Computer Engineering Commons^™

Graph-Based Temporal Analysis In Digital Forensics, Nikolai A. Adderley

Theses and Dissertations

Establishing a timeline as part of a digital forensics investigation is a vital part of understanding the order in which system events occurred. However, most digital forensics tools present timelines as histogram or as raw artifacts. Consequently, digital forensics examiners are forced to rely on manual, labor-intensive practices to reconstruct system events. Current digital forensics analysis tools are at their technological limit with the increasing storage and complexity of data. A graph-based timeline can present digital forensics evidence in a structure that can be immediately understood and effortlessly focused. This paper presents the Temporal Analysis Integration Management Application (TAIMA) to …

Machine Learning Models Of C-17 Specific Range Using Flight Recorder Data, Marcus Catchpole

Theses and Dissertations

Fuel is a significant expense for the Air Force. The C-17 Globemaster eet accounts for a significant portion. Estimating the range of an aircraft based on its fuel consumption is nearly as old as flight itself. Consideration of operational energy and the related consideration of fuel efficiency is increasing. Meanwhile machine learning and data-mining techniques are on the rise. The old question, "How far can my aircraft y with a given load cargo and fuel?" has given way to "How little fuel can I load into an aircraft and safely arrive at the destination?" Specific range is a measure of …

A Quantitative Analysis Of The Fusion Of 3-D Scanning Lidar Systems And 2-D Imaging Systems, Michael F. Milton Jr.

Theses and Dissertations

This research will demonstrate the feasibility of fusing the superior spatial resolution of a 2-D imaging system with the precise range to target information of a 3-D imaging system to create a LIDAR imaging system that can accurately find what and where a target is. The 3-D imaging system will use a scanning method as opposed to a flash method that has been used in similar research. The goal of this research is to improve performance of scanning LIDAR so it has better spatial resolution. The research in this thesis proves that incorporating 2-D imaging data into 3-D scanning LIDAR …

Modeling A Consortium-Based Distributed Ledger Network With Applications For Intelligent Transportation Infrastructure, Luis A. Cintron

Theses and Dissertations

Emerging distributed-ledger networks are changing the landscape for environments of low trust among participating entities. Implementing such technologies in transportation infrastructure communications and operations would enable, in a secure fashion, decentralized collaboration among entities who do not fully trust each other. This work models a transportation records and events data collection system enabled by a Hyperledger Fabric blockchain network and simulated using a transportation environment modeling tool. A distributed vehicle records management use case is shown with the capability to detect and prevent unauthorized vehicle odometer tampering. Another use case studied is that of vehicular data collected during the event …

High Resolution Low-Bandwidth Real-Time Reconnaissance Using Structure From Motion With Planar Homography Estimation, Christian M.A. Arnold

Theses and Dissertations

Aerial real-time surveillance exists in a paradigm balancing the constraints of delivering high quality data and transporting data quickly. Typically, to have more of one, sacrifices must be made to the other. This is true of the environment in which an Unmanned Aerial Vehicle (UAV) operates, where real-time communication may be done through a low-bandwidth satellite connection resulting in low-resolution data, and serves as the primary limiting factor in all intelligence operations. Through the use of efficient computer vision techniques, we propose a new Structure from Motion (SfM) method capable of compressing high-resolution data, and delivering that data in real-time. …

Instantaneous Bandwidth Expansion Using Software Defined Radios, Nicholas D. Everett

Theses and Dissertations

The Stimulated Unintended Radiated Emissions (SURE) process has been proven capable of classifying a device (e.g. a loaded antenna) as either operational or defective. Currently, the SURE process utilizes a specialized noise radar which is bulky, expensive and not easily supported. With current technology advancements, Software Defined Radios (SDRs) have become more compact, more readily available and significantly cheaper. The research here examines whether multiple SDRs can be integrated to replace the current specialized ultra-wideband noise radar used with the SURE process. The research specifically targets whether or not multiple SDR sub-band collections can be combined to form a wider …

Confidence Inference In Defensive Cyber Operator Decision Making, Graig S. Ganitano

Theses and Dissertations

Cyber defense analysts face the challenge of validating machine generated alerts regarding network-based security threats. Operations tempo and systematic manpower issues have increased the importance of these individual analyst decisions, since they typically are not reviewed or changed. Analysts may not always be confident in their decisions. If confidence can be accurately assessed, then analyst decisions made under low confidence can be independently reviewed and analysts can be offered decision assistance or additional training. This work investigates the utility of using neurophysiological and behavioral correlates of decision confidence to train machine learning models to infer confidence in analyst decisions. Electroencephalography …

Autonomous Association Of Geo Rso Observations Using Deep Neural Networks, Ian W. Mcquaid

