Digital Commons Network^™

Electrically Evoked Emg Decomposition Using The Perturbative Generalized Series Expansion, William R. Walker

Master's Theses

Electrically evoked electromyography records the muscle's response to nerve stimulation and has been used for clinical assessment of neuromuscular function, as well as treatments such as functional electrical stimulation or stroke rehabilitation. In every muscular contraction, an electrical signal is generated. These signals come from the summation of simultaneously recruited motor units, enabling decomposition techniques to reveal information about motor unit recruitment.  By improving the ability to decompose and analyze these signals, clinical treatments involving neuromuscular electrical stimulation can be optimized and better understood. Much of the research on EMG analysis focuses on voluntary EMG, leaving a gap in the …

Develop & Test Radar Assignments For Future Graduate Curriculum, Thomas Ky Hong, Christopher William Hoferer

Electrical Engineering

With Cal Poly’s switch to semesters, many upper-level electives lack enough depth and structured content for the new 15-week format. In addition, while the Electrical Engineering department currently offers approximately five weeks of RADAR-focused laboratory material within EE 424: Intro to Remote Sensing, this limited exposure is insufficient to provide students with a comprehensive understanding of RADAR theory and practical applications. The semester conversion presents a timely opportunity to expand this existing five-week RADAR segment into a full 15-week course that integrates both lecture and laboratory content. To address the current curriculum gap, we have seamlessly extended the RADAR exercises …

Shared Language For Responsible Ai Integration, Asa B. Stone, Mark C. Stone, Alisha Bevins, Jean Claude Niyomugabo, Irene Magara, Jacob Abaare, Derek M. Heeren, Mubarak Abu Zouriq

PRAIRIE: Pioneering Responsible AI for Research, Innovation, and Education

As AI rapidly reshapes how we work and learn, employers increasingly seek graduates who can think before they prompt, exercising judgment under pressure rather than merely producing output. Yet students are praised for AI use in one course and penalized for it in the next, and faculty are left to lead responsibly on shifting ground, with no shared language to guide them.

This paper introduces the PRAIRIE Framework for AI Integration, a shift from reactive gatekeeping toward proactive stewardship. It emerged from a qualitative sentiment analysis of three communities (students, faculty, and industry partners) whose concerns converged on one need: …

Teaching Learning Newsletter Vol 6 Issue 1, Sastra Deemed To Be University

TLC Newsletter

No abstract provided.

Validation Of Electromagnetic Shower Development In A Reduced Alice Focal-E Detector Model Using Geant4, Isaac Lara Romo

Physics

This project focuses on the preliminary simulation of a reduced FoCal-E detector model for the ALICE experiment at CERN. Beamline tests were simulated using an electron gun at various incident energies, and the results were compared with those of the FoCal group. The longitudinal charge deposition per sensor layer was compared between the two setups to verify that the reduced model exhibited expected calorimeter behavior. Additionally, the transverse signal across the high-granularity sensor layers was examined to determine whether the reduced model reproduced the expected lateral development of electromagnetic showers. The results obtained from these measurements show that the reduced …

Low-Cost Path-Loss Characterization For Underground Mine Tunnels Using Lora Transceivers At 915 Mhz, Hilary Kelechi Anabi, Samuel Frimpong, Muhammad Azeem Raza

Mining Engineering Faculty Research & Creative Works

Accurate path-loss models are essential for planning reliable wireless networks in underground mines, yet existing characterization studies rely on specialized channel sounders and vector network analyzers costing tens of thousands of dollars, placing them beyond the reach of most mine operators. This paper demonstrates that LoRa transceivers costing approximately US $15 per node can serve as a self-contained path-loss measurement instrument, logging the received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) directly to a CSV file over a standard USB serial connection. A measurement campaign conducted at the Missouri S&T Experimental Mine on 31 March 2026 collected 4801 packets …

Custom Animatronic Puppet, Jake Schuman, Fredy Herrarte, Madison Reedy, Jackson Reynolds

Mechanical Engineering

This project involved the design, fabrication, and implementation of a custom audio-animatronic puppet modeled after Dr. Brian Davis for the California Polytechnic State University Mechanical Engineering Department. The objective was to create an expressive, lifelike character capable of synchronized speech, head movement, arm gestures, and seated-to-standing motion while demonstrating the integration of mechanical design, controls, manufacturing, and artistic fabrication.

