Engineering Commons^™

Modeling The Human Learning Process Using An Industrial Steam Boiler Analogy To Design A Psychophysiological-Based Hypermedia Adaptive Automation System, Liliana María Villavicencio López

USF Tampa Graduate Theses and Dissertations

This dissertation aims to address the existing gap in the integration of various dimensions within the student learning system, encompassing cognitive, emotional, and physical variables. The primary objective is to construct a Personalized Learning Adaptive Automation model using Electroencephalography (EEG) technology.

To provide deeper insight into the intricate nature of the Human Learning Process, this study introduces a novel analogy with an Industrial Steam Boiler. This analogy serves as a distinctive contribution to research in the field.

The research methodology involved the collection of brainwaves data from engineering students while they undertook educational tasks of varying levels of difficulty, categorized …

Effects Of Unobservable Bus States On Detection And Localization Of False Data Injection Attacks In Smart Grids, Moheb Abdelmalak

USF Tampa Graduate Theses and Dissertations

In an era increasingly marked by sophisticated cyber-attacks, this thesis investigates the critical issue of bus unobservability in smart grids and its impact on the effectiveness of cyber-attack detection and localization models. Given that unobservability is a prevalent challenge in smart grids due to various factors, researchers have developed numerous algorithms for optimal Phasor Measurement Unit (PMU) placement under scenarios of limited observability. However, these models primarily focus on enhancing network observability, often without considering whether this placement optimally facilitates attack detection. This research is driven by the hypothesis that a deeper understanding of the effects of unobservable buses can …

Development Of A Plant Growth And Health Monitoring System Using Imaging And Sensor Array Information For Cubesat Applications, Kat-Kim Phan

USF Tampa Graduate Theses and Dissertations

Space exploration has been a topic of interest in the scientific community, such as the planned missions to Mars. To accomplish this would require being able to provide astronauts with a steady supply of food beyond freeze-dried foods leading to the need to grow food in space. Although this is a topic still being investigated, the CubeSat platform opens the possibility of carrying out studies on plant growth under more strenuous space conditions unlike that in the International Space Station (ISS). Developing a plant-focused mission for a CubeSat, though, entails being able to develop a system to sustain plant life …

Novel Systems Engineering Framework Analysis Of Photovoltaic Models And Equations, Peter R. Michael

USF Tampa Graduate Theses and Dissertations

This dissertation analyzes Photovoltaic PV equations and models for silicon based systems from a Systems Engineering framework. Background information includes an introduction, a summary of the state of PV use, a brief history of photovoltaics, and the detailed derivation of equations that enable the finding of the PV parameters contained in the PV models.The novel inquiry, leveraging systems engineering frameworks, includes three areas useful in analyzing PV equations and models. The first is a statistical verification of common simplifications of PV equations at the primary conditions of short circuit, open circuit, and maximum power. Additional analysis shows other simplifications that …

Cyber-Physical Multi-Robot Systems In A Smart Factory: A Networked Ai Agents Approach, Zixiang Nie

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on addressing the technical challenges of non-stationarity in smart factories through the use of cyber-physical AI agents. Industry 4.0 and smart manufacturing with smart factories as a central role, have a growing demand for Just-in-Time (JIT) and on-demand production, as well as mass customization—all while maintaining high productivity, resource efficiency and resilience. This research positions Multi-Robot Systems (MRS)-driven smart factories. The heterogeneous production and transportation robots in an MRS collaborate to form multiple real-time adjusted production flows achieving the flexibility to accommodate such on-demand, mass customization.

However, the implementation of MRS introduces new sets of challenges, including …

Enhancing Smart Grid Security And Reliability Through Graph Signal Processing And Energy Data Analytics, Md Abul Hasnat

USF Tampa Graduate Theses and Dissertations

Situational awareness in a large, dynamic, and complex cyber-physical critical infrastructure, such as a smart grid, is vital for ensuring its smooth and uninterrupted operation. With the evolving realities of the modern-day smart grids, new challenges associated with the situational awareness of these systems are emerging that demand intelligent and efficient solutions. This dissertation intends to address several problems for enhancing situational awareness by studying the dynamic interaction among the components of the smart grids through energy data analytics using various data-driven, machine learning, and graph signal processing (GSP) techniques. The presented work provides valuable insight into the data-driven analysis …

Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen

USF Tampa Graduate Theses and Dissertations

Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality …

Fabric-Based Organic Electrochemical Transistor Towards Wearable Ph Sensing Electronics, Nestor Osvaldo Marquez Rios

USF Tampa Graduate Theses and Dissertations

Wearable electronics interest has attracted the attention of a few sectors because of their applicability in different areas like healthcare, military, and our daily basis. Still building circuits on fabrics to make these wearable electronics is challenging. As transistors are the building blocks of electronic circuits and most of the biosensors, recently, fiber-shaped electrochemical transistors have been studied extensively for a lot of applications including bioelectronics. Fiber-based devices are getting popular in different applications due to their low fabrication cost, lightweight, and flexibility without losing their properties. Additionally, they are potentially suitable for making sensors on garments.

In this work, …

Analysis And Model Of Sensor-Less Modified Direct Torque Control Surface Permanent Magnet Synchronous Machine For Electrical Submersible Pumping Applications, Mulu Woldeyohannes

USF Tampa Graduate Theses and Dissertations

This dissertation examines a novel sensor-less Direct Torque Control (DTC) strategy for Electrical Submersible Pump (ESP) systems using Surface Mounted Permanent Magnet Synchronous Motors (SPMSM) that are used for oil and gas production. As oil and gas are the two largest fuels in use today to generate energy, the technologies to improve efficiency, increase reliability and reduce carbon footprint are essential. SPMSM is one of the main motor topologies in use to improve the reliability and efficiency of ESP systems. Due to the absence of damper winding, SPMSM cannot be started using Direct On-Line (DOL) control. Instead, Variable Speed Drives …

Multiple Access Techniques Enabling Diverse Wireless Services, Mehmet Mert Şahin

USF Tampa Graduate Theses and Dissertations

The growing interest in diverse wireless applications such as virtual reality (VR), digital twin, ultra-massive broadband communication (u-MBB) has led the cellular industry to look for new multiple accessing schemes and new signal processing techniques for utilization of next-generation wireless technologies. Throughout the PhD duration, different studies are performed in regards to dual-functional and interference robust multiple accessing and control of electromagnetic radiation for cognitive radio systems. Firstly, a novel concept on non-orthogonal multiple access (NOMA), which is the coexistence of different waveform structures in the same resource elements is proposed. Secondly, a novel JCR waveform is proposed to serve …

Development And Implementation Of Telemetry Devices To Identify And Characterize Sources Of Intraocular Pressure Variability In Rats, Christina M. Nicou

USF Tampa Graduate Theses and Dissertations

Eye health depends partially on intraocular pressure (IOP) as abnormal levels can lead to ocular tissue damage. Glaucoma is a neurodegenerative disease that affects nearly 80 million people worldwide [1]. It is associated with elevated IOP, which can lead to irreversible blindness. Relatively little is known about IOP dynamics and the physiological factors that affect it as IOP is typically monitored using tonometry. Tonometry is a common tool used by clinicians and researchers to measure IOP noninvasively. It provides a good estimate of IOP mean but not variance because data collection takes time. Readings can also be influenced by subject …

Remote Medical Diagnosis Via Infrared Thermography And Augmented Reality, Frederick M. Selkey

USF Tampa Graduate Theses and Dissertations

Fast, accurate, and non-invasive diagnostic techniques are required by the medical industry to increase the success of medical treatments and enhance the quality of patient care. Medical IRT has been demonstrated reasonably effective at diagnosing and monitoring several physiological conditions. Diversities in the human body, physical and psychological condition, measurement equipment, and environment all influence the sensitive readings obtained by passive IR measurement devices. New standards for medical IRT and fever screening have been demonstrated effective, but there is limited adherence to the guidelines [36]. Absolute temperature readings require regular calibration checks and can easily be thrown off by noise. …

Process Automation And Robotics Engineering For Industrial Processing Systems, Drake Stimpson

