Computer Sciences Commons^™

Laser Illuminated Imaging: Beam And Scene Deconvolution Algorithm, Benjamin W. Davis

Theses and Dissertations

Laser illuminated imaging systems deal with several physical challenges that must be overcome to achieve high-resolution images of the target. Noise sources like background noise, photon counting noise, and laser speckle noise will all greatly affect the imaging systems ability to produce a high-resolution image. An even bigger challenge to laser illuminated imaging systems is atmospheric turbulence and the effect that it will have on the imaging system. The illuminating beam will experience tilt, causing the beam to wander off the center of the target during propagation. The light returning to the detector will similarly be affected by turbulence, and …

Automated Network Exploitation Utilizing Bayesian Decision Networks, Graeme M. Roberts

Theses and Dissertations

Computer Network Exploitation (CNE) is the process of using tactics and techniques to penetrate computer systems and networks in order to achieve desired effects. It is currently a manual process requiring significant experience and time that are in limited supply. This thesis presents the Automated Network Discovery and Exploitation System (ANDES) which demonstrates that it is feasible to automate the CNE process. The uniqueness of ANDES is the use of Bayesian decision networks to represent the CNE domain and subject matter expert knowledge. ANDES conducts multiple execution cycles, which build upon previous action results. Cycles begin by modeling the current …

Comparison Of Conic Ray Tracing For Occlusion Determination On 3d Point Cloud Data, Henry Cho

Theses and Dissertations

The US Air Force has been increasing the use of automation in its weapon systems to include the remotely piloted aircraft (RPA) platforms. The RPA career field has had issues with poor pilot retention due to job stressors. For example, RPA operators spend a lot of time and attention surveilling a suspect on the ground for many hours, so adding automation to this activity could help improve pilot retention. The research problem in this thesis attempted to automate the process of observing a ground target. This thesis presents a method termed conic ray tracing for determining visibility and occlusion of …

Designing And Building A Radar Simulation Using The Entity Component System, Brennen T. Garland

Theses and Dissertations

This research explores the implementation of a "medium fidelity" radar simulation using the Entity-Component-System (ECS) architecture. The radar implemented mimics the fundamental characteristics of entities in the open-source Mixed Reality Simulation Platform (MIXR) project, supporting real-time interaction. Previous research has shown the potential benefits of using an ECS-based architecture to support improved execution performance relative to Object-Oriented Programming (OOP) approaches, thus improved real-time interaction requirements. This research implements a well-documented radar model that supports the development of soft real-time human-based interaction simulations. The radar system modeled in this research mimics the "out-of-the-box" fidelity defined in the OOP-based MIXR architecture. This …

Optimizing A Bank Of Kalman Filters For Navigation Integrity, Luis E. Sepulveda

Theses and Dissertations

Alternative navigation is an area of research which employs a variety of sensor technologies to provide a navigation solution in Global Navigation Satellite System degraded or denied environments. The Autonomy and Navigation Technology Center at the Air Force Institute of Technology has recently developed the Autonomous and Resilient Management of All-source Sensors (ARMAS) navigation framework which utilizes an array of Kalman Filters to provide a navigation solution resilient to sensor failures. The Kalman Filter array size increases exponentially as system sensors and detectable faults are scaled up, which in turn increases the computational power required to run ARMAS in areal-world …

Exploring Fog Of War Concepts In Wargame Scenarios, Dillon N. Tryhorn

Theses and Dissertations

This thesis explores fog of war concepts through three submitted journal articles. The Department of Defense and U.S. Air Force are attempting to analyze war scenarios to aid the decision-making process; fog modeling improves realism in these wargame scenarios. The first article "Navigating an Enemy Contested Area with a Parallel Search Algorithm" [1] investigates a parallel algorithm's speedup, compared to the sequential implementation, with varying map configurations in a tile-based wargame. The parallel speedup tends to exceed 50 but in certain situations. The sequential algorithm outperforms it depending on the configuration of enemy location and amount on the map. The …

Improving Text Classification With Semantic Information, Joshua H. White

Theses and Dissertations

The Air Force contracts a variety of positions, from Information Technology to maintenance services. There is currently no automated way to verify that quotes for services are reasonably priced. Small training data sets and word sense ambiguity are challenges that such a tool would encounter, and additional semantic information could help. This thesis hypothesizes that leveraging a semantic network could improve text-based classification. This thesis uses information from ConceptNet to augment a Naive Bayes Classifier. The leveraged semantic information would add relevant words from the category domain to the model that did not appear in the training data. The experiment …

A Mobile Application For Optimally Matching Real Estate Clients, Yu Karen Asai, Steven Andrew Luu

Computer Science and Software Engineering

Real estate agents are often tasked with finding their clients’ ideal properties. This can be difficult because multiple clients may have varying preferences, such as number of bedrooms, square footage, or price. Furthermore, different clients may weight their individual preferences differently. Existing applications do not consider multiple clients’ satisfaction, nor do they allow clients to weigh their preferences, potentially leading to less-than-ideal matchings between clients and properties.

