Digital Commons Network^™

Navigating Together: The Conav Testbed And Framework For Benchmarking Cooperative Localization, Rohith Boyinine, Jayanth Ammapalli, Anusna Chakraborty, Rajnikant Sharma, Kevin Brink, Clark N. Taylor

Faculty Publications

This paper presents CoNaV, a comprehensive framework for creating a multi-vehicle cooperative localization (CL) testbed designed to support the benchmarking, development, and deployment of cooperative navigation algorithms. Given the essential role of CL in improving localization accuracy for both defense and civilian applications, CoNaV provides a robust environment for rigorously validating algorithms under real-world conditions. By establishing a benchmark for CL algorithms, CoNaV lays a foundation for advancing research into more sophisticated and distributed CL solutions. This framework highlights the potential of cooperative navigation to enhance multi-vehicle operations and offers a scalable, practical approach for future developments in CL technology.

Finite Element Simulation Of Interstitial–Lymphatic Fluid Flow And Nanodrug Transport In A Solid Tumor: An Intratumoral Injection Approach, Gobinda Debnath, Buddakkagari Vasu, Rama Subba Reddy Gorla

Faculty Publications

Objective: This study presents a mathematical model and finite element simulations to investigate interstitial fluid flow and nanodrug transport in a solid tumor, incorporating transvascular exchange, convection–diffusion–reaction dynamics, and intratumoral injection mechanisms. Impact Statement: Optimizing nanodrug distribution remains a critical challenge in cancer therapy. The proposed model advances nanomedicine by enhancing the mechanistic understanding of nanodrug transport in a solid tumor. Introduction: Cancer, a global threat, often manifests as solid tumors driven by uncontrolled cell growth. The heterogeneous microenvironment, lymphatic drainage, nano-bio interactions, and elevated interstitial fluid pressure (IFP) hinder effective nanodrug delivery. Nanoparticle (NP)-based drug delivery systems offer a …

Editorial: Artificial Intelligence For Smart Health: Learning, Simulation, And Optimization, Bing Yao, Nathan Gaw, Hyo Kyung Lee

Faculty Publications

With rapid developments in medical sensing and imaging, we now live in an era of data explosion in which large amounts of data are readily available in clinical environments. The fast-growing biomedical and healthcare data provide unprecedented opportunities for data-driven scientific knowledge discovery and clinical decision support. Our Research Topic aims to catalyze synergies among biomedical informatics, machine learning, computer simulation, operations research, systems engineering, and other related fields with three specific goals: (1) develop cutting-edge data-driven models to accelerate scientific knowledge discovery in biomedicine using healthcare data collected from laboratory systems, imaging systems, and medical and sensing devices; (2) …

Ultraviolet And Blue Stimulated Emission From Cs Alkali Vapor Pumped Using Two-Photon Absorption And Four-Wave Mixing, Ricardo C. Davila, Christopher A. Rice, Nathan B. Terry, Glen P. Perram

Faculty Publications

Stimulated emission on the ultraviolet and blue transitions in Cs has been achieved by pumping via two-photon absorption and four-wave mixing for the pump transition 6²S½ → 8²D_3/2,5/2. The emission performance of the optically pumped cesium vapor laser operating in ultraviolet and blue has been extended to 650 nJ/pulse for 387 nm, 1 to 3 μ J / pulse for 388 nm, 200 nJ/pulse for 455 nm, and 500 nJ/pulse for 459 nm. Emission performance improves dramatically as the cesium vapor density is increased, and no scaling limitations associated with energy pooling or …

Machine Visual Perception From Sim-To-Real Transfer Learning For Autonomous Docking Maneuvers, Derek Worth, Jeffrey Choate, Ryan M. Raettig, Scott L. Nykl, Clark N. Taylor

