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Improving Rogue Radio Emitter Detection Using Siamese Networks, Mason Wright
Improving Rogue Radio Emitter Detection Using Siamese Networks, Mason Wright
Theses and Dissertations
Radio Frequency Fingerprinting (RFF) is the process of creating discerning signatures of emitted radio signals, most often with the goal of identifying specific devices again in the future. The security benefits of this task are intended to build upon current software-based authentication by making use of multi-factor authentication (MFA), but the related task of being able to reject unwanted emitters is limited. This paper presents a Siamese network trained on two different extracted fingerprints of raw Wi-Fi signals, along with a verifier to perform classification and rogue device detection. It was found that fingerprints using the Distortion Reconstruction (DR) technique …
Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch
Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch
Theses and Dissertations
Encompassing the Earth, the ionosphere presents unique challenges to modern communication, navigation, and surveillance technologies. Intense ionization enhancements known as sporadic-E (Es), can degrade and disrupt signals in unpredictable ways. Much work has been done to understand this phenomenon and more recent efforts have attempted to model its behavior and impacts. However, there have been limited efforts at modeling global Es occurrence rates (OR) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E (fbEs) occurrence rates using a Karhunen-Lo´eve Expansion (KLE) of global fbEs OR climatologies built with Global Positioning System radio occultations (GPS-RO) and …
Gamma Protection Factor For A Surrogate Armored Vehicle Exposed To The Us Army Fast Burst Reactor, Ian J. Poropat
Gamma Protection Factor For A Surrogate Armored Vehicle Exposed To The Us Army Fast Burst Reactor, Ian J. Poropat
Theses and Dissertations
This work measures the Gamma Protection Factor (GPF) of a steel cube, which is meant to serve as a surrogate for armored vehicles. The GPF is measured at the White Sands Missile Range Fast Burst Reactor (FBR) and is modeled using MCNP® v6.2 transport simulation software. When exposed to the FBR, the GPF is found to depend roughly linearly on the distance between the reactor core and steel cube. The GPF is determined to be 1.176 ± 0.00067 at 9.21 m, 1.098 ± 0.0011 at 26.16 m, and 1.033 ± 0.00082 at 42.92 m. MCNP® estimates the GPF for the …
Investigation Of High-Latitude Gnss Radio Occulation Sporadic-E And Auroral-E Measurements, Kyle D. Roberts
Investigation Of High-Latitude Gnss Radio Occulation Sporadic-E And Auroral-E Measurements, Kyle D. Roberts
Theses and Dissertations
Abnormal sporadic-E (Es) occurrences were found in the high latitude regions during a recent climatology study by (Hodos, 2022), that calculated sporadic-E occurrence rates derived from a data set of GPS radio occultation (GPS-RO) and ionosondes. In this study, sporadic-E GNSS-RO techniques are shown to falsely attribute sporadic-E events to auroral-E (Ea) events. A comparative study is conducted on GPS-RO measurement techniques to find false occurrence rates for various RO techniques using a single ionosonde site in Gakona, Alaska. Phase-based RO techniques were found to be more likely to falsely attribute sporadic-E as auroral-E, while amplitude …
Modeling Lightning Flashes In Dissimilar Non-Homogeneous Clouds, Scott R. Wolff
Modeling Lightning Flashes In Dissimilar Non-Homogeneous Clouds, Scott R. Wolff
Theses and Dissertations
A new methodology for near-infrared lightning radiative transfer through clouds is presented. High-resolution weather modeling is used to generate realistic three-dimensional non-homogeneous clouds, which are then used as environments for a Monte Carlo simulation of photon multiple scattering from approximations of lightning flashes. Resultant emissions from the cloud tops are in line with previous studies and satellite observations.
