Engineering Commons^™

Home Automation System By Voice Commands, Noor Kamil Abdalhameed

Theses and Dissertations

The Home Automation System is one of the most important technologies that are used by humans for controlling electrical devices to reduce manual efforts in their daily tasks. The home automation system by voice has the ability to understand thousands of voice commands and perform the required action to control various electrical devices. The voice recognition is a bit complex and challenging task since each person has his accent. Therefore, Bitvoicer Server used in the home automation system in this thesis since it supports 17 languages from 26 countries and regions, and has the ability to recognize an unlimited number …

Mitigating Pilot Contamination Through Optimizing Pilot Allocation In Massive Mimo Systems, Rand Abdul Hussain

Theses and Dissertations

This dissertation has proposed several algorithms to optimize the allocation of pilots to the users’ equipment (UEs) to mitigate the effect of the pilot contamination problem in the massive MIMO systems. Pilot contamination reduces the performance of massive MIMO systems due to the reduction in the quality of the estimated channel between a UE and the serving base station (BS). The limitation of the number of samples in a coherence block limits the number of unique mutually orthogonal pilots, and hence, reusing the set of pilots across the cells causes inter-cell interference during pilot transmission, which is called pilot contamination. …

Socialization Of Veterans Using Virtual Reality, Joan M. Savage

Theses and Dissertations

Virtual reality, augmented reality, mixed reality, and video games are growing in popularity and fulfilling genuine human needs that the real world is currently unable to satisfy. Games are providing rewards that reality is not. They are teaching and inspiring and engaging us in ways that reality is not (McGonigal, 2011). The purpose of this study was to capture the essence of socialization in virtual reality as a Ph.D. dissertation topic at Florida Institute of Technology - Human-Centered Design. This study used a phenomenology methodology to capture the experiences of beneficial features of players who use virtual reality. The study …

Machine Learning In The State Design Pattern, Timothy Matthew Von Friesen

Theses and Dissertations

As the Internet of Things revolution continues to become more prevalent in humanity's daily routine, securing these devices is paramount. Society has seen a substantial increase in activity in the cyber-warfare battle space, resulting in an increasing amount of security breaches every year. The responsibility of securing our devices can no longer rely solely on cyber-security engineers keeping systems hardened through Security Technical Implementation Guides and vulnerability scans; it must shift towards the developer. Previous research has been done in this area of securing our devices. However, these solutions rely heavily on cloud computing resources to perform computationally expensive algorithms. …

Mobile Ad Hoc Networks In Transportation Data Collection And Dissemination, Kardigue Konte

Theses and Dissertations

The field of transportation is rapidly changing with new opportunities for systems solutions and emerging technologies. The global economic impact of congestion and accidents are significant. Improved means are needed to solve them. Combined with the increasing numbers of vehicles on the road, the net economic impact is measured in the many billions of dollars. Promising methodologies explored in this thesis include the use of the Internet of Things (IoT) and Mobile Ad Hoc Networks (MANET). Interconnecting vehicles using Dedicated Short Range Communication technology (DSRC) brings many benefits. Integrating DSRC into roadway vehicles offers the promise of reducing the problems …

Development Of A National-Scale Big Data Analytics Pipeline To Study The Potential Impacts Of Flooding On Critical Infrastructures And Communities, Nattapon Donratanapat

Theses and Dissertations

With the rapid development of the Internet of Things (IoT) and Big data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management and decision making. FAIS allows the user …

A Novel And Inexpensive Solution To Build Autonomous Surface Vehicles Capable Of Negotiating Highly Disturbed Environments, Jason Moulton

Theses and Dissertations

This dissertation has four main contributions. The first contribution is the design and build of a fleet of long-range, medium-duration deployable autonomous surface vehicles (ASV). The second is the development, implementation, and testing of inex-pensive sensors to accurately measure wind, current, and depth environmental vari- ables. The third leverages the first two contributions, and is modeling the effects of environmental variables on an ASV, finally leading to the development of a dynamic controller enabling deployment in more uncertain conditions.

The motivation for designing and building a new ASV comes from the lack of availability of a flexible and modular platform …

Adaptive-Hybrid Redundancy For Radiation Hardening, Nicolas S. Hamilton

Theses and Dissertations

An Adaptive-Hybrid Redundancy (AHR) mitigation strategy is proposed to mitigate the effects of Single Event Upset (SEU) and Single Event Transient (SET) radiation effects. AHR is adaptive because it switches between Triple Modular Redundancy (TMR) and Temporal Software Redundancy (TSR). AHR is hybrid because it uses hardware and software redundancy. AHR is demonstrated to run faster than TSR and use less energy than TMR. Furthermore, AHR allows space vehicle designers, mission planners, and operators the flexibility to determine how much time is spent in TMR and TSR. TMR mode provides faster processing at the expense of greater energy usage. TSR …

Survival Theory Modelling For Information Diffusion, Akshay Aravamudan

Theses and Dissertations

Information diffusion is the spread of information within a network. In this thesis, we model information diffusion as a survival process. We have adopted an existing algorithm called NetRate for modelling information diffusion. This model involves finding the distribution of trasmission time between two nodes in the network. We modify NetRate’s concave-down log-likelihood expression by adding partial parentage information and formulate an Expectation-Minimization (EM) algorithm to learn the parameters. We also describe a simulation scheme for NetRate inspired by point process simulation strategies. Using the assumptions of the NetRate model, we derive a a method to model popularity as a …

