Computer Sciences Commons^™

Use Of Lidar In Automated Aerial Refueling To Improve Stereo Vision Systems, Michael R. Crowl

Theses and Dissertations

The United States Air Force (USAF) executes five Core Missions, four of which depend on increased aircraft range. To better achieve global strike and reconnaissance, unmanned aerial vehicles (UAVs) require aerial refueling for extended missions. However, current aerial refueling capabilities are limited to manned aircraft due to technical difficulties to refuel UAVs mid-flight. The latency between a UAV operator and the UAV is too large to adequately respond for such an operation. To overcome this limitation, the USAF wants to create a capability to guide the refueling boom into the refueling receptacle. This research explores the use of light detection …

Electronic Image Detectability Under Varying Illumination Conditions, Jeremy J. Miller

Theses and Dissertations

Light in the built environment plays an essential role in the vision and the health of humans through non-visual receptors in the eyes. Unfortunately, image analysts and other Air Force personnel who engage in the detection of objects on softcopy displays are often required to work in very dimly-lit or dark environments as higher illumination reduces the contrast of displayed information. Literature has shown that increases in light exposure improves circadian rhythm entrainment and reduces the negative health consequences of insufficient lighting. This research examines the effects of indoor lighting to determine if increases in ambient illumination or changes to …

The Impact Of Changing The Size Of Aircraft Radar Displays On Visual Search In The Cockpit, Justin R. Marsh

Theses and Dissertations

Advances in sensor technology have enabled our fighter aircraft to find, fix, track, target, engage (F2T2E) at greater distances, providing the operator with more data within the battlefield. Modern aircraft are designed with larger displays while our legacy aircraft are being retrofitted with larger cockpit displays to enable display of the increased data. While this modification has been shown to enable improvements in human performance of many cockpit tasks, this effect is often not measured nor fully understood at a more generalizable level. This research outlines an approach to comparing human performance across two display sizes in future F-16 cockpits. …

Interoperable Ads-B Confidentiality, Brandon C. Burfeind

Theses and Dissertations

The worldwide air traffic infrastructure is in the late stages of transition from legacy transponder systems to Automatic Dependent Surveillance - Broadcast (ADS-B) based systems. ADS-B relies on position information from GNSS and requires aircraft to transmit their identification, state, and position. ADS-B promises the availability of high-fidelity air traffic information; however, position and identification data are not secured via authentication or encryption. This lack of security for ADS-B allows non-participants to observe and collect data on both government and private flight activity. This is a proposal for a lightweight, interoperable ADS-B confidentiality protocol which uses existing format preserving encryption …

Mobile Data Analysis Using Dynamic Binary Instrumentation And Static Analysis, Christopher Dukarm

Theses and Dissertations

Mobile classified data leakage poses a threat to the DoD programs and missions. Security experts must know the format of application data, in order to properly classify mobile applications. This research presents the DBIMAFIA methodology to identify stored data formats. DBIMAFIA uses DBI and static analysis to uncover the structure of mobile application data and validate the results with traditional reverse engineering methods. DBIMAFIA was applied to fifteen popular Android applications and revealed the format of stored data. Notably, user PII leakage is identified in the Rago Games application. The application's messaging service exposes the full name, birthday, and city …

Automated Detection And Mitigation Of Inefficient Visual Searching Using Electroencephalography And Machine Learning, Joshua P. Gallaher

Theses and Dissertations

Decisions made during the high-stress and fast-paced operations of the military are extremely prone to cognitive biases. A commonly known cognitive bias is a confirmation bias, or the inappropriate bolstering of an unknown hypothesis. One such critical military operation that can fall prey to a confirmation bias is a visual search. During a visual search, a military operator must perform a visual scan of an environment for a specific target. However, the visual search process can fall prey to the same confirmation bias which can cause inefficient searches. This study elicits inefficient visual search patterns and applies various mitigation techniques …

