Engineering Commons^™

Enhancing Frequency Event Detection In Power Systems Using Two Optimization Methods With Variable Weighted Metrics, Hussain A. Alghamdi, Midrar A. Adham, Umar Farooq, Robert B. Bass

Electrical and Computer Engineering Faculty Publications and Presentations

This research presents a novel technique that refines the performance of a frequency event detection algorithm with four adjustable parameters based on signal processing and statistical methods. The algorithm parameters were optimized using two well-established optimization techniques: Grey Wolf Optimization and Particle Swarm Optimization. Unlike conventional approaches that apply equally weighted metrics within the objective function, this work implements variable weighted metrics that prioritize specificity, thereby strengthening detection accuracy by minimizing false-positive events. Realistic small- and large-scale frequency datasets were processed and analyzed, incorporating various events, quasi-events, and non-events obtained from a phasor measurement unit in the Western Interconnection. An …

Securing Biometric Data, Alyssa F. Carroll

Cybersecurity Undergraduate Research Showcase

Biometric data has been widely adopted across various sectors, including digital identity, artificial intelligence (AI), border control, digital wallets, and national identification systems. While biometric identifiers—such as fingerprints, retina scans, and facial recognition—offer reliable and convenient authentication, they also raise significant concerns regarding privacy and security. This paper examines how biometric data is stored, the vulnerabilities it faces, and the most effective methods for safeguarding it. By highlighting the critical importance of biometric data protection, this study reviews current research on approaches, strategies, and policies that enhance security while preserving the functionality and efficiency of biometric systems.

Utilizing Machine Learning To Predict The Charge Storage Capability Of Lithium-Ion Battery Materials, Manoj Chhetri, Karen S. Martirosyan

Physics & Astronomy Faculty Publications

With the increasing demand for high-performance batteries in applications such as electric vehicles and portable electronics, accurately predicting the charge storage capacity of battery materials is crucial for developing more efficient and reliable energy storage systems. Machine Learning (ML) and data-driven approaches, plays a vital role in enhancing our understanding of Li-ion battery performance, guiding materials design, optimizing system efficiency, and accelerating innovation in energy storage technologies. In this study, an ML-based approach was applied to a dataset of 2345 rechargeable Li-ion battery materials, obtained from the Materials Project online portal, to predict gravimetric charge storage capacity ─ a key …

Sustainable Poultry Farming: A Concept Of Iot-Based Poultry Management System For Small-Scale Farmers, Ayodeji Akinsoji Okubanjo, Ignatius Kema Okakwu, Olufemi Peter Alao, Nurudeen Samuel. Lawal, Ayoola Abiola Babalola, Abisola Olayiwola

Al-Bahir

Conventional poultry management techniques are failing to meet increased demand for poultry products as the population continues to grow. As a result, this issue has become a major concern for small-scale farmers, particularly those in low-income areas, in terms of food security. One of the main reasons for this is that the farmers rely on intensive farming methods which are inefficient for automating daily poultry operations. However, intensive farming methods pose major environmental concerns to ecosystems and poultry health. Also, the environmental conditions, welfare, and productivity of poultry operations may be harmed by the global climate crisis and poultry waste …

Electric Field Profiles Under High Voltage Overhead Transmission Lines With Under-Build Distribution Circuit, Aminu Haruna

Electrical Engineering Theses

The increasing use of underbuilt lines in recent transmission projects alters electric field profile under transmission lines. By assessing electric field intensity in these transmission and distribution network configurations, the study identifies regions with high electric fields that may exceed standard exposure limits, posing potential risks. This work employs the charge simulation method and the method of imaging to analyze electric fields beneath transmission lines with underbuilt distribution networks. Key objectives include calculating the electric fields at 1 meter above ground, and evaluating the field at conductor surfaces. The study considers transmission line voltages of 69kV, 161kV, 220kV, 345kV and …

Cyber Physical Emulation Of Power Grids: Co-Simulation Of Exata Cps And Hypersim, Mckayla Snow