Faculty Publications

This paper presents a comprehensive approach to enhancing autonomous docking maneuvers through machine visual perception and sim-to-real transfer learning. By leveraging relative vectoring techniques, we aim to replicate the human ability to execute precise docking operations. Our study focuses on autonomous aerial refueling as a use case, demonstrating significant advancements in relative navigation and object detection. We introduce a novel method for aligning digital twins using fiducial targets and motion capture data, which facilitates accurate pose estimation from real-world imagery. Additionally, we develop cost-efficient annotation automation techniques for generating high-quality You Only Look Once training data. Experimental results indicate that …

Temporal Metadata Analysis: A Learning Classifier System Approach, Michael C. Todd, Gilbert L. Peterson

Faculty Publications

Digital forensics is a complex field that requires expert knowledge (EK) and specialized tools to collect, analyze, and report on digital evidence. Temporal metadata analysis is particularly challenging, requiring expert knowledge to understand and interpret underlying traces and associate them with their source. This paper introduces Digital Trace Inspector (DTI), a Learning Classifier System (LCS)-based decision support tool for temporal metadata analysis. DTI leverages a binary Michigan-style LCS to locate and group corroborating temporal digital traces of targeted user activity. Rules are built from expert-created atomics encoded as feature vectors using patterns defined in a structured EK rule framework. The …

Training-Informed Air Traffic Control Workforce Scheduling, Eli Winett

Theses and Dissertations

The Radar Control Facility (RCF) at Eglin Air Force Base creates monthly workforce schedules. Adherence to regulations must be verified manually and no evaluation criteria exist to compare schedules. A mixed-integer program model for air traffic controllers allocates workers across 19 positions for a user-defined number of weeks of 24-hour operations. Across eight consecutive months, this model produced schedules to nearly eliminate all shifts with critical shortages from the reference schedules. Without civilian overtime work, gains in positional coverage require a 35% increase in overtime work for military workers and 35% reduction in monthly training instances. We recommend users adopt …

Mitigating Code Reuse Attacks On Risc-V Binaries: Minimizing Gadget Availability Using The Compressed Extension, Heitor Vieira

Theses and Dissertations

Embedded systems are vital in civilian and military applications, requiring high performance and security. The open RISC-V Instruction Set Architecture (ISA) offers significant advantages, including security through community review and strategic independence in microchip supplies. Brazil’s recent partnership with RISC-V highlights its potential for national technological sovereignty. However, RISC-V is not inherently resistant to code reuse attacks (CRAs), highlighting the need to integrate security measures early in development. The RISC-V Compressed extension, while beneficial for optimizing performance and code flexibility, introduces security trade-offs. As RISC-V adoption grows, particularly in critical systems, addressing these security challenges from the start is crucial …

Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill

Theses and Dissertations

This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …

Applying Machine‐Learning Methods To Laser Acceleration Of Protons: Lessons Learned From Synthetic Data, Ronak Desai, Thomas Zhang, J. J. Felice, Ricky Oropeza, Joseph R. Smith, Alona Kryshchenko, Chris Orban, Michael L. Dexter, Anil K. Patnaik

Faculty Publications

In this study, we consider three different machine-learning methods—a three-hidden-layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine-learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus …

Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl

Faculty Publications

Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

Time Domain Spectral Lidar Enabled By Cascaded Raman In A Hydrogen-Filled Transmitter, Richard K. Martin, Trevor L. Courtney, Arielle M. Adams, Daniel E. Leaird, Luke Ausley, Christian K. Keyser

Faculty Publications

We introduce what we believe to be novel spectral light detection and ranging (LiDAR) architectures that enable ultra-compact systems by a transition from spectral signal processing in space (gratings) to processing in time. The architectures leverage temporal dispersion and the unique spectro-temporal waveforms produced from the cascaded Raman scattering generated in the (H₂) filled hollow core fiber. The characterized Raman source yields as many as six Raman orders from 1.06-1.70 μm; their unique spectro-temporal waveforms are measured. System performance simulations based on measured Raman waveforms show that high accuracy measurement of range and reflectivity are possible with proper …