In this project, we design and implement an iOS application whereby real estate agents can match multiple clients with individually weighted preferences to properties scraped from web listings. We model this client-property matching …

Node Classification On Relational Graphs Using Deep-Rgcns, Nagasai Chandra

Master's Theses

Knowledge Graphs are fascinating concepts in machine learning as they can hold usefully structured information in the form of entities and their relations. Despite the valuable applications of such graphs, most knowledge bases remain incomplete. This missing information harms downstream applications such as information retrieval and opens a window for research in statistical relational learning tasks such as node classification and link prediction. This work proposes a deep learning framework based on existing relational convolutional (R-GCN) layers to learn on highly multi-relational data characteristic of realistic knowledge graphs for node property classification tasks. We propose a deep and improved variant, …

Design And Implementation Of Kawaii Robots By Japanese And American University Students Using Remote Collaboration, Dave Berque, Hiroko Chiba, Michiko Ohkura, Midori Sugaya, Peeraya Sripian, Tipporn Laohakangvalvit

Computer Science Faculty publications

This paper describes our approach to the design and implementation of virtual Kawaii robots and spaces by Japanese and American university students using remote collaboration. Because of the COVID-19 pandemic, we had to change our planned 7-week collaboration from in-person to virtual with a resultant change in the target product of our collaboration from real robots to virtual robots. Based on our new plan, students designed virtual spaces with robot pairs, proposed evaluation items for the robot pairs, evaluated their designs, and analyzed the results. The students designed each robot pair with the goal that one robot would be more …

Wg2An: Synthetic Wound Image Generation Using Generative Adversarial Network, Salih Sarp, Murat Kuzlu, Emmanuel Wilson, Ozgur Guler

Engineering Technology Faculty Publications

In part due to its ability to mimic any data distribution, Generative Adversarial Network (GAN) algorithms have been successfully applied to many applications, such as data augmentation, text-to-image translation, image-to-image translation, and image inpainting. Learning from data without crafting loss functions for each application provides broader applicability of the GAN algorithm. Medical image synthesis is also another field that the GAN algorithm has great potential to assist clinician training. This paper proposes a synthetic wound image generation model based on GAN architecture to increase the quality of clinical training. The proposed model is trained on chronic wound datasets with various …

3d Printing Of Human Microbiome Constituents To Understand Spatial Relationships And Shape Parameters In Bacteriology, Jacques Izard, Teklu Kuru Gerbaba, Shara R. P. Yumul

Department of Food Science and Technology: Faculty Publications

Effective laboratory and classroom demonstration of microbiome size and shape, diversity, and ecological relationships is hampered by a lack of high-resolution, easy-to-use, readily accessible physical or digital models for use in teaching. Three-dimensional (3D) representations are, overall, more effective in communicating visuospatial information, allowing for a better understanding of concepts not directly observable with the unaided eye. Published morphology descriptions and microscopy images were used as the basis for designing 3D digital models, scaled at 20,000×, using computer-aided design software (CAD) and generating printed models of bacteria on mass-market 3D printers. Sixteen models are presented, including rod-shaped, spiral, flask-like, vibroid, …

Efficient Algorithms For Trajectory-Aware Mobile Crowdsourcing, Chung-Kyun Han

Dissertations and Theses Collection (Open Access)

Mobile crowdsourcing, a subclass of crowdsourcing dealing with location-specific tasks, is prevalent in our daily life. From sensing urban environment such as noise, air pollution to package delivery, various location-specific tasks are posted on mobile crowdsourcing platforms to tap on the pool of crowdsourced workers. Many digital platforms compete with each other to expand and retain their pool of crowdsourced workers. Comparing with the traditional workforce, crowdsourced workers do not dedicate their time to do tasks fully and have strong spatiotemporal preferences. The ignorance of crowdsourced workers’ mobility patterns and the lack of personalization would lead to crowdsourced workers’ exodus, …

Accurate Covariance Estimation For Pose Data From Iterative Closest Point Algorithm, Rick H. Yuan

Theses and Dissertations

One of the fundamental problems of robotics and navigation is the estimation of relative pose of an external object with respect to the observer. A common method for computing the relative pose is the Iterative Closest Point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in down-stream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this thesis a novel method for estimating uncertainty from sensed data is introduced. …

Unsupervised Clustering Of Rf-Fingerprinting Features Derived From Deep Learning Based Recognition Models, Christian T. Potts

