Digital Commons Network^™

Machine Learning In Baseball Analytics: Sabermetrics And Beyond, Wenbing Zhao, Vyaghri Seetharamayya Akella, Shunkun Yang, Xiong Luo

Electrical and Computer Engineering Faculty Publications

In this article, we provide a comprehensive review of machine learning-based sports analytics in baseball. This review is primarily guided by the following three research questions: (1) What baseball analytics problems have been studied using machine learning? (2) What data repositories have been used? (3) What and how machine learning techniques have been employed for these studies? The findings of these research questions lead to several research contributions. First, we provide a taxonomy for baseball analytics problems. According to the proposed taxonomy, machine learning has been employed to (1) predict individual game plays; (2) determine player performance; (3) estimate player …

An Empirical Evaluation Of Communication Technologies And Quality Of Delivery Measurement In Networked Microgrids, Ruairí De Fréin, Yasin Emir Kutlu

Articles

Networked microgrids (NMG) are gaining popularity as an example of smartgrids (SG), where power networks are integrated with communication technologies. Communication technologies enable NMGs to be monitored and controlled via communication networks. However, ensuring that communication networks in NMGs satisfy quality of delivery (QoD) metrics such as the round trip time (RTT) of NMG control data is necessary. This paper addresses the communication network types and communication technologies used in NMGs. We present various NMG deployments to demonstrate real-life applicability in different contexts. We develop a real-time NMG testbed using real hardware such as Cisco 4331 Integrated Services Routers (ISR). …

Simultaneous Observations Of Irregular Sporadic E Structures Using The Lwa And A Dps4d, Kenneth S. Obenberger, C. A. Taylor, Jonah J. Colman, Eugene Dao, J. Dowell, J. D. Eccles, Daniel J. Emmons, C. T. Fallen, J. M. Holmes, G. B. Taylor

Faculty Publications

Multi-instrument studies have recently shed new light on the morphology of sporadic E, especially intense sporadic E. Here we present simultaneous observations of dense sporadic E (E_s) structures using the Long Wavelength Array (LWA) radio telescopes and a Digisonde Portable Sounder 4D (DPS4D). Our coordinated observations show that the LWA radio telescopes in central New Mexico can reliably locate regions of dense Es structures as they pass over a Digisonde located over 500 km away in Texas. The LWA appears to be most sensitive to the densest E_s structures, which also appear to contain irregularities with vertical …

Scalable Cyber-Physical Testbed For Cybersecurity Evaluation Of Synchrophasors In Power Systems, Shuvangkar Chandra Das, Tuyen Vu, Hebert L. Ginn Iii

Faculty Publications

This paper presents a synchrophasor-based real-time cyber-physical power system testbed with a novel security evaluation tool, pySynphasor, that can emulate different real attack scenarios on the phasor measurement unit (PMU). The testbed focuses on real-time cyber-security emulation using different components, including a real-time digital simulator, virtual machines (VM), a communication network emulator, and a packet manipulation tool. The script-based VM deployment and software-defined network emulation facilitate a highly scalable cyber-physical testbed, which enables emulations of a real power system under different attack scenarios such as address resolution protocol (ARP) poisoning attack, man-in-the-middle (MITM) attack, false data injection attack (FDIA), and …

Graphtreemed: A Hybrid Graph-Tree Rag Architecture For Mission-Critical Medical Applications, Joshit Mohanty, Sandeep Kumar Nayak, Sumit Lahiri

Graduate Student Government Association Research Conference

Studies within engineering management indicate that decision-making is often based on the cognitive processing of grouped and pictographic information clusters entangled with high-level pattern recognition. Similarly, graph-based retrieval-augmented generation (RAG) architectures substantially improve diagnostic accuracy and interpretability, while tree-structured systems reduce critical misses through hierarchical reasoning. However, existing solutions often lack a unified framework that seamlessly integrates these two paradigms to address the multifaceted demands of mission-critical healthcare settings. This proposal introduces GraphTreeMed, a novel hybrid RAG architecture designed to harness the complementary strengths of graph-based and tree-based retrieval mechanisms, thereby advancing the safety and efficacy of clinical decision support …

Using Eeg Signal For Smart Health: An Overview, Addison Clark, Chelsea Bonyata, Ishfaq Ahmad, Hansheng Lei, Khalid Saifullah, Victor Ibarra

Informatics and Engineering Systems Faculty Publications

This survey paper aims to provide an overview of the current state of EEG (Electroencephalography) signal technology as it relates to people with disabilities. It will highlight the various methods and techniques employed, discussing their advantages and disadvantages. The paper will also examine the applications of EEG technology in assisting individuals with disabilities, specifically focusing on Brain-Computer Interfaces (BCIs) and assistive device control. By understanding the current state of EEG signal technology, we can identify the opportunities and challenges involved in utilizing this technology to improve the lives of people with disabilities.

Disturbance Observer-Based Control For Power Converters In Renewable Energy Systems, Amulya Viswambharan

Thesis/ Dissertation Defenses

Renewable energy systems face significant challenges in ensuring stability, efficiency, and reliability due to model uncertainties, unmeasured disturbances, and grid variabilities. This thesis addresses these challenges through the development and experimental validation of disturbance observer-based control (DOBC) strategies for power converters. The first study focuses on DC-DC boost converters, employing a backstepping-based DOBC to achieve zero steady-state error, robust stabilization, and compensation for control saturation without requiring anti-windup schemes. Experimental results confirmed the controller effectiveness under diverse load types and operating conditions. The second study presents a robust current control scheme for grid-tied inverters with LCL filters, combining state-feedback control …

Smart Traffic Intersections: Leveraging Isac And Millimeter-Waves For Advanced Vehicle Platooning, Mohammed Risal Thadathil

Thesis/ Dissertation Defenses

The rapid advancement of self-driving cars is reshaping the transportation industry and accelerating the development of smart cities. Vehicle platooning, a key capability of autonomous vehicles, has the potential to enhance traffic efficiency, reduce congestion, and improve safety at intersections. However, maintaining platoon cohesion, minimizing latency, and optimizing traffic signal interactions remain significant challenges. This study addresses these issues by leveraging Integrated Sensing and Communication (ISAC) technology with millimeter-Waves (mmWaves) signals to optimize platooning performance at traffic signal intersections. The research identifies gaps in existing Vehicle-to-Everything (V2X) communication frameworks, particularly in managing platoon movements in urban traffic intersections. To bridge …

Antenna Analysis Using Mixed Computer Platforms (Aacp), Keith H. Bova

Electrical and Computer Engineering ETDs

This thesis reviews the design, implementation, and benchmarking of commercially available, previous generation (CAPG) computer systems, and how they can be used with computational electromagnetics. The thesis focuses on their architecture, performance, and practical applications. It begins with an overview of CAPG system components, including different node types such as login/controllers, compute nodes, block storage, and object storage. The process of assembling these systems is outlined. The conjugate gradient method and direct linear solvers were benchmarked. The performance of CAPG systems is compared with modern computing architectures. A detailed analysis provides recommendations for designing CAPG systems around the performance of …

Single-Entity Protein Electrochemistry Of A Diffusion-Limited Enzyme, Ziwen Zhao, Nikolaos Kostopoulos, Sagar Ganguli, Paul Bergstrom, Alina Sekretareva

Michigan Tech Publications

In this work, we present single-entity protein electrochemistry (SEPE) experiments on catalase, along with a theoretical model to simulate its enzymatic activity and predict the experimentally observed current responses. We perform SEPE measurements at various enzyme concentrations and use protein film voltammetry to investigate the origin of the observed electrochemical signals in SEPE. The modeling approach we develop combines finite element simulations in COMSOL Multiphysics with random walk simulations in MATLAB. The enzyme is represented as a partially active sphere, with an arc on the surface corresponding to the enzyme’s diffusion channel leading to the active site. Notably, the model …

The Role Of Counter Electrode In Perovskite Solar Cell On Silicon Substrate To Enhance Power Conversion Efficiency For Cmos-Compatible Applications, Sameh O. Abdellatif, Eman Sawires

Electrical Engineering

This study investigates the impact of various counter-electrode materials on the overall power conversion efficiency (PCE) of perovskite solar cells (PSCs) fabricated on silicon substrates. We examined four distinct configurations using fluorine-doped tin oxide (FTO) as bare cell, copper (Cu), aluminum (Al), and highly p-doped silicon wafers as counter electrodes. The results indicate that the PSCs with Cu achieved the highest short-circuit current density (JSC) of 17.11 mA/cm2, while the p-doped silicon and aluminum showed lower JSC values of 16.19 mA/cm2 and 16.51 mA/cm2, respectively. Open-circuit voltage (VOC) values remained competitive across all cells, with FTO achieving a VOC of …

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.

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 …

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 …

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 …

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 …

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 …

Design And Characterization Of Radiation-Hardened Gaas Hbt-Based Optical Transceivers, Collin Burt

Electrical and Computer Engineering ETDs

Optical transceivers are desirable for their galvanic isolation and natural immunity to electromagnetic interference compared with direct electrical connections. This is true in normal operating environments as well as radiation environments. This work realizes and characterizes GaAs HBT-based optical transceivers across temperature and a range of radiation environments including high dose rate, total ionizing dose, neutron, and heavy ion. The transceiver designs are discussed and specific environmental design considerations are highlighted. Final results for each environment are provided and compared with existing transceivers.

