Computer Engineering Commons^™

Guided Autonomy For Quadcopter Photography, Saif Alabachi

Electronic Theses and Dissertations

Photographing small objects with a quadcopter is non-trivial to perform with many common user interfaces, especially when it requires maneuvering an Unmanned Aerial Vehicle (C) to difficult angles in order to shoot high perspectives. The aim of this research is to employ machine learning to support better user interfaces for quadcopter photography. Human Robot Interaction (HRI) is supported by visual servoing, a specialized vision system for real-time object detection, and control policies acquired through reinforcement learning (RL). Two investigations of guided autonomy were conducted. In the first, the user directed the quadcopter with a sketch based interface, and periods of …

Genet-Cnv: Boolean Implication Networks For Modeling Genome-Wide Co-Occurrence Of Dna Copy Number Variations, Salvi Singh

Graduate Theses, Dissertations, and Problem Reports (ETD)

Lung cancer is the leading cause of cancer-related death in the world. Lung cancer can be categorized as non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC makes up about 80% to 85% of lung cancer cases diagnosed, whereas SCLC is responsible for 10% to 15% of the cases. It remains a challenge for physicians to identify patients who shall benefit from chemotherapy. In such a scenario, identifying genes that can facilitate therapeutic target discoveries and better understanding disease mechanisms and their regulation in different stages of lung cancer, remains an important topic of research.

In this …

Leveraging Writing And Photography Styles For Drug Trafficker Identification In Darknet Markets, Wei Song

Graduate Theses, Dissertations, and Problem Reports (ETD)

Due to its anonymity, there has been a dramatic growth of underground drug markets hosted in the darknet (e.g., Dream Market and Valhalla). To combat drug trafficking (a.k.a. illicit drug trading) in the cyberspace, there is an urgent need for automatic analysis of participants in darknet markets. However, one of the key challenges is that drug traffickers (i.e., vendors) may maintain multiple accounts across different markets or within the same market.

To address this issue, in this thesis, we propose and develop an intelligent system named uStyle-uID leveraging both writing and photography styles for drug trafficker identification at the first …

Personal Universes: A Solution To The Multi-Agent Value Alignment Problem, Roman V. Yampolskiy

Faculty and Staff Scholarship

AI Safety researchers attempting to align values of highly capable intelligent systems with those of humanity face a number of challenges including personal value extraction, multi-agent value merger and finally in-silico encoding. State-of-the-art research in value alignment shows difficulties in every stage in this process, but merger of incompatible preferences is a particularly difficult challenge to overcome. In this paper we assume that the value extraction problem will be solved and propose a possible way to implement an AI solution which optimally aligns with individual preferences of each user. We conclude by analyzing benefits and limitations of the proposed approach.

Quantum Metalanguage And The New Cognitive Synthesis, Alexey V. Melkikh, Andrei Khrennikov, Roman V. Yampolskiy

Faculty and Staff Scholarship

Problems with mechanisms of thinking and cognition in many ways remain unresolved. Why are a priori inferences possible? Why can a human understand but a computer cannot? It has been shown that when creating new concepts, generalization is contradictory in the sense that to be created concepts must exist a priori, and therefore, they are not new. The process of knowledge acquisition is also contradictory, as it inevitably involves recognition, which can be realized only when there is an a priori standard. Known approaches of the framework of artificial intelligence (in particular, Bayesian) do not determine the origins of knowledge, …

The Social Presence Of Jibo, Parisa Farhadi

Graduate Research Theses & Dissertations

This research is an attempt to investigate how a social robot is perceived by users at the first encounter. And to find out whether a social robot like Jibo is understood merely as a kind of a technological object or a social entity. Previous studies in Human-Robot Interaction have considered social presence as a mediator in users’ social responses toward robots; however, the focus of this study is the social presence itself and investigates whether Jibo produces a sense of presence during initial encounters. To this end, the current study examines individuals’ perceptions of and responses to Jibo. Participants (N=8) …

A Graph-Based Reinforcement Learning Method With Converged State Exploration And Exploitation, Han Li, Tianding Chen, Hualiang Teng, Yingtao Jiang

Civil and Environmental Engineering and Construction Faculty Research

In any classical value-based reinforcement learning method, an agent, despite of its continuous interactions with the environment, is yet unable to quickly generate a complete and independent description of the entire environment, leaving the learning method to struggle with a difficult dilemma of choosing between the two tasks, namely exploration and exploitation. This problem becomes more pronounced when the agent has to deal with a dynamic environment, of which the configuration and/or parameters are constantly changing. In this paper, this problem is approached by first mapping a reinforcement learning scheme to a directed graph, and the set that contains all …

Textured Contact Lens Based Iris Presentation Attack In Uncontrolled Environment, Daksha Yadav

Graduate Theses, Dissertations, and Problem Reports (ETD)

The widespread use of smartphones has spurred the research in mobile iris devices. Due to their convenience, these mobile devices are also utilized in unconstrained outdoor conditions. At the same time, iris recognition in the visible spectrum has developed into an active area of research. These scenarios have necessitated the development of reliable iris recognition algorithms for such an uncontrolled environment. Additionally, iris presentation attacks such as textured contact lens pose a major challenge to current iris recognition systems.

