Electrical and Computer Engineering Commons^™

A Hands-On Lab On Agentic Scientific Discovery For Time Series With The Model Context Protocol, Dhaval Patel, Miaomiao Feng, Chathurangi Shyalika, Shuxin Lin, Hasnat Md. Abdullah

Publications

Agentic AI offers new opportunities to accelerate scientific discovery for time series by enabling autonomous systems to inspect data, formulate hypotheses, compose experiments, and refine models based on empirical evidence. However, building agents that make sound modeling decisions requires more than connecting a language model to forecasting tools. Key challenges include exposing large model catalogues without overwhelming the agent, grounding parameter choices in data characteristics, supporting iterative experimentation, and evaluating the quality of the reasoning behind each decision.

This hands-on lab introduces participants to agentic scientific discovery for time series using the Model Context Protocol (MCP). Through five interactive sprints, …

Thermal Modelling And Numerical Simulation Of Nanofluid-Based Battery Thermal Management System Using Lattice Boltzmann Method Under High Discharge Rate, Bahram Mahjoobkarambasti, Barun K. Das, Hussein A. Mohammed, Iftekhar Ahmad

Research outputs 2022 to 2026

Effective battery thermal management systems (BTMS) are essential to ensure performance, safety, and lifetime of lithium-ion battery packs in electric vehicles, especially under high charge/discharge conditions. In this regard, this study investigates a nanofluid-based cooling system for a battery module operating under 5C discharge rate. Batteries in a module are placed in a wavy cooling channel, and the proposed design is analysed for different parameters, including coolant mass flow rate (0.002 to 0.05 kg/s), nanoparticle volume fraction (0 to 0.04), battery spacing (0 to 5 mm) and channel width (4, 5 and 6 mm). For the numerical simulation, a two-dimensional …

Evolving Ai Integration In Complex Medical Decision-Making And Multidisciplinary Transplant Care: A Systematic Review Of Human-Ai Collaboration, Rachel L. Dzieran, Cihan H. Dagli, Robert J. Marley

Engineering Management and Systems Engineering Faculty Research & Creative Works

Purpose of Review: Artificial intelligence (AI) in healthcare has evolved dramatically from early expert systems, which were initially considered replacements for clinical judgment, to today's collaborative frameworks that aim to augment physician decision-making. This evolution is particularly crucial in domains such as transplant surgery, where decisions carry irreversible consequences and require the integration of complex, often ambiguous data. Drawing on peer-reviewed literature from 2019 to 2025, we conducted a systematic review that analyzed key elements distinguishing successful human-AI partnerships from those that fail. Recent Findings: The ideal balance incorporates human expertise into AI systems through weighted integration approaches, rather than …

Extending Geometric Acoustic Ray Tracing To Multi-Room Environments: A Case Study On Gunshot Sound Transmission Between Adjacent Rooms, Tyler Ton

Theses and Dissertations

Accurate localization of gunshots in multi-room building environments remains a challenging problem in acoustic forensics and public safety applications. Existing approaches model sound propagation within a single room, neglecting the transmission of acoustic energy through walls and other building materials. This thesis presents a study on modeling multi-room gunshot acoustic transmission, combining geometric ray tracing with structural acoustic transmission-loss modeling to generate impulse responses for two horizontally adjacent rooms separated by a shared wall, providing a foundation for future inter-room gunshot localization work. The proposed system uses GSound-SIR, a geometric acoustics engine, to simulate sound propagation in both of the …

Adaptive Active Snore Noise Control Using Microphone And Loudspeaker Arrays, Nathan Walker

All Graduate Theses and Dissertations, Fall 2023 to Present

Snoring can wreck a good night’s sleep, not just for the snorer but for their partner, and over time it can strain relationships. Common fixes like CPAP machines, nasal strips, and earplugs either have to be worn or don’t do much against the deep, low-pitched rumble that makes snoring so hard to ignore.