USF Tampa Graduate Theses and Dissertations

Automation in industrial systems applications has emerged as the fundamental solution for improving quality, production rate, and efficiency of a process. Much of the recent popularity surrounding the transition of processes from manually operated tasks to automated systems can be attributed to the concept of Industry 4.0, which outlines the fundamental guidelines for integrating cyber-physical systems into industrial processes. Due to rapid advancement of technology in robotics and automation as well as the increase in accessibility of resources to this technology, the capability to develop automated systems has become feasible for small-scale enterprise. This work presents a two-part initiative to …

Deep Reinforcement Learning Based Optimization Techniques For Energy And Socioeconomic Systems, Salman Sadiq Shuvo

USF Tampa Graduate Theses and Dissertations

Optimization, which refers to making the best or most out of a system, is critical for an organization's strategic planning. Optimization theories and techniques aim to find the optimal solution that maximizes/minimizes the values of an objective function within a set of constraints. Deep Reinforcement Learning (DRL) is a popular Machine Learning technique for optimization and resource allocation tasks. Unlike the supervised ML that trains on labeled data, DRL techniques require a simulated environment to capture the stochasticity of real-world complex systems. This uncertainty in future transitions makes the planning authorities doubt real-world implementation success. Furthermore, the DRL methods have …

Research On Stability And Resilience Of Modern Power Systems, Yi Zhou

USF Tampa Graduate Theses and Dissertations

With more and more renewable energy generation integrated into the utility grid, the modern power system circumstance and its operating characteristics have changed significantly. Since most renewable energy resources are inverter-based resources (IBR), the prominent characteristics of the power electronics and the cyber distribution in the renewable energy generation system make the power grid stability more diverse and complex.

This dissertation addresses the power system resilience problems from two points of view. The first one is from the inverter level, which deals with the problems of short circuit ratio (SCR) drop. With low SCR, the IBR system operation point may …

Reducing Instrumentation Barriers Of Diffuse Correlation Spectroscopy For Low-Cost Deep Tissue Blood Flow Monitoring, Arindam Biswas

USF Tampa Graduate Theses and Dissertations

Cerebral blood flow (CBF) is a good indicator of brain health as blood carries necessary nutrients, oxygen, and metabolic byproducts. Quantitative blood flow information can be used in several clinical and therapeutic applications such as stroke detection, measuring autoregulation, evaluating brain injury, or determining neuronal activity. Over the past few decades, light-based deep tissue hemodynamic detection modalities have become popular for non-invasive CBF measurements. In particular, noninvasive Diffuse Correlation Spectroscopy (DCS), has become a tool of choice for research and clinical applications due to its depth sensitivity (>1 cm), portability, validity against other technologies such as Magnetic Resonance Imaging …

Frequency Domain Diffuse Optics Spectroscopies For Quantitative Measurement Of Tissue Optical Properties, Sadhu Moka

USF Tampa Graduate Theses and Dissertations

Tissue oxygen saturation, blood flow and blood volume are physiological bio-markers of tissue health. Diffuse Optical Spectroscopy(DOS) and Diffuse Correlation Spectroscopy (DCS) are two complementary approaches to measure tissue oxygen saturation and blood flow respectively. Quantitative Diffuse Optical Spectroscopy (DOS) uses multi-spectral intensities of near-infrared light that have been modulated at RF frequencies to estimate static tissue optical properties and hence concentrations of oxygenated and de-oxygenated hemoglobin. Diffuse Correlation Spectroscopy estimates tissue dynamics - i.e., blood flow, by measuring temporal intensity auto-correlation function of backscattered light diffusing through the tissue. Conventionally, DCS instruments use coherent light sources with constant intensity. …

Recognition Of Modern Modulated Waveforms With Applications To Abms And Vdats Test Program Set Development, Sylwester Sobolewski

USF Tampa Graduate Theses and Dissertations

A newly developed, near real-time, well-performing and potentially universally applicable Automatic Modulation Recognition (AMR) technique for discrimination of numerous modern modulated waveforms found in commercial as well as military communication systems applicable to the new Air Force’s Advanced Battle Management & Surveillance (ABMS) framework as well as Versatile Depot Automatic Test Station (VDATS) Test Program Set (TPS) development is presented. It involves generating complex feature vectors composed of High-Order Direct Cumulant, Cyclostationary and Fourier of Wavelet Transform features created with the help of Principal Component Analysis and Variance Data Compression.

