Digital Commons Network^™

Miniature, Submersible Electromagnetic Pumps Of Molten Lead And Sodium For Gen-Iv Nuclear Reactors Development, Ragai M. Altamimi

Nuclear Engineering ETDs

Heavy metals and alkali Liquid Metals are suitable coolants for Generation IV terrestrial nuclear reactors for operating at elevated temperatures for achieving plant thermal efficiency more than 40% and the thermochemical generation of hydrogen fuel. In addition, the low vapor pressure of these liquids eliminates the need for a pressure vessel and instead operates slightly below ambient pressure. A primary issue with the uses of these coolants is their compatibility with nuclear fuel, cladding and core structure materials at elevated temperatures more than 500oC. Therefore, in pile and out-of-pile test loops have been constructed or being considered for quantifying the …

Digital Twins And Artificial Intelligence For Applications In Electric Power Distribution Systems, Deborah George

All Theses

As modern electric power distribution systems (MEPDS) continue to grow in complexity, largely due to the ever-increasing penetration of Distributed Energy Resources (DERs), particularly solar photovoltaics (PVs) at the distribution level, there is a need to facilitate advanced operational and management tasks in the system driven by this complexity, especially in systems with high renewable penetration dependent on complex weather phenomena.

Digital twins (DTs), or virtual replicas of the system and its assets, enhanced with AI paradigms can add enormous value to tasks performed by regulators, distribution system operators and energy market analysts, thereby providing cognition to the system. DTs …

Tunable Linewidth Chip-Level Lasers Using Hybrid Integration Methods, Charles Porter

All Theses

Photonic integrated circuits (PICs) have significant value in today’s world of research. They enable devices to experience large reductions in size, weight, operation power, and cost (SWAPc), making photonic technology more accessible than ever. The functionality of PICs is greatly enhanced due to the realization of both active and passive devices within a single device structure. This is made possible through hybrid integration, which utilizes various coupling methods to transfer light from an active chip to a passive chip. Hybrid integration technology tremendously expands the capabilities of PICs, leading to a plethora of applications. One significant application is that of …

Comparison Of Random Coupling Model To Stochastic Dyadic Green’S Function When Coupling To A Distributed Port, Evelyn Anne Dohme

Electrical and Computer Engineering ETDs

The Random Coupling Model (RCM) provides statistical representation of coupling between ports in a highly overmoded resonant cavity based on knowledge of radiation impedances of system ports and cavity properties. A recent variant of RCM, the Stochastic Dyadic Green’s Functions (S-DGF), provides the statistical distribution of fields in the cavity. By using Source Reconstruction Methods (SRM) to approximate equivalent surface currents, I hypothesized that the S-DGF can calculate the fields at their location, enabling a statistical estimate of the coupled power. Using nearfield radiation from simple patch antenna simulations and the SRM to estimate the equivalent fields and account for …

Evalattai: A Holistic Approach To Evaluating Attribution Maps In Robust And Non-Robust Models, Ian E. Nielsen, Ravi Ramachandran, Nidhal Carla Bouaynaya, Hassan M. Fathallah-Shaykh, Ghulam Rasool

Henry M. Rowan College of Engineering Departmental Research

Eyes are one of the main critical organs of the body that provide our brain with the most information about the surrounding environment. Disturbance in the activity of this informational organ, resulting from different ocular diseases, could affect the quality of life, so finding appropriate methods for treating ocular disease has attracted lots of attention. This is especially due to the ineffectiveness of the conventional therapeutic method to deliver drugs into the interior parts of the eye, and the also presence of barriers such as tear film, blood-ocular, and blood-retina barriers. Recently, some novel techniques, such as different types of …

Study Of Radiation Effects In Gan-Based Devices, Han Gao

Electrical Engineering Theses and Dissertations

Radiation tolerance of wide-bandgap Gallium Nitride (GaN) high-electron-mobility transistors (HEMT) has been studied, including X-ray-induced TID effects, heavy-ion-induced single event effects, and neutron-induced single event effects. Threshold voltage shift is observed in X-ray irradiation experiments, which recovers over time, indicating no permanent damage formed inside the device. Heavy-ion radiation effects in GaN HEMTs have been studied as a function of bias voltage, ion LET, radiation flux, and total fluence. A statistically significant amount of heavy-ion-induced gate dielectric degradation was observed, which consisted of hard breakdown and soft breakdown. Specific critical injection level experiments were designed and carried out to explore …

