Confidence Framing In Prompt Engineering, Micah Parrilla

Doctoral Dissertations and Master's Theses

While prompt engineering is pivotal for shaping Large Language Model (LLM) outputs, the impact of confidence framing on behavioral calibration remains underexplored. This study investigates the ways in which psychological framing, utilizing techniques such as capability praise, role amplification, and doubt induction, affects linguistic tone, objective accuracy, and internal calibration. A 1,080-trial experimental matrix evaluated six diverse models across factual, logical, coding, and cyber security domains. Analysis using the Kruskal-Wallis H-test revealed highly significant behavioral shifts across all measured dimensions, providing conclusive evidence that the applied frames exert a substantial influence on model performance.

The findings identify a distinct cognitive …

Guardrail Height Safety Requirements Given Recent Crash History And Evolution Of Vehicle Design, Delaney Morgan

Doctoral Dissertations and Master's Theses

Roadside barriers such as guardrails are fundamental to roadway safety, providing a critical last line of defense in preventing vehicles from leaving the roadway and impacting potential hazards. As vehicles are ever changing, it is necessary for guardrail design to adapt in response. Many contemporary vehicles such as sport utility vehicles and light trucks have higher centers of gravity than previous years, while electric vehicles have increased weight. Having sufficient guardrail height is essential to ensure proper engagement with vehicles to prevent loss of containment during impact.

The purpose of this study is to evaluate the performance of current Florida …

Safety-Aware Trajectory Generation For Increased Autonomy In Advanced Air Mobility, Edison Alberto Martinez Samaniego

Doctoral Dissertations and Master's Theses

Advanced Air Mobility (AAM) envisions highly automated aircraft that will enable short and medium range transportation. Unlike conventional aviation, these vehicles are expected to operate closer to populated areas and with increased levels of autonomy, making safe operation under abnormal or degraded conditions a critical requirement. Failures or performance degradation can reduce the maneuvering capability of an aircraft, causing trajectories planned under nominal conditions to become dynamically unfeasible.

This thesis presents a trajectory generation and replanning framework designed to maintain safe and feasible flight under reduced flight envelope conditions for a lift+cruise eVTOL aircraft. A unified control architecture based on …

Quantification Of The Depth-Of-Field In A Self-Aligned Focusing Schlieren System, Alexander Ephraim

Doctoral Dissertations and Master's Theses

This thesis investigates self-aligned focusing schlieren (SAFS) as a step toward future volumetric and quantitative measurements of three-dimensional compressible flows. Conventional schlieren imaging provides valuable visualization of density gradients, but it records only a line-of-sight projection and therefore does not directly resolve the spatial distribution of structures through the depth of the flowfield. SAFS addresses part of this limitation by introducing depth sensitivity, but its depth response has not been well characterized quantitatively. To help lay the foundation for future volumetric and quantitative SAFS methods, this work addresses two related problems. First, calibrated quantitative schlieren imaging is applied to an …

Adaptive Methods Of Resident Space Object Identification For Space Situational Awareness, Evan Pavetto-Stewart

Doctoral Dissertations and Master's Theses

One of the fundamental tenets of Space Situational Awareness (SSA) is the detection and sub sequent identification of Resident Space Objects (RSOs) within unresolved optical space imagery. This function is vital to the documentation and tracking of RSOs in their respective operational orbits, knowledge that is necessary for collision avoidance efforts and Space Domain Awareness (SDA) applications. In previous work, development was begun on a MATLAB program called RSOID to fulfill this purpose by accepting a collection (or ’collect’) of unresolved imagery and outputting a sequence of RSO locations (called a ’tracklet’) that can be used to determine the RSO’s …

Developing A Multi-Band Gnss Remote Sensing Station To Measure Precipitable Water Vapor For Flash Flood Nowcasting, Madison Gleydura

Doctoral Dissertations and Master's Theses

Flash flood nowcasting in Central and Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at long range and signal blockage by mountains. GNSS-meteorology offers an established alternative for measuring precipitable water vapor and is currently integrated into several numerical weather models. Recent research demonstrates that commercial-grade GNSS receivers can produce tropospheric products comparable to those from geodetic-grade equipment. The gaps in mountain coverage can be addressed by developing a low-cost, self-contained embedded system that …