Theses and Dissertations

RF-Fingerprinting is focus of machine learning research which aims to characterize wireless communication devices based on their physical hardware characteristics. It is a promising avenue for improving wireless communication security in the PHY layer. The bulk of research presented to date in this field is focused on the development of features and classifiers using both traditional supervised machine learning models as well as deep learning. This research aims to expand on existing RF-Fingerprinting work by approaching the problem through the lens of an unsupervised clustering problem. To that end this research proposes a deep learning model and training methodology to …

Enhanced Space Object Detection Without Prior Knowledge Of The Point Spread Function, Grant F. Graupmann

Theses and Dissertations

Since the point detector was created, other detection algorithms have been created that increase the probability of detection, while still keeping the same probability of false alarm. The point detector still has uses, such as when there is no prior knowledge of the point spread function (PSF). The matched filter correlator (MFC) detector is reliant on prior knowledge of the PSF. This has been an issue in cases where the PSF information is potentially inaccurate or unknown. This thesis utilizes MFC detector in a manner that it has never been used before, along with a new detection algorithm, the Pearson's …

A Cyber Threat Taxonomy And A Viability Analysis For False Injections In The Tcas, John W. Hannah

Theses and Dissertations

This thesis provided background information on the Traffic Collision Avoidance System (TCAS). Additionally, the thesis developed a threat taxonomy for TCAS, resulting in the determination that a false injection attack presents the most comprehensive risk. Moreover, the thesis presents the development of a program to determine what ranges, altitudes, and relative bearings are most vulnerable to a false injection attack. The program includes test for all requirements of a successful false injection. Furthermore, the thesis presents an analysis of results and creates threat maps as situational awareness tools. Lastly, the thesis discusses potential solutions to the false injection attack, covers …

Performance Of Various Low-Level Decoder For Surface Codes In The Presence Of Measurement Error, Claire E. Badger

Theses and Dissertations

Quantum error correction is a research specialty within the area of quantum computing that constructs quantum circuits that correct for errors. Decoding is the process of using measurements from an error correcting code, known as error syndrome, to decide corrective operations to perform on the circuit. High-level decoding is the process of using the error syndrome to perform corrective logical operations, while low-level decoding uses the error syndrome to correct individual data qubits. Research on machine learning-based decoders is increasingly popular, but has not been thoroughly researched for low-level decoders. The type of error correcting code used is called surface …

Comparison Of Machine Learning Techniques On Trust Detection Using Eeg, James R. Elkins

Theses and Dissertations

Trust is a pillar of society and is a fundamental aspect in every relationship. With the use of automated agents in todays workforce exponentially growing, being able to actively monitor an individuals trust level that is working with the automation is becoming increasingly more important. Humans often have miscalibrated trust in automation and therefore are prone to making costly mistakes. Since deciding to trust or distrust has been shown to correlate with specific brain activity, it is thought that there are EEG signals which are associated with this decision. Using both a human-human trust and a human-machine trust EEG dataset …

Infiniband Network Monitoring: Challenges And Possibilities, Kyle D. Hintze

Theses and Dissertations

Within the realm of High Performance Computing, the InfiniBand Architecture is among the leading interconnects used today. Capable of providing high bandwidth and low latency, InfiniBand is finding applications outside the High Performance Computing domain. One of these is critical infrastructure, encompassing almost all essential sectors as the work force becomes more connected. InfiniBand is not immune to security risks, as prior research has shown that common traffic analyzing tools cannot effectively monitor InfiniBand traffic transmitted between hosts, due to the kernel bypass nature of the IBA in conjunction with Remote Direct Memory Access operations. If Remote Direct Memory Access …

Low-Cost Terrestrial Demonstration Of Autonomous Satellite Proximity Operations, Zackary R. Hewitt

Theses and Dissertations

The lack of satellite servicing capabilities significantly impacts the development and operation of current orbital assets. With autonomous solutions under consideration for servicing, the purpose of this research is to build and validate a low-cost hardware platform to expedite the development of autonomous satellite proximity operations. This research aims to bridge the gap between simulation and existing higher fidelity hardware testing with an affordable alternative. An omnidirectional variant of the commercially available TurtleBot3 mobile robot is presented as a 3-DOF testbed that demonstrates a satellite servicing inspection scenario. Reference trajectories for the scenario are generated via optimal control using the …

Stereo Camera Calibrations With Optical Flow, Joshua D. Larson

Theses and Dissertations

Remotely Piloted Aircraft (RPA) are currently unable to refuel mid-air due to the large communication delays between their operators and the aircraft. AAR seeks to address this problem by reducing the communication delay to a fast line-of-sight signal between the tanker and the RPA. Current proposals for AAR utilize stereo cameras to estimate where the receiving aircraft is relative to the tanker, but require accurate calibrations for accurate location estimates of the receiver. This paper improves the accuracy of this calibration by improving three components of it: increasing the quantity of intrinsic calibration data with CNN preprocessing, improving the quality …

