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 …

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 …

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 …

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 …

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 …

Technical, Environmental And Institutional Readiness For Nuclear Power Deployment In Seismically Active Power Systems, King Harold A. Recto, Izech Brian O. Edwin

Electronics, Computer, and Communications Engineering Faculty Publications

The deployment of nuclear power in seismically active power systems presents significant engineering, environmental, and institutional challenges. Although nuclear energy offers a low-carbon option capable of supporting long-term energy transition goals, its adoption requires careful evaluation of readiness factors beyond reactor technology alone. This study investigates the technical, environmental, and institutional determinants influencing nuclear power deployment in seismically active regions. A systematic review of engineering, environmental, and policy literature was conducted to identify key factors affecting nuclear energy integration, including grid capacity requirements, seismic safety considerations, hydrological conditions for reactor cooling, and long-term radioactive waste management strategies. These factors are …

A Nonlinear Viscoelastic 3d Bellows Microspring Fabricated With Two-Photon Polymerization, Harris J. Hall, Tyler Grunzke, Samuel Campbell, Hengky Chandrahalim

Faculty Publications

In the past decade, two-photon polymerization (TPP) based 3D printing has gained popularity for prototype fabrication of micro and nanoscale structures and mechanical metamaterials. However, practical considerations including response uniformity, nonlinear behaviors, and material aging effects remain challenges for device maturation and are seldom reported. In this work, a pair of cylindrical microscale bellows springs (~ 100 μm diameter), fabricated with a commercial TPP 3D printer, are shown to exhibit nonlinear stiffness profiles under compression (up to ~ 7%), with distinct linear and nonlinear regions, that change significantly after ~ 9–10 months of open air exposure in ambient lab conditions. …

Graphene Oxide–Driven In Situ Bismuth Reduction Enables Highly Efficient Electroreduction Of Co2 To Formic Acid, Hao Feng, Bohong Zhang, Jie Huang, Xinhua Liang

Electrical and Computer Engineering Faculty Research & Creative Works

To enable large scale efficient electrochemical CO₂ reduction reaction (CO₂RR) to formic acid (HCOOH), it is important to develop catalysts that can be operated in a wide potential window with good stability. Herein, we successfully synthesized Bi₂O₃ catalyst supported on graphene oxide (GO) and graphene (G) and found that Bi₂O₃/GO catalyst had a better overall performance than Bi₂O₃/G. The Bi₂O₃/GO catalyst demonstrated an outstanding CO₂RR performance with a greater than 90% faradaic efficiency (FE) across a wide applied potential window …

Thermo-Mechanical Behaviour Of Metal-Coated Optical Fibers For Distributed High-Temperature Sensing: From Laboratory Characterization To Industrial Case Validation, Koustav Dey, Rony Kumer Saha, Bohong Zhang, Laura Bartlett, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Reliable distributed temperature sensing in high-temperature environments remains a significant challenge due to the thermal and mechanical limitations of conventional optical fibers. In particular, polymer-coated fibers degrade above ∼300 °C due to coating failure, mechanical fragility and hydrogen ingress. Metal-coated optical fibers offer a robust alternative for harsh environments such as Electric Arc Furnaces (EAFs), aerospace engines, nuclear systems, and oil and gas wells, owing to their superior mechanical strength and hermetic sealing. In this work, a first comprehensive experimental investigation of the thermo-mechanical behavior of metal-coated optical fibers for distributed high temperature sensing is presented over a wide temperature …

Ph-Compensated Hydrogen Peroxide Quantification Using A Dual-Modal Fiber-Optic Probe, Homayoon Soleimani Dinani, Bohong Zhang, Maryam Karimi, Rex E. Gerald, Shelley D. Minteer, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

We report a dual-modal fiber-optic probe that integrates electrochemical quantification of hydrogen peroxide (H₂O₂) with co-localized fluorescent pH sensing for pH-indexed interpretation of the H₂O₂ response. H₂O₂ is a reactive oxygen species involved in oxidative stress, inflammation, and cellular signaling, and local pH modulates both its production and electrochemical response. Many electrochemical H₂O₂ sensors exhibit pH-dependent sensitivity, creating ambiguity unless pH is measured and used for compensation, which is difficult in small, heterogeneous, or rapidly changing microenvironments. A three-electrode configuration—working (WE), counter (CE), and Ag/AgCl pseudo-reference (pRE) electrodes—is fabricated directly on the cylindrical surface of a 710-µm-diameter optical fiber using …

