Engineering Commons^™

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 …

Design And Analysis Of High Performance Magnetic Gears For Magnetomechanical Thrusters, Ho Yin Wong

Dissertations and Theses

This dissertation is devoted to investigating the performance potential of coaxial radial flux modulating magnetic gears for use in propulsive thruster applications. The research has focused on studying magnetic gear designs suitable for use in electric aircraft drivetrains and electric boat thrusters. Consequent pole, flux concentration and Halbach rotor magnetic gear typologies have been analyzed and compared with respect to their torque density performance for the first time. It is shown that the Halbach rotor magnetic gear holds the greatest potential with respect to achieving volumetric and mass torque densities parity with an equivalent mechanical gear.

A high volumetric torque …

Implementing Commercial Inverse Design Tools For Compact, Phase-Encoded, Plasmonic Digital Logic Devices, Michael Efseaff, Kyle Wynne, Krishna Narayan, Mark C. Harrison

Engineering Faculty Articles and Research

Numerical simulations have become an essential design tool in the field of photonics, especially for nanophotonics. In particular, 3D finite-difference-time-domain (FDTD) simulations are popular for their powerful design capabilities. Increasingly, researchers are developing or using inverse design tools to improve device footprints and performance. These tools often make use of 3D FDTD simulations and the adjoint optimization method. We implement a commercial inverse design tool with these features for several plasmonic devices that push the boundaries of the tool. We design a logic gate with complex design requirements as well as a y-splitter and waveguide crossing. With minimal code changes, …

Utilizing Inverse Design To Create Plasmonic Waveguide Devices, Michael Efseaff, Kyle Wynne, Mark C. Harrison

Engineering Faculty Articles and Research

In modern communications networks, data is transmitted over long distances using optical fibers. At nodes in the network, the data is converted to an electrical signal to be processed, and then converted back into an optical signal to be sent over fiber optics. This process results in higher power consumption and adds to transmission time. However, by processing the data optically, we can begin to alleviate these issues and surpass systems which rely on electronics. One promising approach for this is plasmonic devices. Plasmonic waveguide devices have smaller footprints than silicon photonics for more compact photonic integrated circuits, although they …

Compressive Sensing Via Variational Bayesian Inference Under Two Widely Used Priors: Modeling, Comparison And Discussion, Mohammad Shekaramiz, Todd K. Moon

Electrical and Computer Engineering Faculty Publications

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli-Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the compounds of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery …

Statistical Analysis And Degradation Pathway Modeling Of Photovoltaic Minimodules With Varied Packaging Strategies, Sameera Nalin Venkat, Xuanji Yu, Jiqi Liu, Jakob Wegmueller, Jayvic Cristian Jimenez, Erika I. Barcelos, Hein Htet Aung, Roger H. French, Laura S. Bruckman

Faculty Scholarship

Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power …

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. …

Full-Scale Testing Of Power Transfer Roadways, Oscar Moncada

Purdue Road School

A dynamic wireless power transfer roadway refers to the integration of wireless power transfer technology into a new and existing road infrastructure to provide motive power, battery charging, or both to electric vehicles. The objective of this presentation is to evaluate the mechanical and thermal performance of the system when incorporated in both flexible and rigid pavement structures.

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

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 …

Low-Power Redundant-Transition-Free Tspc Dual-Edge-Triggering Flip-Flop Using Single-Transistor-Clocked Buffer, Zisong Wang, Peiyi Zhao, Tom Springer, Congyi Zhu, Jaccob Mau, Andrew Wells, Yinshui Xia, Lingli Wang

Engineering Faculty Articles and Research

In the modern graphics processing unit (GPU)/artificial intelligence (AI) era, flip-flop (FF) has become one of the most power-hungry blocks in processors. To address this issue, a novel single-phase-clock dual-edge-triggering (DET) FF using a single-transistor-clocked (STC) buffer (STCB) is proposed. The STCB uses a single-clocked transistor in the data sampling path, which completely removes clock redundant transitions (RTs) and internal RTs that exist in other DET designs. Verified by post-layout simulations in 22 nm fully depleted silicon on insulator (FD-SOI) CMOS, when operating at 10% switching activity, the proposed STC-DET outperforms prior state-of-the-art low-power DET in power consumption by 14% …

