Digital Commons Network^™

Design And Realization Of Concurrent Cryptosystem For Medical Image Privacy On Reconfigurable Hardware, Vinoth Raj R

Theses and Dissertations

The protection of medical image privacy plays a crucial role in maintaining confidentiality for the secure storage and transmission of patient’s sensitive healthcare data. Medical images are the widely used data type in the e-healthcare sector. Traditional cryptographic algorithms have limitations when applied to large-scale medical image datasets due to their high computational requirements. The primary goal of this research work is to design and implement indigenous algorithms to provide confidentiality for grayscale and color DICOM (Digital Imaging and Communications in Medicine) images through an encryption process. The research leverages the benefits of reconfigurable hardware, namely the Field-Programmable Gate Arrays …

Extraction Of Contact Resistivity For Transition Metal Oxide-Based Silicon Heterojunction Solar Cell, Shanmugam K

Theses and Dissertations

Contact resistivity quantifies the charge transport barrier, which is one of the key parameters for choosing the carrier selective contact for silicon solar cells. Optically transparent and electrically selective contacts such as Transition Metal Oxide (TMO)–based contacts are employed in solar cell applications. Therefore, extracting contact resistivity for such Schottky contacts requires apposite validation.

In this work, the contact resistivity of TiOx/LiFx/Al stack over an n-type c-Si wafer is extracted using two conventional techniques: i) Shockley’s Transfer Length Method (TLM) and ii) Cox and Strack Method (CSM). The extracted contact resistivity is validated by comparing it with the solar cell’s …

Efficiency Of Enzyme Induced Carbonate Precipitation (Eicp) By Crude Extract Urease For Geotechnical And Geoenvironmental Applications, Kaniz Roksana

Theses and Dissertations

Geotechnical engineering is currently facing challenges, including accommodating rapid urbanization, adapting to climate change impacts, and prioritizing sustainability, all while ensuring the safety and stability of critical infrastructure. Biocementation is a recently developed new branch in geotechnical engineering that offers innovative and eco-friendly solutions by leveraging natural processes to enhance soil properties. This dissertation examines the potential of soybean extract urease as a cost-effective substitute for analytical-grade enzymes in Enzyme Induced Carbonate Precipitation (EICP) applications for biocementation. This research examines its effectiveness in various aspects, such as coastal erosion control, desiccation crack remediation, and the removal of heavy metals. Essential …

Lgbtq+ Identity Development And Individual Vs Group Psychotherapy Experience: A Consensual Qualitative Analysis, Aileen Marie Rands

Theses and Dissertations

Multicultural topics, such as LGBTQ+ experience, have accrued predominance within the field of psychology. It has become clear that better understanding minority experience is of utmost importance. Pervasive stigma, discrimination, and mistreatment culminate into minority stress; thus LGBTQ+ individuals face greater risk for poor mental health and seek out psychotherapy at higher rates. Despite this reality, limited psychotherapy research exists in this area generally and essentially no research has looking into the experience of LGBTQ+ individuals in group therapy. The current study investigated experiences of LGBTQ+ clients who have participated in both individual and group psychotherapy. Eleven participants were interviewed …

Mind Matters: Investigating The Impact Of Mental Health First Aid Training On High School Staff, Sarah Jo O'Neill

Theses and Dissertations

This dissertation, titled Mind Matters: Investigating the Impact of Mental Health First Aid Training on High School Staff, examines whether Mental Health First Aid (MHFA) training equips high school personnel with the necessary tools to identify and support students in crisis. Conducted over six weeks during a series of Kitchen Table Conversations, the study involved eight high school staff members who participated in a structured MHFA training program. Qualitative data collected from participant discussions revealed that while the current MHFA training format is a good start, it lacks school-specific contextualization. More importantly, the opportunity to discuss and process the material …

Comparison Of A Terrestrial And Handheld 3d Laser Scanner In As-Built Building Information Model (Bim) Creation, Cedric Scott Jankowski