A Graph Attention Network-Based Framework For Reconstructing Missing Lidar Beams, Khalfalla Awedat, Mohamed Abidalrekab, Mohammad El-Yabroudi

Electrical and Computer Engineering Faculty Publications and Presentations

Vertical beam dropout in spinning LiDAR sensors -triggered by hardware aging, dust, snow, fog, or bright reflections - removes entire vertical slices from the point cloud and severely degrades 3D perception in autonomous vehicles. This paper proposes a Graph Attention Network (GAT)-based framework that reconstructs these missing vertical channels using only the current LiDAR frame, with no camera images or temporal information required. Each LiDAR sweep is represented as an unstructured spatial graph: points are nodes and edges connect nearby points while preserving the original beam-index ordering. A multi-layer GAT learns adaptive attention weights over local geometric neighborhoods and directly …

Surrogate-Guided Conditional Generative Adversarial Network For Generative Design Of 3d-Printable Terahertz Holographic Metasurfaces, Manxu Lv, Zhikai Hu, Zhichuan Xie, Huizhen Feng, Dongping Zhang, Manna Gu, Chee Leong Tan, Bo Fang, Yongxing Jin, Ying Tian, Xufeng Jing

Electrical and Computer Engineering Faculty Publications and Presentations

Terahertz metasurface holography requires unit cells with prescribed complex transmission responses and sufficient compatibility with practical fabrication processes. For elementary low-bit phase quantization at 0.1 THz, conventional parameter sweeping of simple geometries remains an effective and reliable approach. However, when the design objective is extended from a single phase state to simultaneous consideration of complex transmission matching, transmission amplitude, structural connectivity, and fabrication-related geometric constraints, the design problem becomes a constrained multi-objective inverse-design task. Here, we propose a generative-model-assisted inverse-design framework for 3D-printable terahertz metasurface unit cells. The framework combines a VGG-based surrogate model with a modified WGAN-GP generator. The …

Context Matters: Evaluating Llm-Generated Knowledge Graph Schemas, Ritvik Garimella, Riju Marwah, Atishay Jain, Khusham Bansal, Amit Sheth

Publications

Knowledge graph (KG) schema engineering is labor-intensive and resists automation at scale. We investigate whether LLMs can generate domain-specific KG schemas of sufficient quality for downstream symbolic reasoning. We propose a tiered contextual framework that varies domain context richness across four levels: zero context, domain scope, task requirements, and data distribution. Generated schemas are evaluated intrinsically on BioRED (600 PubMed abstracts, multi-type entities and relations), where automated tiered schemas match an established KG construction baseline at 79.9% EC, with edge conformance rising from 47.5% at L1 to a stable 78–80% from L2 onward. Extrinsic evaluation on a 50-record MedHop controlled …

Scheduling The Charging Of Battery-Electric Vehicles Under Heterogeneous Scheduling Constraints, Justin Whitaker, Greg Droge, Mario Harper

Electrical and Computer Engineering Student Research

Adopting battery electric vehicles (EVs) for vehicle fleets requires scheduling charging alongside day-to-day operations. This problem is complicated by complex utility cost structures, limited battery capacity, and competition for shared charging resources. Existing methods that simultaneously schedule routes and charging neither address the full cost structure nor employ high-fidelity charging models, and no prior work analyzes fleets containing vehicles with heterogeneous routing constraints. This work addresses these gaps by combining a state-of-the-art flexible-schedule formulation with time-of-use (TOU) demand costs and a non-linear, variable-rate charging model as drawn from additional state-of-the-art works. The proposed method is validated against two state-of-the-art methods, …

Superiorgat: Graph Attention Networks For Sparse Lidar Point Cloud Reconstruction In Autonomous Systems, Khalfalla Awedat, Mohamed Abidalrekab, Gurcan Comert, Mustafa Ayad, Negash Begashaw

Electrical and Computer Engineering Faculty Publications and Presentations

LiDAR-based perception in autonomous systems is fundamentally limited by sparse vertical sampling and further degraded by structured beam dropout caused by occlusions, sensor faults, or reduced-cost LiDAR hardware. These degradations disrupt vertical geometric continuity and negatively affect downstream perception tasks such as object detection, localization, and scene understanding. Existing reconstruction approaches often struggle to balance reconstruction accuracy with the computational efficiency required for real-time autonomous operation. This paper presents SuperiorGAT, a graph attention–based framework for reconstructing missing elevation information in sparse LiDAR point clouds under structured beam loss. The proposed method models LiDAR scans as beam-aware graphs and enhances standard …

