Electrical and Computer Engineering Commons^™

Are We Ready For Synchronous Conceptual Modeling In Augmented Reality? A Usability Study On Causal Maps With Hololens 2, Anish Shrestha, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Participatory modeling requires combining individual views to create a shared conceptual model. While remote collaboration tools have enabled synchronous online modeling, they are limited to desktop settings. Augmented reality (AR) offers a new approach by potentially providing the sense of presence found in physical collaboration, which may better support participants in achieving the sense of presence found in physical locations, thus supporting them in negotiating meaning and building a shared model. (2) Methods: Building on prior works that developed technology, we performed a usability study with pairs of modelers to examine their ability at performing key conceptual modeling …

Data-Driven Insights For Optimizing Ev Charging Infrastructure: A Case Study On Efficiency And Utilization, Kazi Zehad Mostofa, Md Fokrul Islam, Mohammad Aminul Islam, Mohammad Khairul Basher, Tarek Abedin, Boon Kar Yap, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The increasing global adoption of electric vehicles (EVs) has led to a growing demand for a cost-effective and reliable charging infrastructure. This study presents a novel data-driven approach to assessing EV station performance by analyzing power consumption efficiency, station utilization rates, no-power session occurrences, and CO2 reduction metrics. A dataset of 17,500 charging sessions from 305 stations across a regional network was analyzed to identify operational inefficiencies and opportunities for infrastructure optimization. Results indicate a strong correlation between station utilization and energy efficiency, highlighting the importance of strategic station placement. The findings also emphasize the impact of no-power sessions on …

Visible Modulation Of Epsilon Near Zero Materials, Jonathan M. Weber

Theses and Dissertations

While Epsilon-Near-Zero materials have offered many benefits from their introduction to the field of nonlinear optics, those materials have also narrowed the range of wavelengths often studied. With reliable and proven enhancements to light-matter interaction in the ENZ region, many researchers have had little reason to extend the spectrum measured in experiments. The purpose of this work is to measure the intensity dependent refractive index of Indium Tin Oxide across a broad spectral range, from ultraviolet to near infrared, to improve the understanding of semiconductor nonlinearities far from Lorentz and Drude resonances. Furthermore, the visible region is naturally low-loss in …

Implementation Of Quantized Artificial Neural Networks With Spintronic Stochastic Computing, Saadi Sabyasachi Mr.

Theses and Dissertations

Artificial intelligence or machine learning is going through a rapid expansion. It also incurs significant costs for power and device footprints. Various approaches are being explored to design energy and hardware efficient machine learning models. Stochastic computing has been proposed for efficient machine learning implementation. It requires a source of random number generation which poses some practical challenges. So spintronic solutions such as magnetic tunnel junction has been used for random number generation. Again, spintronic random number generation to implement high precision circuit is prone to device-to-device variations. Hence we designed quantized artificial neural network with spintronic stochastic computing which …

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

Forecasting Data-Driven System Strength Level For Inverter-Based Resources-Integrated Weak Grid Systems Using Multi-Objective Machine Learning Algorithms, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Mohammad A.S. Masoum

Research outputs 2022 to 2026

Shortage of grid-fault level, known as system strength inadequacy, impacts on grid instability and can lead to blackouts. System strength is generally measured by short circuit ratio index at point of coupling (POC) of inverter-based resources (IBRs) and the grid system. Nowadays, accurate knowledge of system strength forecasting for ‘next day’ to ‘next week’ duration is essential to power system operators, owing to the higher-growth of IBRs. However, releavant publications about this subject remain limited when compared with load demand, active and reactive power prediction. Therefore, a data-driven system strength forecasting scheme is presented in this paper to surmount these …

Characterization, Optimization, And Performance Evaluation Of Pcm With Al2o3 And Zno Hybrid Nanoparticles For Photovoltaic Thermal Energy Storage, Md Golam Kibria, Utpol K. Paul, Md Shahriar Mohtasim, Barun K. Das, N. N. Mustafi