Theses and Dissertations

Ground-based non-resolved optical observations of resident space objects (RSOs) in geosynchronous orbit (GEO) represent the majority of the space surveillance network’s (SSN’s) deep-space tracking. Reliable and accurate tracking necessitates temporal separation of the observations. This requires that subsequent observations be associated with prior observations of a given RSO before they can be used to create or refine that RSO’s ephemeris. The use of astrometric data (e.g. topocentric angular position) alone for this association task is complicated by RSO maneuvers between observations, and by RSOs operating in close proximity. Accurately associating an observation with an RSO thus motivates the use of …

A Multi-Vehicle Cooperative Localization Approach For An Autonomy Framework, Edwin A. Mora

Theses and Dissertations

Offensive techniques produced by technological advancement present opportunities for adversaries to threaten the operational advantages of our joint and allied forces. Combating these new methodologies requires continuous and rapid development towards our own set of \game-changing" technologies. Through focused development of unmanned systems and autonomy, the Air Force can strive to maintain its technological superiority. Furthermore, creating a robust framework capable of testing and evaluating the principles that define autonomy allows for the exploration of future capabilities. This research presents development towards a hybrid reactive/deliberative architecture that will allow for the testing of the principles of task, cognitive, and peer …

A Framework For Cyber Vulnerability Assessments Of Infiniband Networks, Daryl W. Schmitt

Theses and Dissertations

InfiniBand is a popular Input/Output interconnect technology used in High Performance Computing clusters. It is employed in over a quarter of the world’s 500 fastest computer systems. Although it was created to provide extremely low network latency with a high Quality of Service, the cybersecurity aspects of InfiniBand have yet to be thoroughly investigated. The InfiniBand Architecture was designed as a data center technology, logically separated from the Internet, so defensive mechanisms such as packet encryption were not implemented. Cyber communities do not appear to have taken an interest in InfiniBand, but that is likely to change as attackers branch …

Unresolved Object Detection Using Synthetic Data Generation And Artificial Neural Networks, Yong U. Sinn

Theses and Dissertations

This research presents and solves constrained real-world problems of using synthetic data to train artificial neural networks (ANNs) to detect unresolved moving objects in wide field of view (WFOV) electro-optical/infrared (EO/IR) satellite motion imagery. Objectives include demonstrating the use of the Air Force Institute of Technology (AFIT) Sensor and Scene Emulation Tool (ASSET) as an effective tool for generating EO/IR motion imagery representative of real WFOV sensors and describing the ANN architectures, training, and testing results obtained. Deep learning using a 3-D convolutional neural network (3D ConvNet), long short term memory (LSTM) network, and U-Net are used to solve the …

Examining Effectiveness Of Web-Based Internet Of Things Honeypots, Lukas A. Stafira

Theses and Dissertations

The Internet of Things (IoT) is growing at an alarming rate. It is estimated that there will be over 25 billion IoT devices by 2020. The simplicity of their function usually means that IoT devices have low processing power, which prevent them from having intricate security features, leading to vulnerabilities. This makes IoT devices the prime target of attackers in the coming years. Honeypots are intentionally vulnerable machines that run programs which appear as a vulnerable device to a would-be attacker. They are placed on a network to entice and trap an attacker and then gather information on them, including …

From Decoder Rings To Deep Fakes: Translating Complex Technologies For Legal Education, Rachel S. Evans, Jason Tubinis

Presentations

“Technological developments are disrupting the practice of law” is a common refrain, but the last few years has seen some particularly complex pieces of technology become the hot new thing in legal tech. This session will look at blockchain, quantum computing, artificial intelligence, and ‘Deep Fakes’ as examples of how librarians can stay abreast of technological developments and inform themselves about their impacts in the legal profession. Then we will look at how to translate the complexities and jargon of these examples into lessons for for-credit courses, one-off informational sessions, or meetings with stakeholders.

Hyper-Parameter Optimization Of A Convolutional Neural Network, Steven H. Chon

Theses and Dissertations

In the world of machine learning, neural networks have become a powerful pattern recognition technique that gives a user the ability to interpret high-dimensional data whereas conventional methods, such as logistic regression, would fail. There exists many different types of neural networks, each containing its own set of hyper-parameters that are dependent on the type of analysis required, but the focus of this paper will be on the hyper-parameters of convolutional neural networks. Convolutional neural networks are commonly used for classifications of visual imagery. For example, if you were to build a network for the purpose of predicting a specific …

A Stochastic Game Theoretical Model For Cyber Security, Michael T. Larkin

Theses and Dissertations

The resiliency of systems integrated through cyber networks is of utmost importance due to the reliance on these systems for critical services such as industrial control systems, nuclear production, and military weapons systems. Current research in cyber resiliency remains largely limited to methodologies utilizing a singular technique that is predominantly theoretical with limited examples given. This research uses notional data in presenting a novel approach to cyber system analysis and network resource allocation by leveraging multiple techniques including game theory, stochastic processes, and mathematical programming. An operational network security problem consisting of 20 tactical normal form games provides an assessment …