Limitations Of Beam-Control Compensation, Matthew Kalensky, Darren Getts, Matthias T. Banet, Derek J. Burrell, Milo W. Hyde, Mark F. Spencer

Faculty Publications

In this paper, we use wave-optics simulations to explore the limitations of beam-control compensation. We evaluate performance in terms of the normalized power in a diffraction-limited bucket for the cases of no beam-control compensation, perfect phase compensation, and perfect full-field compensation. From these results, we are able to arrive at the following conclusions: (1) without any form of beam-control compensation, performance begins to degrade when D/r₀ > 1; (2) with perfect phase compensation, performance begins to degrade when D/r₀ > 1 and (λ/r₀)/θ₀ > 1; and (3) with perfect full-field compensation, performance begins to degrade when D/r_{0 …}

Inferring Tlb Configuration With Performance Tools, Cristian Agredo, Tor J. Langehaug, Scott R. Graham

Faculty Publications

Modern computing systems are primarily designed for maximum performance, which inadvertently introduces vulnerabilities at the micro-architecture level. While cache side-channel analysis has received significant attention, other Central Processing Units (CPUs) components like the Translation Lookaside Buffer (TLB) can also be exploited to leak sensitive information. This paper focuses on the TLB, a micro-architecture component that is vulnerable to side-channel attacks. Despite the coarse granularity at the page level, advancements in tools and techniques have made TLB information leakage feasible. The primary goal of this study is not to demonstrate the potential for information leakage from the TLB but to establish …

Constraining Accreted Neutron Star Crust Shallow Heating With The Inferred Depth Of Carbon Ignition In X-Ray Superbursts, Zachary P. Meisel

Faculty Publications

Evidence has accumulated for an as-yet unaccounted for source of heat located at shallow depths within the accreted neutron star crust. However, the nature of this heat source is unknown. I demonstrate that the inferred depth of carbon ignition in X-ray superbursts can be used as an additional constraint for the magnitude and depth of shallow heating. The inferred shallow heating properties are relatively insensitive to the assumed crust composition and carbon fusion reaction rate. For low-accretion rates, the results are weakly dependent on the duration of the accretion outburst, so long as accretion has ensued for enough time to …

Rubrics Informed By The Cognitive Theory Of Multimedia Learning That Support Research On Personalized Learning Paths, Sean A. Mochocki, Mark G. Reith, Jonathan Zemmer

AFIT Documents

Personalized Learning Paths (PLP)s are a popular area of research in E-Learning where sequences of Learning Materials (LM)s and activities are returned based on a learner profile, the LM metadata, and a knowledge structure that describes the relationship between the underlying topics. Unfortunately, PLP researchers tend to not use an empirically supported cognitive science framework for their research, instead relying on such unsupported theories as learning styles or developing their own ad hoc approaches. While many of these researchers present and solve challenging PLP problems using a variety of algorithmic approaches, the PLP community in general would benefit from a …

Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch, Anthony L. Franz, Eugene V. Dao, Dong L. Wu, Nimalan Swarnalingam, Cornelius C. J. H. Salinas, Daniel J. Emmons

Faculty Publications

Intense ionization enhancements in the Earth’s ionosphere, known as sporadic-E (E_s), can degrade and severely disrupt the propagation of radio signals. Although many previous studies have analyzed the characteristics and morphologies of sporadic-E, few efforts have attempted to model global E_s occurrence rates (ORs) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E occurrence rates using a Karhunen–Loéve Expansion (KLE) of a global OR climatology built with Global Navigation Satellite System radio occultation (GNSS-RO) and ionosonde observations. Using an fbE ≥ threshold of 3 MHz, the model outputs a blanketing sporadic-E …

Personalized Learning Path Problem Variations: Computational Complexity And Ai Approaches, Sean A. Mochocki, Mark Reith, Brett J. Borghetti, Gilbert L. Peterson, John Jasper, Laurence D. Merkle