Efficient Scopeformer: Towards Scalable And Rich Feature Extraction For Intracranial Hemorrhage Detection Using Hybrid Convolution And Vision Transformer Networks, Yassine Barhoumi

Theses and Dissertations

The field of medical imaging has seen significant advancements through the use of artificial intelligence (AI) techniques. The success of deep learning models in this area has led to the need for further research. This study aims to explore the use of various deep learning algorithms and emerging modeling techniques to improve training paradigms in medical imaging. Convolutional neural networks (CNNs) are the go-to architecture for computer vision problems, but they have limitations in mapping long-term dependencies within images. To address these limitations, the study explores the use of techniques such as global average pooling and self-attention mechanisms. Additionally, the …

Molecular And Cellular Level Characterization Of Cytoskeletal Mechanics Using A Quartz Crystal Microbalance, Emily M. Kerivan, Lyle Tobin, Mihir Basil, Dana N. Reinemann

Faculty and Student Publications

A quartz crystal microbalance (QCM) is an instrument that has the ability to measure nanogram-level changes in mass on a quartz sensor and is traditionally used to probe surface interactions and assembly kinetics of synthetic systems. The addition of dissipation monitoring (QCM-D) facilitates the study of viscoelastic systems, such as those relevant to molecular and cellular mechanics. Due to real-time recording of frequency and dissipation changes and single protein-level precision, the QCM-D is effective in interrogating the viscoelastic properties of cell surfaces and in vitro cellular components. However, few studies focus on the application of this instrument to cytoskeletal systems, …

Design And Optimization With Quantum And Memristor Platforms, Yiwei Li

Dissertations and Theses

The complexity and diversity of modern computing challenges have made it difficult for traditional computers to efficiently handle tasks such as computer-aided design, design optimization, and combinatorial problems within a reasonable time frame. In applications such as bitcoin mining and robotic control, power consumption and circuit size are critical factors. To overcome these limitations, this dissertation examines alternative computing technologies such as quantum computers and hybrid memristive circuits. The research demonstrates that these technologies hold the potential to offer more efficient and effective solutions for particular problems, whether in terms of faster computation or reduced power consumption.

An Evaluation Of A Computational Technique For Measuring The Embeddedness Of Sustainability In The Curriculum Aligned To Aashe-Stars And The United Nations Sustainable Development Goals, Philippe Lemarchand, Cormac H. Macmahon Dr, Mick Mckeever, Philip Owende

Articles

Introduction: SDG 4.7 mandates university contributions to the United Nations (UN) Sustainable Development Goals (SDGs) through their education provisions. Hence, universities increasingly assess their curricular alignment to the SDGs. A common approach to the assessment is to identify keywords associated with specific SDGs and to analyze for their presence in the curriculum. An inherent challenge is associating the identified keywords as used in the diverse set of curricular contexts to relevant sustainability indicators; hence, the urgent need for more systematic assessment as SDG implementation passes its mid-cycle.

Method: In this study, a more nuanced technique was evaluated with notable capabilities …

Deep Learning For Power Flow Estimation And High Impedance Fault Detection, Kun Yang

Electronic Theses and Dissertations

My thesis is divided into two parts.

The first part is: “Optimal Power Flow Estimation Using One-Dimensional Convolutional Neural Network [1]“. Optimal power flow (OPF) is an important research topic in power system operation and control decisions. Traditional OPF problems are solved through dynamic optimization with nonlinear programming techniques. For a large power system with large amounts of variables and constraints, the solving process would take a long time. This paper presents a new method to quickly estimate the OPF results using a one-dimensional convolutional neural network (1D-CNN). The OPF problem is treated as a high-dimensional mapping between the load …

Unsupervised Learning Algorithm For Noise Suppression And Speech Enhancement Applications, Abdullah Zaini Alsheibi

Electronic Theses and Dissertations

Smart and intelligent devices are being integrated more and more into day-to-day life to perform a multitude of tasks. These tasks include, but are not limited to, job automation, smart utility management, etc., with the aim to improve quality of life and to make normal day-to-day chores as effortless as possible. These smart devices may or may not be connected to the internet to accomplish tasks. Additionally, human-machine interaction with such devices may be touch-screen based or based on voice commands. To understand and act upon received voice commands, these devices require to enhance and distinguish the (clean) speech signal …