This research explores a different approach, a device that doesn’t need to be worn at all. It uses a small set of microphones to listen to the snoring and a set of speakers to play back carefully timed anti-snore noise, which is sound designed to cancel out …

Efficient Charging Scheduling Through Coordination Of Electric Vehicle Platoons And Charging Stations, Liwan Qi, Bochun Wu, Shoubo Li, Yi Gong, Wei Ni

Research outputs 2022 to 2026

This paper focuses on charging allocation in a vehicle-to-infrastructure (V2I) communications-enabled electric vehicle (EV) network with heterogeneous traffic flows, where manned EVs and EV platoons coexist, and each EV platoon may have a different size and travel speed. In such a network, hybrid traffic flows pose significant challenges since platoons with multiple EVs can easily cause severe station overloading and increase the total time cost for charging service, particularly when large platoons occur. To tackle this issue, a centralized approach is proposed to plan charging allocation and optimize the velocities of manned EVs and EV platoons with the assistance of …

Multimodal Deep Learning Architecture For Biomass Prediction Using Aerial Imagery, Sadikul Alim Toki

All Graduate Theses and Dissertations, Fall 2023 to Present

Grape growers rely on knowing how much leaf area their vines have, since canopy size affects how much water a vine needs, how healthy it is, and how much fruit it will produce. Measuring this by hand, plant by plant, across an entire vineyard takes enormous time and labor, so researchers have turned to cameras mounted on drones and satellites as a faster alternative. Drones capture very detailed images but only over small areas per flight, while satellites can photograph an entire farming region at once but cannot make out individual plants. 

This thesis builds a computer vision model, a …

Dataset For Integrity Attacks On Time Synchronized Synchrophasor Data, Taylah Griffiths, Mohiuddin Ahmed, Chadni Islam

Research outputs 2022 to 2026

Phasor measurement units, also known as synchrophasors, are a vital component within smart grids to determine the stability of the grid. These devices send synchrophasor data to phasor data concentrators that collate and analyse the data. Recently, synchrophasor communication data has become beneficial for the research community. However, datasets covering cyberattacks on synchrophasor data are not public. Having access to this data would aid in investigating mitigations against cyberattacks. This paper describes a public specialized dataset, known as ECU-PMU-FDI/TSA. The dataset contains synchrophasor communication data for cybersecurity mitigation testing. Three hours of communication data was captured, from a simulated testbed. …

Scalable And Interpretable Fraud Detection In Fintech: Evidence From A Hybrid Tabnet–Graph Neural Network Framework, Haseeb Ahmad, Istiaque Bhuiyan, Tanvir Bhuiyan, Ariful Hoque

Research outputs 2022 to 2026

This study proposes a fraud detection framework for FinTech that integrates tabular deep learning with relational graph reasoning. We address a key limitation of state-of-the-art tabular fraud models: their assumption that transactions are independent, despite evidence that modern fraud often arises from coordinated infrastructure reuse (e.g., shared devices, email domains, accounts, and addresses). Using the IEEE-CIS Fraud Detection dataset (≈590,540 e-commerce transactions), we develop a hybrid architecture that combines TabNet, chosen for its built-in interpretability on tabular data, with a GraphSAGE encoder trained on a leakage-free heterogeneous entity graph constructed strictly from historical training transactions. Unseen entities in validation and …

Switching Configurations For Reconfigurable Battery Packs: A Review, Kenneth Tetteh Akiti, Quoc Viet Phung, Iftekhar Ahmad, Daryoush Habibi

Research outputs 2022 to 2026

Conventional Battery Energy Storage Systems (BESS) are designed with fixed cell topologies, where the electrical connection between cells remains unchanged throughout their life. Although Battery Management Systems (BMS) and conventional cell-balancing techniques are employed to monitor and maintain performance, they often fail to address key challenges such as uneven cell aging, capacity mismatch, and fault isolation that arise especially during the second life of a battery pack. These limitations can result in reduced efficiency, reduced reliability, and shortened battery life. As large-scale battery packs become increasingly common in Electric Vehicles (EVs), smart grids, robotics, and renewable energy systems, the limitations …

Ncf Sensor Coated With Cofe1.96la0.04o4/Lafeo3 Heterostructures For Room-Temperature Detection Of Liquefied Petroleum Gas Concentration, Ziqiang Liu, Fujian Tang, Yufang He, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Liquefied petroleum gas (LPG) is a highly flammable fuel widely used for cooking, heating and transportation, making real-time leak detection essential for preventing fire, explosion, and suffocation hazards. In this study, a room-temperature no-core fiber (NCF) sensor coated with CoFe_1.96La_0.04O₄/LaFeO₃ heterostructures was developed for LPG detection. The heterostructures were characterized using XRD, Raman spectroscopy, SEM, TEM, XPS, PL spectroscopy, and UV–vis spectroscopy. Experimental results showed that La incorporation and heterostructure formation refined the particles size, improved surface accessibility, and modulated the electronic structure and band gap, thereby enhancing LPG adsorption, carrier redistribution, and …