The final system consists of an 18-degree-of-freedom animatronic platform utilizing servo-driven facial and upper-body actuation combined with a pneumatic standing mechanism. A modular aluminum and 3D-printed structural framework was developed using SolidWorks and validated through engineering analysis. Character shells were digitally …

Compressor Rotor Joints And Interference Fit Study, Isabela Jimenez, Aubrey Perez, Isaiah Guerrero, Jireh Velasquez

Mechanical Engineering

Solar Turbines need a comprehensive study on the effects axial and radial inference fits have on turbine compressor rotor stiffness. The aft weldment of the compressor has press-fit dowel pins and pilot joints to connect one weldment section to another. These fits need to be assessed through a finite element modal analysis, as well as practically tested through modal impacts to investigate how the fits affect the stiffness of the turbine’s connected weldments. The experimental data will be used to validate the finite element analysis (FEA) model.

Mass - Modular Acoustic Suppression System, Julia Megargle, Michael A. Milner, Lucas Kaemmererer, Jonathan Corvera, Gabriel Choi, Emmi Cayer

Mechanical Engineering

The Modular Acoustic Suppression System (MASS) is a mass tuned damper that reverberates a membrane atop a cavity to absorb select acoustic frequencies produced by the Environmental Control and Life Support Systems (ECLSS) within NASA's Artemis Human Lander. The height of the reverberation chamber is adjusted to mitigate a range of varying tonal frequencies within the human lander.

Xylem: A Comparative Analysis Of Gpu Dispatch Pipelines For Large-Scale, Procedural Environments, Srinivas Sundararaman

Master's Theses

The real-time rendering of large-scale, procedural scenes presents a significant performance challenge for traditional CPU-bound rendering pipelines. The high volume of draw calls and the need for complex culling and level-of-detail management create bottlenecks that limit scene complexity and visual fidelity. This thesis investigates the evolution of GPU-driven rendering paradigms via the Xylem renderer within NVIDIA’s Donut rendering framework as a solution to these challenges.

A comprehensive benchmarking framework is developed to implement and quantitatively analyze three distinct rendering strategies for a procedurally generated, parameterizable, large-scale forest scene. The evaluated pipelines include: (1) traditional instanced rendering, (2) compute-driven indirect rendering …

Autonomous Vision-Based Litter Collection Rover, Dante Michael Benedetti, Benjamin Scott Tavares, Nathan Heil

Electrical Engineering

This report documents the design, implementation, and testing of an autonomous litter-collection rover developed as a Senior Project Design Lab (EE 460/463/464) at California Polytechnic State University. The rover integrates autonomy, computer vision, embedded real-time control, mecanum-wheel omnidirectional mobility, and a two-degree-of-freedom robotic arm to detect, approach, and collect small ground-level litter such as bottles, wrappers, and paper fragments.

The system uses a two-layer compute architecture: an NVIDIA Jetson Orin Nano running ROS 2 for perception, SLAM, and path planning, paired with an STM32L4A6ZG microcontroller for real-time motor control and odometry. The robot is built on a multi-level aluminum frame …

Solving Linearly Constrained Quadratic Programs Using Field Programmable Analog Arrays, Tyler Wynn, Alonzo Arroyo

Electrical Engineering

This paper presents a field-programmable analog array (FPAA) implementation for solving linearly constrained quadratic programs (LCQPs) directly in the analog domain. The solver is based on a continuous-time primal-dual control architecture with integral action, anti-windup compensation, and a piecewise-linear nonlinearity for enforcing affine inequality constraints. A switched-capacitor implementation using three AN231E04 FPAAs is developed, and coefficient scaling methods are introduced to keep internal and output signals within the voltage limits of the hardware. A global scaling factor is used to reduce internal signal excursions, while solution-space scaling is shown to modify the implemented optimization coefficients and alter the local closed-loop …

Table Tennis Training Instrument (Ping Pongers), Derick Alvarado, Peter Sandberg, Andrew Wong

Mechanical Engineering Senior Theses

Table Tennis is an extremely popular sport worldwide, boasting an extensive market. However, it is a sport that generally requires other people to both play and practice with. The goal of this project was to create a machine that would serve table tennis balls for the purpose of individual practice. The machine was meant to fill the niche that other machines of the same type filled by allowing individuals to practice on their own without the assistance of another player or a coach. The main goal of the project was to offer a market competitive device, sharing the same basic …

Semi-Automated Wine Case Palletizer, Ashley A. Corona, Joshan Darji, Russell Williams, Brett Beaudoin

Mechanical Engineering

The Semi-Automated Wine Case Palletizer project was sponsored by Saucelito Canyon Winery to reduce the physical demands of manually palletizing wine cases during bottling operations.