Empowering 5g Mmwave: Leveraging Kml Placemarks For Enhanced Rf Design And Deployment Efficiency, Gustavo A. Fernandez

School of Mathematical & Statistical Sciences Faculty Publications

This publication explores the significance of Keyhole Markup Language (KML) in telecommunications, particularly in the context of 5G mmWave RF design and planning. With the advent of 5G mmWave technology, the demand for seamless and efficient network deployments has never been greater. The deployment of small cells and repeaters for 5G mmWave necessitates utmost precision in location accuracy and rapid information exchange during site surveys and evaluations. The challenges of mmWave frequencies, including their limited range and susceptibility to attenuation, intensify the complexity and criticality of this process. Network operators must ensure that the chosen location is devoid of obstacles …

Deep Learning For Multi-Structured Javanese Gamelan Note Generator, Arik Kurniawati, Eko Mulyanto Yuniarno, Yoyon Kusnendar Suprapto

Knowledge Engineering and Data Science

Javanese gamelan, a traditional Indonesian musical style, has several song structures called gendhing. Gendhing (songs) are written in conventional notation and require gamelan musicians to recognize patterns in the structure of each song. Usually, previous research on gendhing focuses on artistic and ethnomusicological perspectives, but this study is to explore the correlation between gendhing as traditional music in Indonesia and deep learning technology that replaces the task of gamelan composers. This research proposes CNN-LSTM to generate notation of ricikan struktural instruments as an accompaniment to Javanese gamelan music compositions based on balungan notation, rhythm, song structure, and gatra …

Recent Progress Of Bifunctional Electrocatalysts For Oxygen Electrodes In Unitized Regenerative Fuel Cells, Tian-Long Zheng, Ming-Yu Ou, Song Xu, Xin-Biao Mao, Shi-Yi Wang, Qing-Gang He

Journal of Electrochemistry

Unitized regenerative fuel cells (URFCs), which oxidize hydrogen to water to generate electrical power under thefuel cells (FCs) mode and electrolyze water to hydrogen under the water electrolysis (WE) mode for recycling, areknown as clean and sustainable energy conversion devices. In contrast to the hydrogen oxidation reaction (HOR) andhydrogen evolution reaction (HER) on the hydrogen electrode side, the sluggish kinetics of oxygen reduction reaction(ORR) and oxygen evolution reaction (OER) on the oxygen electrode side requires highly efficient bifunctional oxygencatalysts. Conventional precious metal oxygen catalysts combine Pt and IrO₂ with excellent ORR and OER activities toachieve bifunctional electrocatalysis performance, but …

Band Alignments Of Metal/Oxides-Water Interfaces Using Ab Initio Molecular Dynamics, Yong-Bin Zhuang, Jun Cheng

Journal of Electrochemistry

Band alignments of electrode-water interfaces are of crucial importance for understanding electrochemical interfaces. In the scenario of electrocatalysis, applied potentials are equivalent to the Fermi levels of metals in the electrochemical cells; in the scenario of photo(electro)catalysis, semiconducting oxides under illumination have chemical reactivities toward redox reactions if the redox potentials of the reactions straddle the conduction band minimums (CBMs) or valence band maximums (VBMs) of the oxides. Computational band alignments allow us to obtain the Fermi level of metals, as well as the CBM and VBM of semiconducting oxides with respect to reference electrodes. In this tutorial, we describe …

Designing Intelligent Energy Efficient Scheduling Algorithm To Support Massive Iot Communication In Lora Networks, Jui Mhatre

Master of Science in Computer Science Theses

We are about to enter a new world with sixth sense ability – “Network as a sensor -6G”. The driving force behind digital sensing abilities is IoT. Due to their capacity to work in high frequency, 6G devices have voracious energy demand. Hence there is a growing need to work on green solutions to support the underlying 6G network by making it more energy efficient. Low cost, low energy, and long-range communication capability make LoRa the most adopted and promising network for IoT devices. Since LoRaWAN uses ALOHA for multi-access of channels, collision management is an important task. Moreover, in …

