Digital Commons Network^™

Active Microwave-Thermographic Signal Reconstruction, Logan M. Wilcox, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT) which is subsequently spatiotemporally imaged with an infrared (IR) camera. As all antennas have spatial variation in their radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT. This nonuniform heating causes uncertainty in defect detection and has the potential to lead to false positives and/or negatives. To this end, thermographic signal reconstruction (TSR), a well-established thermographic signal …

Reinforcement Learning-Based Nonlinear Optimal Discrete-Time Control Of Power Systems, Vijay Kumar Singh, Behzad Farzanegan, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a partially model-free adaptive optimal tracking control method for power systems, specifically targeting a synchronous generator connected through a reactive transmission line. By integrating the tracking error dynamics with reference trajectory dynamics, an augmented system is created. A discounted performance function is introduced to address the nonlinear tracking problem optimally. Unlike traditional methods that compute feedforward and feedback terms separately, the proposed approach calculates both simultaneously by minimizing the discounted performance function. The discrete-time tracking Bellman and Hamilton-Jacobi-Bellman (HJB) equations are derived, and a reinforcement learning (RL)-based technique is employed to solve the optimal policy online without …

Active Microwave-Thermographic Signal Reconstruction With Adaptive Polynomial Regression, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Active microwave thermography (AMT) is a coupled electromagnetic (EM) and thermographic nondestructive testing and evaluation (NDT&E) technique. AMT utilizes a radiating EM source (e.g., an antenna) that induces dielectric/magnetic heating on a specimen under test (SUT). The inspection surface of the SUT is imaged with an infrared (IR) camera over the inspection time. As the thermal excitation originates from a spatially varying radiated power density, a nonuniform thermal excitation results within (or on the surface of) the SUT that is directly related to this power density. This nonuniform heating causes uncertainty in defect detection and has the potential to lead …

Enhancing Fiber Optic Interferometric Sensing With Microwave Photonics-Based Dispersion Fourier Transform And Integrated Magnitude–Phase Analysis, Chen Zhu, Ruimin Jie, Chenxi Huang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Fiber optic inline interferometers are widely used for high-precision sensing due to their sensitivity, compactness, and immunity to electromagnetic interference. Traditional optical spectral analysis methods suffer from limited dynamic range due to free spectral range (FSR) constraints, while microwave photonic filtering (MPF) techniques based on dispersion Fourier transform (DFT) provide an alternative by mapping optical signals into the radio frequency (RF) domain. However, conventional passband frequency tracking in MPF systems has limited sensitivity, and the recently demonstrated phase-based methods, though highly sensitive, are constrained by phase wrapping beyond 2π. In this work, we propose and experimentally demonstrate an integrated magnitude–phase …

A Cost-Effective Nilm Solution With Three-Point Labelling And Non-Causal Convolution Technique, Yanan Zhang, Gan Zhou, Yanjun Feng, Zhan Liu, Li Huang, Zhi Li, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

Although deep learning is increasingly promising in the field of Non-Intrusive Load Monitoring (NILM) these days, the high costs of data recording and labelling represent a significant challenge for the training of supervised models. To address this, a cost-effective sequence-to-points NILM solution is proposed, integrating three-point labelling with non-causal convolution techniques. The approach introduces a semi-automatic labelling framework for obtaining NILM three-point data, which provides a low-cost data collection and labelling solution for large-scale applications. Then, a novel loss function combining coordinate loss and confidence loss is developed to address the positional misalignment and negative sample confusion in sequence-to-points scenario …

Distributed Sapphire Fiber Bragg Grating-Based Thermal Profiling Of Submerged Entry Nozzles, Farhan Mumtaz, Hanok W. Tekle, Bohong Zhang, Xiaodong Li, Sunday Abraham, Bryant Mathis, Jeffrey D. Smith, Ronald J. O'Malley, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

This article focuses on the application of sapphire fiber Bragg gratings (FBGs) for instrumentation in submerged entry nozzles (SENs) within the steelmaking industry. The SEN is pivotal for transferring molten steel from a tundish to a mold, while preventing the infiltration of oxygen and nitrogen from the surrounding environment. Maintaining optimal flow conditions in the mold is crucial for ensuring casting process stability and maintaining high-quality steel. Sapphire FBG sensors have been instrumented in SENs to enable distributed thermal mapping for monitoring the health of the SEN. The optical sensor comprises three cascaded sapphire FBGs inscribed using femtosecond (FS) laser …