Assessing Hardware Security Threats Posed By Hardware Trojans In Power Electronics, Quinn Kinzie

All Theses

This study investigates the threat of hardware Trojans (HTs) in power electronics applications, a rising concern due to the growing demand for cost-effective embedded solutions in power systems. With the supply chain for electronic hardware devices expanding globally, particularly to low-cost foundries in foreign locations, there is an increasing risk of HT attacks. While there has been extensive research on HTs in computer applications, little consideration has been given to their threat in power electronics. This study demonstrates the effectiveness of a power electronics HT by implementing a novel HT design into a gate drive circuit. Additionally, the research proposes …

Regenerative Power System Itech Itm-3633, Yang Naing Myint

Electrical Engineering

In EE295 classes, students have been using variable resistors and a 125V DC source from the wall. Both of them have weaknesses and therefore, the regenerative power system Itech IT-M3633 is used in this project. Moreover, two of the experiments, 7 and 8, have some problems with the EE295 lab manual. There is accuracy in experiment 7 and experiment 8 has been stopped for a while now by a few professors if not all. These experiments were conducted again with ITech IT-M3633 for better accuracy.

Solar Farm Utilizing A Battery Energy Storage System, Hannah Nicole Bonderov

Master's Theses

According to the US Energy and Information Administration, between 2022-2023 60% of planned new electricity generation consists of solar farms with a battery energy storage system [1]. The demand for these paired systems has increased since batteries can be charged during the day with the energy captured from the solar farm then released to the customer in the evening during peak energy demand. This achieves peak load shaving which reduces the cost of electricity for the customer and is ecologically friendly.

This thesis aims to create an efficient solar farm with a battery energy storage system for a farmer in …

Comparison Of Federated Kalman Filter And Autonomous Resilient Management Of All-Source Sensors (Armas) Framework For Fault Detection And Exclusion, Flavio Fernandes

Theses and Dissertations

In recent times, there is a great demand for resilient and robust PNT solutions. Current navigation systems rely heavily on GNSS to provide this solution. This dependence is a major concern and therefore it is prudent to use additional aids to provide the best solution possible. However, traditional methods to address this need require a nontrivial number of Kalman filters to maintain a fault-free solution. The federated filter approach provides an attractive alternative to the current solutions. In this thesis, we examine the use of the federated filter design to address this need in today’s integrated navigation systems. We compared …

Design And Modeling Of A Pvdf-Trfe Flexible Wind Energy Harvester, Berkay Kullukçu, Levent Beker

Turkish Journal of Electrical Engineering and Computer Sciences

This study presents the simulation, experimentation, and design considerations of a Poly(vinylidene fluoride co-trifluoroethylene)/ Polyethylene Terephthalate (PVDF-TrFe / PET), laser-cut, flexible piezoelectric energy harvester. It is possible to obtain energy from the environment around autonomous sensor systems, which can then be used to power various equipment. This article investigates the actuation means of ambient vibration, which is a good candidate for using piezoelectric energy harvester (PEH) devices. The output voltage characteristics were analyzed in a wind test apparatus. Finite element modeling (FEM) was done for von Mises stress and modal analysis. Resonance frequency sweeps, quality factors, and damping ratios of …

Suppressing White-Noise Interference For Orbital Angular Momentum Waves Via The Forward-Backward Dynamic Mode Decomposition, Yanming Zhang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

When the orbital angular momentum (OAM)-carrying beam propagates in a highly boisterous environment, it causes the degradation of the OAM modes' purity, which brings the crosstalk in the demultiplexing process. To address this issue, we extend the dynamic mode decomposition (DMD) method to suppress white-noise interferences of OAM by using the forward-backward DMD (FBDMD) approach. The FBDMD-based scheme retrieves the noise-free DMD mapping matrix corresponding to the actual OAM's topological charges by combining the forward and backward DMD mapping matrix in the noisy environment and consequently reduces the crosstalk, particularly for sorting the superposed OAM modes. Numerical examples are provided …

Deep Learning To Predict The Hydration And Performance Of Fly Ash-Containing Cementitious Binders, Taihao Han, Rohan Bhat, Sai Akshay Ponduru, Amit Sarkar, Jie Huang, Gaurav Sant, Hongyan Ma, Narayanan Neithalath, Aditya Kumar

