Engineering Commons^™

How Cloud, Edge, And Mist Computing Affects Resource Management In Vanets, Andrew Trombly, Izzat Alsmadi

Masters Theses (Archived)

With public interest in automated vehicles as well as self-aware and responsive smart cities the demand for fast and efficient communication and computation will push our current infrastructure beyond its limits. Vehicular networks
(VANETs) look to solve this issue but suffer from extremely dynamic topologies
with unpredictable times and locations of high network and computational de-
minds from highly mobile units. This thesis looks into how current scholarly
works attempt resource management in VANETs and simulate VANETs with
cloud, edge, or mist computing architectures. The data of are compared and
discussed to drive a data-driven understanding and conversation of architecture …

Control Of Fully-Actuated Aerial Manipulators And Omni-Directional Multirotors, Riley M. Mccarthy

Mechanical Engineering ETDs

This thesis details the system modeling, design, control, simulation, construction, and
testing of both a fully-actuated and omni-directional multirotor aerial system created
for the primary purpose of performing active tasks with their environment. This work
verifies the capabilities of both systems through empirical testing, and demonstrates
how through the use of new control methods and physical designs multirotors can
expand their purpose from passive inspection based tasks to active contact based
tasks. These systems take advantage of newly implemented control allocation features present in the PX4 flight control software, version 1.14. The use of which makes designing controllers for such …

Terahertz Permittivity Parameters Of Monoclinic Single Crystal Lutetium Oxyorthosilicate, Sean Knight, Steffen Richter, Alexis Papamichail, Megan Stokey, Rafał Korlacki, Vallery Stanishev, Philipp Kühne, Mathias Schubert, Vanya Darakchieva

Department of Electrical and Computer Engineering: Faculty Publications

The anisotropic permittivity parameters of monoclinic single crystal lutetium oxyorthosilicate, Lu₂SiO₅ (LSO), have been determined in the terahertz spectral range. Using terahertz generalized spectroscopic ellipsometry (THz-GSE), we obtained the THz permittivities along the a, b, and c^⋆ crystal directions, which correspond to the ε_a; ε_b, and ε_c? on-diagonal tensor elements. The associated off diagonal tensor element ε_ac? was also determined experimentally, which is required to describe LSO’s optical response in the monoclinic a–c crystallographic plane. From the four tensor elements obtained in the model fit, we calculate the …

Ai’S Next Frontier — Dermatology? How It May Help Close The Health Gap For Dark-Skinned Patients, Gretchen B. Smail

Capstones

This project examines how engineers are creating artificial intelligence systems to help with dermatology diagnosis. Researchers believe that these systems can help close the health care gap in lower income and Black and brown communities, but dermatologists of color worry that these systems are not being trained to recognize skin conditions on patients of color.

Survey Of Transfer Learning Approaches In The Machine Learning Of Digital Health Sensing Data, Lina Chato, Emma Regentova

Electrical & Computer Engineering Faculty Research

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these …

Turnstile File Transfer: A Unidirectional System For Medium-Security Isolated Clusters, Mark Monnin, Lori L. Sussman

Journal of Cybersecurity Education, Research and Practice

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. …

Chronic Kidney Disease Android Application, Paul Le

Computer Science and Engineering Senior Theses

Chronic kidney disease is increasingly recognized as a leading public health problem over the world that affects more than 10 percent of the population worldwide, where electrolytes and wastes can build up in your system. Kidney failure might not be noticeable until more advanced stages where it may then become fatal if not for artificial filtering or a transplant. As a result, it is important to detect kidney disease early on to prevent it from progressing to kidney failure. The current main test of the disease is a blood test that measures the levels of a waste product called creatine …

Finding Diffs Of Pull Request Commits, Chalinee Karawek, John Businge

Undergraduate Research Symposium Posters

Github is a social network that allows developers' projects to be forked for various uses, such as developing the existing repository or use the code to steer development into a new direction. As more forks are made, the more bugs that can occur due to new pull requests not synchronizing with the upstream repository. PaReco is a clone detection tool by Ramkisoen et al. (Ramkisoen et al. 2022) that identifies the changes inside the files inside a pull request by performing diff on every file. However, in this study, we take a different approach to identifying the changes inside the …

Safety-Aware Autonomous Robot Navigation, Mapping And Control By Optimization Techniques, Tingjun Lei