Twelve modulated waveforms are used to evaluate the performance of …

Information Dissemination And Perpetual Network, Harshit Srivastava

USF Tampa Graduate Theses and Dissertations

Social networks have attracted increasing attention from both physical and social scientists. Social networks are essential elements in societies, serving as channels for exchanging various benefits, such as innovation, information, and social support. Moreover, research in social networks helps explain macro-level social phenomena, such as social polarization and social contagion. An understanding of social networks has significant implications, such as improving social welfare and political participation. Modeling social network formation has typically employed game theory or agent-based modeling. These studies typically propose simple and tractable micro-level rules for link formation mechanisms and show that these rules have implications for known …

Modeling, Control, And Operation Of A Grid-Tied Solar Photovoltaic Inverter In Unbalanced Conditions, Abdulhakim Alsaif

USF Tampa Graduate Theses and Dissertations

With the increasing penetration of inverter-based resources (IBRs) integrated into powersystem grids as well as in microgrids (MGs), operational challenges and stability issues have been identified by the industry. In the last few years, solar Photovoltaic (PV) farm plants were allowed to be disconnected (isolated from the grid) during abnormal operation conditions to not only avoid stability issues but also to protect any damage to the solar PV inverter from such overcurrent or overvoltage. Most solar PV inverters operate in a conventional grid-following (GFL) control, where unbalanced operation becomes a critical issue. This control scheme regulates only positive sequence currents. …

Dynamic Study Of Inverter-Based Resources In Weak Grids, Zhengyu Wang

USF Tampa Graduate Theses and Dissertations

As the main technology behind renewable energy resources, such as solar PV and wind turbines, inverter-based generation penetration is increasing aggressively. Besides the congestion concerns when interconnecting a large-scale inverter-based resource (IBR) into the power grid, stability issues are commonly existing. The stability events occur more frequently when the resources are in remote area because the transmission line is long and grid strength is weak. One of the most representative and widely observed phenomena of IBR instability is low-frequency oscillation. And this dissertation focuses on investigating the low-frequency oscillations of IBR in weak grids.

The main objectives of the investigation …

Development Of An Automated Platform For Sensing And Differentiating Vapor-Phase Btex Constituents, Jonathan Samuelson

USF Tampa Graduate Theses and Dissertations

Light aromatic hydrocarbons are an inevitable byproduct of fossil fuel extraction, refinement, distribution, and use. The four lightest and most prevalent of these are benzene, toluene, ethylbenzene, and xylene, which are known collectively as BTEX. In spite of their chemical similarity these species have markedly different effects on human health and substantially different concentrations are permitted by OSHA in workplaces and by the EPA in ambient air and groundwater. Real-time detection, identification, and quantification of these species is therefore of great importance wherever they see industrial use.This work represents the continuation and advancement of a line of research in which …

Accelerating Multiparametric Mri For Adaptive Radiotherapy, Shraddha Pandey

USF Tampa Graduate Theses and Dissertations

MR guided Radiotherapy (MRgRT) marks an important paradigm shift in the field of radiotherapy. Superior tissue contrast of MRI offers better visualization of the abnormal lesions, as a result precise radiation dose delivery is possible. In case of online treatment planning, MRgRT offers better control of intratumoral motion and quick adaptation to changes in the gross tumor volume. Nonetheless, the MRgRT process flow does suffer from some challenges that limit its clinical usability. The primary aspects of MRgRT workflow are MRI acquisition, tumor delineation, dose map prediction and administering treatment. It is estimated that the acquisition of MRI takes around …

Improving Wireless Networking From The Learning And Security Perspectives, Zhe Qu

USF Tampa Graduate Theses and Dissertations

Due to the high development of wireless networking and artificial intelligence, most of the data are generated from mobile devices, which distribute in different environments. As such, how to improve the performance of machine learning-based networking and its security should be carefully considered. To reduce the communication burden and protect private information from users, Federated Learning (FL) is a possible solution for learning-based wireless networking. Although FL achieves much success until now, it also remains some specific issues to be solved. In this dissertation, we propose two FL wireless networking frameworks and discuss two potential security issues.