Centralized And Federated Heart Disease Classification Using Uci Dataset: A Benchmark With Interpretability Analysis, Mario Padilla Rodriguez, Eyiara Oladipo, Mohamed Nafea

Electrical and Computer Engineering Faculty Research & Creative Works

Cardiovascular disease (CVD) is a leading cause of global mortality, highlighting the need for accurate diagnostic methods. This study benchmarks centralized and federated learning (FL) algorithms for heart disease binary classification using the UCI dataset, which includes 920 patient records from four hospitals in the USA, Hungary, and Switzerland. Our benchmark is supported by Shapley-value as well as Local Interpretable Model-agnostic Explanations (LIME) interpretability analyses to quantify feature importance for classification. In the centralized setup, various classification algorithms are trained on pooled data, with the Naive Bayes classifier achieving the highest test accuracy of 81.1%. Further, FL algorithms with four …

Benchmarking Deep Learning Architectures For Ecg-Based Multi-Label Heart Disease Prediction Using Mimic-Iv Database, Eyiara Oladipo, Sarwar Nazrul, Mohamed Nafea

Electrical and Computer Engineering Faculty Research & Creative Works

Cardiovascular disease (CVD) is a leading cause of global mortality, accounting for an estimated 17.9 million deaths annually. CVD is broadly defined as a group of medical conditions influenced by modifiable or non-modifiable risk factors that affect the heart's ability to function properly. Machine learning (ML) has emerged as a powerful tool for analyzing complex medical data, aiding in early detection and accurate diagnosis of CVD and improving patient outcomes. Recent studies proposed various deep learning (DL) architectures for detecting CVD, yet there is a lack of robust benchmarks for comparing their performance on large-scale databases. In this work, we …

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 …

Synthesis And Characterization Of Free Radical Scavenging Dendrimer Nanogels Via Cross-Linking Reaction-Enabled Flash Nanoprecipitation, Lin Qi, Da Huang, Huari Kou, Anna Chernatynskaya, Nuran Ercal, Hu Yang

Chemistry Faculty Research & Creative Works

This work reports the development and evaluation of dendrimer-based nanogels based on Poly amidoamine (PAMAM) dendrimer generation 5, engineered to act as a carrier with reactive oxygen species (ROS)-scavenging capabilities. We developed a cross-linking reaction-enabled flash nanoprecipitation method in which the cross-linking reaction occurs during the flash nanoprecipitation process to form a cross-linked nanostructure. Using this approach, an N-hydroxy succinimide (NHS)-functionalized ROS-responsive thioketal cross-linker (TK-NHS) was synthesized and utilized to cross-link DAB-core PAMAM dendrimer G5, resulting in the formation of G5-TK nanogels. The resulting nanogels were characterized using dynamic light scattering and transmission electron microscopy, and their cytocompatibility, irritancy, cellular …

Scalable Fabrication Of High-Payload Dendrimer-Based Nanoparticles For Targeted Atherosclerosis Therapy, Huari Kou, Lin Qi, Da Huang, Jiandong Wu, Honglan Shi, Hu Yang

Chemistry Faculty Research & Creative Works

Nanoparticle-based therapeutics hold promise for the treatment of atherosclerosis, but challenges such as low drug-loading capacity and a lack of scalable, controllable production hinder their clinical translation. Flash nanoprecipitation, a continuous synthesis method, offers a potential solution for scalable and reproducible nanoparticle production. In this study, we employed a custom-designed multi-inlet vortex mixer to perform cross-linking reaction-enabled flash nanoprecipitation, facilitating controlled and scalable synthesis of cross-linked Poly amidoamine (PAMAM) dendrimer nanoparticles. Notably, this approach allows simultaneous nanoparticle cross-linking and drug loading in a single step. The mannose moiety enabled specific targeting of macrophages via mannose receptors, enhancing the localization of …

Enhanced Cholesterol Efflux And Atherosclerosis Regression Via Ceh Gene Delivery Using Galactose-Functionalized Dendrimeric Nanoparticles, Huari Kou, Jing Wang, Paul J. Yannie, Da Huang, William J. Korzun, Genta Kakiyama, Siddhartha S. Ghosh, Hu Yang