The Application Of Digital Health To The Assessment And Treatment Of Substance Use Disorders: The Past, Current, And Future Role Of The National Drug Abuse Treatment Clinical Trials Network, Lisa A. Marsch, Aimee Campbell, Cynthia Campbell, Ching-Hua Chen, Emre Ertin, Udi Ghitza, Chantal Lambert-Harris, Saeed Hassanpour, August F. Holtyn, Yih-Ing Hser, Petra Jacobs, Jeffrey D. Klausner, Shea Lemley, David Kotz, Andrea Meier, Bethany Mcleman, Jennifer Mcneely, Varun Mishra, Larissa Mooney, Edward Nunes, Chrysovalantis Stafylis, Catherine Stanger, Elizabeth Saunders, Geetha Subramaniam, Sean Young

Dartmouth Scholarship

The application of digital technologies to better assess, understand, and treat substance use disorders (SUDs) is a particularly promising and vibrant area of scientific research. The National Drug Abuse Treatment Clinical Trials Network (CTN), launched in 1999 by the U.S. National Institute on Drug Abuse, has supported a growing line of research that leverages digital technologies to glean new insights into SUDs and provide science-based therapeutic tools to a diverse array of persons with SUDs.

This manuscript provides an overview of the breadth and impact of research conducted in the realm of digital health within the CTN. This work has …

Algorithm Selection Framework For Cyber Attack Detection, Marc W. Chalé, Nathaniel D. Bastian, Jeffery D. Weir

Faculty Publications

The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the …

Review Of Fundamental To Know About The Future, Hannarae Lee

International Journal of Cybersecurity Intelligence & Cybercrime

What we consider fundamental elements can be easily overlooked or perceived as facts without the process of empirical testing. Especially in the field of cybercrime and cybersecurity, there are more speculations regarding the prevalence and the scope of harm carried out by wrongdoers than empirically tested studies. To fill the void, three articles included in the current issue addresses empirical findings of fundamental concerns and knowledge in the field of cybercrime and cybersecurity.

Illegal Gambling And Its Operation Via The Darknet And Bitcoin: An Application Of Routine Activity Theory, Sinyong Choi, Kyung-Shick Choi, Yesim Sungu-Eryilmaz, Hee-Kyung Park

International Journal of Cybersecurity Intelligence & Cybercrime

The Darknet and Bitcoins have been widely utilized by those who wish to anonymously perform illegal activities in cyberspace. Restricted in many countries, gambling websites utilize Bitcoin payments that allow users to freely engage in illegal gambling activities with the absence of a formal capable guardian. Despite the urgency and limited knowledge available to law enforcement regarding this issue, few empirical studies have focused on illegal gambling websites. The current study attempts to examine the characteristics and operations of online gambling websites on both the Darknet and Surface Web, which allow Bitcoin payments. The findings suggest that both websites on …

An Empirical Study To Determine The Role Of File-System In Modification Of Hash Value, Kumarshankar Raychaudhuri, M. George Christopher

International Journal of Cybersecurity Intelligence & Cybercrime

In digital forensics, maintaining the integrity of digital exhibits is an essential aspect of the entire investigation and examination process, which is established using the technique of hashing. Lack of knowledge, while handling digital exhibits, might lead to unintentional alteration of computed hash, rendering the exhibit unacceptable in the court of Law. The hash value of a physical drive does not solely depend upon the data files present in it but also its file-system. Therefore, any change to the file-system might result in the change of the disk hash, even when the data files within it remain untouched. In this …

A Reverse Digital Divide: Comparing Information Security Behaviors Of Generation Y And Generation Z Adults, Scott M. Debb, Daniel R. Schaffer, Darlene G. Colson

International Journal of Cybersecurity Intelligence & Cybercrime

How individuals conceptualize their accountability related to digital technology. There may also be age-based vulnerabilities resulting from personal perceptions about the importance of engaging in best-practices. However, age may not be as critical as experience when it comes to implementation of these behaviors. Using the Cybersecurity Behaviors subscale of the Online Security Behaviors and Beliefs Questionnaire (OSBBQ), this study compared the self-reported cybersecurity attitudes and behaviors across college-aged individuals from Generation Y and Generation Z. Data were derived from a convenience sample of predominantly African-American and Caucasian respondents (N=593) recruited from two public universities in Virginia, USA. Four of the …