A Flexible, Natural Deduction, Automated Reasoner For Quick Deployment Of Non-Classical Logic, Trisha Mukhopadhyay

USF Tampa Graduate Theses and Dissertations

Automated Theorem Provers (ATP) are software programs which carry out inferences over logico-mathematical systems, often with the goal of finding proofs to some given theorem. ATP systems are enormously powerful computer programs, capable of solving immensely difficult problems. Currently, many automated theorem provers exist like E, vampire, SPASS, ACL2, Coq etc. However, all the available theorem provers have some common problems: (1) Current ATP systems tend not to try to find proofs entirely on their own. They need help from human experts to supply lemmas, guide the proof, etc. (2) There is not a single proof system available which provides …

Sustainability In An It Context, Dikran Kassabian

Sustainability Research & Practice Seminar Presentations

No abstract provided.

Effective Geometric Modeling For Scientific Computing And Forensic Reconstruction, Celong Liu

LSU Doctoral Dissertations

Geometric modeling is a fundamental and critical problem for many computer graphic and computer vision problems. We usually want to build a parameterization which establishes a low-distortion one-to-one map between geometric data and canonical parametric domain, and it is a fundamental tool to support many visual data processing tasks such as mesh generation, finite element simulations, visual analytics, geometric reconstruction, etc. In this dissertation, I studied two problems. The first is to construct low- distortion canonical (polycube) parameterization of large-scale geometric regions, with which high-quality semi-structured quad meshes can be generated and used to facilitate more efficient scientific computing. The …

An Extensive Game-Based Resource Allocation For Securing D2d Underlay Communications, Oleksii Rudenko, Yuhong Liu, Chenwei Wang, Susanto Rahardja

Computer Science and Engineering

Device-to-device (D2D) communication has been increasingly attractive due to its great potential to improve cellular communication performance. While resource allocation optimization for improving the spectrum efficiency is of interest in the D2D-related work, communication security, as a key issue in the system design, has not been well investigated yet. Recently, a few studies have shown that D2D users can actually serve as friendly jammers to help enhance the security of cellular user communication against eavesdropping attacks. However, only a few studies considered the security of D2D communications. In this paper, we consider the secure resource allocation problem, particularly, how to …

Stochastic Methods To Find Maximum Likelihood For Spam E-Mail Classification, Seyed M. -H. Mansourbeigi

Computer Science Student Research

The increasing volume of unsolicited bulk e-mails leads to the need for reliable stochastic spam detection methods for the classification of the received sequence of e-mails. When a sequence of emails is received by a recipient during a time period, the spam filters have already classified them as spam or not spam. Due to the dynamic nature of the spam, there might be emails marked as not spam but are actually real spams and vice versa. For the sake of security, it is important to be able to detect real spam emails. This paper utilizes stochastic methods to refine the …

High-Performing Pgm-Free Aemfc Cathodes From Carbon-Supported Cobalt Ferrite Nanoparticles, Xiong Peng, Varchaswal Kashyap, Benjamin Ng, Sreekumar Kurungot, Lianqin Wang, John R. Varcoe, Mustain E William

Faculty Publications

Efficient and durable non-precious metal electrocatalysts for the oxygen reduction reaction (ORR) are highly desirable for several electrochemical devices, including anion exchange membrane fuel cells (AEMFCs). Here, cobalt ferrite (CF) nanoparticles supported on Vulcan XC-72 carbon (CF-VC) were created through a facile, scalable solvothermal method. The nano-sized CF particles were spherical with a narrow particle size distribution. The CF-VC catalyst showed good ORR activity, possessing a half-wave potential of 0.71 V. Although the intrinsic activity of the CF-VC catalyst was not as high as some other platinum group metal (PGM)-free catalysts in the literature, where this catalyst really shined was …

Diagnosis Of Brain Diseases Via Multi-Scale Time-Series Model, Zehua Zhang, Junhai Xu, Jijun Tang, Quan Zou, Fei Guo

Faculty Publications

The functional magnetic resonance imaging (fMRI) data and brain network analysis have been widely applied to automated diagnosis of neural diseases or brain diseases. The fMRI time series data not only contains specific numerical information, but also involves rich dynamic temporal information, those previous graph theory approaches focus on local topology structure and lose contextual information and global fluctuation information. Here, we propose a novel multi-scale functional connectivity for identifying the brain disease via fMRI data. We calculate the discrete probability distribution of co-activity between different brain regions with various intervals. Also, we consider nonsynchronous information under different time dimensions, …

Recipe For Disaster, Zac Travis

MFA Thesis Exhibit Catalogs

Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.