Chemical and Biochemical Engineering Faculty Research & Creative Works

Cholesteryl ester hydrolase (CEH) is a critical enzyme in cholesterol ester hydrolysis, influencing cholesterol metabolism and efflux. This study demonstrates that CEH overexpression promotes free cholesterol efflux from macrophages, thereby reducing the lipid burden in existing atherosclerotic plaques. To enable targeted delivery, galactose-functionalized polyamidoamine (PAMAM) dendrimeric nanoparticles were utilized as nanocarriers for hepatic delivery of the CEH expression vector. The therapeutic potential of CEH plasmid-loaded dendrimeric nanoparticles was evaluated in Ldlr-/- mice. Results showed a significant reduction in total lesion area (21%) and aortic arch lesion area (23%) compared to baseline. Lesion component analysis revealed marked decreases in total cholesterol …

Cold Atmospheric Plasma Induced Degradation Of Organophosphate Pesticides On Kevlar Swatches, Ta Chun Lin, Victor Somtochukwu Mbanugo, Boluwatife Stephen Ojo, Yue-Wern Huang, Marek Locmelis, Frank Daoru Han

Biological Sciences Faculty Research & Creative Works

Cold atmospheric plasma (CAP) was evaluated for degrading organophosphate pesticide (OP) residues (acephate, malathion, and dimethoate) from Kevlar fabrics. Fourier transform-Infrared (FTIR) spectroscopy, scanning electron microscope (SEM) imaging and tensile strength testing confirmed that CAP treatment preserved Kevlar's structural integrity and mechanical properties. Results show degradation efficiency increased with higher power, shorter discharge gaps, and longer exposure duration. Hyperspectral imaging supported the detection of plasma-induced spectral changes, reinforcing the presence of reactive species. The strong oxidative potential of CAP facilitated the rapid breakdown of OPs into nontoxic byproducts. These findings demonstrate the potential of CAP as a portable, energy-efficient solution …

Leveraging Intrinsic Properties For Classification Of Coal Seams Towards Spontaneous Combustion Proclivity And Predicting Susceptibility Using Machine Learning: Smart And Sustainable Mining Approach, Siddhartha Agarwal, Pradeep K. Gautam, Yuhao Zou, Rishabh Dwivedi, Durga C. Panigrahi, Cihan H. Dagli, A. Singh

Engineering Management and Systems Engineering Faculty Research & Creative Works

Mine fires and other hazards caused by spontaneous coal combustion are a pervasive and longstanding issue in Jharia coalfields, India. This study proposes a novel approach to classify coal seams based on their propensity to spontaneous combustion using the intrinsic properties of 30 coal samples from different seams. This method eliminates the need for expensive and time-consuming experimental determinations of susceptibility indices (SI) such as crossing point temperature (CPT), critical air blast (CAB), and differential thermal analysis (DTA). All clustering models, viz. hierarchical, k-means, and multidimensional scaling, aptly classify coal seams into three categories: highly risky, medium risky, and low …

Enhancing Energy Efficiency In Solar Thermal Systems: The Role Of Hybrid Nanofluids In Sustainable Energy Harvesting And Storage, Seyedehzahra Haeri

Theses: Doctorates and Masters

Global energy demand continues to grow, making the reliance on fossil fuels increasingly unsustainable due to dwindling reserves and environmental impacts. Among these, solar thermal systems have emerged as a practical and environmentally friendly solution, with applications ranging from industrial heating and solar water heating to concentrated solar power (CSP) plants and solar desalination units. Solar-based thermal systems offer a promising alternative, with nanofluids (NFs) emerging as a transformative solution. Engineered by dispersing nanoparticles into base fluids, NFs enhance thermal conductivity, heat absorption, and efficiency. Notably, nanoparticles such as titanium nitride also act as nano-catalysts, improving chemical reactions and reducing …

Immersive Extended Reality For Lower Limb Rehabilitation: Design, Deployment, And Pilot Study, Jeremy Varghese

Electronic Theses & Dissertations (2024 - present)

This thesis presents the design, deployment, and pilot study of an immersive extended-reality (XR) rehabilitation system integrated with a ceiling-mounted dynamic body-weight support device (Vector Gait and Safety System), aimed at improving lower-limb rehabilitation out- comes. The implemented system combined immersive virtual tasks—such as Touch Wall, Ball Launcher, Obstacle Dodge, and Stepping Stones—with real-time movement tracking, enabling detailed kinematic analysis and personalized therapy. A pilot study conducted at Sunnyview Rehabilitation Hospital involved seven patients with various mobility impairments, providing quantitative performance metrics and quali- tative user feedback. Results demonstrated consistent patient engagement, measurable im- provements in gait speed and task …

Food Rescue, Food Waste, And Policy In New York: A Mixed-Methods Study Of Food System Impacts, Mariana Torres Arroyo

Electronic Theses & Dissertations (2024 - present)

This dissertation investigates how food donation policies in New York State influence the recovery, redistribution, and waste of surplus fresh produce, with a focus on fruits and vegetables. Using an interdisciplinary, systems-based approach that integrates system dynamics modeling with qualitative, community-engaged research, the study analyzes the effectiveness and unintended consequences of three major policies: Nourish New York, the Farm to Food Bank tax credit, and the Food Donation and Food Scraps Recycling Law.