Book Review: The Cyber Risk Handbook By Domenic Antonucci, Stanley Mierzwa

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

Artificial Intelligence: A New Paradigm In Obstetrics And Gynecology Research And Clinical Practice, Pulwasha Iftikhar, Marcela V. Kuijpers, Azadeh Khayyat, Aqsa Iftikhar, Maribel Degouvia De Sa

Publications and Research

Artificial intelligence (AI) is growing exponentially in various fields, including medicine. This paper reviews the pertinent aspects of AI in obstetrics and gynecology (OB/GYN) and how these can be applied to improve patient outcomes and reduce the healthcare costs and workload for clinicians.

Herein, we will address current AI uses in OB/GYN, and the use of AI as a tool to interpret fetal heart rate (FHR) and cardiotocography (CTG) to aid in the detection of preterm labor, pregnancy complications, and review discrepancies in its interpretation between clinicians to reduce maternal and infant morbidity and mortality. AI systems can be used …

The Rise And Infiltration Of Pac-Man And Street Fighter, Angelic Phan

University Honors Theses

With the social, cultural, and economic influence of video games, it is important to examine why they have become such popular forms of entertainment. Particularly, why certain franchises have continued to persist among the growing industry. Two notable franchises are Pac-Man and Street Fighter, which are also most frequently discussed in scholarly texts. I supplement a literature review with an analysis of marketing texts to illuminate a series of shared factors that help explain both games' popularity despite the apparent dissimilarities of their content. First, my work helps us look across multiple scholarly papers to create a bigger picture …

Improving Ocr Accuracy Of Damaged Pictures With Generative Adversarial Networks, Pu Du

LSU Master's Theses

In this thesis, we focus on resolving the inpainting problem and improving Optical Character Recognition (OCR) accuracy of damaged text images at character level. We present a Generative Adversarial Network (GAN)-based model conditioned on class labels for image inpainting. This model is a deep convolutional neural network with encoder-decoder style architecture which can process images with holes at random locations. Experiments on the character images dataset demonstrate that our proposed model generates promising inpainting results and significantly improve OCR accuracy by reconstructing missing parts of damaged character images.

Apparatus For Securely Configuring A Target Device And Associated Methods, Timothy J. Pierson, Xiaohui Liang, Ronald Peterson, David Kotz

Other Faculty Materials

Apparatus and method securely transfer first data from a source device to a target device. A wireless signal having (a) a higher speed channel conveying second data and (b) a lower speed channel conveying the first data is transmitted. The lower speed channel is formed by selectively transmitting the wireless signal from one of a first and second antennae of the source device based upon the first data. The first and second antenna are positioned a fixed distance apart and the target device uses a received signal strength indication (RSSI) of the first signal to decode the lower speed channel …

Cyber-Physical Security With Rf Fingerprint Classification Through Distance Measure Extensions Of Generalized Relevance Learning Vector Quantization, Trevor J. Bihl, Todd J. Paciencia, Kenneth W. Bauer Jr., Michael A. Temple

Faculty Publications

Radio frequency (RF) fingerprinting extracts fingerprint features from RF signals to protect against masquerade attacks by enabling reliable authentication of communication devices at the “serial number” level. Facilitating the reliable authentication of communication devices are machine learning (ML) algorithms which find meaningful statistical differences between measured data. The Generalized Relevance Learning Vector Quantization-Improved (GRLVQI) classifier is one ML algorithm which has shown efficacy for RF fingerprinting device discrimination. GRLVQI extends the Learning Vector Quantization (LVQ) family of “winner take all” classifiers that develop prototype vectors (PVs) which represent data. In LVQ algorithms, distances are computed between exemplars and PVs, and …

Reliability Analysis Of Power Grids And Its Interdependent Infrastructures: An Interaction Graph-Based Approach, Upama Nakarmi