Theses and Dissertations

The realm of autonomous robotics has seen impressive advancements in recent years, with robots taking on essential roles in various sectors, including disaster response, environmental monitoring, agriculture, and healthcare. As these highly intelligent machines continue to integrate into our daily lives, the pressing imperative is to elevate and refine their performance, enabling them to adeptly manage complex tasks with remarkable efficiency, adaptability, and keen decision-making abilities, all while prioritizing safety-aware navigation, mapping, and control systems. Ensuring the safety-awareness of these robotic systems is of paramount importance in their development and deployment. In this research, bio-inspired neural networks, nature-inspired intelligence, deep …

Adaptive Traction, Power And Torque Control Strategies And Optimization In An All-Electric Powertrain, Aymane Hidara

Theses and Dissertations

Electric and hybrid-electric vehicles lean heavily on intricate control algorithms to provide smooth, reliable, and secure operations under any driving conditions. Three distinct supervisory control strategies have been developed, each aiming to improve reliability and vehicle performance of a dual-motor electric vehicle equipped with an all-wheel-drive, fully electric powertrain. These algorithms are adept at dynamically modulating and constraining the torque provided to the wheels, leveraging two autonomous permanent magnet electric drive units. This study utilizes a vehicle model jointly provided by MathWorks and General Motors in partnership with industry sponsors. The these strategies were implemented in the model and enhanced …

Timeseries Forecasting Of U.S. Housing Price Index Using Machine Learning And Deep Learning Models, Krishna Chaitanya Nunna

Dissertations

Time series forecasting is a promising technique for various applications which predicts future values or patterns by taking historical data as base. Forecasting future trends is very beneficial for different industries to make valuable decisions and strategies. One such industry is housing market; it has biggest influence on U.S. economy. Housing price index (HPI) is a one of the crucial economic indices published by various government funded and private agency to benefit several industries and individuals for better analysis of future trends of housing market.

Several factors influence the HPI, economical, geographical, and demographic features. Development of traditional time series …

Data Science And The Ethics Of Private Information, Faria R. Promi

Publications and Research

This research focuses on the moral questions linked to modern data technologies like Big Data and the Internet of Things. These technologies can be, but they are also about privacy and keeping data secure. We've looked at what experts say about these concerns and discovered that most agree we need to safeguard people's privacy and data. Our research encourages everyone to use these technologies, finding a balance between technological progress and responsible, ethical behavior. This way, we can enjoy the benefits of these technologies while also respecting individuals' privacy and data rights. The study reviews what experts have said about …

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

A Low-Power Analog Cell For Implementing Spiking Neural Networks In 65 Nm Cmos, John S. Venker, Luke Vincent, Jeff Dix

Computer Science and Computer Engineering Faculty Publications and Presentations

A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, such as speech recognition. The proposed network uses a leaky integrate and fire neuron scheme for computation, interleaved with a Spike Timing Dependent Plasticity (STDP) circuit for implementing synaptic-like weights. The low-power, asynchronous analog neurons and synapses are tailored for the VLSI environment needed to effectively make use of hardware SSN systems. To demonstrate functionality, a feedforward Spiking Neural Network composed of two layers, …

Pollutant Forecasting Using Neural Network-Based Temporal Models, Richard Pike

Masters Theses & Specialist Projects

The Jing-Jin-Ji region of China is a highly industrialized and populated area of the country. Its periodic high pollution and smog includes particles smaller than 2.5 μm, known as PM2.5, linked to many respiratory and cardiovascular illnesses. PM2.5 concentration around Jing-Jin-Ji has exceeded China’s urban air quality safety threshold for over 20% of all days in 2017 through 2020.

The quantity of ground weather stations that measure the concentrations of these pollutants, and their valuable data, is unfortunately small. By employing many machine learning strategies, many researchers have focused on interpolating finer spatial grids of PM2.5, or hindcasting PM2.5. However, …

Ethical Implications Of Ai-Based Algorithms In Recruiting Processes: A Study Of Civil Rights Violations Under Title Vii And The Americans With Disabilities Act, Vanessa Rodriguez

Cyber Operations and Resilience Program Graduate Projects

This research paper analyzes the ethical implications of utilizing artificial intelligence, specifically AI-based algorithms in business selection and recruiting processes, with a focus on potential violations under Title VII of the Civil Rights Act of 1964 and Title 1 of the Americans with Disabilities Act (ADA). Amazon’s attempt at launching AI recruiting tools is examined. This paper will assess the fairness of AI recruiting practices, considering data collection, potential biases, and accuracy concerns in its implementation process. Additionally, the paper will provide an overview of federal civil rights statutes enforced by the U.S. Equal Employment Opportunity Commission (EEOC) and recent …