Chapter 2 uses simulation modeling to examine how the tax credit and waste ban affect produce recovery and redistribution. Results show that while both policies can boost …

Safeguard Cyberspace In Ransomware Era: Risk Analysis & Cyber Insurance, Li Huang

Electronic Theses & Dissertations (2024 - present)

The increasing frequency and severity of ransomware attacks pose significant challenges for organizational cybersecurity. Fragmentation across disciplines in cyber defense has created practical gaps in the development of the necessary capabilities needed to address rapidly evolving cyber threats. This study explores the impact of ransomware attacks and the evolving role of cyber insurance as a proactive cybersecurity partner. Bridging the gap between actuarial science and cyber risk management, it proposes an interdisciplinary framework that quantifies the impact of ransomware and integrates cyber insurance into cybersecurity strategies.

The primary contribution of this study is methodology. We present a framework that remains …

Mobile Air Quality Assessment Using Public Transportation: A Community-Focused Study In The Capital Region, Allen Pronith Reddy Yeddula

Electronic Theses & Dissertations (2024 - present)

The monitoring of air quality plays a crucial role in establishment of concentrations of air pollutants over space and time in relation to human health, urban planning, and environmental conservation. This research offers a new strategy of monitoring the air quality in the Capital Region of New York State utilizing public transit buses as air quality moving sampling platforms. To assess the level of pollutants, a Capital District Transportation Authority (CDTA) bus network was used across the urban, suburban, and rural areas thus filling a research gap where public transit has most of the time been disregarded as a mobile …

Whipped Into Shape: Infrastructure As Punishment For The Unruly Citizen, Eliza Mortimer

Anthós

This article interrogates how inflexible designs and policies of airline seating, as well as critical socio-political rhetoric, discipline and marginalize bodies that do not conform to normative standards. Situating airline seating within fat and disability studies models, Mortimer argues that these punitive measures are not neutral, but rather reflective of cultural narratives of productivity, morality, and bodily discipline. Mortimer draws from ethnographic analyses of first-person accounts and policy reviews to demonstrate how shrinking seats, rigid policies, and moralized discourse construct a “space of calculability” that pressures fat individuals to monitor and minimize their bodies to justify their presence in public …

Exploring Large Language Models For Summarizing And Interpreting An Online Brain Tumor Support Forum, Christy Muasher-Kerwin, M. Courtney Hughes, Michelle L. Foster, Ibrahiim Al Azher, Hamed Alhoori

Faculty Articles, Papers, and Other Scholarship

Objective

Methods

Comparison Of Dynamic Mode Decomposition With Other Data-Driven Models For Lung Cancer Incidence Rate Prediction, L. Raymond Guo, Jifu Tan, M. Courtney Hughes

Faculty Articles, Papers, and Other Scholarship

Introduction: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools but can be computationally expensive and require specialized knowledge. Dynamic mode decomposition (DMD) is an alternative that offers efficient analysis with fewer resources. This study explores applying DMD in public health using lung cancer data and compares it with other machine learning models.

Methods: We analyzed lung cancer incidence data (2000–2021) from 1,013 US counties. Machine learning models (random forest, gradient boosting machine, support vector machine) …

Innovative Biomechanical And Engineering-Based Assessments Of Hip Mechanics In Individuals With Hip-Related Pain, Holly M. Stanze

Theses and Dissertations--Kinesiology and Health Promotion

Individuals with hip-related pain exhibit altered movement patterns, and worse fear of movement (kinesiophobia), compared to asymptomatic controls. Exacerbation of hip pain during movement and the role of hip symptom duration may help further our understanding of specific movement patterns that are associated with hip-related pain. A better understanding of the potential connections between hip-related symptom duration, kinesiophobia, hip pain during movement, and lower limb mechanics in those with hip-related pain may allow for more efficient referrals to clinicians and lead to more effective rehabilitation. In addition, the impact of hip-related pain on intra-articular hip loading patterns is not well …

Ecobuoys For Scalable Oceanography, Anuscheh Nawaz, Michael Steele, Ruth Branch, David Burnett, Kuotian Liao, Mallory Parker, Eleftheria Roumeli