Electrical and Computer Engineering ETDs

This thesis explores the use of cyber-physical emulation of power grids to analyze the impact of different types of cyberattacks. It outlines the co-simulation process of HYPERSIM and EXata CPS within the OPAL-RT real-time digital simulator, demonstrating various cyberattacks on two power system models. The first model is a simple communication-focused testbed for easy demonstration of co-simulation setup, while the second is a power system model of a secondary network used to assess the cybersecurity of network protector units in low-voltage networks through a hardware-in-the-loop (HIL) testbed. Using this testbed, the cybersecurity of the direct transfer trip (DTT) scheme is …

Examining Physiological Responses To Misophonic Triggers, Christian O'Reilly, Xuan Yang, Sewon Oh, Doug Wedell, Svetlana Shinkareva

Faculty Publications

We collected and analyzed an array of biosignals (face electromyogram, skin electrodermal activity, peripheral temperature, and electrocardiogram) in 60 participants with and without misophonia, a condition characterized by decreased tolerance to innocuous sounds. Our goal was to objectively characterize the physiological response to misophonia triggering sounds. We found that misophonic responses can be objectively identified in some cases through atypical physiological reactions to triggering stimuli, though not all participants exhibited this response. Our analyses suggest a large interindividual variability in response to misophonic triggers and highlights the need for methodological adjustments in future experiments to increase the detectability of misophonic …

Removing Eog Artifacts From Eeg Recordings Using Deep Learning, Christian O'Reilly, Scott Huberty

Faculty Publications

The electroencephalogram (EEG) directly measures the electrical activity generated by the brain. Unfortunately, it is often contaminated by various artifacts, notably those caused by eye movements and blinks (EOG artifacts). Such artifacts are usually removed using an independent component analysis (ICA) or other blind source separation techniques. However, it is difficult to assess whether subtracting EOG components estimated through ICA removes some neurogenic activity. It is crucial to address this question to avoid biasing EEG analyses. Toward that objective, we developed a deep learning model for EOG artifact removal that exploits information about eye movements available through eye-tracking (ET). Using …

A Reliable And Efficient Detection Pipeline For Rodent Ultrasonic Vocalizations, Sabah Shahnoor Anis, Devin Mark Kellis, Kris Ford Kaigler, Marlene A. Wilson, Christian O'Reilly

Faculty Publications

Analyzing ultrasonic vocalizations (USVs) is crucial for understanding rodents' affective states and social behaviors, but the manual analysis is time-consuming and prone to errors. Automated USV detection systems have been developed to address these challenges. Yet, these systems often rely on machine learning and fail to generalize effectively to new datasets. To tackle these shortcomings, we introduce ContourUSV, an efficient automated system for detecting USVs from audio recordings. Our pipeline includes spectrogram generation, cleaning, pre-processing, contour detection, post-processing, and evaluation against manual annotations. To ensure robustness and reliability, we compared ContourUSV with three state-of-the-art systems using an existing open-access USV …

An Effective Genetic Algorithm For Mixed Precision, Wanyu Zhang, Yu Shang, Min Tsao, Yiwei Li, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

The precision of floating-point numbers is a critical task in high-performance computing. Many scientific applications rely on floating-point arithmetic, but excessive precision can lead to unnecessary computational overhead. Reducing precision may introduce unacceptable errors. Addressing this trade-off is essential for optimizing performance while ensuring numerical accuracy. In this paper, we present a genetic algorithm-based approach for tuning the precision of floating-point computations. Our method leverages algorithmic differentiation and first-order Taylor series approximation to assess the impact of precision variations efficiently. We employ stochastic partitioning algorithms with multiple precision combinations that meet the error requirements. Moreover, we present a genetic heuristic …

From Adversarial Attacks To Robust Classifiers - A Study In Social Media Spam Detection - Black Box & White Box, Jonathan Jose Penaloza Rumie