Theses and Dissertations

Advances in computer processing have allowed mobile 3D laser scanners to begin to rival terrestrial scanners in terms of accuracy and scanning speed. While terrestrial scanners are the established instrument used in BIM creation for existing buildings, their cost, immobility, and size warrants a comparison to mobile scanners. This evaluation used scans created with both scanner types, the resulting point clouds, and 3D models created by tracing these point clouds to compare the BIM models and resulting floorplans, which were checked against direct on-site measurements. Because terrestrial scanners are considered state-of-the-art, the evaluation focused primarily on comparing mobile scanner and …

Sustainable Well-Being For Young Adults: An Online Course To Build Ecological And Psychological Flourishing In A Changing Climate, Jeremy Stanley Bekker

Theses and Dissertations

Sustainable well-being is "happiness that contributes to individual, community and/or global well-being without exploiting other people, the environment or future generations" (O'Brien, 2010, p. 2). Sustainable well-being education may encourage environmentally sustainable behavior while also teaching people how to live meaningful lives. However, there is currently a lack of research-based disseminable courses for teaching sustainable well-being. We hypothesized that taking the course would result in lower eco-anxiety and higher well-being, as well as increased pro-environmental self-efficacy and pro-environmental values. We used a randomized crossover repeated measures research design to assess pre and post effects. Quantitative results indicated that participating in …

Reentry Vehicle Trajectory Analysis & Performance (Re-Tap) Development And Application To Spacex Starship, Emma L. Webb

Theses and Dissertations

A modeling simulation tool for exo-to-endo atmospheric flight is developed to characterize errors associated with varying simulation fidelities from 3 to 6 degrees-of-freedom (DOF). Engineering approximation errors are quantified and compared with flight data from Apollo 10 and the first Space Shuttle Orbiter reentry (STS-1). The reentry reachability optimal control problem (OCP) is formulated in 6DOF and compared with point-mass solutions, revealing that point-mass solutions oversimplify the problem. A cylindrical reentry vehicle (CRV-1), similar to the SpaceX Starship, is introduced with necessary details for 6DOF analysis. The performance and reentry control capabilities of CRV-1 are captured through 6DOF solutions of …

Event-Based Camera Simulation And Neural Network Processing For Autonomous Aerial Refueling, Stephanie C. Hanson

Theses and Dissertations

Event-based cameras excel in dynamic environments, and do not face challenges like washout and motion blur, like a frame-based camera. This work describes the process used to collect the first EBS data collect for use in AAR, and develops an event simulator to generate synthetic training data for evaluating CNN architectures on asynchronous data. The three models compared are a traditional CNN, a YOLO-based CNN, and an asynchronous sparse CNN. The YOLO-based model achieved the best accuracy, while the sparse CNN, despite being less optimized, maintained an average IoU of 0.9. These results highlight the potential of asynchronous approaches for …

Palindrome: A Bi-Directional Multi-Object Detection Framework For Relative Navigation And Autonomous Docking, Liam A. Weinfurtner

Theses and Dissertations

This work introduces a bi-directional, multi-object detection framework that integrates pose estimates from both receiver- and tanker-mounted cameras to improve accuracy and redundancy. A modular YOLO-based detection pipeline is trained using synthetic and real imagery, leveraging a bootstrap transfer learning approach to enhance sim-to-real performance. System evaluation in both virtual and real-world environments demonstrates improved detection robustness, pose estimation accuracy, and scalability. These advancements contribute to the development of AI-driven vision systems for AAR and other autonomous docking applications.

Misalignment Uncertainty In Near-Field Thz Scattering Experiments, Philip Patterson

Theses and Dissertations

This research investigates the effect of misalignment on the near-field scattering of cylinders in the 550-700 GHz frequency band. A Type-1 calibration is performed on previously collected data, using a near-field physical optics solution to simulate scattering at various positions and orientations. The alignment of the cylinders at the time of measurement is predicted by comparing the range profiles of the theoretical and calibrated responses. The data with the most similar range profiles had a mean calibration difference metric of -2.78 dB and a standard deviation of -0.57 dB, demonstrating the presence of sources of error that are dominant over …

Improvement Of Microcracking And Mechanical Properties Of Tungsten Fabricated Via Laser Powder Bed Fusion Through Alloying With Reactive Secondary Constituents, William S. Mockel