Using customer requirements developed through sponsor meetings and observations, the team designed, manufactured, and tested a semi-automated palletizing system consisting of a rotating carousel platform, pneumatic scissor-lift assembly, airbag jack, and forklift-compatible base. The design allows operators to maintain an ergonomic working height while accessing all pallet locations from a single position.

Engineering analyses, including finite element, fatigue, and buckling analyses, verified the structural integrity of the design. Prototype testing confirmed successful operation of the …

Effects Of Atmospheric Drag On Angles-Only Initial Relative Orbit Determination Accuracy In Low Earth Orbit, Sydney Walsh

Master's Theses

Growing congestion in low Earth orbit is increasing the demand for effective space situational awareness, straining ground station capabilities. Space-based relative orbit determination offers an alternative, using line-of-sight angle measurements from a chaser spacecraft to estimate a target spacecraft's orbit. This research focuses on angles-only initial relative orbit determination (IROD), where no prior target state knowledge is assumed. It investigates how IROD solution accuracy is affected by differential atmospheric drag between the chaser and target spacecraft. A nonlinear batch least squares filter is designed to estimate the target's initial state, which is compared against a truth solution. It was found …

Stock Market Analysis And Volatility Behavior During The Covid-19 Pandemic, Manoj Venkatachalaiah, Soon Leong Yeap, Salman Ahmed Lnu, Sangwhan Cha

Harrisburg University Other Works

This report outlines the structural design, cloud implementation, and analytical findings of a scalable Big Data architecture deployed on Google Cloud Platform (GCP). The primary objective is to investigate the macroeconomic and microeconomic disruption caused by the COVID-19 pandemic on global equities, focusing on two dominant digital business models: online retail/cloud computing (Amazon, Inc. - AMZN) and subscription-based digital streaming entertainment (Netflix, Inc. - NFLX). Through a serverless orchestration pipeline leveraging GCP Cloud Run, automated workflows fetched and blended high-velocity epidemiological metrics alongside daily financial asset layers. Data transformations and parallel analytical calculations were executed utilizing Apache Beam pipelines inside …

A Performance-Weighted Environmental Assessment Of Ultra-High-Volume Fly Ash Substitution In Portland Cement Concrete, Youngguk Seo, M.A. Karim, Teddy Tzvetkov, Joshua Hardy

Faculty Articles

Fly ash substitution for cement in Portland cement concrete (PCC) has been regarded as a sustainable solution, but its widespread application remains constrained by concerns over mechanical performance and durability of PCC, especially at higher replacement rates. This study evaluates PCC mixes incorporating fly ash Type C (FA-C) or Type F (FA-F) across cement replacement rates from 10% to 90%, tracking fresh-state workability, compressive strength, and surface electrical resistivity at 7, 14, and 28 curing days. A process-based life cycle assessment (LCA) with the TRACI 2.1 method quantified global warming potential (GWP, kg CO₂/m³) under a …

Evaluating Design Choices For Gpu-Accelerated Finite-Difference Time-Domain Simulation On Resource-Constrained Devices, Joel Manesh

Master's Theses

The Finite-Difference Time-Domain (FDTD) method is a numerical technique for solving partial differential equations. It was first developed to solve Maxwell’s equations for electromagnetic wave propagation and has since been extended to model other physical phenomena governed by wave propagation. Because the FDTD method is data-parallel, it is well-suited for GPU acceleration; modern FDTD-based simulations run offline on large GPU clusters. There is, however, very little research on running FDTD on resource-constrained embedded GPUs, which are increasingly popular for real-time applications.