Undergraduate Theses

Adversarial attacks pose a significant threat to the reliability of machine learning-based spam detection systems in social media. This undergraduate thesis, "From Adversarial Attacks to Robust Classifiers: A Study in Social Media Spam Detection – Black Box & White Box," systematically examines the impact of both black-box and white-box adversarial attacks on a range of spam classifiers, including Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Bagging, Gradient Boosting, and Support Vector Machines. Leveraging a novel dataset derived from Twitter spam messages and enhanced with adversarial perturbations such as synonym replacement and character-level modifications, this study evaluates classifier performance under …

31 - Shaped Adversarial Patches, Huong Quach

Undergraduate Research Symposium

In recent years, the development and deployment of computer vision models have become widespread, with applications ranging from autonomous vehicles to security systems. Among these, object detection algorithms like YOLO are particularly significant due to their real-time performance and accuracy in identifying and localizing objects within an image. However, the robustness of these models is increasingly challenged by adversarial attacks, which are deliberate manipulations designed to deceive the model's predictions.

In this paper, I present an approach to advancing the deception capabilities of adversarial patches, specifically targeting YOLO-based person detectors. The objective is to design and implement shaped adversarial patches …

Cascaded Hydropower Plant Optimization Using A Bi-Level Mixed Integer Model, Ahmad Heidari, Rui Bo

Miners Solving for Tomorrow Research Conference

No abstract provided.

On The Robustness Of Adaptive Resonance Theory Neural Networks, Shane Cairns, Leonardo Enzo Brito Da Silva, Sasha Petrenko, Donald C. Wunsch

Miners Solving for Tomorrow Research Conference

No abstract provided.

Training Ai Models For Automatic Melanoma Detection, Victoria Wegley, Jason Hagerty, R. Joe Stanley

Miners Solving for Tomorrow Research Conference

No abstract provided.

Impulse, Spring 2025, Jill Fier, Micayla Standish, Jerome J. Lohr College Of Engineering

Impulse (Jerome J. Lohr College of Engineering Publication)

2 | Faculty, Staff Honored
4 | The State of Lithium
6 | 100 Years of Ag and Biosystems Engineering
8 | Major Milestones
12 | Distinguished Engineer Geldert-Murphy
14 | Distinguished Engineering Heitkamp
16 | By the Numbers
18 | Space Grant Consortium at SDSU
20 | Healthcare Systems Engineering Program
22 | Ridgway Three-Peat
23 | Aerospace Club Launches
24 | Power and Energy Scholarships
25 | More Take Part in Nasa Challenges
26 | Weber Recognized as Student Leader
30 | Wild Hare Racing Ready
32| Sebern’s Success as Sole Operator
34 | Meink: From Prairie to …

Smart Home Hvac Digital Twin Ml Meta-Model For Electric Power Distribution Systems With Solar Pv And Cta-2045 Controls, Rosemary E. Alden, Evan S. Jones, Steven B. Poore, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Building modeling, specifically heating, ventilation, and air conditioning (HVAC) load and equivalent energy storage calculations, represent a key focus for decarbonization of buildings and smart grid controls. In this paper, an ultra-fast one minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel contribution in the field of residential physics-based smart home surrogate modeling. Emulation of white box models, or digital twins, with editable parameters through machine learning (ML) meta-modeling serves as an alternative to wide-spread experimental big data collection. The HMLM employs combined k-means clustering with multiple linear regression (MLR) to emulate minutely HVAC power …

Analytical Dispatch Strategies For Pumped Storage Hydro: A Conditional Dynamic Programming Approach To Discontinuous Multi-Period Optimization Problems, Jian Liu, Jianwen Zhang, Zaiwu Gong, Donald C. Wunsch, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing integration of renewable energy sources like wind and solar poses significant challenges to secure and stable grid operation. Energy storage systems, particularly pumped storage hydro (PSH), play a crucial role in balancing power supply and demand. Traditional analytical studies of PSH economic dispatch problems often assume zero lower bounds for generating and pumping rates to simplify analysis and derive analytical solutions for multi-period optimization problems. However, the inherent mechanical design constraints of PSH require non-zero minimum flow rates for efficient operation. We analyze two scenarios, merchants having PSH only and merchants having both PSH and wind farms. In …