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 …

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 …

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 …

Design And Fabrication Of Compact Mimo Array Antenna With Tapered Feed Line For 5g Applications, F. Taher, Mohamed Fathy Abo Sree, Hesham A. Mohamed, Hussein Hamed Ghouz, Sarah Yehia Abdel Fatah

All Works

This paper presents the design, fabrication, measurement, and evaluation of two multiple input multiple output antenna elements (MIMOs) optimized for performance in the 5G frequency band. The MIMO antennas are integrated onto Roger/Druoid 5880 substrates with a dielectric constant of 2.2, housed within a compact volume measuring 20×24.14×0.79mm3. Initially, a thorough parametric study is conducted using the CST Electromagnetic (EM) Simulator to analyze and optimize the antenna's performance according to 5G application requirements. Subsequently, in the antenna design phase, calculations are made for the envelope correlation coefficient (ECC > 0.01dB) and diversity gain (DG > 9.9dB), yielding favorable outcomes. Fabrication and measurement …

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 …

Heat-Pipe-Based Thermal Management System Design For A 250 Kw Gan-Based Integrated Modular Motor Drive, Seyed Iman Hosseini Sabzevari, Salar Koushan, Armin Ebrahimian, Towhid Islam Chowdhury, Nathan Weise, Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

Integrated modular motor drive (IMMD) is an effective approach for realizing high-efficiency, high-power-density, and fault-tolerant electric machines. However, designing an efficient thermal management system (TMS) for the motor drive becomes a challenge, particularly due to space constraints. This article presents the design of a TMS based on 3-mm heat pipes for a 250-kW IMMD intended for aviation applications. The power electronics module is simulated using PLECS software where an electrothermal analysis is conducted. A simplified thermal resistance model of the system is developed to estimate the die junction temperature of gallium nitride (GaN) semiconductors. The performance of the proposed TMS …

Smart Traffic Intersections: Leveraging Isac And Millimeter-Waves For Advanced Vehicle Platooning, Mohammed Risal Thadathil

Theses

The rapid advancement of self-driving cars is reshaping the transportation industry and accelerating the development of smart cities. Vehicle platooning, a key capability of autonomous vehicles, has the potential to enhance traffic efficiency, reduce congestion, and improve safety at intersections. However, maintaining platoon cohesion, minimizing latency, and optimizing traffic signal interactions remain significant challenges. This study addresses these issues by leveraging Integrated Sensing and Communication (ISAC) technology with millimeter-Waves (mmWaves) signals to optimize platooning performance at traffic signal intersections.

The research identifies gaps in existing Vehicle-to-Everything (V2X) communication frameworks, particularly in managing platoon movements in urban traffic intersections. To bridge …

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 …

Adaptive Control System Of Nonlinear Dynamic Object On The Basis Of Neuro-Fuzzy Networks, Isomiddin Xakimovich Siddikov, Dilnoza Maxamadjanovna Umurzakova

Technical science and innovation

The presented paper is devoted to the development of an adaptive control system for a nonlinear dynamic object using neuro-fuzzy networks. The authors note that in real conditions dynamic objects function under conditions of uncertainty, which are characterised by complex and poorly understood relationships between technological variables, the presence of perturbing and random disturbances, as well as nonlinear elements. This makes it difficult to apply traditional linear adaptive control algorithms. The use of self-organising adaptive control is proposed, in which the mathematical model of the controlled system is formed by operational identification during system operation. It is shown that linear …

Enhancing Ai Reliability: Probabilistic Inference And Neural Optimization, Behnam Zeinali Rizi

USF Tampa Graduate Theses and Dissertations

Deep neural networks have recently shown better performance than traditional machine learning algorithms in various applications such as computer vision, natural language processing, indoor navigation, and biomedical signal/bio-informatics data processing tasks. However, some main challenges still exist when harnessing these models for different applications. The first challenge is the performance of these methodologies for some specific types of data, such as Biomedical images, IMU measurements, and even bioinformatics data, such as genome expression. Moreover, deploying these models on mobile and IoT devices is computationally challenging. Most devices use cloud computing, where powerful deep-learning models analyze the data on a server. …

A 0.18 Μm Cmos 40 Ghz Miniaturized Coupled-Line Coupler With Enhanced Directivity, Elsayed Ibrahim Elsaidy, Amr H. Hussein Prof.Dr., Mustafa M. Abd Elnaby Prof. Dr., Anwer S. Abd El-Hameed Dr.

Journal of Engineering Research

No abstract provided.