This thesis explores a CUDA-based FDTD solver for the 3D wave equation on the Nvidia Jetson Orin Nano. This …

Elastic Actuator For Robotic Leg, Bruce Berg, Allan Pham, Madeline Slater, Sofie Zalatimo

Mechanical Engineering

The Cal Poly Legged Robots Group (CPLRG) is developing a 12 degree of freedom robot with the goal of eventually performing highly dynamic movements such as jumps and flips. During such movements, large impact forces will be experienced by the leg, potentially resulting in damage to physical components such as the motors. The scope of this project is to integrate elastic elements into the quadruped legs to reduce shock loads on the motors and improve the efficiency of the legs through energy storage. The key stakeholders for this project are Professor Charlie Refvem and Dr. Simon Xing. Other stakeholders include …

Design And Optimization Of A Bluff Body For Energy Harvesting Of Transverse Galloping Induced By Low-Speed Wind Using Bernstein Polynomial Equations, Youssef Wael Abdelmoneim

Theses and Dissertations

In this thesis, a computational framework is proposed for optimizing the aerodynamic shape of bluff bodies used in galloping-based wind energy harvesters. The system targets low-wind speed environments, where normal wind turbines are not effective, offering a potential alternative to batteries used for powering small electronic devices such as wireless sensors. The design relies on the galloping effect, where airflow around a bluff body induces transverse oscillations that drive an energy conversion mechanism. To generate efficient bluff body geometry, the Class-Shape Transformation (CST) method is used to define a wide range of candidate shapes with minimal design parameters. These shapes …

Quantum And Conventional Informatics Studies Of Synthesis Energetics And Defect Formation In Nitride Crystal Epitaxy, Andrew Steven Messecar

Dissertations

Machine learning is a valuable approach for the processing and analysis of complex information. By estimating relationships from recorded data, machine learning methodologies can be effective strategies for pattern recognition, enabling investigations and technological applications based thereon. The potential for improved understanding of high-dimensional data has drawn interest towards machine learning from across the sciences, including the research and development of new and improved material systems. In the context of experimental materials research, much of the reported efforts to incorporate machine learning into conventional practice have been primarily focused on either the enhanced analysis of characterization experiment data or the …

Can Generative Ai Make Farming Decisions? Current Status And Future Pathways: A Case Study In Row Crop Production With Chatgpt, Nipuna Chamara, Yufeng Ge, Joe Luck, Yu Pan, Saleh Taghvaeian, Cory Walters, Christopher Proctor, Daran Rudnick, Daren Redfearn

Department of Agricultural and Biological Systems Engineering: Faculty Publications

The agricultural decision-making process is experience-based, knowledge-dependent, time-sensitive, complex, and driven by historical data. Planting, fertilization, irrigation, and chemigation are key categories in farm decision-making, and currently there is no one-shot decision-support tool that covers all these activities. Generative Artificial Intelligence (AI) models are more advanced than traditional machine learning and deep learning models. These models have been trained on vast amounts of data from the internet, allowing them to accept unstructured data in various forms and generate human-like text, solutions to problems, and scenario predictions. Given this capability, we became interested in exploring the potential of generative AI in …

Hydpn: A Hybrid Deep Reinforcement Learning, Programming, And Neighborhood Operations Framework For Integrated Scheduling On Parallel Batch Processing Machines, Yuqi Wang, He Luo, Guoqiang Wang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Batch processing machines (BPMs) are widely used in industries such as semiconductors, metal processing, and healthcare, where jobs are processed in batches. As production, inventory, and distribution become increasingly integrated to improve efficiency, research on their joint scheduling in parallel BPM environments remains scarce. This paper addresses the integrated scheduling problem in parallel BPMs, involving production, inventory, and distribution stages, with the objective of minimizing total costs. A unified cost-based model is first formulated, applicable to both in-facility and external distribution scenarios. A hybrid algorithm framework, HyDPN, combining deep reinforcement learning, dynamic programming, and neighborhood operations is proposed. Extensive experiments …

Extensive And Intensive Margin Labor Supply On Ride-Sourcing Platforms, Hao Sun, Hai Wang, Zhixi Wan

Research Collection School Of Computing and Information Systems

The rapid expansion of ride-sourcing platforms has enabled freelance drivers to flexibly determine both their participation and working hours. Understanding this flexible labor supply behavior is essential for managing platform capacity and evaluating the impacts of pricing and incentive policies on driver welfare. This study develops a labor supply model in which drivers optimally choose whether to participate (extensive margin) and how long to work (intensive margin) to maximize their utility from consumption and leisure. The model incorporates heterogeneity in drivers’ other income, idle time, and participation costs, allowing us to analytically characterize equilibrium labor supply decisions. The results show …