Faculty Publications

E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use personalized learning paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs (KGs), student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the nondeterministic polynomial (NP)-hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of …

Numerically Efficient Coherent Mode Representations For Partially Coherent Beams With Separable Phases, Milo W. Hyde, Carolina Rickenstorff

Faculty Publications

We present a method to numerically compute the coherent mode representations (CMRs) for partially coherent beams with separable phases. This special class of random light field has the ability to self-focus and is resistant to turbulence-induced degradation, making it potentially useful in applications such as optical communications. We validate our method by generating (in simulation) two such sources from the literature using their computed CMRs. Lastly, we conclude with a summary of our approach and a discussion of potential applications.

Predictability Limit Of The 2021 Pacific Northwest Heatwave From Deep‐Learning Sensitivity Analysis, P. Trent Vonich, Gregory J. Hakim

Faculty Publications

The traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu‐Weather model forecasts initialized with the GraphCast‐derived optimal, suggesting that model error is an unimportant part of the …

Closed-Loop Adaptive Optics In The Presence Of Speckle And Weak Scintillation, Derek J. Burrell, Mark F. Spencer, Ronald G. Driggers

Faculty Publications

In this paper, we show that speckle averaging helps to improve adaptive-optics (AO) performance in closed loop as a result of reduced measurement error associated with a Shack–Hartmann wavefront sensor (SHWFS); however, this reduction is rendered ineffective with increasing beacon anisoplanatism. We do so operating in a weak-scintillation regime, where the SHWFS offers robust performance, and using in-plane translation of the illuminated rough surface to accomplish frame-to-frame speckle diversity. Understanding these trade-space limitations is critical when performing AO with non-cooperative, extended-source beacons.

Using Machine Learning To Predict State Compliance With International Legal Obligations For Registration Of Space Objects: Comparative Performance Of Logistic Regression And Dense Neural Network Models, Jonathan K. Sawmiller

Student Publications

Approximately 12% of satellites and other objects launched into outer space have not been registered with the United Nations (UN) as required by international law. To predict whether States will register a launched space object and understand what factors influence a registration decision, data from a UN online index of space objects was used to train and select the best machine learning model. After preparation, the dataset had 1938 datapoints with 11 features, with categorical features simplified and converted to binary.

Multiple variations of classical logistic regression models were compared to multiple variations of dense neural network models. The best …

Innovation Challenges In The Air Force Sbir Program: From The Small Businesses' Perspective, Hart J. Holt, Amy M. Cox, Scott Drylie, David S. Long, Alfred E. Thal Jr., Robert D. Fass

Faculty Publications

Every year the United States invests $3.2 billion in the Small Business Innovation Research (SBIR) program to promote innovation among the nation’s small businesses. Half of this investment is from the DoD. This research considers the challenges faced by small businesses innovating with the DoD, particularly those awarded SBIR contracts with the United States Air Force. The authors surveyed 286 unique small businesses that were previously awarded an Air Force SBIR contract. By asking the survey respondents open-ended questions and categorizing their responses, they pinpoint unaddressed challenges from the small business perspective. By categorizing survey responses through Qualitative Content Analysis, …

Production Box Cost Estimating Relationships For Dod Avionics, Carla J. Cisneros, Edward D. White, Brandon M. Lucas, Jonathan D. Ritschel, Robert D. Fass, Shawn M. Valentine

Faculty Publications

The authors use historical information obtained from the Cost Assessment Data Enterprise to estimate recurring production unit cost for DoD avionics via cost estimating relationships (CERs). The specific modeled responses include mean unit cost, median unit cost, and the 100th production unit cost (T100) utilizing learning curve theory. For T100, the authors adopt both a multiplicative and an additive error for CER comparison. Recommended CERs consist of the mean unit cost and the T100 utilizing a multiplicative error. Moreover, results reveal that weight has a significant effect on cost as well as a potential underaccounting of real price change or …