Evaluating Uav Visual-Inertial Odometry Trajectory Error And Feature-Level Metrics Over Repetitive Floor Patterns, Anass El Mekkoussi

Doctoral Dissertations and Master's Theses

Visual-Inertial Odometry (VIO) is a widely used state estimation technique for Uncrewed Aerial Vehicle (UAV) navigation in environments where Global Navigation Satellite System (GNSS) signals are unavailable. VIO systems that rely on visual feature tracking are susceptible to performance degradation when operating over surfaces containing repetitive visual textures, where visually similar features can produce ambiguous correspondences that introduce errors into the trajectory estimate. Despite the prevalence of repetitive textures in indoor UAV operating environments such as warehouses, manufacturing facilities, and infrastructure corridors, the specific impact of different repetitive pattern geometries on per-surface VIO accuracy has received limited systematic study, and …

Behavioral-Centric Team Evaluation Via Consistent Rewards, Clement Kudakwashe Nyanhongo

Dartmouth College Ph.D Dissertations

Across human domains ranging from sports to business and organizational settings, complex tasks are often solved by teams rather than individuals, leveraging benefits such as interaction, mutual support, complementary skills, cohesion, and task allocation. Evaluating team effectiveness, however, is inherently challenging due to the subjectivity of many existing techniques and the limitations of outcome-driven metrics that primarily focus on performance scores while overlooking the team processes that generated the scores. To address these challenges, this dissertation proposes a behavioral-centric, end-to-end framework for team evaluation grounded in reward functions that model sequential team behavior. Reward functions offer compact and interpretable representations …

Advancement And Characterization Of Next-Generation Solid-State Photon-Counting Image Sensors For Astrophysics Applications, Nicholas R. Shade

Dartmouth College Ph.D Dissertations

Astronomers’ pursuit of detecting light from increasingly faint and distant objects in the expanse of space necessitates continuous improvement in signal-to-noise ratio of camera technology. Recent advancements in solid-state detector technologies have enabled the determination of photon-number, including single photon events, enabling observations at the fundamental limits of physics. These developments are instrumental not only for standard two-dimensional imaging but also for advanced spectroscopy, which increasingly drives future astrophysical applications. This thesis presents an evaluation of three next-generation silicon-based detectors capable of photon-counting with deep-sub-electron input-referred read noise: the electron-multiplying charge-coupled device (EMCCD), the single-photon avalanche diode (SPAD), and the …

An Ultrafine-Resolution Numerical Investigation Of The Influence Of Terrain On Tornado Behavior, Jiamin Dang, Jana Houser, Leigh Orf, Peng Yue, Guirong Yan

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study investigates the effects of idealized and realistic terrain on tornado characteristics and behavior. It uses a novel simulation approach, nesting a high-fidelity, ultrafine-resolution, tornado-scale, engineering large-eddy simulation (LES) within a Cloud Model 1 (CM1) simulation of a tornadic supercell. We analyze the effects of terrain on the tornado's central pressure, horizontal and vertical velocities, vortex shape, and path. Seven idealized terrain configurations are used including 1) a control run with flat ground, 2) and 3) an idealized hill with steep and gradual slopes having the height of 25.4 m, 4) and 5) an idealized escarpment with steep and …

Tuning And Performance Of Pid Controlled Low Complexity Systems, Timothy Evans

Honors Theses

Proportional integral derivative (PID) controllers are used for precise position and orientation control in systems such as autonomous underwater vehicles (AUVs). This project supports the University of Southern Mississippi’s (USM) Robotics Club’s RoboSub AUV effort by developing, troubleshooting, and manually tuning PID controllers to characterize tracking performance and settling time across systems of increasing complexity. Initially, the project hypothesized that tracking performance would be reduced and settling times would increase as system complexity advanced from one degree-of-freedom (DOF) to two DOF. However, prior research was found that suggests that for small disturbances around an equilibrium state, separate PID-controlled DOFs can …

Research Days: Case Study: Creation Of A Student-Driven Radio Talk Show To Answer Cybersecurity Questions And Concerns To Boost Power Skills, Joel Leiva, Anthony Bayate, David Abiandu, Keith Fernandez