The Trust-Based Interactive Partially Observable Markov Decision Process, Richard S. Seymour

Theses and Dissertations

Cooperative agent and robot systems are designed so that each is working toward the same common good. The problem is that the software systems are extremely complex and can be subverted by an adversary to either break the system or potentially worse, create sneaky agents who are willing to cooperate when the stakes are low and take selfish, greedy actions when the rewards rise. This research focuses on the ability of a group of agents to reason about the trustworthiness of each other and make decisions about whether to cooperate. A trust-based interactive partially observable Markov decision process (TI-POMDP) is …

Way-Finding: A New Approach To Studying Digital Communications, William Daniel Glade

Theses and Dissertations

This work further develops the way-finding model first proposed by Pearson and Kosicki (2017) which examines the flow of information in the digital age. Way-finding systems are online systems that help individuals find information—i.e. social media, search engines, email, etc. Using a grounded theory methodology, this new framework was explored in greater detail. Way-finding theory was created using the context of the elaboration likelihood model, gatekeeping theory, algorithmic gatekeepers, and the existence of the filter bubble phenomenon. This study establishes the three basic pillars of way-finding theory: the user’s mindset when accessing way-finding systems, the perception of how popular way-finding …

Formal Trust Architecture For Assuring Trusted Interactions In The Internet Of Things, Milankumar Patel

Theses and Dissertations

The Internet of Things (IoT) has provided a flexible platform for a large number of heterogeneous devices to dynamically join and leave the network. This enhances the availability of a diverse range of services provided by a network. However, this dynamic expansion of the network with mobile IoT devices introduces a major challenge to security especially related to management of trust across the IoT platforms. Furthermore, IoT is open and distributed in nature, which allows the integration and registration of diverse entities. Thus arises the necessity for a mechanism that can ensure the selection of secure and trusted devices as …

Drone Path Scheduling For Data Gathering In Disaster Effected Environments, James Bologna

Theses and Dissertations

Creative networks in a disaster effected area have been a concern for a long time. With the innovation of flying ad hoc networks the problem can be tackled from a more practical standpoint. The use of low altitude drones equipped with wireless transmission and receiving capability can be used under the circumstances to enable ad-hoc communication. Drone coordination and route planning is a critical consideration when determining how to gather data from ground nodes, since the drones are constrained by limited battery capacity. In this research, we set out to explore a wide range of policies for determining the trajectories …

Wavelet–Based Functional Data Analysis For Classification And Change Point Detection, Nenad Mijatovic

Theses and Dissertations

Data collection and analysis, performed close to the source and transferred to other devices for different analysis, are the major paradigms of the Internet of Things (IoT). Usually, the raw data comes in the form of a time-series sequence that can be considered as functions, and as such can be examined by the functional analysis apparatus. Among others, the two major tasks in data analysis are (1) categorical signal classification and (2) change detection in signal statistical parameters. Here, we study both problems: featureless signal classification using discriminative interpolation regularized with the ℓ1 norm is performed using Classification by Discriminative …

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 …

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 …

Machine Translation With Image Context From Mandarin Chinese To English, Brooke E. Johnson

Theses and Dissertations

Despite ongoing improvements in machine translation, machine translators still lack the capability of incorporating context from which source text may have been derived. Machine translators use text from a source language to translate it into a target language without observing any visual context. This work aims to produce a neural machine translation model that is capable of accepting both text and image context as a multimodal translator from Mandarin Chinese to English. The model was trained on a small multimodal dataset of 700 images and sentences, and compared to a translator trained only on the text associated with those images. …

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 …

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 …

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 …

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. …

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 …

Near Real-Time Rf-Dna Fingerprinting For Zigbee Devices Using Software Defined Radios, Frankie A. Cruz

Theses and Dissertations

Low-Rate Wireless Personal Area Network(s) (LR-WPAN) usage has increased as more consumers embrace Internet of Things (IoT) devices. ZigBee Physical Layer (PHY) is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 specification designed to provide a low-cost, low-power, and low-complexity solution for Wireless Sensor Network(s) (WSN). The standard’s extended battery life and reliability makes ZigBee WSN a popular choice for home automation, transportation, traffic management, Industrial Control Systems (ICS), and cyber-physical systems. As robust and versatile as the standard is, ZigBee remains vulnerable to a myriad of common network attacks. Previous research involving Radio Frequency-Distinct Native Attribute …

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 Blockchain-Based Anomalous Detection System For Internet Of Things Devices, Joshua K. Mosby

Theses and Dissertations

Internet of Things devices are highly susceptible to attack, and owners often fail to realize they have been compromised. This thesis describes an anomalous-based intrusion detection system that operates directly on Internet of Things devices utilizing a custom-built Blockchain. In this approach, an agent on each node compares the node's behavior to that of its peers, generating an alert if they are behaving differently. An experiment is conducted to determine the effectiveness at detecting malware. Three different code samples simulating common malware are deployed against a testbed of 12 Raspberry Pi devices. Increasing numbers are infected until two-thirds of the …

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 …

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 …

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 …

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 …