Real-Time Synthesis Of A Nonuniformly Correlated, Partially Coherent Beam Using An Optical Coordinate Tansformation, Milo W. Hyde Iv

Faculty Publications

We design, build, and validate an optical system for generating light beams with complex spatial coherence properties in real time. Beams of this type self-focus and are resistant to turbulence degradation, making them potentially useful in applications such as optical communications. We begin with a general theoretical analysis of our proposed design. Our approach starts by generating a Schell-model (uniformly correlated or shift-invariant) source by spatially filtering incoherent light. We then pass this light through an optical coordinate transformer, which converts the Schell-model source into a nonuniformly correlated field. After the general analysis, we discuss system engineering, including trade-offs among …

The Evaluation Of Machine Learning Techniques For Isotope Identification Contextualized By Training And Testing Spectral Similarity, Aaron P. Fjelsted, Tyler J. Morrow, Clayton D. Scott, Yilun Zhu, Darren E. Holland, Azaree T. Lintereur, Douglas E. Wolfe

Faculty Publications

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset …

Malware Classification Through Abstract Syntax Trees And L-Moments, Anthony J. Rose, Christine M. Schubert Kabban, Scott R. Graham, Wayne C. Henry, Christopher M. Rondeau

Faculty Publications

The ongoing evolution of malware presents a formidable challenge to cybersecurity: identifying unknown threats. Traditional detection methods, such as signatures and various forms of static analysis, inherently lag behind these evolving threats. This research introduces a novel approach to malware detection by leveraging the robust statistical capabilities of L-moments and the structural insights provided by Abstract Syntax Trees (ASTs) and applying them to PowerShell. L-moments, recognized for their resilience to outliers and adaptability to diverse distributional shapes, are extracted from network analysis measures like degree centrality, betweenness centrality, and closeness centrality of ASTs. These measures provide a detailed structural representation …

Coarse-Gridded Simulation Of The Nonlinear Schrödinger Equation With Machine Learning, Benjamin F. Akers, Kristina O. F. Williams

Faculty Publications

A numerical method for evolving the nonlinear Schrödinger equation on a coarse spatial grid is developed. This trains a neural network to generate the optimal stencil weights to discretize the second derivative of solutions to the nonlinear Schrödinger equation. The neural network is embedded in a symmetric matrix to control the scheme’s eigenvalues, ensuring stability. The machine-learned method can outperform both its parent finite difference method and a Fourier spectral method. The trained scheme has the same asymptotic operation cost as its parent finite difference method after training. Unlike traditional methods, the performance depends on how close the initial data …

A Panel Data Regression Model For Defense Merger And Acquisition Activity, Corey D. Mack, Clay Koschnick, Michael Brown, Jonathan D. Ritschel, Brandon M. Lucas

Faculty Publications

Excerpt: This paper examines the relationship between a prime contractor's financial health and its mergers and acquisitions (M&A) spending in the defense industry. It aims to provide models that give the United States Department of Defense (DoD) indications of future M&A activity, informing decision-makers and contributing to ensuring competitive markets that benefit the consumer.

The results show a significant relationship between efficiency and M&A spending, indicating that companies with lower efficiency tend to spend more on M&As. However, there was no significant relationship between M&A spending and a company's profitability or solvency. These results were consistent with previous research and …

Integrating Blockchain Technology Into The Software Development Life Cycle To Satisfy The Software Bill Of Materials Requirement For Government Software Systems, Walter T. Scott Ii

Theses and Dissertations

This thesis explores the integration of Blockchain Technology (BT) into the Software Development Life Cycle (SDLC) to satisfy the Software Bill of Materials (SBOM) requirement for government software systems. This study begins by synthesizing a standard SDLC definition from various government and industry references, which may provide the foundation for future efforts to standardize software development practices across the government software development community. This study proceeds to define working definitions for the software supply chain (SSC) and software supply chain management (SCM) before introducing and detailing the SBOM requirement as well as providing an overview of prior research regarding SBOMs …