Recycled Polycarbonate And Polycarbonate/Acrylonitrile Butadiene Styrene Feedstocks For Circular Economy Product Applications With Fused Granular Fabrication-Based Additive Manufacturing, Alessia Romani, Marinella Levi, Joshua M. Pearce

Electrical and Computer Engineering Publications

Distributed recycling and additive manufacturing (DRAM) holds enormous promise for enabling a circular economy. Most DRAM studies have focused on single thermoplastic waste stream. This study takes three paths forward from the previous literature: 1) expanding DRAM into high-performance polycarbonate/ acrylonitrile butadiene styrene (PC/ABS) blends, 2) extending PC/ABS blend research into both recycled materials and into direct fused granular fabrication (FGF) 3-D printing and 3) demonstrating the potential of using recycled PC/ABS feedstocks for new applications in circular economy contexts. A commercial open source large-format FGF 3-D printer was modified and used to assess the different printability and accuracy of …

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …

Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson

Cybersecurity Undergraduate Research Showcase

For this research project I used a Raspberry Pi device and conducted online research to investigate potential security vulnerabilities along with mitigation strategies. I configured the Raspberry Pi by using the proper peripherals such as an HDMI cord, a microUSB adapter that provided 5V and at least 700mA of current, a TV monitor, PiSwitch, SD Card, keyboard, and mouse. I installed the Rasbian operating system (OS). The process to install the Rasbian took about 10 minutes to boot starting at 21:08 on 10/27/2023 and ending at 21:18. 1,513 megabytes (MB) was written to the SD card running at (2.5 MB/sec). …

Are We Day-Dreaming Our Way To The Future?, John O'Connor

Presentations

This paper explores the transformative impact of technology on society, drawing on Marshall McLuhan’s insights. It scrutinizes the consequences of profit-driven technological progress, particularly in VR, brain-computer interfaces, and AI hallucinations. Critiquing the dominance of industry leaders in AI safety discussions, the paper advocates a balanced, inclusive approach.

Philosophical perspectives on AI and VR prompt questions about their impact on human experience. The paper proposes an educational shift to cultivate human attributes alongside technological skills. Examining AI hallucinations and gaming glitches, it raises concerns about the potential blurring of reality and virtuality.

Connecting technological advancements with environmental challenges, the paper …

Resilient, Sustainable, And Secure Systems Support For Ultra-Low-Power Computational Things, Nicole Tobias

All Dissertations

Wireless battery-free and energy-harvesting devices are expanding the reach and vision of the Internet of Things, where trillions of embedded computational things interconnect ubiquitously around us and inform many different aspects of our everyday lives. Designing these systems without batteries and interconnecting wires lowers maintenance, environmental, and economic costs while also extending device lifetime and deployment opportunities. Over the last decade, research on these ultra-low-power embedded sensors and systems has dramatically increased — enabling new and exciting prospects in many different scientific fields, from smart building and health monitoring applications to animal and activity tracking.

These systems are not without …

From Leanstore To Learnedstore: Using A Learned Index To Improve Database Index Search, Sujit Maharjan

Computer Science and Engineering Faculty Publications - Archive

In the realm of database systems, optimizing B+-tree index performance is of paramount importance to overall database performance. LeanStore, a high-performance OLTP storage engine, has extensively optimized its in-memory B+-tree component as well as its B+-tree -indexed database on the disk. However, B+-tree's lookup time increases linearly with the tree height. This is especially problematic when all or part of its lookup path is on the disk. Recently proposed learned index technique has the potential to significantly improve the performance of the B+-tree -based index by predicting location of the search key, instead of the level-by-Ievel path walk. However, this …

Brunet: Disruption-Tolerant Tcp And Decentralized Wi-Fi For Small Systems Of Vehicles, Nicholas Brunet

Master's Theses

Reliable wireless communication is essential for small systems of vehicles. However, for small-scale robotics projects where communication is not the primary goal, programmers frequently choose to use TCP with Wi-Fi because of their familiarity with the sockets API and the widespread availability of Wi-Fi hardware. However, neither of these technologies are suitable in their default configurations for highly mobile vehicles that experience frequent, extended disruptions. BRUNET (BRUNET Really Useful NETwork) provides a two-tier software solution that enhances the communication capabilities for Linux-based systems. An ad-hoc Wi-Fi network permits decentralized peer-to-peer and multi-hop connectivity without the need for dedicated network infrastructure. …

Decentralized Machine Learning On Blockchain: Developing A Federated Learning Based System, Nikhil Sridhar

Master's Theses

Traditional Machine Learning (ML) methods usually rely on a central server to per-
form ML tasks. However, these methods have problems like security risks, data
storage issues, and high computational demands. Federated Learning (FL), on the
other hand, spreads out the ML process. It trains models on local devices and then
combines them centrally. While FL improves computing and customization, it still
faces the same challenges as centralized ML in security and data storage.