Electrical and Computer Engineering Faculty Publications and Presentations

An approach to scalable surface-drifting buoys is needed to enable the high spatial and temporal resolution of oceanographic data that the science and meteorological communities are asking for. With the number of active buoys predicted to increase by a factor of 100 or more, the impact on the environment becomes even more important. Here, we present a pathway to a scalable and sustainable generation of buoys. We identify the main criteria to be used when developing such buoys to be low cost, with reliable data and neutral or even positive environmental impact. For each buoy subsystem—hull, electronics, energy generation and …

Statistical Investigation Of Gypsum Crystallization Kinetics, Azmain A. Akash

LSU Master's Theses

Gypsum, a naturally occurring mineral salt, is frequently encountered in industrial processes such as seawater desalination and power plant cooling. Over the years, significant effort has been devoted to controlling the crystallization of gypsum through various chemical approaches, including acidification, chelating agents, and antiscalant polymers. Nevertheless, the cost associated with chemical treatments may be reduced by incorporating physical operational strategies to complement chemical methods in regulating gypsum crystallization.

This study systematically examines the comparative roles of chemical and physical entropy in the gypsum crystallization process to elucidate their respective influences on gypsum growth kinetics. Chemical entropy is manipulated by adjusting …

Personalized Persuasion In The Digital Age: A Data-Driven Approach To Effective Communication, Annye Braca

Doctoral

This thesis investigates the potential of Machine Learning (ML) to personalize persuasive marketing messages. It explores the identification of individuals receptive to specific persuasion techniques based on their psychometric profiles. By developing ML models that incorporate these profiles, the thesis aims to predict the impact of tailored messages and improve the effectiveness of marketing communication.

Highly Sensitive Diffractive Optical Structures For Detection Of Volatile Organic Compounds, Faolan Radford Mcgovern

Doctoral

From aerospace to agriculture, sensors play a fundamental role in many aspects of modern life. Sensors form an integral part of the complex systems and devices required for the continued functioning and development of services and industries across society. The use of sensors is paramount in areas affecting human health, one such area being the monitoring of indoor air quality, in particular the detection of volatile organic compounds (VOCs). Human contact with VOCs has been associated with many health complications, including skin and eye irritation, cardiovascular damage, and cancers. Optical, electrical, gravimetric, and chemical sensors have been developed for VOC …

Machine Learning Applications In Epigenomics And Its Association With Health And Disease, Trevor Doherty

Doctoral

Epigenetic modifications can lead to altered phenotypes without a change in the DNA sequence itself. Disrupted gene expression regulated by epigenetic processes can result in cancers, autoimmune diseases and various other maladies. Machine learning (ML) involves the use of algorithms and models which are trained to learn patterns in data, and has demonstrated remarkable success in solving diverse, complex challenges. Epigenomic studies, such as those that use DNA methylation (DNAm) data, increasingly make use of ML techniques to process extremely high dimensional data obtained from high throughput platforms e.g., DNAm arrays. These datasets suffer from the curse of dimensionality, increased …

Groundwater Pumping Impacts On Stream Depletion In Humboldt County, Ca: Integrating Analytical Models And Field Data, Andy Hans Heise

Cal Poly Humboldt theses and projects

This study addresses the significant concern of stream depletion from groundwater pumping in Humboldt County, CA. The objective of this study is to develop an analytical model for estimating stream depletion and integrating field data into model parameters.

The methodology involved conducting a field study near three operating wells by a stream, installing stream gages upstream and downstream of the wells, and recording pumping rates and schedules for two of the wells. The third well’s pumping data was synthetically generated, for privacy concerns of the well owner. The StreamDepletR package was used for estimating depletion rates in a stream in …

An Analysis Of Uv Disinfection Of Municipal Wastewater Following Natural Treatment Systems, Jonathan Andrew Kirchubel

Cal Poly Humboldt theses and projects

On January 30, 2024, the City of Arcata replaced chlorine with ultraviolet (UV) irradiation as its disinfection method for treating its wastewater prior to discharging to Humboldt Bay. The Arcata wastewater treatment facility (AWTF) utilizes natural treatment systems for treating its secondary effluent, which can provide issues for UV disinfection through variance in turbidity, total suspended solids (TSS), pH, humic acids, and color. This project aimed to identify whether the water quality characteristics of natural treatment systems in the AWTF would impact UV disinfection of municipal wastewater.

Thirty-one sampling periods occurred between June 8, 2024, and March 1, 2025, recording …