Effect Of Dislocation Density On The Carrier Mobility Of Gan/Algan Hemt, Kelsey M. Colglazier

Caroliniana Undergraduate Research Journal

Gallium nitride (GaN) and aluminum gallium nitride (AlGaN) heterostructures are used in high electron mobility transistors (HEMTs) due to their performance in high-frequency and high-power applications. However, dislocation density in GaN significantly impacts electron mobility and overall device efficiency. In this study, the relationship between dislocation density and electron mobility was investigated using two samples of GaN with thicknesses of 2 µm and 11 µm grown by metal-organic chemical vapor deposition (MOCVD), and HEMTs produced using these samples. Data concerning dislocation density was collected and analyzed using atomic force microscopy and X-ray diffraction, while electrical properties were evaluated through capacitance-voltage, …

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

Solving large-scale PDEs on complex three-dimensional geometries remains a central challenge in scientific and engineering computing, often due to expensive pre-processing stages and high computational overhead. We present Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated deep learning framework for efficient CAE simulations of complex three-dimensional geometries in CAD-driven design analysis. Built upon the Parameter-Conditioned Lagrangian Modeling (PCLM) that embeds physical consistency into the learning process and the Low-Rank Query Attention (LR-QA) module that reduces attention complexity from O(N2) to O(NC2+C3) via covariance decomposition, LRQ-Solver supports multi-configuration analysis within iterative design workflows. On two benchmark datasets, it achieves a 28.6% error …

Adaptive Improved Particle Swarm Optimization-Based Maximum Power Point Tracking And Energy Smoothing For Photovoltaic Hybrid Battery–Supercapacitor Storage Systems, Mohammad Aminul Islam, Guo Shaokai, Jakaria Mahdi Imam, Mohammad Khairul Basher, Nowshad Amin, Tarek Abedin, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The rapid development of photovoltaic (PV) systems has made them an important component of the global clean energy strategy. However, the intermittency and non-linear characteristics of photovoltaic (PV) output remain major challenges for stable renewable energy utilization. This study proposes an adaptive improved particle swarm optimization (IPSO)-based maximum power point tracking (MPPT) strategy integrated with hybrid energy storage coordination for photovoltaic systems. The IPSO introduces adaptive inertia adjustment, velocity clamping, and stagnation reinitialization, which improve the convergence robustness under dynamic irradiance and temperature conditions. The algorithm was benchmarked against Perturb & Observe (P&O), Incremental Conductance (INC), and standard PSO using …

Two-Stage Stochastic Programming For Resource Allocation In Mobile Underwater Acoustic Networks, Sadaf Vahabli, Iftekhar Ahmad, Quoc Viet Phung, Daryoush Habibi

Research outputs 2022 to 2026

Underwater Acoustic Networks (UANs) enable long-range communication for a range of applications. However, UANs are severely limited by scarce acoustic spectrum, which is shared with marine animals and can lead to disruptive interference, contributing to anthropogenic ocean noise. In addition, the underwater environment is highly dynamic, where: marine-animal presence is uncertain, channel conditions fluctuate, and Doppler effects degrade communication reliability. Prior UAN studies have addressed these challenges separately, despite their combined effect on resource allocation. Accordingly, this paper proposes a two-stage Scenario-based Stochastic Programming (SSP) framework with a hybrid risk approach that jointly treats these challenges within a single resource …

Influence Of Particle Properties And Grain Size On Aggregation In Dry Particle-Laden Volcanic Jets, Md Miraz Hossain, Stephen A. Solovitz, Josef Dufek, Joshua S. Mendez Harper, Alexa R. Van Eaton, Mary C. Benage, Larry G. Mastin, Benjamin J. Andrews

Electrical and Computer Engineering Faculty Publications and Presentations

Explosive volcanic eruptions can inject large amounts of ash into the atmosphere. These suspended particles pose hazards to aircraft, so a better understanding of the ash residence time and transport distance is needed. Fine particles combine to form larger aggregates, altering their settling behavior. Ash aggregation is influenced by liquid bonding, electrostatic forces, and preferential concentration driven by turbulence. We examine particle aggregation through controlled, low-humidity experiments at similar particle-fluid scaling to conditions near a volcanic vent. Particle-laden jets used compressed air containing three types of particles at controlled mass loadings: hollow glass spheres (∼10 μm diameter), solid nickel spheres …