Long-Term Mine Planning: A Survey Of Classical, Hybrid And Artificial Intelligence-Based Methods, Nurul Asyikeen Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi

Research Collection School Of Computing and Information Systems

The aim of long-term mine planning (LTMP) is two-fold: to maximize the net present value of profits (NPV) and determine how ores are sequentially processed over the lifetime. This scheduling task is computationally complex as it is rife with variables, constraints, periods, uncertainties, and unique operations. In this paper, we present trends in the literature in the recent decade. One trend is the shift from deterministic toward stochastic problems as they reflect real-world complexities. A complexity of growing concern is also in sustainable mine planning. Another trend is the shift from traditional operational research solutions — relying on exact or …

Design And Parametric Study Of A Mems-Based Reservoir Computer For Reinforcement Learning, Andrew P. Carr

Master's Theses

Single-node reservoir computing (RC) is a hardware-efficient approach to machine learning, leveraging the dynamics of physical systems. In this work, two reinforcement learning algorithms, Q-learning and Proximal Policy Optimization (PPO), are applied to a simulated micro-electro-mechanical system (MEMS)-based reservoir computer to solve both discrete and continuous control tasks. MEMS-based reservoirs are low-power, compact, and their natural frequencies (kHz to MHz) pair well with real-time control loops. To explore the relationship between reservoir dynamics and learning performance, a parametric study is conducted on two reservoir hyperparameters, reservoir size and neuron separation, using CartPole-v1 and MountainCar-v0. The RC successfully learns multiple tasks …

Rf Fingerprinting: Neural Networks For Device Identification, Pranav Chainani, Maxwell Gertner, Genevieve Patmore

Electrical and Computer Engineering Senior Theses

Conventional cybersecurity protocols authenticate devices using digital credentials that can be stolen, copied, or extracted from compromised hardware. Radio frequency (RF) fingerprinting offers a complementary physical-layer authentication mechanism that binds device identity to the unforgeable manufacturing variations present in every transmitter’s analog hardware. This thesis explores the application of convolutional neural networks (CNNs) to RF fingerprinting, focusing on the identification of nominally identical IoT transmitters from raw I/Q samples of the LoRa preamble’s turn-on transient.

We developed an end-to-end system consisting of a modular data collection testbench using a USRP B210 software-defined radio, a 1D CNN trained directly on raw …

Triangle Sorting As A Real-Time 3d Renderer, Alexander Green, Xinyi Wang

Computer Science and Engineering Senior Theses

This project explores the design and implementation of a 3D rendering system based on triangle sorting, integrated into a simple interactive game environment. The goal of the project is to evaluate whether a triangle-sorting–based renderer can function effectively in real time while maintaining visual correctness and performance.

To test the renderer in a practical setting, it is embedded within a 3D environment that supports camera control and real-time navigation through the rendered scene. The system focuses on real-time navigation, model loading, camera movement, and rendering of a low-poly 3D scene. The use of a low-poly visual style helps reduce computational …

Optimization Of Repeat Ground Track Constellation Design For Complex Discontinuous Regional Coverage, Paige Jewell

Master's Theses

The continued growth of low Earth orbit (LEO) satellite constellations motivates efficient constellation design methods to reduce cost and complexity. Although brute-force methods are commonly employed in the preliminary design of satellite constellations, pairing analytic methods with optimization algorithms provides a more efficient means of searching the design space. Constellation design for continuous global and regional coverage is well-established; however, strategies for discontinuous regional coverage remain underdeveloped. This thesis focuses on an analytic geometry-based method for computing satellite coverage and revisit metrics, coupled with genetic algorithm optimization to efficiently explore the design space. The viability of this method is shown …

A Higher Order Panel Method With Vortex Particle Wake, Luca Flick-Kaiser

Master's Theses

Panel codes were some of the earliest methods for computing the flow around aircraft. Despite the availability of higher fidelity CFD methods, panel methods remain an essential tool for the development of modern aircraft, allowing engineers to quickly iterate during the conceptual design phase.

This work develops and implements a high order panel method using Python. The wake is modeled using vortex particles, resulting in an accurate wake representation with minimal user input. The integration of the higher order singularity distributions has been extended to include the calculation of the velocity gradients necessary for the vortex particle implementation. The use …