Center for Cybersecurity

Power skills are essential in any professional career. Oftentimes, college students don’t feel prepared enough to enter the workforce. Having good power skills in a group can greatly increase production and efficiency. This case study aims to develop these power skills in a group of college students through the creation of a student-driven radio talk show answering cybersecurity questions and concerns. A qualitative approach was used via the creation of the C.Y.B.E.R. radio show. This show enhanced the participants’ power skills such as collaboration, teamwork, and communication skills. The findings from this case study prove the alternate hypothesis of boosting …

Adopting Zero Trust Security In Cloud: A Comparative Study, Landy Jimenez

Center for Cybersecurity

Adopting Zero Trust Security in Cloud: A Comparative StudyLandy Jimenez, Dr. Jiaxin LeiDepartment of Computer Science & Technology, Kean UniversityAbstract:As organizations transition to cloud-native environments, ensuring security across distributed systems has become more difficult. Modern cyberthreats like insider breaches and lateral movement attacks have shown that traditional perimeter-based security strategies, which rely on implicit trust within internal networks, are inadequate. Zero Trust Architecture (ZTA) addresses these challenges by requiring continuous authentication, authorization, and encryption for every access request, regardless of network location. However, cloud native Zero Trust presents concerns about scalability, latency, and resource overhead.This study evaluates Zero Trust at …

The Impact Of Ai Ethics Education On Student Engagement And Ethical Perspectives, Diana Medina

Center for Cybersecurity

Artificial Intelligence (AI) has become a cornerstone of technological innovation. The world has come to see the many advancements AI has to offer and the impact it has on everyday life. The benefits of AI are promising, and institutions are learning how to implement AI to further advance productivity and efficiency. However, AI-based products may produce harmful or unjust consequences, especially when ethical considerations are not deliberated during the developmental stages. This study investigates student engagement and examines the impact in infusing ethical reasoning in AI education. With five participating computer science professors and two historians, ethics modules were introduced …

Stamp-V: Steganographic Traceability For ​ Ai-Generated Images With Multimodal Verification, Xinlei Guan, David Arosema, Tejaswi Dhandu, Meng Xu, Kuan Huang, Tida Umamheswara Rao, Bingya Shen

Center for Cybersecurity

The rapid growth of generative AI has intensified challenges in content moderation and digital forensics, particularly when benign AI-generated images are paired with harmful or misleading text. This contextual misuse undermines traditional moderation systems and complicates attribution, as synthetic images typically lack persistent metadata or device signatures. We introduce STAMP-V, a steganography-enabled provenance framework that embeds cryptographically signed identifiers into images at creation time and verifies provenance through multimodal harmful content detection. Our system evaluates five watermarking methods across spatial, frequency, and wavelet domains, and integrates a CLIP-based fusion model that performs multimodal harmful-content detection as part of the provenance …

Control And Stability Analysis Of Three-Phase Grid-Connected Inverters In Renewable Energy Systems, Yasser Ayeva

Electrical Engineering and Computer Science Faculty Publications and Presentations

The increase in demand for renewable energy sources such as solar and wind systems has led to widespread use and integration of a three-phase grid connected inverters in electric modern electric power systems. The inverters are essential to convert DC power into AC power and to control the delivered power to the grid. However, the use of the inverters in a renewable energy system has some challenges related to stability and control due to the presence of power electronic interfaces and filter dynamics. This paper analyzes the control and stability of three phase grid connected inverter through an LCL filter. …

Competitive Reaction Pathways In The Fully Green Synthesis And Surface Modification Of Silver Nanowires Under L-Ascorbic Acid-Limited Conditions, Finley Neal

Honors Theses

Silver nanowires (AgNWs) are critical building blocks for transparent conductive films (TCFs) in flexible electronics, yet their commercial viability is hindered by environmentally hazardous, energy-intensive synthesis methods and limited intrinsic chemical stability. This study investigated a fully green, room-temperature (25℃) batch synthesis of pristine AgNWs using tannic acid as a biodegradable dual reducing and capping agent under strict kinetic pH control (pH 1.25). To enhance oxidative resistance, a subsequent surface modification protocol was evaluated to deposit a protective palladium (Pd) shell using L-ascorbic acid (LAA) as a secondary reducing agent. The pristine green synthesis yielded one-dimensional Ag nanostructures with a …

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 …

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%. …

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 …

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 …