Integrated Automation Framework For Generation Retirement Studies In Transmission Planning, Mohammed Sleiman, Tianwei Xia, Zhangxin Zhou, Nathaniel Rice

Electrical and Computer Engineering Faculty Research & Creative Works

Generation retirement studies are a critical part of long-term transmission planning, as power systems transition toward higher penetration of renewable energy and the retirement of conventional generation. However, existing study workflows are often fragmented across multiple tools and platforms such as PSS/E, TARA, Python, and Excel resulting in time-intensive processes, limited scalability, and reduced reproducibility. This paper showcases an integrated automation tool developed for generation retirement studies that is designed to streamline and unify the end-to-end analytical workflow.

The proposed framework consolidates key study components, including generator retirement modeling, power flow redispatch, contingency analysis (N-1 and N-1-1), and mitigation cost …

Regularization-Based Crystallite Size Distribution Analysis Of Pt/C Catalysts From X-Ray Diffraction, Cheng-Long Ma, Wei-Yu Shi, Zhong-Jun Hou

Journal of Electrochemistry

Accurate determination of the platinum crystallite size distribution in carbon-supported platinum (Pt/C) catalysts is critical for evaluating both the initial performance and long-term durability of proton exchange membrane fuel cells (PEMFCs). Conventional characterization approaches—including transmission electron microscopy (TEM), small-angle X-ray scattering (SAXS), and the Scherrer equation—are limited by restricted statistical sampling, sensitivity to support interference, or the inability to resolve the full breadth of the size distribution. In this work, we present a regularization-based inversion framework that extracts the volume-weighted crystallite size distribution directly from X-ray diffraction (XRD) line profiles, without requiring any a priori assumption regarding the functional form …

Comprehensive Variable-Temperature Raman And Spectroscopic Ellipsometry Investigation Of Bulk Undoped, Fe-Doped, And Ge-Doped Gan: Correlated Phonon, Carrier, Optical, And Disorder Dynamics, Manika Tun Nafisa, Sm Atiqur Rahman, Zhe Chuan Feng, Sandip Das, James N. Hilfiker, Nina Jong, Ian T. Ferguson, Benjamin Klein

Faculty Articles

Bulk gallium nitride (GaN) substrates with controlled electrical compensation and doping are foundational materials for high-power electronics, optoelectronics, and emerging spintronic devices. Despite extensive studies on epitaxial GaN thin films, a complete and correlated understanding of crystal, phonon, free-carrier, birefringence, optical, and disorder-driven processes in high-quality bulk GaN crystals remains limited. In this work, a comprehensive temperature-dependent (25–300 °C) investigation of free-standing bulk undoped, Fe-doped, and Ge-doped GaN substrates using Raman spectroscopy, spectroscopic ellipsometry (SE), and atomic force microscopy (AFM) has been presented. A detailed analysis of phonon evolution, apparent Raman-shift-derived biaxial stress and in-plane strain, longitudinal optical phonon–plasmon coupling …

Distinguishing Auroral-E And Sporadic-E In Gnss Radio Occultation Measurements, Sharjeel Hyder, Jeffrey M. Holmes, Kyle E. Fitch, Kyle D. Roberts, Also: Themens, David R.; Emmons, Daniel J., Nimalan Swarnalingam, Dong L. Wu, Cornelius C. J. H. Salinas, David R. Themens, Daniel J. Emmons

Faculty Publications

GNSS radio occultation (RO) is widely used to detect metallic-ion sporadic-E layers on a global scale, but in auroral environments, particle precipitation-driven ionization can produce auroral-E structures with similar RO signatures. This overlap complicates reliable identification of sporadic-E and may bias high-latitude occurrence rate estimates. In this study, we quantify auroral contamination in RO-based sporadic-E detection and evaluate the performance of both threshold-based and multivariate classifier approaches. A dataset of 106 conjunction events was constructed using RO observations from the Spire and COSMIC-1 constellations, together with co-located ionosonde and magnetometer measurements at two high-latitude sites and an ionosonde at a …