Adaptive Gps Antenna Array Beam Nulling Effectiveness Under Varying Antenna Element Positioning, Aadesh Neel

Electrical and Computer Engineering ETDs

Global Positioning System (GPS) is an essential part of modern life but is susceptible to same frequency jamming. GPS jamming can add excessive noise to a received low power signal and have the capability to change or completely distort information being sent through the GPS signal. Adaptive antenna arrays have long since been a solution to mitigating GPS jamming via beamnulling algorithms. However, there is little research on the effectiveness of these beamnulling algorithms under varying element positioning. In this work, an adaptive antenna array, consisting of Right-Hand Circularly Polarized (RHCP) nearly square GPS antenna elements, was constructed and tested …

A Unified Switched Nonlinear Dynamic Model Of An Electric Vehicle For Performance Evaluation, Dibyendu Khan, Kuntal Mandal, Anjan Kumar Ray, Abdelali El Aroudi

Department of Electrical and Computer Engineering: Faculty Publications

The advanced modeling and estimation of overall system dynamics play a vital role in electric vehicles (EVs), as the selection of components in the powertrain and prediction of performance are the key market qualifiers. The state-space averaged model and small-signal transfer function model are useful for assessing long-term behavior in system-level analysis and for designing the controller parameters, respectively. Both models take less computation time but ignore the high-frequency switching dynamics. Therefore, these two models could be impractical for the development and testing of EV prototypes. On the other hand, the multi-domain model in available simulation tools gives in-depth information …

Nasa Weather Data Based Neural Network Grid Connected Pv System Maximum Power Point Tracking, Oluwatobiloba Mausi Johnson

Theses and Dissertations

Research and development for alternative energy sources that are cleaner, renewable, and have little to no environmental impact have been pushed by the ongoing rise in energy demand, the possibility of a decline in the use of conventional petroleum fuels, and concerns about environmental degradation. Electricity from photovoltaic (PV) systems is significantly better regarded among these alternative sources as a renewable energy source such as wind power, bioenergy, tidal energy, and hydroelectric with a wide application range because it is clean, accessible, and abundant with little to no environmental effect. However, solar energy usage is significantly impacted by the landscape, …

An Ai-Based Intervention For Improving Undergraduate Stem Learning, Mohammad Rashedul Hasan, Bilal Khan

Department of Electrical and Computer Engineering: Faculty Publications

We present results from a small-scale randomized controlled trial that evaluates the impact of just-in-time interventions on the academic outcomes of N = 65 undergraduate students in a STEM course. Intervention messaging content was based on machine learning forecasting models of data collected from 537 students in the same course over the preceding 3 years. Trial results show that the intervention produced a statistically significant increase in the proportion of students that achieved a passing grade. The outcomes point to the potential and promise of just-in-time interventions for STEM learning and the need for larger fully-powered randomized controlled trials.

An Enhanced Adaptive Learning System Based On Microservice Architecture, Abdelsalam Helmy Ibrahim, Mohamed Eliemy, Aliaa Abdelhalim Youssif

Future Computing and Informatics Journal

This study aims to enhance Adaptive Learning Systems (ALS) in Petroleum Sector in Egypt by using the Microservice Architecture and measure the impact of enhancing ALS by participating ALS users through a statistical study and questionnaire directed to them if they accept to apply the Cloud Computing Service “Microservices” to enhance the ALS performance, quality and cost value or not. The study also aims to confirm that there is a statistically significant relationship between ALS and Cloud Computing Service “Microservices” and prove the impact of enhancing the ALS by using Microservices in the cloud in Adaptive Learning in the Egyptian …

Systematic Characterization Of Power Side Channel Attacks For Residual And Added Vulnerabilities, Aurelien Tchoupou Mozipo

Dissertations and Theses

Power Side Channel Attacks have continued to be a major threat to cryptographic devices. Hence, it will be useful for designers of cryptographic systems to systematically identify which type of power Side Channel Attacks their designs remain vulnerable to after implementation. It’s also useful to determine which additional vulnerabilities they have exposed their devices to, after the implementation of a countermeasure or a feature. The goal of this research is to develop a characterization of power side channel attacks on different encryption algorithms' implementations to create metrics and methods to evaluate their residual vulnerabilities and added vulnerabilities. This research studies …