Research outputs 2022 to 2026

The electrical efficiency of the photovoltaic (PV) panel is affected significantly with increased cell temperature. Among various approaches, the use of Phase Change Materials (PCMs) with nanoparticles is currently one of the most effective for reducing and managing the temperature of PV panels. In this study, paraffin wax as PCM with different loading levels (0.5 %, 1 %, and 2 %) of hybrid nanoparticles Al2O3 and ZnO were successfully synthesized and their effects on the performance of the Photovoltaic-Thermal (PVT) system were investigated experimentally. Additionally, a prediction model was developed to analyze the interaction between the operating factors (independent variable) …

Design Of An Improved Robust Fractional-Order Pid Controller For Buck–Boost Converter Using Snake Optimization Algorithm, Seyyed Morteza Ghamari, Hasan Molaee, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

With the increasing complexity of modern power systems, effective control of DC–DC converters has become crucial to ensure stability and efficiency. This paper focuses on optimizing the parameters of a known fractional-order proportional–integral–derivative (FOPID) controller for the control of a DC–DC buck–boost converter. The control of a DC–DC buck–boost converter is achieved using aFOPID approach. The gains of this technique have been enhanced utilizing the snake optimization (SO) algorithm. This converter exhibits unfavourable behaviour due to its non-minimum structure, necessitating a well-regulated controller to guarantee stability. The fractional concept is suggested here to enhance the dynamics of the classical PID …

Assessing Techno-Economic Performance Of Synchronous Condensers And Static Synchronous Compensators In Renewable Energy-Integrated Weak-Grids Using Hedge Feedforward Feedback-Based Online Gated Recurrent Unit, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Mohammad A.S. Masoum

Research outputs 2022 to 2026

To achieve net-zero targets in many counties, renewable energy generators (REGs) are integrated with grids where less fault current is produced compared to synchronous generators. Accordingly, fault level availability, known as ‘system strength’, is reduced at point of coupling (POC) buses. A minimum system strength level is crucial for REGs to accurately detect and ride-through faults. To maintain adequate system strength in renewable energy-based weak grids, researchers and engineers have recommended supplementary devices, such as synchronous condensers (SynCons) or static synchronous compensators (STATCOMs), to provide the required fault-current. Because SynCons and STATCOMs are costly devices, their optimal sizes and placement …

Beyond Assessment Security: A Critical Policy Analysis Of Four Alternative Strategies To Uphold Academic Integrity And Adopt The Genai Transformation Of Teaching And Learning For An Accredited Engineering Degree, Sasha Nikolic, Montserrat Ros, Yasir M. Al-Abdeli, Helen Fairweather

Research outputs 2022 to 2026

Generative Artificial Intelligence (GenAI), exemplified by tools such as ChatGPT, has posed significant challenges and opportunities in the realm of academic integrity, particularly in engineering education. This commentary critically examines alternative strategies that go beyond traditional assessment security, aiming to uphold academic integrity while embracing the transformative potential of GenAI in teaching and learning. In the context of programs that rely on unit level outcomes to determine the overall student progression (not the programmatic approach to progression), this study identifies and explores four strategies that may offer potential improvements to assessment security: I-risk-level analysis, which aligns the mix of supervised …

Energy-Efficient Resource Allocation For Mission-Critical Applications In Underwater Acoustic Networks, Walid K. Hasan, Iftekhar Ahmad, Quoc Viet Phung, Yue Rong, Haitham Khaled, Daryoush Habibi

Research outputs 2022 to 2026

The Internet of Underwater Things (IoUT) has emerged as a vital technological domain, significantly advancing underwater exploration and communication capabilities. At the core of this paradigm is Underwater Acoustic Communication (UAC), which acts as the primary medium for data transmission in underwater environments, including environmental monitoring, security and surveillance, and underwater exploration. Despite its significance, UAC systems face inherent challenges such as limited bandwidth, high signal attenuation, and long propagation delays. These limitations become particularly significant in mission-critical maritime operations, such as warning systems, underwater navigation and control, and diver safety or emergency communications, where timely and reliable data transmission …