Group Convolutional Decoders For Toric Codes, Jim Wang
Group Convolutional Decoders For Toric Codes, Jim Wang
Theses and Dissertations
Quantum Error Correction (QEC) enables both industrial and defense applications of quantum computing. Toric codes and other quantum Low-Density Parity-Check (LDPC) codes are promising and well-researched methods of QEC. However, their decoding cost increases exponentially with a computer’s qubit count. Neural Network (NN) decoders have been shown to decode a code’s error syndrome both accurately and fast enough for a real-time error correcting scheme. Recent key developments introduced Convolutional Neural Network (CNN) to implement a translationally equivariant decoder for a toric code. These CNN decoders both outperform NN decoders and require less training data. This research applies a Group Convolutional …
Comparing Yolov8 To Yolov5 For Pose Estimation Supporting Automated Aerial Refueling, Dawson Friesenhahn
Comparing Yolov8 To Yolov5 For Pose Estimation Supporting Automated Aerial Refueling, Dawson Friesenhahn
Theses and Dissertations
This work used the newly released YOLO version 8 Object Detection as a feature detector for a monocular pose estimation pipeline and showed up to an 83.6% reduction in translation error when compared to YOLOv5. YOLOv8’s new Pose task’s utility as a feature detector was also explored. It performed consistently worse than the Object Detection task, with typical translation error magnitudes between 3 and 8 times worse than Object Detection. Finally, we investigated the use of constant-sized bounding box labels for Object Detection. Previous approaches have used Bounding Box Corrections (BBC). We found that using constant sized labels increases the …
Quantum Circuit Reduction Using Three Layer Transposition, Christian L. Grauberger
Quantum Circuit Reduction Using Three Layer Transposition, Christian L. Grauberger
Theses and Dissertations
The potential of quantum computing to revolutionize critical military applications has led the US Department of Defense to recognize it as a keen interest. However, the practical implementation of these theoretical applications on physical quantum devices is currently limited by inherent reliability and accuracy issues in quantum hardware. To mitigate errors stemming from these limitations, the incorporation of software-based solutions is imperative. Quantum circuit optimization stands out as a primary method of increasing the accuracy of quantum computations. One of the key components of this approach is circuit reduction, whereby circuits are condensed to realize the same computation using fewer …
Neutron Spectrum Unfolding From Activation Foils Irradiated In Gamble Ii Using Deuterated Polyethylene Anodes, Christopher J. Smith
Neutron Spectrum Unfolding From Activation Foils Irradiated In Gamble Ii Using Deuterated Polyethylene Anodes, Christopher J. Smith
Theses and Dissertations
Neutrons can cause irreparable harm to electronics, and experiments are needed to understand their effects fully. This work explores using pulsed power, which is low-cost compared to other sources, to create a useful neutron-rich environment. Deuterated polyethylene was used as anode and catcher material to generate the neutrons from the Deuterium-Deuterium (DD) fusion reaction. A simple, effective, and repeatable method was employed to directly cast deuterated polyethylene onto polyethylene sheets to fabricate the anode and catcher. The DD reactions were made by an ion beam-driven pulsed power generator, Gamble II, with the catcher located in the cathode. Zinc, copper, aluminum, …
Enhancing Port Efficiency And Lead Time Reduction Through Predictive Analysis: A Case Study Of Container Management At Khalifa Bin Salman Port, Abdulaziz A. Aljalahma
Enhancing Port Efficiency And Lead Time Reduction Through Predictive Analysis: A Case Study Of Container Management At Khalifa Bin Salman Port, Abdulaziz A. Aljalahma
Theses and Dissertations
Khalifa bin Salman Port (KBSP), a key pillar in Bahrain's maritime infrastructure, is the focal point of this study, highlighting the significant role of predictive analytics in optimizing port operations. This thesis analyzes container throughput data from 2017 to 2022, provided by Bahrain's Ministry of Transportation database. This data forms the basis for forecasting the 2023 throughput. The study thoroughly compares these predictions with the actual 2023 data, assessing the predictive model's accuracy. The findings underscore the importance of predictive analytics in strategic decision-making for port management, focusing on enhancing operational efficiency and reducing lead times. This research offers a …
Optimizing Deployment Kit, Introducing Acceptance Threshold, Saleh A. Alshuhri
Optimizing Deployment Kit, Introducing Acceptance Threshold, Saleh A. Alshuhri
Theses and Dissertations
This article addresses the optimization of a specialized deployment kit crucial for air force operations, emphasizing the need for rapid and efficient aircraft deployment. Through a comprehensive analysis of historical data, the study aims to streamline the kit's contents without compromising effectiveness. The research suggests a potential reduction of 7.8%, with an impressive 60.5% decrease if a minimal 5% threshold is deemed acceptable. While the primary focus is on air force deployment, the broader implications extend to military entities, such as armies and navies, highlighting the applicability of the findings in enhancing deployment efficiency across various defense sectors.
Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach, Katelyn M. Barton
Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach, Katelyn M. Barton
Theses and Dissertations
This research provides insights into a mixed integer linear programming model that finds the ideal number and type of Electric Vehicle Support Equipment (EVSE) required to meet U.S. military installations’ electric energy demands. Executive Order No. 14057 (2021) requires federal agencies to transition to electric non-tactical vehicles by 2035. This research determines minimum cost solutions to implement the transition incorporating real-world constraints, including the weekly vehicle mileage demand, EVSE cost, charging time, and EVSE capacity. This study contributes to the broader effort of the U.S. military to combat climate change and enhances the understanding of efficient EVSE deployment strategies in …
Modeling & Engineering Of Usmepcom Business Intelligence Data, Merrick A. Bedford
Modeling & Engineering Of Usmepcom Business Intelligence Data, Merrick A. Bedford
Theses and Dissertations
This thesis investigates the USMEPCOM’s issue of modeling and engineering Business Intelligence data centralized around the MEPS of Excellence (MOE) program. MEPS around the US conduct military personnel in-processing and in doing so have a vested interest in the standardization and application of the data associated with such processes. There are currently 65 MEPS stations and one RPS that handle military personnel onboarding paperwork and make determinations for military eligibility. This topic is important due to the MEPS cloud data processing system modernization efforts requiring data processing adaptation to ensure relevant and meaningful usage of current data.
Examining The Effect Of Contractor Logistics Support On The Reliability Of Military Aircraft, Rodrigo S. Campos De Moura
Examining The Effect Of Contractor Logistics Support On The Reliability Of Military Aircraft, Rodrigo S. Campos De Moura
Theses and Dissertations
This study utilizes survival analysis for examining the effect of Contractor Logistics Support (CLS) on the reliability of military aircraft before and after implementing CLS. The provider of CLS in this study is the original equipment manufacturer that designed and produced the target aircraft of this study, the Embraer A-29 Super Tucano.
Federated Medical Scoring Systems, Jacob F. Bryant
Federated Medical Scoring Systems, Jacob F. Bryant
Theses and Dissertations
Federated Learning (FL) is a recent framework of machine learning implementation that trains models on a distributed network of clients as opposed to housing and analyzing this data centrally. This has data communication and practical data privacy advantages, the latter of which is particularly attractive to the medical community where patient privacy is closely safeguarded. We apply FL to a family of sparse linear integer models called Medical Scoring Systems (MSSs). We create a novel methodology for creating these MSSs in a simulated federated environment that involves an lo constrained Logistic Regression (LR), loss-surface examination, and rounding procedure. We tested …
On Intrinsic Dimensionality Of Data Sets And Neural Networks, Ori Chachmo
On Intrinsic Dimensionality Of Data Sets And Neural Networks, Ori Chachmo
Theses and Dissertations
The concept of Intrinsic Dimensionality (ID) is of special interest in the field of Neural Networks (NNs) since it promotes both (a) a deeper understanding of the underlying mechanisms, and (b) embraces parsimonious modeling (that is, building the right-sized model for the task) with associated benefits to processing speed and storage requirements. This thesis explores the concept of ID via two separate, but related, questions. First, we study the potential of NN ID prediction by exploiting easily obtained quantities measured on the data. We then explore NN ID as an independent concept by comparing the results of different methods for …
Systematic Review Of Supply Chain Control Tower Critical Success Factors And Resilience Effects, Clay H. Chaffin
Systematic Review Of Supply Chain Control Tower Critical Success Factors And Resilience Effects, Clay H. Chaffin
Theses and Dissertations
Supply chain control towers (SCCTs) are emerging as a vital component of modern supply chain management (SCM); however, research on SCCTs is limited and disjointed. This paper aims to uncover the critical success factors (CSFs) necessary for high-performing SCCTs, their relationship to the enablers and phases of supply chain resilience (SCRES), and the underlying theoretical framework of this relationship.