Distributed Deep Learning Optimization Of Heat Equation Inverse Problem Solvers, Zhuowei Wang, Le Yang, Haoran Lin, Genping Zhao, Zixuan Liu, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

The inversion problem of partial differential equation plays a crucial role in cyber-physical systems applications. This paper presents a novel deep learning optimization approach to constructing a solver of heat equation inversion. To improve the computational efficiency in large-scale industrial applications, data and model parallelisms are incorporated on a platform of multiple GPUs. The advanced Ring-AllReduce architecture is harnessed to achieve an acceleration ratio of 3.46. Then a new multi-GPUs distributed optimization method GradReduce is proposed based on Ring-AllReduce architecture. This method optimizes the original data communication mechanism based on mechanical time and frequency by introducing the gradient transmission scheme …

Well-Conditioned T-Matrix Formulation For Scattering By A Dielectric Obstacle, Murat Enes Hati̇poğlu, Fati̇h Di̇kmen

Turkish Journal of Electrical Engineering and Computer Sciences

The classic formulation of the extended boundary condition method is revisited to inject the regularization operators for the unknown coefficients of the eigen-function expansions for the travelling and standing waves throughout the dielectric scatterer. It is shown that, using the new definitions, the existing algorithm of the scattering field calculation can be kept the same for its well-conditioned version. This is exemplified for scalar 2D problems for both TM and TE polarization under illumination of a line source. The condition numbers of the matrix operators in the new version of the algorithm are drastically reduced when the regularization interfaces are …

Lightweight Deep Neural Network Models For Electromyography Signal Recognition For Prosthetic Control, Ahmet Mert

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing methods namely, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), root mean square (RMS), and waveform length (WL) are adopted to convolutional neural network (CNN), and long short-term memory neural network (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset are used for training and testing the proposed models considering prosthetic control strategies. Six class hand movements with three contraction levels are applied to WL and RMS-based feature extraction. After that, they …

A Practical Framework For Early Detection Of Diabetes Using Ensemble Machine Learning Models, Qusay Saihood, Emrullah Sonuç

Turkish Journal of Electrical Engineering and Computer Sciences

The diagnosis of diabetes, a prevalent global health condition, is crucial for preventing severe complications. In recent years, there has been a growing effort to develop intelligent diagnostic systems for diabetes utilizing machine learning (ML) algorithms. Despite these efforts, achieving high accuracy rates using such systems remains a significant challenge. Recent advancements in ensemble ML methods offer promising opportunities for early detection of diabetes, as they are known to be faster and more cost-effective than traditional approaches. Therefore, this study proposes a practical framework for diagnosing diabetes that involves three stages. The data preprocessing stage encompasses several crucial tasks, including …

Improving Unet Segmentation Performance Using An Ensemble Model In Images Containing Railway Lines, Mehmet Sevi̇, İlhan Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

This study aims to make sense of the autonomous system and the railway environment for railway vehicles. For this purpose, by determining the railway line, information about the general condition of the line can be obtained along the way. In addition, objects such as pedestrian crossings, people, cars, and traffic signs on the line will be extracted. The rails and the rail environment in the images will be segmented with a semantic segmentation network. In order to ensure the safety of rail transport, computer vision, and deep learning-based methods are increasingly used to inspect railway tracks and surrounding objects. In …

Exploring The Impact Of Students Demographic Attributes On Performance Prediction Through Binary Classification In The Kdp Model, Issah Iddrisu, Peter Appiahene, Obed Appiah, Inusah Fuseini

Knowledge Engineering and Data Science

During the course of this research, binary classification and the Knowledge Discovery Process (KDP) were used. The experimental and analytical capabilities of Rapid Miner's 9.10.010 instructional environment are supported by five different classifiers. Included in the analysis were 2334 entries, 17 characteristics, and one class variable containing the students' average score for the semester. There were twenty experiments carried out. During the studies, 10-fold cross-validation and ratio split validation, together with bootstrap sampling, were used. It was determined whether or not to use the Random Forest (RF), Rule Induction (RI), Naive Bayes (NB), Logistic Regression (LR), or Deep Learning (DL) …