In the FL …

Healthcare Iot System And Network Design, Halil Ibrahim Deniz

USF Tampa Graduate Theses and Dissertations

The developing IoT concept offers many opportunities to service providers in the medical field. However, the functionality of the developed systems is increasing day by day, and it brings many different problems. One of the most important problems is the transmission of biomedical data of real-time monitoring systems to the medical server with the least delay. Network architectures are changing to meet the changing needs of densely connected devices, and “computing at the edge” is the new architectural approach emerging in IoT networks. This architecture is more dynamic than computation in the cloud because it enables data processing at each …

Stability And Interaction Analysis Of Inverter-Based Resources In Power Grids, Li Bao

USF Tampa Graduate Theses and Dissertations

The increasing penetration of inverter-based resources (IBRs) introduces some unexpected dynamic issues, including low-frequency oscillations. To investigate these phenomenon, a laboratory-scale grid-following voltage-source converter (VSC) system is implemented to demonstrate weak grid oscillations. Grid-following control is applied in VSC to provide active power, reactive power or ac voltage control. The test bed is also replicated in electromagnetic transient (EMT) simulation environment (MATLAB/SimPowerSystems) for benchmark purpose. Case studies are carried out to demonstrate low-frequency oscillations under real power/ac voltage or real power/reactive power control. An analytical model is carried out to examine the stability condition and compared with EMT or hardware …

Deep Learning And Feature Engineering For Human Activity Recognition: Exploiting Novel Rich Learning Representations And Sub-Transfer Learning To Boost Practical Performance, Ria Kanjilal

USF Tampa Graduate Theses and Dissertations

A significant gap exists in our knowledge of how domain-specific feature extraction compares to unsupervised feature learning in the latent space of a deep neural network for a range of temporal applications including human activity recognition. This dissertation aims to address this gap specifically for human activity recognition using acceleration data. To ensure reproducibility, we use two publicly available datasets, UniMiB-SHAR and ExtraSensory, with a well-established history in the human activity recognition literature. We methodically analyze the performance of 64 different combinations of i) learning representations (in the form of raw temporal data or extracted features), ii) traditional and modern …

Explainable And Cooperative Autonomy Across Networks Of Distributed Systems, Peter Joseph Jorgensen

USF Tampa Graduate Theses and Dissertations

Large networks of complex systems-of-systems are commonplace and evermore present in both mundane and extraordinary facets of human existence. From the exponential growth of connectivity via the internet and other information networks, to the miniaturization of computers and sensors, to cross-domain sensor and communication networks, these networks of distributed systems-of-systems (NDSS) present incredible benefits and challenges. Autonomy is perhaps the most important and most difficult to achieve enabling technology for efficient performance of the NDSS. Giving each individual agent in a network the ability to manage its internal state in dynamic operating environments and in pursuit of multiple complex and …

Security And Privacy Enhancing Technologies In The Deep Learning Era, Gamage Dumindu Samaraweera

USF Tampa Graduate Theses and Dissertations

Our search queries, online purchase transactions, the videos we watch, and our movie preferences are a few types of information collected and stored daily. This private data collection happens within our mobile devices and computers, on the streets, and in our homes, most of the time even without our consent. Nevertheless, advances in artificial intelligence (AI) in the big data era have increased the capability to capitalize on and benefit from the collection of this private data. Such private data is being used for various machine learning (ML) applications in different domains, such as marketing, insurance, financial services, mobility, social …

Data-Driven Design And Analysis Of Next Generation Mobile Networks For Anomaly Detection And Signal Classification With Fast, Robust And Light Machine Learning, Muhammed Furkan Küçük

USF Tampa Graduate Theses and Dissertations

This research focuses on machine (and deep) learning applications (including clustering,anomaly detection and signal classification) for self-organizing and next generation mobile networks in wireless communications. Specifically, this dissertation document will address the three different topics.

First, in the study titled “Performance analysis of neural network topologies and hyperparameters for deep clustering”, we explore the relationship between the clustering performance and network complexity. Deep learning found its initial footing in supervised applications such as image and voice recognition successes of which were followed by deep generative models across similar domains. In recent years, researchers have proposed creative learning representations to utilize …