USF Tampa Graduate Theses and Dissertations

Large blackouts with significant societal and economic impacts result from cascade of failures in the transmission network of power grids. Understanding and mitigating cascading failures in power grids is challenging due to the large number of components and their complex interactions, wherein, in addition to the physical topology of the system, the physics of power flow and functional dependencies among components largely affect the spatial distribution and propagation of failures. In this dissertation, data-driven interaction graphs, which help in capturing the underlying interactions and influences among the components during cascading failures, are used for capturing the non-local nature of propagation …

Algorithms To Profile Driver Behavior From Zero-Permission Embedded Sensors, Bharti Goel

USF Tampa Graduate Theses and Dissertations

In this dissertation, we design algorithms to profile driver behavior from zero-permission sensors embedded in modern smartphones and wearables. These sensors are typically the accelerometer, gyroscope, magnetometer, pressure sensor and a few more than are now available in most modern smartphones and wearables. In order to profile driving behavior, we devised algorithms for detecting distraction while driving due to the use of modern-day smartphones (e.g., calling, texting and reading while driving) in real-time.

To do so, we conduct an experiment with 16 subjects on a realistic driving simulator, where each subject, where each subject carries a smartphone and a wearable …

Lung Nodule Malignancy Prediction From Computed Tomography Images Using Deep Learning, Rahul Paul

USF Tampa Graduate Theses and Dissertations

Lung cancer has a high incidence and mortality rate. The five-year relative survival rate for all lung cancers is 18%. Due to the high mortality and incidence rate of lung cancer worldwide, early detection is essential. Low dose Computed Tomography (CT) is a commonly used technique for screening, diagnosis, and prognosis of non-small cell lung cancer (NSCLC). The National Lung Screening Trial (NLST) compared low-dose helical computed tomography (LDCT) and standard chest radiography (CXR) for three annual screens and reported a 20% relative reduction in lung cancer mortality for LDCT compared to CXR. As such, LDCT screening for lung cancer …

Towards Safe Power Oversubscription And Energy Efficiency Of Data Centers, Sulav Malla

USF Tampa Graduate Theses and Dissertations

Data centers contribute to approximately 1% of the global electricity consumption, and billions of dollars are spent annually worldwide in construction of new data centers to meet the rising demand for cloud-based services. Given the high cost of construction, the power infrastructure in a data center is typically oversubscribed. Power oversubscription leads to efficient use of data center power hierarchy while simultaneously reducing the power provisioning cost. Power overload situations can occur in oversubscribed data centers. Power overload can lead to power capping of servers or even power outages – both of which degrade the performance of the services offered …

Analysis Of Identification Method For Bacterial Species And Antibiotic Resistance Genes Using Optical Data From Dna Oligomers, Ryan L. Wood, Tanner Jensen, Cindi Wadsworth, Mark Clement, Prashant Nagpal, William G. Pitt

Faculty Publications

Bacterial antibiotic resistance is becoming a significant health threat, and rapid identification of antibiotic-resistant bacteria is essential to save lives and reduce the spread of antibiotic resistance. This paper analyzes the ability of machine learning algorithms (MLAs) to process data from a novel spectroscopic diagnostic device to identify antibiotic-resistant genes and bacterial species by comparison to available bacterial DNA sequences. Simulation results show that the algorithms attain from 92% accuracy (for genes) up to 99% accuracy (for species). This novel approach identifies genes and species by optically reading the percentage of A, C, G, T bases in 1000s of short …

Demand Forecasting Of Wartime Spares Based On Evidence Theory, Yunjing Zhang, Xinxin Wang, Wang Yang, Guangming Tang

Journal of System Simulation

Abstract: A new method based on evidence theory is proposed to solve the lack of data for the demand prediction of spares in wartime. Utilizing Markov chain model to research the rule of spares demand of peacetime from the historical data. A Markov chain transfer probability adjustment strategy, based on the change of combat intensity, is designed, which can be used to simulation the rule of spares demand in wartime. Under the experience of experts and the combination of the Fuzzy theory and evidence theory, the spares demand is wartime is forecasted. The simulation example shows that the …

Moea/D Algorithm Based On The Hybrid Framework For Multi-Objective Evolutionary Algorithm, Hongjun Tian, Wang Lei, Qidi Wu