Application Of Edge-Enhanced Phase Analysis To Active Microwave Thermographic Measurements, Douglas Blaine Fleetwood

Masters Theses

Nondestructive testing and evaluation (NDT&E) encompasses a range of inspection techniques that assess materials, components, and structures while maintaining their integrity and performance. Among these techniques, thermography is particularly attractive due to its non-contact imaging approach with easy-to-interpret results. Active Microwave Thermography (AMT) has emerged as a promising NDT&E inspection technique that utilizes electromagnetic energy to heat materials through dielectric and magnetic absorption, with subsequent infrared imaging used to detect subsurface defects. While AMT offers significant advantages including material-specific heating optimization and lower power requirements compared to conventional thermographic methods, the technique faces a persistent challenge related to the thermal …

Biological Computing From Microscopic To Macroscopic Evolution, Xiaofeng Ding

Masters Theses

A unified theory from microscopic to macroscopic DNA-based biological systems is explained in terms of the rule components used when the system size is increased. Even though the eight female rules in each ruleset are provided with an equal probability of the computation-state outcomes, the initial ensemble of the computation states will exponentially settle into one dominant rule that supports the nutrition needed for growth. The remaining seven rules form into two minority groups to provide the biological characteristics of the system's growth from a micro to macroscopic evolution. The object of this study is to prove that such a …

Global Spectral Clustering Of Temporally Stable Pixels For Epics Identification, Validation, And Hyperspectral Assignment For Satellite Calibration, Juliana Maria Fajardo Rueda

Electronic Theses and Dissertations

No abstract provided.

Refinement Of Trend-To-Trend Cross Calibration Total Uncertainties Utilizing Extended Pseudo Invariant Calibration Sites (Epics) Global Temporally Stable Target, Minura Samaranayake

Electronic Theses and Dissertations

Cross-calibration is an essential technique for calibrating Earth Observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare uncalibrated sensor to a well-calibrated reference sensor. This study introduces the hyperspectral Trend-to-Trend (T2T) cross-calibration technique utilizing EPICS Cluster 13 Global Temporally Stable (Cluster 13-GTS) as the calibration target, offering better temporal stability than previous targets used in T2T cross-calibration by an absolute difference of 0.4%, between coefficients of variation across all bands excluding CA band. A multispectral sensor-specific normalized hyperspectral profile was developed using the EO-1 Hyperion hyperspectral profile over Cluster 13-GTS to improve Spectral Band …

Modeling And Analysis Of Amorphous Steel Transformer For Potential Loss Reduction In Power Systems., Daniel A. Muchow

Theses and Dissertations--Electrical and Computer Engineering

The energy consumed in power systems can be reduced directly by implementing new technologies and materials. The effort to reduce the carbon emissions expelled from the production of electrical energy has become a major focus. According to the U.S. energy Information Administration approximately 5% of all the electric generated and transmitted in the U.S. electrical grid is lost. The energy profile calculated from the usage of electrical power from all 50 states in 2021 is estimated to be 3.8 billion megawatts per hour, with a state average of just over 11 cents per MWh, producing just over 41.8 billion in …

Analysis And Design Optimization Of Synchronous Machines With Excitation Through Stator Dc Coils Or Magnets And Reluctance Rotors, Oluwaseun A. Badewa

Theses and Dissertations--Electrical and Computer Engineering

The design and optimization of electric machines face increasing demands for higher efficiency, improved torque density, manufacturability, and effective utilization of materials, particularly in applications such as electric traction and propulsion, where performance, reliability, and cost are critical. This work explores innovative synchronous machine configurations with reluctance rotors and stator-combined excitation, including permanent magnet and DC-excited topologies, to address these challenges through advanced design methodologies, computational modeling, and experimental validation. This research is relevant to address the growing demand for high-performance electric machines that combine high power density with costeffective manufacturing. The conventional limitations in power and torque density, thermal …

A Multiscale Ai Framework For Forest And Agriculture Health Monitoring: Drone-Based Object Recognition And Segmentation For Automated Ecological Assessment, Sruthi Keerthi Valicharla

Graduate Theses, Dissertations, and Problem Reports (ETD)

Forest and agricultural ecosystems are increasingly at risk due to invasive species, pests, and diseases, necessitating scalable, automated, and intelligent monitoring solutions. Traditional field based forest and agriculture health assessments are limited by cost, time, and spatial coverage. This dissertation presents a multiscale deep learning framework that automates forest and agriculture health monitoring using drone imagery and computer vision techniques. The system operates across three spatial levels: forest level, tree level, and leaf level, combining object detection, segmentation, and classification models to support large scale ecological assessment.