Motivated by these factors, in this thesis, a detailed analysis of the effect of textured contact lenses on iris recognition in …

Security Bug Report Classification Using Feature Selection, Clustering, And Deep Learning, Tanner D. Gantzer

Graduate Theses, Dissertations, and Problem Reports (ETD)

As the numbers of software vulnerabilities and cybersecurity threats increase, it is becoming more difficult and time consuming to classify bug reports manually. This thesis is focused on exploring techniques that have potential to improve the performance of automated classification of software bug reports as security or non-security related. Using supervised learning, feature selection was used to engineer new feature vectors to be used in machine learning. Feature selection changes the vocabulary used by selecting words with the greatest impact on classification. Feature selection was able to increase the F-Score across the datasets by increasing the precision. We also explored …

Immunity-Based Framework For Autonomous Flight In Gps-Challenged Environment, Mohanad Al Nuaimi

Graduate Theses, Dissertations, and Problem Reports (ETD)

In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about …

Kidney Ailment Prediction Under Data Imbalance, Ranaa Mahveen

Graduate Theses, Dissertations, and Problem Reports (ETD)

Chronic Kidney Disease (CKD) is the leading cause for kidney failure. It is a global health problem affecting approximately 10% of the world population and about 15% of US adults. Chronic Kidney Diseases do not generally show any disease specific symptoms in early stages thus it is hard to detect and prevent such diseases. Early detection and classification are the key factors in managing Chronic Kidney Diseases.

In this thesis, we propose a new machine learning technique for Kidney Ailment Prediction. We focus on two key issues in machine learning, especially in its application to disease prediction. One is related …

An Empirical Analysis Of An Algorithm For The Budgeted Maximum Vertex Cover Problem In Trees, Mujidat Abisola Adeyemo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Covering problems are commonly studied in fields such as mathematics, computer science, and engineering. They are also applicable in the real world, e.g., given a city, can we build base-stations such that there is network availability everywhere in the city. However, in the real world, there are usually constraints such as cost and resources. The Budgeted Maximum Vertex Cover is a generalization of covering problems. It models situations with constraints. In this thesis, we empirically analyze an algorithm for the problem of finding the Budgeted Maximum Vertex Cover in undirected trees (BMVCT). The BMVCT problem is defined as follows: Given …

Virtual Morphology As A Method Of Robotic Control, Conner Todd Castle

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis presents Virtual Morphology (VM), a method that explores a different perspective on the design of robot autonomy using inspiration from morphological computing and programmed computation. Morphological computation offers physical solutions that solve complex tasks, like robotic grasping of unknown objects, with relative ease. Unfortunately, these physical solutions are difficult to adjust post-development, and are usually designed to complete only one or a few specific tasks. Programmed computational approaches are more flexible because they can be implemented and adjusted through software, but unfortunately, these approaches can become rather complex as tasks become more difficult. This thesis explores the potential …

Investigation And Development Of Exhaust Flow Rate Estimation Methodologies For Heavy-Duty Vehicles, Chakradhar Reddy Vardhireddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Exhaust gas flow rate from a vehicle tailpipe has a great influence on emission mass rate calculations, as the emission fractions of individual gases in the exhaust are calculated by using the measured exhaust flow rate. The development of high-end sensor technologies and emission pollutant measurement instruments, which can give instantaneous values of volume concentration of pollutants flowing out of the engine are gaining importance because of their ease of operation. The volume concentrations measured can then be used with the instantaneous exhaust flow rate values to obtain mass flow rates of pollutants.