Journal of System Simulation

Abstract: Aimto the difficulties of designing the bonding mechanism of global optimization algorithm and local search strategy for hybrid multi-objective evolutionary algorithm, and of improving the performance of multi-objective evolutionary algorithms, based on the feedback control idea, a systematic and modular hybrid MOEA/D algorithm combining the global optimization and local search is proposed. In the algorithm, a diversity measure method based on crowded entropy is designed; a local search strategy based on simplified quadratic approximation and population diversity enhancement strategy for MOEA/D is proposed. The numerical experiments show that the proposed HMOEA/D can achieve a balance between diversity …

Tele-Robot Control System With Online Virtual Simulation Prediction, Lingyan Hu, Kangbai Shi, Bichun Zhang, Shaoping Xu

Journal of System Simulation

Abstract: When a tele-robot with virtual simulation system works in a complicated environment, the virtual scene may not synchronize with the real one. This may cause the operator on the master side to give wrong instruction. This paper presents a tele-robot with online virtual model correction. The virtual simulation system on the master side builds a virtual model of environment and robot on the slave side. It predicts the motion of the slave robot in real time according the commands from the operator. In order to ensure the virtual scene synchronize with the real one, the measured data on the …

Research On Cavitation Evolution Model In Venturi Tube, Guihua Han, Yanan Liu, Junpeng Shao, Cijun Zhang

Journal of System Simulation

Abstract: The dynamic cavitation model of venturi tube air-water two-phase flow was established according to the time variation of air precipitation and digestion process in water. FLUENT three-dimension numerical simulation were studied to simulate the air-water two-phase flow, and the variation law of venturi tube throat diameter and inlet pressure on vapor volume fraction were obtained. The results of numerical simulation and experiments show that the vapor volume fraction increases following the increase of the inlet pressure. If the throat diameter is increased, the inlet pressure can be increased as the compensation to achieve the same cavitation effect. The …

Self-Organizing Aggregation Behavior Modeling Of Swarm Robots Based On Ant Colony Algorithm, Yang Wei, Zeng Liang, Xinchen Kang

Journal of System Simulation

Abstract: Aiming at the formation of aggregation behavior model of swarm robots, a self-organizing motion model based on particle system mechanics and perceptual state weighting is established to realize the autonomous transfer of robot motion state. The aggregation degree and uniformity index of aggregation behavior are established, and the aggregation performances of different models are analyzed by experiment, which lay a foundation for the formation of self-organized aggregation behavior model of swarm robots. Using the selected model, the self-organizing aggregation behavior simulation experiments are conducted in two cases with boundary constraints and no boundary constraints. The maximum coverage of …

Auv Vertical Plane Control Based On Improved Pid Neural Network Algorithm, Runan Huang, Ding Ning

Journal of System Simulation

Abstract: An improved PID neural network controller is designed for the movement control of a small low-speed autonomous underwater vehicle (AUV) in vertical plane, and the global control of the depth and pitch angle of the underwater vehicle in vertical plane is obtained. The AUV simulation control system is built by using REMUS underwater vehicle model in Simulink. The simulation results show that the improved control method with better dynamic performance has solved the original excessive saturation issue, and can adapt to different learning rates and network initial weight,and is of certain reference value to the practical application …

Integrated Simulation Test Platform For Environment Perception And Planning Decision Of Intelligent Vehicle, Sun Jian, Runhan Huang, Li Lin, Qiyuan Liu, Yudi Li

Journal of System Simulation

Abstract: The simplicity of testing modules and the lack of high-fidelity and high-density traffic scenes are the primary issues in the intelligent vehicle' simulation test. To solve these problems, a "software in the loop" integration test platform is built. The environmental perception module is integrated on the basis of the PreScan, the planning decision-making module designed by MATLAB/Simulink and the virtual traffic flow environment from Vissim. Based on this platform, high-fidelity traffic flow scenes can be generated rapidly to support the test of environment perception and decision-making integratedly and effectively. Moreover, the intelligent vehicle's influence on the traffic flow …