Digital Commons Network^™

Spacecraft Attitude Control Simulator, Bricen S. Rigby

College of Engineering Summer Undergraduate Research Program

The Spacecraft Attitude Control Simulator is a single-axis attitude control platform which is intended to recreate the frictionless space environment in order to test cold-gas thruster control schemes. The overall goal of this research project was to observe settling data from the platform and compare it to simulated data. In practice, the predicted settling time was only 7% larger than observed settling time.

Empirical Support For Theoretical Conjectures, Luke O. Fanguna, Daniel Frishberg

College of Engineering Summer Undergraduate Research Program

The PI is engaged in ongoing work on experiments designed to gather evidence for or against conjectures in one or both of two active areas in theoretical computer science: Markov chain Monte Carlo (MCMC) mixing times, and greedy approximation algorithms for combinatorial optimization problems. Research in the area of MCMC mixing seeks to prove theoretical upper and lower bounds on the mixing times—or convergence times—of random walk-based algorithms for sampling from various distributions. The PI has published three papers,,, in 2023, on this research, which were entirely theoretical. Two of these papers2,3—comprising the PI’s dissertation—left open a significant gap between …

Remote Operated Amateur Radio Using Raspberry Pi And Limesdr, Esteban I. Perez

College of Engineering Summer Undergraduate Research Program

This project presents the design and implementation of a portable, remotely programmable amateur radio system operating on the 2-meter and 70-cm bands. The system utilizes Raspberry Pi 5 and LimeSDR hardware to facilitate communication between aerial and ground units, allowing for efficient transmission and reception of synchronization and calibration waveforms. A custom communication protocol incorporating BPSK modulation and a two-tone signaling mechanism enables time-of-flight measurements for calculating distances between nodes. This low-cost solution has potential applications in civilian sectors, such as search and rescue operations, where accurate direction finding and target localization are critical. Full-duplex operation enhances the system's capability …

Optimizing Resume Authenticity And Ats Compatibility With Llm Feedback Integration, Katie He, Justin Lau

College of Engineering Summer Undergraduate Research Program

Resume generation using Large Language Models (LLMs) like ChatGPT is becoming increasingly popular for automating the creation of customized resumes, but significant user modification is often required before submission. Common issues include poor alignment with job descriptions, inflated qualifications, and lack of authenticity, which undermine the effectiveness of LLM-generated resumes. This project addresses these challenges by integrating feedback from Applicant Tracking Systems (ATS) to guide LLMs in producing resumes that accurately reflect an applicant’s qualifications and better align with job-specific requirements. By optimizing the model's output through ATS feedback, the project aims to create more authentic, tailored, and ATS-compatible resumes, …

Solubility Characterization Of Organic Molecules For Aqueous Organic Redox Flow Batteries, Anthony W. Ferrell, Harkeerith K. Vij, Seamus D. Jones

College of Engineering Summer Undergraduate Research Program

The major obstacle to renewable energy sources is a lack of long-term energy storage capabilities. Energy produced during the day dissipates, leaving insufficient electricity for at night. The goal of the project is to design an Aqueous Organic Redox Flow Battery (AORFB) to act as long-term energy storage. Work has been done using machine learning to identify suitable compounds for the batteries. In this work there was no indication as to the aqueous solubility of the molecules; this controls the device’s energy storage capabilities. We used a machine learning model to determine the aqueous solubility of slightly more than 3000 …

Targeting Federated Learning: A Study Of Membership Inference Attacks On Healthcare Data, Brett W. Hillyard, Aditi S. Lappathi

College of Engineering Summer Undergraduate Research Program

This study investigates the vulnerabilities of federated learning models in the healthcare domain, specifically focusing on membership inference attacks (MIA). Federated learning allows local models to train on sensitive healthcare data without sharing the data itself, making it an attractive method for protecting privacy. However, even in this decentralized framework, models remain vulnerable to MIAs, where attackers can infer whether certain data points were used to train a model by analyzing model updates. Using the Texas100 dataset, this study demonstrates that as the number of local models increases, the attack accuracy of MIAs also increases due to higher bias …

Fast And High-Resolution View Synthesis From A Single Input Panorama, Nam Nguyen, Angela V. Chen, Theresa Zhu, Seth Johnson, Pranav Dumpa, Benjamin Geil