Application Of The Monte-Carlo Tree Search To Multi-Action Turn-Based Games With Hidden Information, Connor M. Pipan

Theses and Dissertations

Traditional search algorithms struggle when applied to complex multi-action turn-based games. The introduction of hidden information further increases domain complexity. The Monte-Carlo Tree Search (MCTS) algorithm has previously been applied to multi-action turn-based games, but not multi-action turn-based games with hidden information. This thesis compares several Monte Carlo Tree Search (MCTS) extensions (Determinized/Perfect Information Monte Carlo, Multi-Observer Information Set MCTS, and Belief State MCTS) in TUBSTAP, an open-source multi-action turn-based game, modified to include hidden information via fog-of-war.

Anomaly Detection And Encrypted Programming Forensics For Automation Controllers, Robert W. Mellish

Theses and Dissertations

Securing the critical infrastructure of the United States is of utmost importance in ensuring the security of the nation. To secure this complex system a structured approach such as the NIST Cybersecurity framework is used, but systems are only as secure as the sum of their parts. Understanding the capabilities of the individual devices, developing tools to help detect misoperations, and providing forensic evidence for incidence response are all essential to mitigating risk. This thesis examines the SEL-3505 RTAC to demonstrate the importance of existing security capabilities as well as creating new processes and tools to support the NIST Framework. …

Two Published Flight Dynamics Models Rewritten In Rust And Structures As An Ecs, Chad A. Willis

Theses and Dissertations

This thesis explores using the Entity-Component System (ECS) architecture to implement a Flight Dynamics Model (FDM) by re-implementing two published versions in the Rust programming language using the Specs Parallel ECS (SPECS) [1] for military simulation advancement. One FDM is based on Grant Palmers published textbook titled Physics for Game Programmers [2], and another is based on David Bourgs textbook titled Physics for Game Developers [3]. Furthermore, this thesis uses these models within an interactive flight simulator.The ECS architecture is based on the Data-Oriented Design (DOD) paradigm, where Components contain the data and the Systems implement the behavior which transforms …

Aircraft Inspection By Multirotor Uav Using Coverage Path Planning, Patrick H. Silberberg

Theses and Dissertations

All military and commercial aircraft must undergo frequent visual inspections in order to identify damage that could pose a danger to safety of flight. Currently, these inspections are primarily conducted by maintenance personnel. Inspectors must scrutinize the aircraft’s surface to find and document defects such as dents, hail damage, broken fasteners, etc.; this is a time consuming, tedious, and hazardous process. The goal of this work is to develop a visual inspection system which can be used by an Unmanned Aerial Vehicle (UAV), and to test the feasibility of this system on military aircraft. Using an autonomous system in place …

Enumerating And Locating Bluetooth Devices For Casualty Recovery In A First-Responder Environment, Justin M. Durham

Theses and Dissertations

It is difficult for first-responders to quickly locate casualties in an emergency environment such as an explosion or natural disaster. In order to provide another tool to locate individuals, this research attempts to identify and estimate the location of devices that would likely be located on or with a person. A variety of devices, such as phones, smartwatches, and Bluetooth-enabled locks, are tested in multiple environments and at various heights to determine the impact that placement and interference played in locating the devices. The hypothesis is that most Bluetooth devices can be successfully enumerated quickly, but cannot be accurately located …

A Framework For Autonomous Cooperative Optimal Assignment And Control Of Satellite Formations, Devin E. Saunders

Theses and Dissertations

A decentralized, cooperative multi-agent optimal control framework is presented to offer a solution to the assignment and control problems associated with performing multi-agent tasks in a proximity operations environment. However, the framework developed may be applied to a variety domains such as air, space, and sea. The solution presented takes advantage of a second price auction assignment algorithm to optimally task each satellite, while model predictive control is implemented to control the agents optimally while adhering to safety and mission constraints. The solution is compared to a pseudospectral collocation method, and a study on tuning parameters is included.

Amplitude Estimation For The Large Clutter Discrete Removal Algorithm, Hannah Gjermo Chomitz

Theses and Dissertations

A large clutter discrete (LCD) is spectrally bright localized clutter that can cause a false alarm or missed target detection in space-time adaptive processing (STAP) radar data. For passive bistatic STAP, the four step LCD removal (LCDR) algorithm estimates the spatial/Doppler frequency and complex amplitude of the LCD and then removes it from the data. Once the LCD is removed from the data, homogeneous clutter suppression techniques can be used to process the data and search for targets. This research focuses on reducing the complexity of estimating the LCDs complex amplitude. This research proposes a method that directly solves for …

Choosing Isds As A Major: Predictive Analysis, Sarah Johnson

Honors Capstones

No abstract provided.