High Performance Scene Generator For Testing Of Imaging Sensors, Austin Modoff

Electrical Engineering Theses and Dissertations

Since the invention of modern solid-state imaging sensors in the 1980s, considerable advancements have been made, improving aspects such as frequency response, dynamic range, resolution, and wavelength diversity, as well as inventing many different types of sensor architectures such as framing sensors and event sensors. While considerable improvements in this field are evolving every day, the methods by which to test and validate these systems remain unchanged. Many of these test platforms require specialized designs to test only one or two specific qualities. For example, a spinning disk in front of a black body source to measure the frequency response …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Progression Of Surface Flashover In Vacuum With Polymer Insulators, Kimberly M. Faris

Electrical and Computer Engineering ETDs

In pulsed power devices, an insulator is needed to isolate the transmission line from vacuum chambers. Vacuum is used as the insulator because it contains no atoms. Since it is being used as an insulator in these pulsed power insulation systems, surface flashover in vacuum is one of the most extensively studied areas since it being the greatest constraint in providing power due to dielectrics that are not able to sustain the voltage the system is operating under. These dielectrics are used in between high voltage electrodes in various pulsed power applications as electrical insulators to limit the electric current …

A Comparative Effectiveness Study On Opioid Use Disorder Prediction Using Artificial Intelligence And Existing Risk Models, Sajjad Fouladvand, Jeffery Talbert, Linda Phyliss Dwoskin, Heather M. Bush, Amy L. Meadows, Lars E. Peterson, Yash R. Mishra, Steven K. Roggenkamp, Fei Wang, Ramakanth Kavuluru, Jin Chen

Markey Cancer Center Faculty Publications

Opioid use disorder (OUD) is a leading cause of death in the United States placing a tremendous burden on patients, their families, and health care systems. Artificial intelligence (AI) can be harnessed with available healthcare data to produce automated OUD prediction tools. In this retrospective study, we developed AI based models for OUD prediction and showed that AI can predict OUD more effectively than existing clinical tools including the unweighted opioid risk tool (ORT). Data include 474,208 patients’ data over 10 years; 269,748 were females with an average age of 56.78 years. Cases are prescription opioid users with at least …

Resilience Model For Teams Of Autonomous Unmanned Aerial Vehicles (Uav) Executing Surveillance Missions, Robert Koeneke

Doctoral Dissertations and Master's Theses

Teams of low-cost Unmanned Aerial Vehicles (UAVs) have gained acceptance as an alternative for cooperatively searching and surveilling terrains. These UAVs are assembled with low-reliability components, so unit failures are possible. Losing UAVs to failures decreases the team's coverage efficiency and impacts communication, given that UAVs are also communication nodes. Such is the case of a Flying Ad Hoc Network (FANET), where the failure of a communication node may isolate segments of the network covering several nodes. The main goal of this study is to develop a resilience model that would allow us to analyze the effects of individual UAV …

Inter-Frame Video Compression Based On Adaptive Fuzzy Inference System Compression Of Multiple Frame Characteristics, Arief Bramanto Wicaksono Putra, Rheo Malani, Bedi Suprapty, Achmad Fanany Onnilita Gaffar, Roman Voliansky

Knowledge Engineering and Data Science

Video compression is used for storage or bandwidth efficiency in clip video information. Video compression involves encoders and decoders. Video compression uses intra-frame, inter-frame, and block-based methods. Video compression compresses nearby frame pairs into one compressed frame using inter-frame compression. This study defines odd and even neighboring frame pairings. Motion estimation, compensation, and frame difference underpin video compression methods. In this study, adaptive FIS (Fuzzy Inference System) compresses and decompresses each odd-even frame pair. First, adaptive FIS trained on all feature pairings of each odd-even frame pair. Video compression-decompression uses the taught adaptive FIS as a codec. The features utilized …

Ant Colony Optimization For Resistor Color Code Detection, Slamet Wibawanto, Kartika Candra Kirana, Hani Ramadhan

Knowledge Engineering and Data Science

In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other …