Theses and Dissertations

Tungsten (W), a Group VI transition metal, possesses a number of advantageous properties, most notably its impressive mechanical performance at extreme temperatures. While tungsten's nature render traditional manufacturing methods difficult, additive manufacturing through laser powder bed fusion (LPBF) presents a promising avenue for fabricating tungsten components. However, the material’s high ductile-to-brittle transition temperature combined with the embrittling effect of impurities mean that the residual stresses imparted by LPBF result in microcracking in tungsten, degrading its usefulness. This study sought to improve the characteristics of LPBF-W through the removal of embrittling oxygen content via alloying with low concentrations of reactive elements, …

Mechanical Response Of Triply Periodic Minimal Surface Gyroid Structures Under Combined Loading, Jay B. Patel

Theses and Dissertations

This work explored combined tensile and torsional loads applied to additively manufactured Inconel 718 specimens employing Triply Periodic Minimal Surface (TPMS) structures. The gyroid TPMS unit cell was selected with two variations of cylindrical cell maps, a rectangular cell map, and a spherical cell map. All four variants were tested in an axial-torsion test frame at room temperature using equal parts of vertical and angular displacement control until failure. The combined loading in these tests utilized tension and torsion. The data from the tests were compared to finite element analysis (FEA) models to visualize when yielding was predicted. Finally, the …

Impact Of Stochastic Travel Times On The Military Port Selection Problem: A Stochastic Programming Approach, William M. Titus

Theses and Dissertations

This research models and analyzes the impact of stochastic travel times on port selection during a large-scale mobilization of equipment from continental United States installations to deployment locations using sealift ships. A stochastic mixed-integer programming model is developed to minimize the average arrival time of equipment into theater. The model is solved using Sample Average Approximation. In the first stage, the model selects ports to open and assigns installations, equipment, and ships to open ports. In the second stage, travel times are realized, and equipment is assigned to specific ships that are scheduled to depart. Results show that the marginal …

Advancing Defense Software Cost Estimation Through Regression, Probabilistic, And Machine Learning Models, Stephen D. Chatterton

Theses and Dissertations

Accurately estimating software costs is critical for effective project management within the Department of Defense (DoD), where early decisions shape resource allocation and risk management. This work evaluates regression-based Cost Estimating Relationships (CERs), probabilistic models, and machine learning techniques to address limitations of traditional estimation methods. Using records from two DoD repositories, the analysis applied Ordinary Least Squares (OLS) regression, Multinomial Logistic Regression (MLR), Random Forest, and neural networks to model and classify software costs, with key predictors including Source Lines of Code (SLOC), Equivalent Source Lines of Code (ESLOC), and programming hours. The findings highlight strengths and trade-offs of …

The Location Set Covering Disruption Problem, Richard A. Sheldon

Theses and Dissertations

This research models and analyzes a variant of the Location Set Covering Problem (LSCP) in a bilevel, game theoretic setting by posing the LSCP as a non-cooperative attacker-defender Stackelberg game, where facilities are to be emplaced by the defender from a boarder set of potential facility locations to cover a set of demands; however, an attacker removes the possibility of emplacing q specific facility locations with the objective to remove the maximum weighted value demands, and then lexicographically maximize the cost of coverage of remaining demands. A novel methodology leveraging lexicographic programming computed an optimal solution for 98% of all …

Tracking News Narratives: Topic Modeling, Sentiment, And Media Coverage Patterns, Alexandria G. Lai

Theses and Dissertations

This study introduces a novel content-driven influence measurement framework, built around a custom influence formula that integrates Non-negative Matrix Factorization (NMF) topic modeling, sentiment analysis, and influence metrics to analyze media narratives over time. Applied to news coverage of the 2020 U.S. presidential election and the COVID-19 pandemic, the framework identifies key topics, sentiment patterns, and influential sources. Results demonstrate its ability to distinguish between transient political controversies and sustained public health discourse while capturing shifts in media influence. While effective, refinements in topic separation, sentiment analysis, and temporal weighting could enhance adaptability. This study highlights the novel influence formula …