College of Engineering Summer Undergraduate Research Program

We introduce a novel method to convert a single input panorama into a 3D colored mesh representation of the scene. Unlike recent methods based on neural rendering, which are limited to low-resolution inputs and offline rendering, our approach supports 4k resolution inputs and real time rendering in a virtual reality headset. We first estimate a depth map and produce an initial layered depth image (LDI) representation. We fill unseen regions behind objects by iteratively cutting and inpainting the LDI. We then convert the LDI into an optimized, texture mapped mesh to achieve a compact representation

Enhancing Semantic Search With Human-Crafted Knowledge In Sentence Embeddings, Zachary Weinfeld

College of Engineering Summer Undergraduate Research Program

Semantic search plays a critical role in many domains, with numerous algorithms developed to address it. A common approach involves using sentence transformers to generate embeddings for both search queries and documents, allowing for the comparison of their vectors. While many different embedding models are widely used, our approach integrates these models with human-crafted knowledge in a novel way, resulting in an improvement in the Mean Average Precision (MAP) scores. Traditional embeddings often rely heavily on the specific words used in a query or document. Our technique mitigates this dependency by refining the vectors to capture the overall semantic meaning, …

Advanced Grasping Sensor Technologies For Autonomous Robotic Apple Harvesting Using Tactile Data And Cnns, Chris Bae

College of Engineering Summer Undergraduate Research Program

This research investigates how to achieve an optimal grasp of an apple using a four-finger soft robotic grasper equipped with force-resistive sensors. Specifically, we sought to determine whether a convolutional neural network (CNN) could accurately classify the grasper's state and recommend adjustments ("in," "out," or "good" grasp) based on tactile data from the sensors. Spatiotemporal tactile images were developed from the sensors and fed into our CNN, achieving near 100% accuracy on unseen test data. This work suggests that CNN-based processing of tactile images can be a powerful tool for real-time control of soft robotic grippers.

Cryogenic Fuel Applications To Sustainable Commercial Aircraft, Jack R. Kisling, Lillian Panning

College of Engineering Summer Undergraduate Research Program

Finding a source of clean and renewable energy is one of the greatest challenges humanity will face in the 21st century. With populations expanding, developing countries increasing their use of resources, and the rapid and alarming effects of climate change, a shift to a sustainable future is an existential prospect.

The goal of our project is to take the first steps toward getting safety procedures and lab space established, so mechanical engineering senior projects can quickly begin to experiment with cryogenic fuels. They will explore the application of cryogenic fuels to commercial aviation.

Optimizing Sensor Placements For Fixed Source Localization: A Distinct Subset Distance Sum Problem, Peter Chinh

College of Engineering Summer Undergraduate Research Program

This research addresses the problem of optimizing sensor placements for fixed source localization using distinct subset distance sums. Given a line L in R² and a set P of n points on one side of L, we seek to locate a minimal set S of points on L such that for any two distinct subsets Q and R of P, there exists a point s∈S where the sum of reciprocal distances from Q to s uniquely identifies Q. Our results show that a minimal sensor set S of size 1 is always feasible, but computing this set exactly proves …

"Deep Learning For Microscope Image Denoising", Nasreen Buhn, Sriya Adunur, Guy Hagen, Jonathan Ventura

College of Engineering Summer Undergraduate Research Program

In order to avoid damaging live cells, optical microscope imaging must be conducted under low-excitation light intensity and/or short exposure times, resulting in low signal-to-noise ratios (SNR). Deep learning methods offer an effective solution for removing microscope noise, utilizing algorithms that are able to reconstruct finer features in low SNR images. This research explores the denoising capability of several deep learning methods based on PSNR and SSIM. Tested methods include traditional approaches (BMED), supervised learning (CARE and Restormer), and unsupervised methods (Noise2Fast, N2V, SSD-Unsupervised, and SASSID). The Restormer model, which employs an encoder-decoder transformer architecture and progressive learning, stood out …

Incorporation Of Gnss Technology For Water-Level Instruments, Armaan S. Oberai, Toma Grundler, Serena B. Lee, Stefan A. Talke

College of Engineering Summer Undergraduate Research Program

Our goal was to take an existing water-level measuring embedded system and upgrade the GNSS module for the purpose of getting the elevation of the device within an error of 1 cm. Main milestones for the project included deploying a successful field test at a known survey point, using software-based post processing to improve the GNSS solution point, and implementing robust hardware and software for the water-level embedded system such that it was easily scalable.