The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as a …

Wiwear: Wearable Sensing Via Directional Wifi Energy Harvesting, Huy Vu Tran, Archan Misra, Jie Xiong, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Energy harvesting, from a diverse set of modes such as light or motion, has been viewed as the key to developing batteryless sensing devices. In this paper, we develop the nascent idea of harvesting RF energy from WiFi transmissions, applying it to power a prototype wearable device that captures and transmits accelerometer sensor data. Our solution, WiWear, has two key innovations: 1) beamforming WiFi transmissions to significantly boost the energy that a receiver can harvest ~23 meters away, and 2) smart zero-energy, triggering of inertial sensing, that allows intelligent duty-cycled operation of devices whose transient power consumption far exceeds what …

Long-Term Trajectories Of Human Civilization, Seth D. Baum, Stuart Armstrong, Timoteus Ekenstedt, Olle Häggström, Robin Hanson, Karin Kuhlemann, Matthijs M. Maas, James D. Miller, Markus Salmela, Anders Sandberg, Kaj Sotala, Phil Torres, Alexey Turchin, Roman V. Yampolskiy

Faculty and Staff Scholarship

Purpose: This paper aims to formalize long-term trajectories of human civilization as a scientific and ethical field of study. The long-term trajectory of human civilization can be defined as the path that human civilization takes during the entire future time period in which human civilization could continue to exist. Design/methodology/approach: This paper focuses on four types of trajectories: status quo trajectories, in which human civilization persists in a state broadly similar to its current state into the distant future; catastrophe trajectories, in which one or more events cause significant harm to human civilization; technological transformation trajectories, in which radical technological …

Statistical Algorithm For Nonuniformity Correction In Focal-Plane Arrays, Majeed M. Hayat, Sergio N. Torres, Ernest Armstrong, Stephen C. Cain, Brian Yasuda

Electrical and Computer Engineering Faculty Research and Publications

A statistical algorithm has been developed to compensate for the fixed-pattern noise associated with spatial nonuniformity and temporal drift in the response of focal-plane array infrared imaging systems. The algorithm uses initial scene data to generate initial estimates of the gain, the offset, and the variance of the additive electronic noise of each detector element. The algorithm then updates these parameters by use of subsequent frames and uses the updated parameters to restore the true image by use of a least-mean-square error finite-impulse-response filter. The algorithm is applied to infrared data, and the restored images compare favorably with those restored …

Hybrid Field Oriented And Direct Torque Control For Sensorless Bldc Motors Used In Aerial Drones, Kellen Carey, Nathan M. Zimmerman, Cristinel Ababei

Electrical and Computer Engineering Faculty Research and Publications

In this study, a sensorless hybrid control scheme for brushless direct current (BLDC) motors for use in multirotor aerial vehicles is introduced. In such applications, the control scheme must satisfy high-performance demands for a wide range of rotor speeds and must be robust to motor parameter uncertainties and measurement noise. The proposed controller combines field-oriented control (FOC) and direct torque control (DTC) techniques to take benefit of the advantages offered by each of these techniques individually. Simulation results demonstrate the effectiveness of the proposed control scheme over a wide range of rotor speeds as well as good robustness against parameter …

Computationally Efficient Strand Eddy Current Loss Calculation In Electric Machines, Alireza Fatemi, Dan M. Ionel, Nabeel Demerdash, Dave A. Staton, Rafal Wrobel, Yew Chuan Chong

Electrical and Computer Engineering Faculty Research and Publications

A fast finite element (FE) based method for the calculation of eddy current losses in the stator windings of randomly wound electric machines is presented in this paper. The method is particularly suitable for implementation in large-scale design optimization algorithms where a qualitative characterization of such losses at higher speeds is most beneficial for identification of the design solutions that exhibit the lowest overall losses including the ac losses in the stator windings. Unlike the common practice of assuming a constant slot fill factor s _f for all the design variations, the maximum s _f in the developed method is …

Freelabel: A Publicly Available Annotation Tool Based On Freehand Traces, Philipe A. Dias, Zhou Shen, Amy Tabb, Henry P. Medeiros

Electrical and Computer Engineering Faculty Research and Publications

Large-scale annotation of image segmentation datasets is often prohibitively expensive, as it usually requires a huge number of worker hours to obtain high-quality results. Abundant and reliable data has been, however, crucial for the advances on image understanding tasks recently achieved by deep learning models. In this paper, we introduce FreeLabel, an intuitive open-source web interface that allows users to obtain high-quality segmentation masks with just a few freehand scribbles, in a matter of seconds. The efficacy of FreeLabel is quantitatively demonstrated by experimental results on the PASCAL dataset as well as on a dataset from the agricultural domain. Designed …

A State-Of-The-Art Survey On Deep Learning Theory And Architectures, Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari

Electrical and Computer Engineering Faculty Publications

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language …