With the recent promulgation of real world …

Roadnet: Robust Adaptive Network For Information Diffusion In Vanet, Priyashraba Misra

Graduate Theses, Dissertations, and Problem Reports (ETD)

The automotive industry has changed more in the last one decade than ever before. Rapid advancements in autonomous driving have opened up opportunities for CAVs (Connected and Automated Vehicles). Vehicles today rely on a sensor-suite to map the surrounding and use that information for safety and navigation. The sensor's view is limited to its line of sight and this drawback can be tapered off by using Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I), generally referred to as Vehicle to Everything (V2X) communication. In this thesis, we specifically focus on utilizing V2V communication using on-board Dedicated Short Range Communication …

Improve Operating Room Utilization Through Distributed Scheduling Workflow And Automation, Miteshkumar Mahendrabhai Vasoya

Browse all Theses and Dissertations

Operating room (OR) plays a crucial role in health care, contributing more than 50% of the hospital’s revenue and incurring over 35% of the hospital’s expense, ultimately determining the hospital’s profitability. Moreover, because the OR is a primary source of admissions, it is virtually impossible to streamline hospital‐wide workflow without first streamlining patient flow through the OR. Unfortunately, current OR scheduling practices often limit the utilization of OR, one of the most expensive resources in the health care industry, to around 60%. On the other hand, many patients have to wait an excessively long time before their surgeries can be …

Human Action Recognition In Videos Using Transfer Learning, Kaiqiang Huang, Sarah Jane Delany, Susan Mckeever

Session 1: Active Vision, Tracking, Motion Analysis

A variety of systems focus on detecting the actions and activities performed by humans, such as video surveillance and health monitoring systems. However, published labelled human action datasets for training supervised machine learning models are limited in number and expensive to produce. The use of transfer learning for the task of action recognition can help to address this issue by transferring or re-using the knowledge of existing trained models, in combination with minimal training data from the new target domain. Our focus in this paper is an investigation of video feature representations and machine learning algorithms for transfer learning for …

Youtube’S Terms Of Service: Posthumanism, Algorithms, And Professional Writing, Sarah Bresnahan

Graduate Research Theses & Dissertations

This thesis aims to examine YouTube’s Terms of Service as it applies to content creators (known as YouTubers) who use the platform as a means of financial gain and how YouTube’s demonetization policy via an algorithm is negatively affecting them. I conducted a case study featuring one creator, Michelle Guido, and attempted to determine why some of her content is demonetized when it fulfills YouTube’s content standards for monetization. This study is meant as an examination through the lens of Dr. N. Katherine Hayles’s theory of posthumanism as stated in her book, How We Became Posthuman, and will offer insight …

Exploring Cyber-Physical Systems, Misbah Uddin Mohammed

Graduate Research Theses & Dissertations

The advances in IOT, Computer Vision, AI and Machine Learning have made these technologies ubiquitous to our daily lives. From Smart Phones to Connected Vehicles, Cyber Physical systems have been interspersed into everything we interact in today’s world. The aim or this thesis was to explore these advances in Cyber Physical Systems and analyze the different sectors they were affecting. We then hand-picked certain domains and explored further by carrying out practical projects using some of the latest software and hardware resources available. Technologies like Amazon Alexa services, NVIDIA Jetson boards, TensorFlow, OpenCV, NodeJS were heavily employed in our various …

Small Non-Profit Website And Social Media Efficacy, Juliana Maria Leprich

Graduate Research Theses & Dissertations

This thesis examines the website design and social media platform usage of small non-profit organizations (defined as less than $1M operating budget and less than ten staff). This research will be completed by first reviewing other studies that have been done on marketing in the non-profit sector, then by exploring marketing challenges firsthand through a mini-ethnographic case study as described in the mini-ethnographic case study approach, and finally through the examination of tax and financial data provided by Guidestar. The researcher was involved in website collaboration with The Gracie Center in a volunteer capacity and was employed with the two …

Accessibility And Decay Of Web Citations In Computer Science Journals, Mohsen Jalali

Library Philosophy and Practice (e-journal)

The aim of this research is to scrutiny the accessibility and decay of web citations (URLs) used in refereed articles published by 27 Computer Science open access journals as indexed by Scopus. To do this, at first, we downloaded 1000 articles of Computer Science open access journals from 2009 to 2018. After acquiring articles, their web citations are extracted and analyzed from the accessibility and decay point of view. Moreover, for initially missed web citations complementary pathways such as using Google search engine are employed. Then, data collected are analyzed using descriptive statistical methods. Research findings indicated that 80.7% of …

Volumetric Error Compensation For Industrial Robots And Machine Tools, Le Ma

Doctoral Dissertations

“A more efficient and increasingly popular volumetric error compensation method for machine tools is to compute compensation tables in axis space with tool tip volumetric measurements. However, machine tools have high-order geometric errors and some workspace is not reachable by measurement devices, the compensation method suffers a curve-fitting challenge, overfitting measurements in measured space and losing accuracy around and out of the measured space. Paper I presents a novel method that aims to uniformly interpolate and extrapolate the compensation tables throughout the entire workspace. By using a uniform constraint to bound the tool tip error slopes, an optimal model with …