This thesis introduces a new approach combining Federated Learning and Decen-
tralized Machine Learning (DML), which operates on an Ethereum Virtual Machine
(EVM) compatible blockchain. The …

On Dyadic Parity Check Codes And Their Generalizations, Meraiah Martinez

Department of Mathematics: Dissertations, Theses, and Student Research

In order to communicate information over a noisy channel, error-correcting codes can be used to ensure that small errors don’t prevent the transmission of a message. One family of codes that has been found to have good properties is low-density parity check (LDPC) codes. These are represented by sparse bipartite graphs and have low complexity graph-based decoding algorithms. Various graphical properties, such as the girth and stopping sets, influence when these algorithms might fail. Additionally, codes based on algebraically structured parity check matrices are desirable in applications due to their compact representations, practical implementation advantages, and tractable decoder performance analysis. …

Development Of A Machine Learning System For Irrigation Decision Support With Disparate Data Streams, Eric Wilkening

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

In recent years, advancements in irrigation technologies have led to increased efficiency in irrigation applications, encompassing the adoption of practices that utilize data-driven irrigation scheduling and leveraging variable rate irrigation (VRI). These technological improvements have the potential to reduce water withdrawals and diversions from both groundwater and surface water sources. However, it is vital to recognize that improved application efficiency does not necessarily equate to increased water availability for future or downstream use. This is particularly crucial in the context of consumptive water use, which refers to water consumed and not returned to the local or sub-regional watershed, representing a …

In Situ Water Sensing Systems: Research On Advancements In Environmental Monitoring, Abigail Seibel

Honors Program: Senior Projects (Public)

In this work, two sensing systems were researched in order to improve in situ environmental monitoring. The first is a pH and Total Alkalinity sensor used to determine these characteristics of sea water. I explored the facets of this sensor over a 7-week internship with Dr. Ellen Briggs in her lab in summer of 2023. The second is a more holistic sensing system that reads temperature, turbidity, and pressure used for studying environmental characteristics of Alaskan bever ponds. Both systems were developed in close collaboration with scientists who are collecting data to better understand the impacts of climate change. Better …

Electronic Note-String Detector, Gavin Garcia-Rossi, Tommy Smail

Electrical Engineering

As the virtual space has become a dominant part of everyone’s day-to-day lives, many normal face-to-face interactions and services have not yet been facilitated by adapting technology. One of these prevailing areas is music lessons. Over Zoom meetings, or other virtual platforms, it is tremendously challenging to teach students. These challenges include recognizing student mistakes audibly and visually, and being able to give confident feedback on the incorrect notes played by learning musicians. Without having to delve into improving the complex systems that would be required to improve audio, video, and connection quality of these connections, we have another solution …

Systematic Literature Review On Ontology-Based Indonesian Question Answering System, Fadhila Tangguh Admojo, Adidah Lajis, Haidawati Nasir

Knowledge Engineering and Data Science

Question-Answering (QA) systems at the intersection of natural language processing, information retrieval, and knowledge representation aim to provide efficient responses to natural language queries. These systems have seen extensive development in English and languages like Indonesian present unique challenges and opportunities. This literature review paper delves into the state of ontology-based Indonesian QA systems, highlighting critical challenges. The first challenge lies in sentence understanding, variations, and complexity. Most systems rely on syntactic analysis and struggle to grasp sentence semantics. Complex sentences, especially in Indonesian, pose difficulties in parsing, semantic interpretation, and knowledge extraction. Addressing these linguistic intricacies is pivotal for …

Eeg Classification While Listening To Murottal Al-Quran And Classical Music Using Random Forest Method, Heni Sumarti, Fahira Septiani, Agus Sudarmanto, Wahyu Caesarendra, Rizki Edmi Edison

Knowledge Engineering and Data Science

This study is aimed to classify the brain activity of adolescents associated with audio stimuli; murottal Al-Quran and classical music. The raw data were filtered using Independent Component Analisys (ICA) and followed by band-pass filter in Python on the Google Colab Extraction was processed with Power Spectral Density (PSD) and the Random Forest Method in Weka Machine Learning was used for classification. The research results showed the same results between the two types of stimulation, namely the order of brain waves from highest to lowest were delta, alpha, theta and beta. The average brain waves of teenagers when given murottal …