Evaluation Of Vtol-Capable Cargo Uavs For Dispersible Airfield Logistics In The Usindopacom Aor, Maria C. Chedzoy
Evaluation Of Vtol-Capable Cargo Uavs For Dispersible Airfield Logistics In The Usindopacom Aor, Maria C. Chedzoy
Theses and Dissertations
This research models and analyzes the ability of commercial cargo UAVs to rapidly evacuate logistics from an airfield to proximal, outlying destinations, particularly in the USINDOPACOM AOR. This is a tenet of Agile Combat Employment by the USAF, which seeks to mitigate the effect of kinetic threats by near-peer adversaries. The analysis sets forth a binary linear program to minimize the total time to evacuate a fixed amount of logistics from an airfield. Parameters include the cargo UAV with its performance specifications, number of cargo loading points at the airfield, number of destinations for cargo evacuation, and subset of destinations …
2033 Digital Modernization At Usmepcom: A Strategic Analysis Of Future Military Applicant Processing, William A. Clay
2033 Digital Modernization At Usmepcom: A Strategic Analysis Of Future Military Applicant Processing, William A. Clay
Theses and Dissertations
This research examines the projected 2033 applicant processing scenario considering the digital modernization efforts of the United States Military Entrance Processing Command (USMEPCOM). The study evaluates the necessary modifications to current processes, with a particular focus on the influence of two key information technology systems, the MEPCOM Integrated Resource System (MIRS) 1.1 and the Military Health System (MHS) Genesis, on manpower at a Military Entrance Processing Station (MEPS). In doing so, the study establishes baseline processing metrics for assessing these impacts. By utilizing discrete event simulation modeling and leveraging current literature, the study proposes strategies for incorporating technological advancements into …
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Theses and Dissertations
Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …
Advanced Intermediate Manufacturing (Aim) Supply Chain Concepts Leading To Reduced Lead Times And Improved Responsiveness, Eric S. Draudt
Advanced Intermediate Manufacturing (Aim) Supply Chain Concepts Leading To Reduced Lead Times And Improved Responsiveness, Eric S. Draudt
Theses and Dissertations
This thesis investigates the optimization of the supply chain for key aircraft components, focusing on the implementation of Advanced Intermediate Manufacturing (AIM) production facilities. Utilizing anyLogistix, the study compares the current supply chain model based on Supply Chain Operations Wing (SCOW) data with various AIM production facility configurations: single, dual, quadruple, and three utilization-driven models (high, medium, and low). The findings demonstrate that integrating AIM production facilities significantly reduces lead times, with even a single facility dramatically cutting down the lead time from over 800 days to approximately 104 days. The utilization models further provide insights into operational flexibility under …
An Integer Programming Model To Optimize Us Army Deployment Cycle And Maximize Unit Availability, Grant R. Engel
An Integer Programming Model To Optimize Us Army Deployment Cycle And Maximize Unit Availability, Grant R. Engel
Theses and Dissertations
The goal of this paper is to determine an optimal cycle length, in months, that minimizes costs and maximizes availability for deploying units in the United States (US) Army. The US Army must be cost efficient while maintaining the flexibility required to adapt to dynamic mission demand. The current practice is to deploy units for a length between the range of 6 to 12 months; however, this varies from unit to unit and the best policy is not clear. We address these issues by forming a mathematical programming model with unique characteristics that distinguish it from others of similar design. …
Techniques For Addressing Extreme Class Imbalance For Artificial Neural Networks Training, Colin W. Foley
Techniques For Addressing Extreme Class Imbalance For Artificial Neural Networks Training, Colin W. Foley
Theses and Dissertations
This research examined the class imbalance problem while training convolutional neural networks (CNN) by applying different techniques to combat this common issue. This research used a modified CIFAR-10 dataset along with a curated aerial image dataset. Methods covered included undersampling, oversampling, synthetic minority oversampling technique, Edited Nearest Neighbors and combinations of the aforementioned methods. This research found that undersampling methods tended to outperform oversampling methods. While undersampling methods showed a decrease in overall accuracy, the increase in minority class prediction performance was promising enough to warrant further investigation.