Hydrogen Fuel Cell Gasket Handling And Sorting With Machine Vision Integrated Dual Arm Robot, Devin C. Fowler

College of Graduate Studies: Theses & Dissertations

Recently demonstrated robotic assembling technologies for fuel cell stacks used fuel cell components manually pre-arranged in stacks (presenters), all oriented in the same position. Identifying the original orientation of fuel cell components and loading them in stacks for a subsequent automated assembly process is a difficult, repetitive work cycle which if done manually, deceives the advantages offered by automated fabrication technologies of fuel cell components and by robotic assembly processes. We present an innovative robotic technology which enables the integration of automated fabrication processes of fuel cell components with robotic assembly of fuel cell stacks into a fully automated fuel …

Eaglebot: A Chatbot Based Multi-Tier Question Answering System For Retrieving Answers From Heterogeneous Sources Using Bert, Muhammad Rana

College of Graduate Studies: Theses & Dissertations

This paper proposes to tackle Question Answering on a specific domain by developing a multi-tier system using three different types of data storage for storing answers. For testing our system on University domain we have used extracted data from Georgia Southern University website. For the task of faster retrieval we have divided our answer data sources into three distinct types and utilized Dialogflow's Natural Language Understanding engine for route selection. We compared different word and sentence embedding techniques for making a semantic question search engine and BERT sentence embedding gave us the best result and for extracting answer from a …

A Hardware-Deployable Neuromorphic Solution For Encoding And Classification Of Electronic Nose Data, Anup Vanarse, Alexander Rassau, Peter Van Der Made

Research outputs 2014 to 2021

In several application domains, electronic nose systems employing conventional data processing approaches incur substantial power and computational costs and limitations, such as significant latency and poor accuracy for classification. Recent developments in spike-based bio-inspired approaches have delivered solutions for the highly accurate classification of multivariate sensor data with minimized computational and power requirements. Although these methods have addressed issues related to efficient data processing and classification accuracy, other areas, such as reducing the processing latency to support real-time application and deploying spike-based solutions on supported hardware, have yet to be studied in detail. Through this investigation, we proposed a spiking …

Effective Plant Discrimination Based On The Combination Of Local Binary Pattern Operators And Multiclass Support Vector Machine Methods, Vi N T Le, Beniamin Apopei, Kamal Alameh

Research outputs 2014 to 2021

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the …

Real-Time Classification Of Multivariate Olfaction Data Using Spiking Neural Networks, Arnup Vanarse, Adam Osseiran, Alexander Rassau, Therese O'Sullivan, Jonny Lo, Amanda Devine

Research outputs 2014 to 2021

Recent studies in bioinspired artificial olfaction, especially those detailing the application of spike-based neuromorphic methods, have led to promising developments towards overcoming the limitations of traditional approaches, such as complexity in handling multivariate data, computational and power requirements, poor accuracy, and substantial delay for processing and classification of odors. Rank-order-based olfactory systems provide an interesting approach for detection of target gases by encoding multi-variate data generated by artificial olfactory systems into temporal signatures. However, the utilization of traditional pattern-matching methods and unpredictable shuffling of spikes in the rank-order impedes the performance of the system. In this paper, we present an …

Modeling Context-Adaptive Energy-Aware Security In Mobile Devices, Preeti Singh

UNF Graduate Theses and Dissertations

As increasing functionality in mobile devices leads to rapid battery drain, energy management has gained increasing importance. However, differences in user’s usage contexts and patterns can be leveraged for saving energy. On the other hand, the increasing sensitivity of users’ data, coupled with the need to ensure security in an energy-aware manner, demands careful analyses of trade-offs between energy and security. The research described in this thesis addresses this challenge by 1)modeling the problem of context-adaptive energy-aware security as a combinatorial optimization problem (Context-Sec); 2) proving that the decision version of this problem is NP-Complete, via a reduction from a …

On Learning And Visualizing Lexicographic Preference Trees, Ahmed S. Moussa

UNF Graduate Theses and Dissertations

Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of …

Smart Control Of Automatic Voltage Regulators Using K-Means Clustering, Brook Abegaz, J. Kueber

Engineering Science Faculty Publications

The future cyber physical systems consist of voltage regulators distributed across wide geographical areas. In this paper, a smart control approach of voltage regulators is presented for cyber physical system applications. The approach is implemented using K-means clustering algorithms that use data from voltage and current sensors, compute the correlation of changes across the regulators and generate a proportional feedback. Advanced estimation methods are used in cases where the data from the sensors was not available. The results show that the approach could be used to improve the performance of networked, power dependent systems by 94.5% in terms of overshoot …