Multi-Solution Ternary Grover’S Algorithm For Logic-Based Quantum Machine Learning With Pseudo-Kronecker Reed–Muller Form Minimization, Sophia Lee, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Optimization problems in machine-learning (ML) applications can be computationally challenging for classical methods, particularly when they involve large, unstructured search spaces. Quantum computing offers a promising approach to combinatorial optimization by utilizing quantum superposition and amplitude amplification. This paper presents a new methodology using logic-based quantum machine learning (QML) as a complete framework for employing a multi-solution ternary Grover’s algorithm to minimize incomplete binary functions represented by Pseudo-Kronecker Reed–Muller (PKRO) expansions, consistent with Occam’s razor principle. Unlike previous quantum approaches based on Kronecker Reed–Muller (KRO) or fixed-polarity Reed–Muller (FPRM) representations, our work introduces the first Grover-based optimization framework for PKRO …

Vehicle Detection And Classification With Compact Sensor Technologies And Convolutional Neural Networks, Jessy Wu, Brett J. Borghetti, Anthony L. Franz, Darren E. Holland, Abigail A. Bickley

Faculty Publications

Unattended ground sensors (UGSs) are passive systems used to detect and classify nearby activity such as the movement of military vehicles or personnel. However, UGS performance is limited in complex environments. This work explores the application of deep learning to classify vehicles as either ‘heavy’ or ‘light’ using seismic, acoustic, and magnetic sensor data collected at varying distances from a road. Two approaches were evaluated: one using features from Short-Time Fourier Transforms (STFTs), and another using convolutional neural networks trained on time series data. Models were also tested under data partitioning strategies involving either spatial or temporal variation. Data fusion …

Fault Recovery For A Class Of Unmanned Autonomous Vehicles Using Gaussian Process Regression, Garrison Greenwood

Electrical and Computer Engineering Faculty Publications and Presentations

Unmanned autonomous vehicles (UAVs) must often operate reliably in remote, hostile environments for extended periods. Fault tolerance is essential, enabling continued operation despite system faults. Fault recovery is challenging due to unknown fault characteristics and no possibility of on-site human intervention. Replacing faulty hardware with operating spares is an effective recovery method, but impractical because UAVs have limited space. Reconfiguration may be the only practical option. This paper discusses correcting faulty behavior in robot arm DC servos, guided by a PID controller, by modifying the controller’s gain values. Although simple in principle, finding new gain values is difficult and time-consuming, …

Doing Less With More: A First-Principles Exploration Of The Suitability Of Agentic Computing Over Alternative Architectural Choices, Ritvik Garimella, Biplav Srivastava, Amit Sheth

Publications

There is growing interest in automating business activities with Agentic Artificial Intelligence (AI) due to latter's seeming ease of use. Never has it been easier, or costlier, to do less with more. However, little is known about when agents are preferable to established alternatives such as local computation, Representational State Transfer (REST), the Simple Object Access Protocol (SOAP), and the Model Context Protocol (MCP), particularly when development speed, performance, and operational cost are considered. We investigate this question using a controlled mathematical task that compares seven methods on a benchmark of 1,000 arithmetic expressions where semantics of operator precedence has …

The Evolution Of Reliability Methods For Nondestructive Evaluation (Nde): From Probability Of Detection (Pod) Conception To Contemporary Practices, Christine E. Knott, Jennifer Brown, John Aldrin, Christine M. Schubert Kabban

Faculty Publications

Nondestructive evaluation (NDE) methods are powerful tools for detecting and characterizing flaws in structural components, but their reliability must be evaluated before they can be used in critical applications. For more than 50 years, probabilistic and statistical methods have been used effectively to estimate reliability by describing an NDE system’s Probability of Detection (POD) for flaws of realistic sizes. The POD methods used by the USAF and NASA, like Hit/Miss, Signal-Response (â vs. a), and Point Estimate method (PEM, a.k.a. 29/29) have evolved, alongside newer approaches like Limited Sample POD (LS-POD) method, and Model Assisted Probability of Detection …