A Multi-Objective Approach To Optimal Deployment Policies For Wireless Sensor Networks Using Drop Points, Noah E. Fox
A Multi-Objective Approach To Optimal Deployment Policies For Wireless Sensor Networks Using Drop Points, Noah E. Fox
Theses and Dissertations
This research addresses the development of deployment policies for aerially dropped sensors in a wireless sensor network (WSN). Multi-objective genetic algorithm (GA) and simulated annealing meta-heuristic techniques, along with Monte Carlo simulation are used to identify policies with the aim of maximizing coverage and minimizing the number of sensors deployed. The policies developed from these techniques are then compared against uniform sensor distribution, as well as initial deployment policies that focus sensors in the center and edge of the region, as well as evenly deployed over the region. A total of 29 non-dominated policies were identified from the GA and …
An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models, Layton C. Hedge
An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models, Layton C. Hedge
Theses and Dissertations
This research examines China and derives insights specific to it and the First Island Chain and the Second Island Chain. In doing so, this research demonstrates a methodology to examine other competitors and their geostrategic interests. In the first phase of analysis, it develops a value hierarchy to depict objectives within subregions of the area of interest and considers four alternative weightings of the value hierarchy. In the second phase of analysis, it applies four location-covering models to assess how the competitor would emplace a range of limited resources to deter and/or control points of interest. Results indicate that land-based …
Analysis And Visualization Of Military Convoy And Supply Utilization In Support Of State-Specific Freight Plans, Madison E. Hofmann
Analysis And Visualization Of Military Convoy And Supply Utilization In Support Of State-Specific Freight Plans, Madison E. Hofmann
Theses and Dissertations
Researching the United States military’s use of highways and interstates is necessary for the allocation of federal infrastructure spending. As of today, there is no established method, tool, or process routinely utilized by the Surface Deployment and Distribution Command to effectively show a system-wide view of military convoy and supply route usage across CONUS. This research advances this endeavor by providing a by-state characterization of interstates and major highways in 2022, categorized by the volume of military freight they support. Some notable methodologies used to conduct the analyses are the shortest path problem, map matching algorithms, and Global Positioning System …
Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski
Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski
Theses and Dissertations
The role of autonomy has evolved recently, demanding tighter integration between human and autonomous systems, particularly in highly contested A2AD environments. Near-peer adversaries have modernized their integrated air defense systems (IADS), diminishing the current advantages of the United States Air Force. To regain air dominance, efforts like the Collaborative Combat Aircraft (CCA) program are underway, aiming to deploy unmanned autonomous alongside manned next-generation fighter aircraft. This research assesses various operational concepts, focusing on autonomous tactics post-manned fighter loss, strike timing of independent teams, and weapon configuration observability. Using the Advanced Framework for Simulation, Integration and Modeling (AFSIM), an agent-based model …
Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig
Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig
Theses and Dissertations
As the recruiting crisis continues to impact the United States Armed Forces, the United States Military Entrance Processing Command (USMEPCOM) continues to search for ways to increase its capability to efficiently determine which applicants are suited for military service. Unfortunately, USMEPCOM does not have a way to evaluate newly suggested alternatives. By leveraging Value-Focused Thinking (VFT), this research describes 28 fundamental objectives that can be applied to a variety of current and future decision problems. Further, this research applies these fundamental objectives to analyze a current decision problem: reengineering the prescreen process to decrease the time from prescreen submission to …
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
Theses and Dissertations
The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.
The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang
The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang
Theses and Dissertations
Deep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict …