Digital Commons Network^™

In-Situ Superconductor Temperature Sensor For Cryogenic Integrated Circuits, Emre Küçükyilmaz, Nazi̇f Orhun Tekci̇, Sasan Razmkhah, Ali̇ Bozbey

Turkish Journal of Electrical Engineering and Computer Sciences

Cryogenic circuits, such as those based on single flux quantum (SFQ) logic, function at extremely low temperatures. Therefore, the designs target the utilization of liquid helium (LHe) temperatures, maintaining them at 4.2 K. These specialized circuits can be subjected to measurement either within liquid helium (LHe) baths or enclosed within closed-cycle cryocoolers.However, when utilizing LHe in cryocooler systems, inherent weak thermal contact can lead to temperature gradients between the circuit chip and the cold head, where conventional temperature sensors are typically placed. To address this challenge, this study introduces an innovative on-chip temperature sensing approach that capitalizes on the temperature …

Fault Diagnosis Of Photovoltaic Array Based On Gated Residual Network With Multi-Head Self Attention Mechanism, Ahmed Mesai Belgacem, Mounir Hadef, Abdesslem Djerdir

Turkish Journal of Electrical Engineering and Computer Sciences

Effective fault identification and diagnosis in photovoltaic (PV) arrays is vital for improving the effectiveness, and safety of solar energy systems. While various artificial intelligence methods have successfully established fault detection and diagnosis models, introducing inefficiencies and potentially overlooking useful features. Moreover, these methods often employ neural networks with limited performance capabilities. In response to these challenges, this paper introduces an innovative intelligent model that integrates a combination of a gated residual neural network (GRN) and a multi-head self-attention mechanism (MHSA). To evaluate the proposed fault diagnosis model, the small-scale PV grid system is implemented, and fault simulation experiments, including …

Model-Based Navigation And Control Of Multirotor Uavs: A Machine Learning Approach, Serhat Sönmez

Electronic Theses and Dissertations

In recent decades, unmanned systems, particularly Unmanned Aerial Vehicles (UAVs), have seen significant advancement and unprecedented growth in military, civilian and public domain applications. Scientists have focused on enhancing UAV navigation and control through cutting-edge technologies and support tools. UAVs find applications in many fields, except military, such as agriculture, infrastructure inspection, wildlife monitoring, search and rescue, emergency response, border protection, to name but a few relevant civilian applications. Given the faster-than-exponential increase of available computational power, learning-based algorithms have emerged as a prominent tool for (real-time) multirotor UAV navigation and control. This dissertation centers around the fusion of conventional …

It's Not As Bad As You Think: Detecting Ai-Generated Voices, Yong Qin Xu

Undergraduate Research Symposium Lightning Talks

Advances in machine learning have opened up the world to a brand new frontier of fraudulent phone calls which the average person may not be in any way prepared for. From imitations of a loved one's voice to lifelike mimicry of human callers, telephone scams may become harder than ever to anticipate or prevent now that criminals have the help of AI on their side. This is why in my research paper, I aim to analyze and compare two existing methods of detecting the authenticity of human voice recordings in order to demonstrate and explain currently available technology that's capable …

Smartphone Haptics Can Uncover Differences In Touch Interactions Between Asd And Neurotypicals, Ivonne Monarca, Franceli L. Cibrian, Isabel López Hurtado, Monica Tentori

Engineering Faculty Articles and Research

Utilizing touch interactions from smartphones for gathering data and identifying digital markers for screening and monitoring neurological disorders, such as Autism Spectrum Disorder (ASD), is an emerging area of research. Smartphones provide multiple benefits for this kind of study, including unobtrusive data collection via built-in sensors, integrated haptic feedback systems, and the capability to create specialized applications. Acknowledging the significant yet understudied presence of tactile processing differences in individuals with ASD, we designed and developed Feel and Touch, a mobile game that leverages the haptic capabilities of smartphones. This game provides vibrotactile feedback in response to touch interactions and collects …

N-Type Cdte Thin Films For Photovoltaic Applications, Wei Wang

USF Tampa Graduate Theses and Dissertations

Cadmium Telluride is widely used as a thin-film solar cell material due to its nearly ideal optical bandgap (~ 1.5eV) and its high absorption coefficient (> 104 cm-1). In the past several decades, most of the research focused on p-type CdTe solar cells. In recent years, the research on CdTe devices is mainly based on CdSexTe1-x (CST) absorbers, leading to substantial advancements in conversion efficiency, comparable to that of silicon-based solar cells. Presently, p-type polycrystalline CST/CdTe thin-film solar cells exhibit a notable efficiency of 23.1%. The state-of-art CST/CdTe solar cell has attained an almost optimal short-circuit current density (JSC) of …

Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl

Faculty Publications

Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

Soc-Based Control System Performance Analysis For Trapped-Ion Quantum Computing, Tiamike Dudley

Electrical and Computer Engineering ETDs

Scatter-gather dynamic-memory-access (SG-DMA) is utilized in applications that require high bandwidth and low latency data transfers between memory and peripherals, where data blocks, described using buffer descriptors (BDs), are distributed throughout the memory system. The data transfer organization and requirements of a Trapped-Ion Quantum Computer (TIQC) possess characteristics similar to those targeted by SG-DMA. In particular, the ion qubits in a TIQC are manipulated by applying control sequences consisting primarily of modulated laser pulses. These optical pulses are defined by parameters that are (re)configured by the electrical control system. Variations in the operating environment and equipment make it necessary to …

Performance Optimization And Application Of P-Type Transparent Semiconductors In The Cdte Solar Cells, Md Zahangir Alom

USF Tampa Graduate Theses and Dissertations

CdTe thin-film solar cells are one of the major interests in the solar industry for their favorable properties. For example, CdTe has a near-ideal bandgap (1.45 eV) for absorption of the solar spectrum, and a high absorption coefficient ( ̴ 105 cm-1) and can capture 99% of light with only 2 µm of film thickness. Moreover, CdTe can be doped both n and p-type. Additionally, low production costs contribute to more affordable panel prices (0.40 $/W) [1]. The highest efficiency achieved to-date for this material is 23.1% demonstrated by First Solar, which is still lower than the theoretical limit [2]. …

Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin, Ruairí De Fréin, Alexander Mutiso Mutua Mr

Articles

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a $6$ km …

Investigation On The Impact Of Loading Effect Of Fruit Juices On The Performance Of Pulsed Electric Field Generators, Devi S

Theses and Dissertations

Pulsed Electric Field (PEF) treatment is one of the efficient non-thermal food processing techniques which is being preferred as a replacement for thermal pasteurization methods. The effectiveness of PEF treatment was measured in terms of reduction in the microbial load in the food, extension of shelf life of the food and retention of nutritional properties of the food. The successful implementation of the PEF treatment depends upon various aspects such as design of pulse generator, parameters of pulse generator, shape and size of the treatment chamber and most importantly the characteristics of each food items. Design and fabrication of a …

A 3d Memristor Architecture For In-Memory Computing Demonstrated With Sha3, Muayad J. Aljafar, Rasika Joshi, John M. Acken

Electrical and Computer Engineering Faculty Publications and Presentations

Security is a growing problem that needs hardware support. Memristors provide an alternative technology for hardware-supported security implementation. This paper presents a specific technique that utilizes the benefits of hybrid CMOS-memristors technology demonstrated with SHA3 over implementations that use only memristor technology. In the proposed technique, SHA3 is implemented in a set of perpendicular crossbar arrays structured to facilitate logic implementation and circular bit rotation (Rho operation), which is perhaps the most complex operation in SHA3 when carried out in memristor arrays. The Rho operation itself is implemented with CMOS multiplexers (MUXs). The proposed accelerator is standby power-free and circumvents …

Measuring Soil Salinity, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

To make sound decisions on managing saline sites, you need to know the source of salt, how salinisation is occurring, the landscape context, and most importantly, the actual salt concentration of the soil.

The most common 'measures' of salt concentration are actually estimates based on electrical conductivity of a soil and water solution. Soil salt content can be measured in a laboratory by measuring the total dissolved solids in a sample. In the field, salt concentration can also be estimated using electromagnetic induction-based soil sensors.

Refining Chlorosulfonation Methods For The Synthesis Of A Perfluoroalkyl Arylsulfonimide (Pfsi) Monomer, Ainsley P. Foster, Benjamin Varney, Hua Mei

Science University Research Symposium (SURS)

Proton-exchange membrane (PEM) fuel cells are sources of energy that are clean, quiet, and highly responsive to changes in power needs, making them promising for use in automobiles and other portable power devices. The electrolyte of a PEM cell–the layer responsible for conductivity–is a polymer membrane, commonly consisting of perfluoroalkyl sulfonic acid (PFSA) polymers. Perfluoroalkyl arylsulfonimide (PFSI) polymers are expected to improve the efficiency of PEM fuel cells through optimal stability and proton conductivity. The trifluovinylether (TFVE) aryl perfluorosulfonamide monomer is a new PFSI monomer proposed to fulfill these benefits once polymerized. A six-step synthesis route was initially designed to …

Autonomous Coalition Formation For Energy Trading In Intelligent Grid Systems, Issiac Baca

Electrical and Computer Engineering ETDs

This paper introduces an interactive model and architectural framework for a smart grid system, emphasizing the prosumers dual roles as energy buyers and sellers within localized energy trading markets, i.e., coalitions. Initially, the utilities of both buyers and sellers are formulated to capture the benefits derived from their energy-related activities. Then, the paper presents the Approximate SMARTFORM (ASMARTFORM) mechanism, based on the principles of matching theory, to enhance the prosumers engagement in coalitions by addressing externalities influencing their decisions. To further improve the coalition formation process, the Accurate SMARTFORM (AccSMARTFORM) mechanism is formulated based on the theory of coalition games, …

Safety-Centric Analysis Of Grounding Systems For Substations In Distribution Grids, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

The safety of grounding systems for substations in distribution grids is paramount to ensuring operational reliability, protecting personnel and equipment, and maintaining the stability of distribution grids while complying with regulatory standards. This paper explores essential safety aspects of grounding systems, including fault current handling strategies, the interdependence between protective devices and grounding systems, and maintenance practices. The integration of grounding systems design with overall substation layout and design considerations by focusing on mitigating Ground Potential Rise (GPR) and optimizing bonding techniques, is examined. Additionally, advanced techniques, such as high-frequency grounding and Transient Ground Potential Rise (TGPR) management, are presented …

Techniques For Modeling Ocean Soundscapes: Detailed Description For Wind Contributionsa, Martin Siderius, Michael A. Ainslie, John Gebbie, Alexandra Schäfke, N Ross Chapman, Bruce Martin, Kay L. Gemba

Electrical and Computer Engineering Faculty Publications and Presentations

Wind over the ocean creates breaking waves that generate air-filled bubbles, which radiate underwater sound. This wind-generated sound is a significant component of the ocean soundscape, and models are essential for understanding and predicting its impact. Models for predicting sound pressure level (SPL) from wind have been studied for many years. However, the terminology and definitions behind modeling approaches have not been unified, and ambiguity has led to differences in predicted SPL. The 2022 Ambient Sound Modeling Workshop was organized to compare ambient sound modeling approaches from different researchers. The main goal of the workshop was to quantify differences in …

An Overview Of Ancillary Services Provided By Vehicle-To-Grid Systems, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

Vehicle-to-Grid (V2G) systems are emerging as a pivotal technology in modern power systems, offering a range of ancillary services that enhance the stability and reliability of power systems. This paper provides an overview of the key ancillary services provided by V2G systems, highlighting their role in grid modernization. Technical challenges, economic implications, and policy considerations associated with the deployment of V2G systems are explored to assess their potential impact on advancing a more resilient and sustainable energy infrastructure.

Analysis Of Multivariable Sensor Responses To Multi-Analyte Gas Samples In The Presence Of Interferents And Humidity, Sakin Sarwar Satter, Florian Bender, Nicholas Post, Antonio J. Ricco, Fabien Josse

Electrical and Computer Engineering Faculty Research and Publications

This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg–Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the …

Addressing Uncertainty In Renewable Energy Integration For Western Australia’S Mining Cector: A Robust Optimization Approach, Mehrdad Ghahramani, Daryoush Habibi, Seyyedmorteza Ghamari, Asma Aziz

Research outputs 2022 to 2026

The mining industry is a key contributor to Western Australia’s economy, with over 130 mining operations that produce critical minerals such as iron ore, gold, and lithium. Ensuring a reliable and continuous energy supply is vital for these operations. This paper addresses the challenges and opportunities of integrating renewable energy sources into isolated power systems, particularly under uncertainties associated with renewable energy generation and demand. A robust optimization approach is developed to model a multi-source hybrid energy system that considers risk-averse, risk-neutral, and risk-seeking strategies. These strategies address power demand and renewable energy supply uncertainties, ensuring system reliability under various …

Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch, Anthony L. Franz, Eugene V. Dao, Dong L. Wu, Nimalan Swarnalingam, Cornelius C. J. H. Salinas, Daniel J. Emmons

Faculty Publications

Intense ionization enhancements in the Earth’s ionosphere, known as sporadic-E (E_s), can degrade and severely disrupt the propagation of radio signals. Although many previous studies have analyzed the characteristics and morphologies of sporadic-E, few efforts have attempted to model global E_s occurrence rates (ORs) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E occurrence rates using a Karhunen–Loéve Expansion (KLE) of a global OR climatology built with Global Navigation Satellite System radio occultation (GNSS-RO) and ionosonde observations. Using an fbE ≥ threshold of 3 MHz, the model outputs a blanketing sporadic-E …

Joint Modeling Of Degradation Signals And Time-To-Event Data For The Prediction Of Remaining Useful Life, Sebastian Brumm, Erik Linstead, Junde Chen, Narayanaswamy Balakrishnan, Yuxin Wen

Engineering Faculty Articles and Research

Accurate prediction of remaining useful life (RUL) for in-service systems plays an important role in ensuring efficient operation of industrial equipment and in preventing unexpected equipment failures. In this paper, we present a prognostic framework for real-time RUL prediction based on joint modeling of both degradation signals and time-to-event data. The proposed model employs a change point-based general path model to capture signal non-linearity and Neural network (NN) based Cox model to link the time-to-event data with the estimated degradation trend. An empirical two-step scheme for hyperparameter estimation is proposed to enhance prognostic accuracy. Furthermore, an efficient Bayesian model updating …

Goodbye With Tears! Deep Condolences To Academician Zhao-Wu Tian!, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

The Adviser Of Journal Of Electrochemistry Professor Christian Amatore Received The Chinese Government Friendship Award, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Journal Of Electrochemistry Is Selected To Entering Ici Journals Master List, Editorial Office Of J.Electrochem.

Journal of Electrochemistry

No abstract provided.

Addressing Output Ripples In Low-Power Cmos-Based Multistage Dc-Dc Boost Converters For Self-Powered Electrochemical Sensors Applications, Sameh O. Abdellatif, Ahmat Abdelwahid

Electrical Engineering

No abstract provided.

Design And Analysis Of Optimal Energy Efficient Routing Protocols For Lifetime Maximization And Qos Enhancement In Wireless Body Area Networks, Karthikeyan S

Theses and Dissertations

In recent times, Wireless Body Area Networks (WBAN) a subsection of Wireless Sensor Networks (WSN) a promising technology for the future healthcare realm with cutting-edge technologies that can assist healthcare professionals like doctors, nurses and biomedical engineers which enables one-to-one care by implementing Tele-Medicine and Tele-Health solutions. Machine Learning (ML) and Internet of Things (IoT) enabled medical big data is the future of the healthcare sector and Medical Technology-based industries leading to applications in other sectors such as fitness tracking for commercial purposes, Sportsperson health monitoring to track their day-to-day activities and wearable devices for critical and emergency care.

Recent …

Multi-Agent Reinforcement Learning For Smart Community Energy Management, Patrick Wilk, Ning Wang, Jie Li

Henry M. Rowan College of Engineering Departmental Research

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the …

Cross-Track Seabed Imaging And Buried Object Detection With A Multibeam Sonar, Charles W. Holland, Samuel Pinson, Daniel L. Orange, Cody R. Henderson

Electrical and Computer Engineering Faculty Publications and Presentations

What lies underneath the ocean floor is of interest to a wide variety of disciplines. The focus here is imaging the upper tens of meters of material beneath the seafloor. The sub-bottom profiler is a valuable tool to that end, producing a 2-D image in depth below the seafloor and along the track of the ship. Geoscientists and engineers are frequently interested in not only the subseabed beneath the ship but also either side or cross-track of the ship. We show an approach to cross-track imaging using a low-frequency multibeam sub-bottom profiler. The main result is the detection of a …

A Deep Autoencoder For Electric Double Layer Capacitance Prediction In Electrochemical Sensors, Mason Mcgarrity, Feng Zhao

Electrical and Computer Engineering Faculty Research & Creative Works

This study explores the application of a deep autoencoder neural network to accurately predict the electric double layer capacitance from real-world parameters in binary, asymmetric electrolytes under low concentration conditions. By utilizing a modest simulation-based dataset of just 250 samples, the deep autoencoder neural network model developed in this study effectively predicted the capacitance by learning the critical features and relationships of the electric double layer model and encoding this learned representation into a low-dimensional latent space. From the latent variables, the decoder block of the neural network learned to effectively recreate the high-dimensional input. To enhance the model's robustness, …