Amplification In Mems Rlc Circuits For Enhanced Sensitivity And Mems Applications, Mutaz Mohd Hamdi Al Fayad

Durham School of Architectural Engineering and Construction: Dissertations, Theses, and Student Research

Micro-electro-mechanical systems (MEMS) have garnered significant attention due to their unique characteristics, including small size, high sensitivity, and cost-effective mass production. While electrostatic actuation offers low power consumption, it requires high voltage to move the MEMS structure, posing a challenge for applications such as RF switches and MEMS resonator-based sensors. Reducing the required input voltage for electrostatic MEMS remains a key research focus.

This thesis investigates voltage amplification in electrostatic MEMS through integration with resonant RLC circuits. By leveraging resonance, optimized configurations are designed to maximize voltage gain and enhance the signal-to-noise ratio. Through theoretical modeling and simulations using MATLAB …

Antenna Multi-Band Enhancement By Employing Negative Impedance Converters, Kevin Pepin

Doctoral Dissertations and Master's Theses

This thesis presents the implementation of a Negative Impedance Converter (NIC) integrated with a planar monopole antenna, originally centered at 2.9 GHz, to create a multi-band network. The network exhibits resonances at frequencies of 0.9 GHz, 1.5 GHz and 1.9 GHz. The work highlights the design process of pairing a NIC with a capacitive, single-band antenna, addressing key challenges such as overcoming the limitations associated with low self-resonant frequency (SRF) inductors. The design process included the use of measurement-based component models and radio-frequency simulations using Keysight Advanced Design Systems (ADS). The developed NIC emulates a -0.99 pF capacitor using two …

Clinical Use Of Sit2stand Ai Application For Kinematic Analysis In Prosthesis Users., Samerial Brown

Posters - 2025

Biomechanical analysis is a tool to evaluate prosthetic and orthotic patient's. These tools offer the clinician capability of understanding the mechanism of injury, gait deviation or prosthesis problem. Video based analysis require expensive hardware, software, and training which sometimes costs $40-100,000.

The recent advent of artificial intelligence (AI) has opened up the possibility of acquiring high speed human motion video analysis using low-cost hardware and open-source machine learning algorithms. Still, free assessments like the Sit2Stand test is a current clinical outcome measure which assesses ability of a patient to stand and sit as fast as possible 5x. The faster the …

Long-Term Techno-Economic Analysis Considering System Strength And Reliability Shortfalls Of Electric Vehicle-To-Grid-Systems Installations Integrated With Renewable Energy Generators Using Hybrid Gru-Classical Optimization Method, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Paul Moses

Research outputs 2022 to 2026

Higher penetration of Electric Vehicle-to-Grid charging stations (EV2GCSs) in power grids integrated with renewable energy generators (REGs) result in system-instabilities raising the risk of blackout issues. Although techno-economic feasibility of EV2GCSs has been identified within the literature addressing power outage and financial concerns as a contemporary solution, no long-term techno-economic analysis has been investigated considering system strength and reliability shortfalls before the EV2GCSs installation. Therefore, this research aims to evaluate the long-term (2025–2045) techno-economic analysis of EV2GCSs into REGs-integrated grid systems by accounting for system strength and reliability factors. A novel optimization framework is developed, combining Gated Recurrent Units (GRU) …

Transforming Optical Vernier Effect Into Coherent Microwave Interference Towards Highly Sensitive Optical Fiber Sensing, Ruimin Jie, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