Electrical and Computer Engineering Faculty Research & Creative Works

Fly ash (FA) – an industrial byproduct – is used to partially substitute Portland cement (PC) in concrete to mitigate concrete's environmental impact. Chemical composition and structure of FAs significantly impact hydration kinetics and compressive strength of concrete. Due to the substantial diversity in these physicochemical attributes of FAs, it has been challenging to develop a generic theoretical framework – and, therefore, theory-based analytical models – that could produce reliable, a priori predictions of properties of [PC + FA] binders. In recent years, machine learning (ML) – which is purely data-driven, as opposed to being derived from theorical underpinnings – …

H-Plane Siw Horn Antenna With Enhanced Front-To-Back Ratio For 5g Applications, Özlem Akgün, Nurhan Türker Tokan

Turkish Journal of Electrical Engineering and Computer Sciences

Millimeter-wave (mmWave) antennas are indispensable components in the fifth-generation (5G) wireless communication systems. With the inherent advantages of integration capability, substrate integrated waveguide (SIW) antenna is an excellent choice for applications in the mmWave frequency bands. However, reflection losses occur at dielectric-filled thin apertures of SIW antennas. These reflections can be overcome by impedance matching between the aperture and the free space. In this study, we introduce an mmWave SIW horn antenna having impedance matching transitions (IMTs) across the horn's aperture width. The designed antenna, operating in the 24-28 GHz band, is simulated with a full-wave analysis tool. The simulation …

Development Of Two Stage Optimization-Based Demand Response Technique For Smart Homes Under Real Time Pricing, Govind Rai Goyal, Shelly Vadhera

Turkish Journal of Electrical Engineering and Computer Sciences

Residential load management deals with two major objectives viz. minimizing the cost of monthly electricity bill and peak demand of power consumption. Both objectives can be achieved by effective operational scheduling of smart home appliances. These two objectives are conflicting in nature because rescheduling of appliances in order to minimize one objective may result in the rise of another. To achieve both objectives concurrently, an algorithm is suggested in this paper based on artificial intelligent techniques like cuckoo search, hybrid GA-PSO, and adaptive cuckoo search. The proposed algorithm is tested successfully on seven households of different monthly power consumption and …

Boomerang Algorithm Based On Swarm Optimization For Inverse Kinematics Of 6 Dof Open Chain Manipulators, Okan Duymazlar, Di̇lşad Engi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, a feasible swarm intelligence algorithm is proposed that computes the inverse kinematics solution of 6 degree of freedom (DOF) industrial robot arms, which are frequently used in industrial and medical applications. The proposed algorithm is named as Boomerang algorithm due to its recursive structure. The proposed algorithm aims to reduce the computation time to feasible levels without increasing the position and orientation errors. In order to reduce the computational time in swarm optimization algorithms and increase feasibility, an alternative definition method was used instead of the DH method in defining the robot arm kinematic configuration. The effect …

3d Point Cloud Classification With Acgan-3d And Vacwgan-Gp, Onur Ergün, Yusuf Sahi̇lli̇oğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning and deep learning techniques are widely used to make sense of 3D point cloud data which became ubiquitous and important due to the recent advances in 3D scanning technologies and other sensors. In this work, we propose two networks to predict the class of the input 3D point cloud: 3D Auxiliary Classifier Generative Adversarial Network (ACGAN-3D) and Versatile Auxiliary Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (VACWGAN-GP). Unlike other classifiers, we are able to enlarge the limited data set with the data produced by generative models. We consequently aim to increase the success of the model by …

Teamwork Optimization Based Dtc For Enhanced Performance Of Im Based Electric Vehicle, Anjan Kumar Sahoo, Ranjan Kumar Jena

Turkish Journal of Electrical Engineering and Computer Sciences

The tailpipe emissions caused by vehicles using internal combustion engines are a significant source of air pollution. To reduce the health hazards caused by air pollution, advanced countries are now adopting the use of electric vehicles (EVs). Due to the advancement of electric vehicles, research and development efforts are being made to improve the performance of EV motors. With a nominal reference stator flux, the classical induction motor drive generates significant flux, torque ripple, and current harmonics. In this work, a teamwork optimization algorithm (TOA)-based optimal stator flux strategy is suggested for torque ripple reduction applied in a classical direct …