At the forest level, high-altitude drone imagery is processed using object detection and …

Deep Target Recognition: Semi-Supervised Annotation, Sensor Fusion And Super-Resolution, Shoaib Meraj Sami

Graduate Theses, Dissertations, and Problem Reports (ETD)

Despite the recent expansion of machine learning algorithms to cover a wide range of disciplines, several areas of automatic target recognition (ATR) remain underexplored. This dissertation presents tools developed to improve performance in three significant aspects of ATR: semi-supervised annotation, sensor fusion, and image super-resolution. The aim of the semi-supervised methods is to automatically annotate targets in scenarios where labeled data are scarce in the target domain but available in the source domain. Secondly, to address the limitations of individual image sensors and enhance robustness under different environmental conditions and man-made constraints, a sensor fusion algorithm was developed to improve …

Rheology Of Alumina Suspensions Subjected To Alternating Current Electric Fields For Freeze-Casting, Sivakumar Chithamallu, Ruksana Baby, Jacob L. Jones, Dipankar Ghosh

Mechanical & Aerospace Engineering Faculty Publications

Alternating current (AC) electric field can extrinsically tune freeze‐cast microstructure, originating from field‐induced increase in viscosity of ceramic suspensions. However, the changes that occur in a ceramic suspension and rheological behavior, ultimately affecting freeze‐cast microstructure, are not well understood. Moreover, the effects of AC electrokinetic forces and temperature on viscosity need to be decoupled. The viscosity and temperature of ceramic suspensions subjected to AC field and direct heating were measured, revealing that the increase in viscosity is due to AC dielectrophoretic forces rather than field‐induced heating of suspension. The shear thinning behavior of suspensions characterized using a power‐law model reveals …

Optimal Control And Structurally-Informed Gradient Optimization Of A Custom 4-Dof Rigid-Body, Brock Marcinczyk, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

This work develops a control-centric framework for a custom 4-DOF rigid-body manipulator by coupling a reduced-order Pontryagin’s Maximum Principle (PMP) controller with a physics-informed Gradient Descent stage. The reduced PMP model provides a closed-form optimal control law for the joint accelerations, while the Gradient Descent module determines the corresponding time horizons by minimizing a cost functional built directly from the full Rigid-Body Dynamics. Structural-mechanics reaction analysis is used only to initialize feasible joint velocities—most critically the azimuthal component—ensuring that the optimizer begins in a physically admissible region. The resulting kinematic trajectories and dynamically consistent time horizons are then supplied to …

Ai-Generated Messaging For Life Events Using Structured Prompts: A Comparative Study Of Gpt With Human Experts And Machine Learning, Christopher Lynch, Erik Jensen, Ross Gore, Virginia Zamponi, Kevin O'Brien, Brandon Feldhaus, Katherine Smith, Joseph Martínez, Madison H. Munro, Timur E. Ozkose, Tugce B. Gundogdu, Ann Marie Reinhold, Hamdi Kavak, Barry Ezell

VMASC Publications

Large Language Models (LLMs) play an increasingly integrated and pivotal role in generating diverse types of texts, such as social media messages, emails, narratives, and technical reports, among other textual communication forms. As AI-generated messaging filters into human communication, a systematic exploration of their effectiveness for mimicking human-like communication of life events is needed. In this study, we employ a zero-shot structured narrative prompt to generate 24,000 life event messages for birth, death, hiring, and firing events using OpenAI's GPT-4. From this dataset, we manually classify 2880 messages and evaluate their validity in conveying these life events through the form …

Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty

VMASC Publications

Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

A Novel Intelligent Thermal Feedback Framework For Electric Motor Protection In Embedded Robotic Systems, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures …

Differential Equations' Coverage In Circuits And Heat Transfer Courses In An Engineering Technology Curricula, Otilia Popescu, Orlando Ayala

Engineering Technology Faculty Publications

Differential equations are at the core of any undergraduate engineering as well as of science programs. While a course in differential equations is required by any engineering degree, it is not a requirement for engineering technology programs, which usually require mathematics up to calculus I. This paper discusses the undergraduate engineering technology curricula, focusing on how differential equations are taught to engineering technology students and how they are covered in the programs. In particular, the paper will discuss the treatment of differential equations in electrical circuits and heat transfer courses.

A Comparative Analysis Of Preprocessing Filters For Deep Learning-Based Equipment Power Efficiency Classification And Prediction Models, Sang-Ha Sung, Chang-Sung Seo, Michael Pokojovy, Sangjin Kim

Mathematics & Statistics Faculty Publications

The quality of input data is critical to the performance of time-series classification models, particularly in the domain for industrial sensor data where noise and anomalies are frequent. This study investigates how various filtering-based preprocessing techniques impact the accuracy and robustness of a Transformer model that predicts power efficiency states (Normal, Caution, Warning) from minute-level IIoT sensor data. We evaluated five techniques: a baseline, Simple Moving Average, Median filter, Hampel filter, and Kalman filter. For each technique, we conducted systematic experiments across time windows (360 and 720 min) that reflect real-world industrial inspection cycles, along with five prediction offsets (up …

Stack Bonding In Pentacene And Its Derivatives, Craig A. Bayse

Chemistry & Biochemistry Faculty Publications

Understanding the nature of π-stacking interactions is important to molecular recognition, self-assembly, and organic semiconductors. The stack bonding order (SBO) model of π-stacking has shown that the conformations of dimers occur when the combinations of monomer MOs are overall stack bonding in character. DFT calculations show that minima found on the potential energy surface for the π-stacked dimers of pentacene and perfluoropentacene occur when the dimer MOs are constructed from combinations of monomer MOs with an allowed SBO. An ex-amination of the MOs of π-stacked dimers extracted from X-ray structures of alkynyl derivatives like TIPS-pentacene pack at one or more …

Identification Of Π-Stacking Motifs In Naphthalene Diimides Via Solid-State Nmr, Jennifer E. Mejia, Hannah E. Butler-Au, Nalaya E. Thompson, Karcher D. Goodman, Elizabeth R. Zengel, Robert D. Pike, Jingdong Mao, Craig A. Bayse

Chemistry & Biochemistry Faculty Publications

Organic electronics, featuring π-conjugated small molecules and polymers, have gained significant attention for their potential in flexible, lightweight devices. However, characterization of the ordered, π-stacking domains within these materials using microscopy or X-ray diffraction (XRD) is challenging with complex systems or when crystallography is impractical. This study applied 1D ¹³C multiple cross-polarization magic angle spinning (multiCP/MAS) and 2D ¹H-¹³C heteronuclear correlation (HetCor) solid-state nuclear magnetic resonance (ssNMR) to systematically characterize π-stacking motifs in a series of N,N'-dialkyl naphthalene diimides (NDIs). These techniques were shown to distinguish between the electronic environments attributed to different π-stacking motifs adopted in these NDIs, such …

Transformer-Based Symbolic Music Generation, Ben Buentello

Master’s Theses

This thesis investigates the capacity of transformer-based architectures to learn generalized musical patterns through symbolic generation. To support this exploration, a complete music generation pipeline was developed, beginning with the construction and classification of a large-scale dataset of over 170,000 MIDI files. The dataset was processed using rule-based heuristics and custom neural classifiers to separate tracks by musical function and contour. A novel tokenization scheme, MINTii, was introduced to encode musical information compactly through interval-based representations, reducing redundancy and promoting generalization. Using this infrastructure, a transformer model was trained to generate single-track melodic sequences. Its performance was evaluated through both …