Forecasting Army Recruiting Mission Distribution, Maxwell A. Deihl

Theses and Dissertations

The Army’s recruiting landscape has changed markedly in recent years, raising questions about whether forecasting methods of Army contracts remain robust. This thesis recreates the presented models in Joshua McDonald’s 2015 thesis. It replicates and evaluates the models with updated data (2018–2023) to assess their current validity and compare them to novel alternative approaches, such as simpler regression models or neural networks. While the 2015 model remains a valuable baseline, results suggest that either refining its variables or adopting alternative methods can improve predictive accuracy and interpretability. Ultimately, the United States Army Recruiting Command has many options regarding how it …

Machine Learning Techniques To Detect Anomalies In T-38 Flight Sensor Data, Sydney M. Wekamp

Theses and Dissertations

Accurate sensors are critical for ensuring the safety of aircrew. However, detecting faulty sensors remains a significant challenge for the Test Pilot School at Edwards Air Force Base in California. Current methods rely on either student pilots identifying anomalies or waiting for sensors to fail completely before repairs are made—an approach that lacks reliability and consistency. This research aims to address these shortcomings by implementing machine learning techniques to detect sensor faults proactively. To date, applying machine learning to a dataset of this size, encompassing numerous sensors on the same aircraft, is unprecedented. The project focuses on establishing strong baseline …

Geo-Spatial Mapping Of Sentiment Analysis With Transformer-Based Models, Dugan J. Turnbow

Theses and Dissertations

The public sentiment of events of interest, and their impacts, is vital for decision makers to allocate resources. This research develops a robust algorithm for aggregating sentiment analysis from social media and published articles, while contextualizing results through spatial and temporal mapping. The methodology employs two transformer-based language models for sentiment analysis and named entity recognition (NER). Sentiment scores are generated and augmented using explicit location data, such as latitude and longitude, and implicit location data derived through NER or location features. Results are mapped using a geo-tagged location dictionary, enabling visualization of sentiment trends at state and county levels …

Hyperparameter Tuning For Robust Autonomous Vehicle Vision, Nico D. De Ros

Theses and Dissertations

Classification “flickering,” where the classification of an object changes inconsistently between consecutive video frames, remains a persistent issue in modern object classification algorithms. This problem undermines the reliability of autonomous vision systems and poses significant risks in high-stakes applications such as autonomous vehicles. This thesis explores the use of response surface methodology, a statistical design of experiments technique, to optimize hyperparameters across three object classification pipelines. The first pipeline combines YOLOv8 with SORT to establish a benchmark. The second integrates a Bayesian back-end, while the third employs an exponential smoothing back-end. Hyperparameter tuning was conducted using a two-step process: an …

Evaluating Educational Benefits Of A Custom Cyber Game: ‘Hvac Attack!’, Jillian S. Valente

Theses and Dissertations

Cyber competition and conflict remain an enduring concern for the Department of Defense (DoD). Positive control of cyberspace is crucial across the vast diversity of military operations and supporting activities. Military members play an important role in cyber prevention, detection, and remediation, but most receive relatively little training outside of the annual Cyber Awareness Challenge. Particular career fields within the DoD may benefit from specialized training in cybersecurity, in particular the civil engineering (CE) community supporting critical infrastructure protection. Prior research has suggested that game-based learning (GBL) can be beneficial for teaching cyber concepts.

Evaluating Learning Outcomes In A Serious Game: A Practical And Model Checking Approach, Matthew D. Douglas

Theses and Dissertations

This research introduces a novel computational framework to evaluate and predict the educational impact of serious games during development. By using finite state machines (FSM) and model-checking techniques, this study evaluates two serious games. Traditional evaluation approaches, often reliant on resource-intensive human trials, lack scalability and fail to provide early insight into the alignment of game mechanics with learning objectives. This study addresses these challenges of traditional evaluation methods.