The optical Vernier effect has garnered significant research attention and found widespread applications in enhancing the measurement sensitivity of optical fiber interferometric sensors. Typically, Vernier sensor interrogation involves measuring its optical spectrum across a wide wavelength range using a high-precision spectrometer. This process is further complicated by the intricate signal processing required for accurately extracting the Vernier envelope, which can inadvertently introduce errors that compromise sensing performance. In this work, we introduce a novel approach to interrogating Vernier sensors based on a coherent microwave interference-assisted measurement technique. Instead of measuring the optical spectrum, we acquire the frequency response of the …

Vessel Trajectory Prediction With Recurrent Neural Networks: An Evaluation Of Datasets, Features, And Architectures, Isaac Slaughter, Jagir Laxmichand Charla, Martin Siderius, John Lipor

Electrical and Computer Engineering Faculty Publications and Presentations

Maritime situational awareness tasks such as port management, collision avoidance, and search-and-rescue missions rely on accurate knowledge of vessel locations. The availability of historical vessel trajectory data through the Automatic Identification System (AIS) has enabled the development of prediction methods, with a recent focus on trajectory prediction via recurrent neural networks (RNNs) and other deep learning architectures. While these methods have shown promising performance benefits over kinematic and clustering-based models, comparing among RNN-based models remains difficult due to variations in evaluation datasets, region sizes, vessel types, and numerous other design choices. As a result, it is not clear whether recent …

Swvl: A Custom Ai-Powered Face Tracking Camera Gimbal, Alexander J. Anderson-Mcleod, Jakub Jerzmanowski, Michael Laitarovsky, Trevor Allison, Jagger Tanner

Senior Theses

In response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an …

Seabed Characterization Using Ambient Sound For A Range-Dependent Track In The New England Mud Patcha, Martin Siderius, Stan E. Dosso, Brian Granger

Electrical and Computer Engineering Faculty Publications and Presentations

Wind-generated, ocean ambient sound data were used to characterize seabed properties along a track in the New England Mud Patch. A 15-m vertical array, consisting of 16 hydrophones, collected ambient sound data across the 50–5000 Hz frequency band. The array drifted for 1 h, covering a 1.7 km track. Seabed characterization was performed using beamforming techniques, which limited the analysis to the 400–700 Hz band. Passive fathometer processing was applied to estimate the water–seabed interface and sub-bottom layering. Additionally, the data were used to estimate the power reflection coefficient, which was then used as input for a trans-dimensional geoacoustic inversion. …

On The Provenance Of Software Systems: Automating Software Traceability With Knowledge Graph And Large Language Model Synergy, Tyler Procko

Doctoral Dissertations and Master's Theses

The present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life …

Developing Fixed-Bias Langmuir Probes For Multi-Point Constellation Deployments, Henry Carter Valentine

Doctoral Dissertations and Master's Theses

Since their initial development in the early 20th century, electrostatic Langmuir probes have proved invaluable in terrestrial and interplanetary ionospheric sounding applications. When deployed aboard rocket and satellite platforms, these probes yield high-cadence, in-situ measurements of key plasma parameters such as electron density, ion density, and electron temperature. Furthermore, the efficacy of Langmuir probes in characterizing the three-dimensional structure and dynamics of ionospheric plasmas can be augmented by the technique of multi-payload deployments. In this work, we discuss the design, development, and analysis of fixed-bias Langmuir probes constructed for two multi-point science campaigns: the Mars-bound, dual-satellite Escape and Plasma Acceleration …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Streamer Discharge Simulation For Plasma-Assisted Combustion, Stuart Jairo Reyes

Electrical & Computer Engineering Theses & Dissertations

A common and successful method to achieve atmospheric pressure fuel-air plasma-assisted combustion is through repetitive ns pulsed discharges and dielectric-barrier discharge. The transient phase in these discharges is dominated by transport influenced by strong space charges produced by ionization fronts, this can be best represented by the streamer model. The function of non-thermal plasma in these discharges is to excite the species in the fuel-air mixture to produce radicals which accelerate the chemical conversion reactions which directly lead to temperature rise, ultimately culminating in ignition. Therefore, the characterization of the streamer and its energy partitioning is essential to developing a …