E1.01 Crew Laptop
Sponsor: NASA - Texas Space Grant Consortium (TSGC)
Student Team: Rabeea Fatima, Javier Garcia, Matthew Giddens, Cameron Guillies
Faculty Advisor: Mr. Jeffrey Stevens
Our project is to design a USB hub development board that demonstrates the architecture required for NASA’s Radiation Tolerant Crew Laptop. The Crew Laptop must support connectivity to at least fifteen downstream USB devices, but the system currently provides only four upstream USB interfaces. This means that integrating additional USB hubs to expand the system’s port count, manage data flow, and safely deliver power to connected devices is required. This hub prototype will validate the electrical design, power delivery behavior, and overall feasibility of the USB subsystem for the final deployment ready motherboard.
E1.02 Foundation Monitoring
Sponsor: Stable Solutions
Student Team: Anthony Cook, Marcos Navarro, Townes Brown, Sofia Burgos
Faculty Advisor: Mr. Mark Welker
Foundation monitoring system that continuously tracks structural integrity of homes. The system will send data wirelessly and display an easy-to-read UI for the user using Home Assistant. Sensor data is logged and analyzed to identify structural movement and/or anomalies before sending alerts through Home Assistant to notify the user.
E1.03 Watts Up
Sponsor: Ingram School of Engineering (ISoE)
Student Team: David Bendeck Cortes, Grant Koop, David Castillo
Faculty Advisor: Dr. Sercan Iscan
Our project develops a PowerWorld simulation of the existing San Marcos power grid to integrate an LED bulb factory and a utility-scale solar plant. The simulation will focus on transmission-level planning, evaluating system performance under base-case and N-1 contingency conditions. This project provides hands-on experience using industry-standard power system planning tools and design practices, preparing students for careers in transmission planning and grid integration for renewable energy.
E1.04 PLC Traffic Light Operation
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Madeline Pope, Frederick Jones, Jose Rodriguez
Faculty Advisor: Mr. Jeffrey Stevens
Our project is an PLC traffic light control system for a four-way intersection. It will be able to detect vehicles in every direction and change lights according to the way the vehicle needs to proceed, as well as communicate with other intersections to optimize traffic flow. Deliverables include a simulation in OpenPLC with an HMI model, a SPICE simulation of LED driver circuit, custom PCB designs, and functional selected controllers
E1.05 Radiant Street
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Alex Conti, Marko Milosevic, Stephen Peterson
Faculty Advisor: Mr. Mark Welker
Our project is a system of traffic lights that sense vehicle activity and communicate between each other to manage traffic flow. The traffic lights will be directed by OpenPLC to manage the light pattern while sensors will detect traffic and alter the timing of the lights. A physical model will be placed on a street map course where it can interact with autobots and change in real time. By implementing our lights onto the traffic model, we increase the efficiency of the traffic flow and allow an environment where bots will be able to practice routing.
E1.06 Autobots
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Melissa Corrales, Tyler Larson, Siena Rea, Andres Rocha
Faculty Advisor: Mr. Jeffrey Stevens
The goal of this project is to develop an Autonomous Driving system capable of navigating a miniature cityscape. The systems detect lane boundaries, interpret traffic lights and stop signs, and execute precise closed-loop motor control to maintain safe and accurate movement. A custom PCB, regulated power system, and integrated sensor architecture support real-time processing and decision-making
E1.07 Talos
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Arturo Ott, Andrea Quan, Luke Windham
Faculty Advisor: Mr. Jeffrey Stevens
Our project is an autonomous vehicle that navigates a standardized streetscape, watching traffic lights, stop signs, and other vehicles. It uses a camera and image processing to determine lane position and detect traffic signals and uses an onboard navigation system to follow a preassigned route without additional user input. This is important because the Autobot project showcases building a safe autonomous vehicle capable of handling the complexities of real-world traffic that can be delivered on a constraining budget. By implementing these features on affordable and low power hardware, it validates autonomous technology that can be safe and cost effective.
E1.08 Autobot: Magnetronics
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Ignacio Rubio. Troy Hamilton, Micheal Orduna, Ana Carlos
Faculty Advisor: Mr. Jeffrey Stevens
An autonomous ground vehicle capable of operating without human intervention. The system is pre-programmed with navigation directives prior to deployment and uses onboard sensing to detect lane boundaries, traffic signals, stop signs, and surrounding obstacles in real time, enabling safe and adaptive decision-making during operation.
E1.09 Autonomous Prime: Autobots
Sponsor: Ingram School of Engineering (ISoE)
Student Team: Marshall Morris, Kaleb W Smith, Colin Wilder, Ryan Reyes
Faculty Advisor: Mr. Mark Welker
We are an autonomous driving bot that will navigate a traffic grid with traffic obstructions, signs, and lights. The bot will compete to finish the course the quickest, and will display the time elapsed once completed, arriving at the stop sign. Delivering high speed, inspiring engineers, and the ability to develop a working solution to autonomous driving.