Calibration And Demonstration Of A Dropped Channel Polarimetric Compressive Sensing Radar System, Cameron G. Goodbar

Theses and Dissertations

The Air Force Institute of Technology (AFIT) Dropped Channel Polarimetric Compressive Sensing (DCPCS) Radar System is a polarimetric radar utilizing four horn antennae with a unique cross-coupling architecture that enables direct control of system parameters to embed signals into adjacent channels. This thesis characterizes the nature of the system, develops system calibration, and illustrates the performance of the DCPCS technique under multiple system configurations. As shown in the results, DCPCS can successfully reconstruct full-polarization data from a subset of polarization measurements. In many cases, the target estimation and signal reconstruction is precise despite less-than-ideal conditioning of the canonical target dictionary …

Graph Neural Network-Based Uav Coverage Planning For Robust And Efficient 3d Environments, Gal Tsfaty

Theses and Dissertations

This thesis addresses the challenge of generating optimized UAV waypoints for complete coverage of complex 3D environments, utilizing graph-based computational techniques. The proposed framework replaces computationally intensive steps—triangulation and three-coloring—within the Vantage Waypoint Set Generation Algorithm (VWSGA) pipeline with Graph Neural Networks (GNNs). By learning structural patterns, the GNN achieves scalable and robust triangulation and node classification, enabling enhanced coverage planning in irregular geometries. A novel penalty mechanism ensures alignment with graph structure during adjacency prediction. Experimental results demonstrate the effectiveness of GNNs in balancing accuracy, computational efficiency, and adaptability, advancing UAV coverage optimization.

Evaluating A Military Digital Badging System Prototype, Benjamin T. Pederson

Theses and Dissertations

The Department of Defense is committed to developing and maintaining a highly skilled workforce capable of defending the United States and associated interests abroad. Digital badging systems, a form of micro-credentialing, offer a way to record service member competencies. By providing decision-makers with granular data, this technology could augment the military’s development of a highly skilled workforce, especially in technical career fields including cyber operations. Mixed-method data from thirty-six participants suggest that establishing a digital badging program could increase deterrence and operational effectiveness.

Transforming Defense: A Case Study On Digital Cots Implementation, Dara A. Armstrong

Theses and Dissertations

While research on digital transformation efforts and their challenges are widespread, the introduction and use of digital commercial off-the-shelf (COTS) tools in the Department of Defense (DOD) remains under explored. To investigate this problem, this research focuses on a fledgling digital transformation effort within a division of the Air Force Nuclear Weapons Center. Due to inefficiencies caused by fragmented workflows, management introduced Jira and Confluence, two digital tools known for their broad use in various industries, to the workforce.

Autonomous Vehicle Path Planning Under Uncertainty, Madison C. Gillan

Theses and Dissertations

Autonomous vehicles are increasingly being deployed for use in high-stakes and uncertain environments where safe and efficient navigation is critical. In these scenarios, traditional path planning approaches, which rely primarily on deterministic models and fixed assumptions, fall short due to the inherent uncertainty of dynamic threats, sensor inaccuracies, and incomplete information. This research addresses these challenges by developing a novel path-planning methodology that combines the Chance-Constrained Rapidly Exploring Random Tree* (CC-RRT*) algorithm with a probabilistic risk assessment heuristic. This method models uncertainty in sensor detection zones, obstacles in the environment, and the Autonomous Vehicle itself, which allows for uncertainty during …

Spatiotemporal Prediction Of Atmospheric Events Through Recurrent Deep Learning Model, Brian W. F. Popick

Theses and Dissertations

The main contributions of this research is to add to the growing library of literature on the use of deep learning algorithms for the spatiotemporal prediction of dangerous atmospheric and hydrologic phenomena. Specifically, we develop novel attention-based and non-attention-based recurrent neural network frameworks to produce short-range sequential forecasts for lightning and tornado occurrences. Additionally, we introduce methods that account for and include error in the model tuning process to generate more reliable models. Furthermore, we have created a lightweight spatiotemporal tornadic prediction dataset that we plan to make publicly available. The first component of this research develops three novel spatiotemporal …

Atmospheric Characterization For Optical Paths In Lunar Proximity, Patrick D. Carattini

Theses and Dissertations

This paper presents a novel technique for estimating the Fried seeing parameter (r0) for optical paths around the Moon, where traditional methods fail due to the Moon's intensity. Using image processing, it derives the atmospheric optical transfer function (OTF) by using the Moon's edge as a step-input. A simulation chain validates the approach, achieving r0 estimation within 0.0012 m. Real-world tests confirm accuracy through visual and statistical analysis, offering an effective alternative for atmospheric characterization of optical paths close to the moon.