Promoting Retention And Success Of Underrepresented Minorities In Engineering, Sebastian Macias-Gonzalez

College of Engineering Summer Undergraduate Research Program

Equity gaps in higher education have long been a pressing concern, with factors like race, ethnicity, and socioeconomic status influencing college attendance, retention, and success rates. While CSU-wide initiatives have been implemented to reduce these disparities, Cal Poly's distinctive role as the only campus transitioning into a Hispanic-serving institution calls for heightened efforts on our campus. Additionally, the persistence of equity gaps in STEM fields, combined with Cal Poly’s strong engineering program, makes it essential to focus on retaining and supporting underrepresented minorities (URM) in engineering disciplines.

Biomechanical Effects Of Worn-Out Shoe Outsoles And Insoles On The Human Foot, Yuka M. Iwashita, James A. Engebretson, Shreeshan Jena

College of Engineering Summer Undergraduate Research Program

The combination of outsole, midsole and insole layers of new shoes provide proper cushioning to the foot during the daily activities of life. Subsequently, the wear-based degeneration or damage to one or more of these sole layers can reduce the cushioning effect to the plantar surface of the foot. This reduction of cushioning effect can impact the distribution of stress concentration regions along the foot plantar surfaces and lead to foot pathologies. The present proposed research intends to study these effects of shoe-wear on the plantar surface of the human foot, using FEA (ABAQUS) and experimental methods. Commonly, the worn-out …

This Is An Automated Software Platform For Grid Interconnection Of Renewable Energy Resources., Cathy Chen, Lani Nguyen

College of Engineering Summer Undergraduate Research Program

To help curb climate change, the United States is rapidly electrifying many sectors of the energy economy, such as transportation (electric vehicles) and home heating (electric heat pumps). Thus, recent years have witnessed an explosion of businesses that want to connect and operate energy resources on the electric grid. However, getting an energy resource connected to the grid is an arduous process that takes years or more, requires specialized technical knowledge, and requires navigating complex and varying regulatory procedures. This project will develop a software platform that leverages AI and high-performance computing to automate and accelerate grid interconnection and energy …

Development Of Python Library For Spacecraft Modeling, Shreya Kale

College of Engineering Summer Undergraduate Research Program

This project involved developing a python library to be used in modeling spacecraft attitude dynamics and control

Empirical Support For Algorithmic Conjectures, Shayan Daijavad

College of Engineering Summer Undergraduate Research Program

Our project focuses on a particular Markov Chain Monte Carlo algorithm, with applications in statistical physics, known as hardcore model Glauber dynamics. The target distribution of Glauber dynamics is a distribution of all of the independent sets within a graph. An independent set is a set of vertices within a graph with no two vertices in the set containing an edge between them. Our goal is to find whether or not the Glauber dynamics for sampling independent sets on trees mixes in time O(nlogn), and determining how the mixing time changes if we bias the algorithm in favor of larger …

Deploying A Distributed Cloud-Based Microservice Application Benchmark Suite, Sai Dhruthik Murthy, Srish Maulik, Joydeep Mukherjee

College of Engineering Summer Undergraduate Research Program

Cloud services have lately transitioned from a single monolithic application to several loosely coupled microservices. This design shift from a single complex monolithic application to tens or hundreds of single-purpose microservices has enabled modern cloud services to be deployed, scaled, and managed in a more efficient way. Microservices can be deployed inside their own lightweight containers hosted on a cloud platform, and they can be individually developed, updated, tested, and elastically scaled. Each microservice can also be developed in its own suitable language, with only a common API for microservices to communicate with each other. Due to these benefits, large …

Advanced Localization Technologies For Autonomous Robotic Apple Harvesting, Gabriel K. Basus

College of Engineering Summer Undergraduate Research Program

No abstract provided.

Gamified Machine Embroidery, Sai Rama Balakrishnan, Vince Thanh Doan

College of Engineering Summer Undergraduate Research Program

This project aims to improve accessibility to the embroidery field by developing an application that captures the utility of a embroidery software (controlling an embroidery machine to produce embroidered output) and combines it with the playfulness of a game, to put those advanced controls in the hands of novices.

Agentic Ai In Search & Rescue, Belal Elshenety

College of Engineering Summer Undergraduate Research Program

No abstract provided.

Ai For Search And Rescue: Probabilistic Reasoning, Emi Dinh, Sydney Lynch, Swayam Chidrawar

College of Engineering Summer Undergraduate Research Program

No abstract provided.

