Digital Commons Network^™

Exploration Of Esp32 Vulnerabilities And Malware, Charles T. Moreno

College of Engineering Summer Undergraduate Research Program

The rapid expansion of the Internet of Things (IoT) has revolutionized industries by enabling connected smart devices to monitor, communicate, and automate tasks in real-time. Central to the functioning of many IoT systems are microcontrollers like the ESP32, a versatile and low-cost microcontroller known for its integrated Wi-Fi and Bluetooth capabilities. The ESP32's powerful processing, energy efficiency, and adaptability make it a popular choice for IoT applications ranging from smart home devices to industrial automation. However, as IoT adoption grows, so too do the security challenges posed by vulnerabilities in these devices, particularly within ESP32-based systems. For this project, I …

Distributed Multi-Robot Localization And Coordination Framework For Mobile Robots, Dmitri Dobrynin, Indigo T. Garcia

College of Engineering Summer Undergraduate Research Program

This project aims to develop an experimental framework for multiple mobile robots, both in simulation and real-world hardware, using ROS 2 as the primary operating system. Utilizing TurtleBot 3 platforms, the team will establish a robust setup that enables tasks such as distributed localization, autonomous navigation, path planning, and formation control for mobile robots. Leveraging simulation environments like Gazebo, the project will replicate real-world setups in a simulated environment for testing and development. All tasks, communication, and sensor integration will be implemented using ROS 2, ensuring seamless coordination and interoperability among the robots. The project involves integrating various sensors and …

Enhancing Place-Based Interaction With Emotion Ai And Augmented Reality, Jake Maier, Ivan Martinez

College of Engineering Summer Undergraduate Research Program

This project explores the integration of augmented reality (AR) and Emotion AI technologies to enhance user experiences in physical environments. By seamlessly merging virtual elements with real-world contexts, we aim to deepen individuals’ interactions and perceptions of their surroundings. Leveraging AR technology enables users to access contextual information, engage with interactive content, and navigate spaces with heightened immersion and understanding. Additionally, Emotion AI enhances these experiences by detecting and responding to users’ emotional states, fostering personalized and emotionally resonant interactions. We aim to integrate digital content within physical environments using mixed-reality headsets equipped with eye-tracking capabilities and consumer-grade wireless EEG …

Drive To Succeed: Empowering Underserved Communities Through Autonomous Driving Education, Amanda I. Chan

College of Engineering Summer Undergraduate Research Program

Autonomous vehicle (AV) technology represents the leading edge of innovation in transportation and holds immense potential to transform industries across the globe, with California at the forefront. However, access to education and technical training in this field has often been out of reach for students in underserved communities. The “Drive to Succeed” project endeavors to bridge this gap by introducing autonomous driving technology to high school students in communities where access has traditionally been limited. We propose an outreach program specifically designed to address the underrepresentation of marginalized groups in technology sectors and cultivate the next generation of engineers from …

Shake Table Testing Of Full-Scale Fully Equipped Timber Assembly In Residential Timber Homes, Derek A. Quintanilla

College of Engineering Summer Undergraduate Research Program

No abstract provided.

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, …

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.

Quantifying Ethics And Trust In Human-Ai Collaboration, Oliver Lane, Kevin Klave

College of Engineering Summer Undergraduate Research Program

As AI Chatbots continue to evolve in both prevalence and capability, their role in education is becoming increasingly prominent. With chatbots like ChatGPT becoming commonplace in higher education, there is an evident need to understand the ethics and trust dynamics of human-AI collaboration. This research contributes to the ongoing discussion on AI in education, highlighting the importance of trust when utilizing AI in academic settings. By conducting an empirical analysis, this research seeks to quantify trust in human-AI collaboration in higher education with the aim of offering actionable items for higher education institutions to follow to promote ethical and responsible …

Right To Repair: Promoting Equity In Self-Repair Experience, Lilyane Stessman

College of Engineering Summer Undergraduate Research Program

No abstract provided.

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.

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.

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 …

Hardware Control Unit For Trusted Program Verification System, Jake Owen Alt

Master's Theses

Trust in the underlying hardware is the foundational step towards trusting the correctness and integrity of a software application. However, verifying that today's extremely complex processors work exactly as intended has not been feasible, as evidenced by several recent hardware bugs. Trustworthy, formally verified processors currently forego intricate performance enhancements such as out-of-order execution, hampering them substantially versus their less secure counterparts.

The Containment Architecture with Verified Output (CAVO) system solves this problem by isolating the host system and requiring the result of each instruction to be validated by a small, trusted hardware module called the Sentry. Any transmissions to …

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 …

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, …

"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 …

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.

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

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 …

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.

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.

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 …