Measurement Automation & Measurement System Research Endowment, Brian Bivinetto, Beneda Loya, Shiron Bendrihem

College of Engineering Summer Undergraduate Research Program

Road travel safety is always the most important issue in transportation systems. In general, several factors cause road accidents, such as human error, vehicle mechanical failure, roadway limitations (e.g. pavement, lane geometry, etc.), and inclement weather conditions. The major focus of today’s transportation developments is related to making highway transportation safer, smarter, and greener to enhance livability. Many accidents are caused when drivers lack a better understanding of the surrounding traffic conditions because the driver not only needs to control his/her vehicle but also needs to be aware of the movements of the vehicles around him/her. A driver cannot be …

Wearable Sensor-Based Gait And Balance In Elementary And Middle Schools, Sofie Zalatimo, Catherine Semaan

College of Engineering Summer Undergraduate Research Program

No abstract provided.

Understanding And Enhancing Sustainability Literacy In Cal Poly's College Of Engineering, Kenna Toohill

College of Engineering Summer Undergraduate Research Program

This study uses survey data collected from Cal Poly students to analyze engineering students' sustainability knowledge and their perspectives on Cal Poly's efficacy in teaching concepts in sustainability. It also compares the College of Engineering to other colleges to determine if, and how, CENG is behind in environmental education at Cal Poly.

Mechanical Properties Of Polymer Matrix Nanocomposites Reinforced With Varying Carbon Nanotube Geometries, Ethan Gaigalas, Zoe Kelemen

College of Engineering Summer Undergraduate Research Program

This study serves as part of an introduction to nanocomposite research at California Polytechnic State University, San Luis Obispo, and attempts to form an observational comparison between the performance of various CNT-Epoxy nanocomposites under drop weight testing. As a continuation of research on CNT-Epoxy nanocomposites, this study specifies three geometries of Multi-walled carbon nanotubes: straight (MWSCNTs), helical (MWHCNTs), and OH-functionalized straight (FMWSCNTs); and it compares their impact resistances to neat epoxy samples. Based on the total number of failed samples under a normalized impact energy of 0.335J/mm, it is observed that FMWSCNTs increase the impact resistance of Epoxy panels at …

Significance Of Noninvasive Endothelial Dysfunction And Other Cardiovascular Measurements In Various Communities, Sofi Z. David, Lily Wilcox, Kalia Richard

College of Engineering Summer Undergraduate Research Program

This pilot study aims to raise awareness of endothelial dysfunction in underrepresented communities, where cardiovascular disease is prevalent. By delivering educational presentations and materials, the study seeks to increase participants' understanding and interest in endothelial dysfunction, potentially leading to proactive health behaviors. The results will inform future initiatives to enhance public knowledge and reduce the burden of cardiovascular disease in these communities.

In the future, there will also be a 3 vs 5-minute hyperemia study. Thirty participants without a history of cardiovascular disease will undergo testing using the Cordex SmartCuff™ device. If the three-minute hold proves to accurately measure endothelial …

Impact Of Composites Material Research On Underrepresented Communities, Neil Bedagkar

College of Engineering Summer Undergraduate Research Program

The purpose of this research is to qualify and quantify the amount of research into composite materials applicable to certain engineering industries, then examine the impact these industries have on communities of a lower socioeconomic status. Of a random sample of papers from ScienceDirect over the last 24 years, it was found that the amount of research applicable to industries cascades as follows: the most is applicable to the construction industry, followed by automotive, defense, renewable energy, and biomedical. The effects of each industry have been examined and are presented as well.

Digital Twin For Shelf Intelligence: Ai-Driven Inventory Management For Minimizing Food Waste, Charlotte Maples, Marvin Velazquez

College of Engineering Summer Undergraduate Research Program

This project aims to develop a solution for improving grocery store inventory management by leveraging AI-driven image recognition. Traditional inventory methods, which rely on manual counting or barcode scanning, are inefficient, labor-intensive, and prone to human error. Over an 8-week period, we designed and developed a basic iPad app capable of identifying specific types of fruit and automatically updating inventory records in real time. By utilizing the iPad’s camera and machine learning algorithms, the app demonstrates the potential to streamline inventory tracking, reduce manual labor, and improve accuracy in managing perishable goods. Future work will focus on expanding the app’s …