Electrical & Computer Engineering
Electrical and Computer Engineering students are equipped to apply a variety of skills in both hardware and software to the success of their design projects. The focus of the capstone project is to use their skills and knowledge to develop solutions to complex, real-world engineering design problems under the guidance of faculty and industry engineers.
Projects start in fall and spring and last 2 semesters (excluding summer). Project proposals are due mid-March (fall project start) and/or mid-October (spring project start). Submit your proposal here:
FALL PROJECT START INTAKE FORM
SPRING PROJECT START INTAKE FORM
Student Skills:
- Design and Analysis of Electronic Systems: Designing and building discrete electronic circuits and digital logic circuits and analyzing them using circuit laws, simulation and signal analysis.
- Computing and Embedded Systems: Understanding of the use of MATLAB and C coding to program sophisticated microprocessors.
- Electrical Systems: Demonstrating and applying knowledge on power and renewable energy, signals and controls, electromagnetics and nano systems.
- Software Systems: Demonstrating and applying knowledge of software engineering, data structures, digital electronics and hardware-software interfacing.
- Verification and Validation: Assessing both hardware and software systems for their agreement with both methodical specifications and customer needs.
- Project Management: Balancing customer requirements and engineering specifications and constraints with respect to risk management and safety assessment.
- Additional Skills:
- Consideration of ethical, global and cultural implications of design projects
- Dissemination of results through oral presentations
- Documentation including written reports, operating manuals and test plans
Previous Projects:
AI-Powered Defect Detection System
Sponsored by Schneider Electric
Team 06’s project aimed to develop an AI-driven robotic inspection and rejection system that automatically identifies and removes defective packages from a conveyor line. Using computer vision and machine learning, the prototype – via Schneider Electric’s Ecostruxure Automation Expert (EAE) platform based on the IEC 61499 universal automation standard - detects product defects in real time and triggers a robotic mechanism to remove faulty items while recording inspection data through a custom human-machine interface. By integrating diverse hardware under a single software environment, the prototype highlights the potential of vendor-agnostic automation to reduce downtime, training requirements and engineering costs.
Team members: Klayton Caballero, Matthew DeSouza, Brandon Moran, Zach Sparks, Moises Ramirez
Cairdio Embedded
Sponsored by HealthSeers
The Cairdio Platform is an easy-to-use, intuitive system designed to assess a user’s hearth condition using phonocardiography (PCG) and an AI-powered data analysis platform to determine potential need for further cardiological intervention. This team’s project focused on enhancing device capability via noise reduction, classification and feedback via comparative microphone analysis (electret, MEMS, piezoelectric) and the implementation of an adaptive FIR filter using LMS and NLMS algorithms. These enhancements serve to help close healthcare gaps in under-resourced settings and reduce strain on existing healthcare systems without the need for traditional expensive equipment.
Team members: Randall Adams, Claire Lewis, Mohammed Mohiuddin, Chinyere Nosike, Mostafa Saleh
Acoustics Detection for Sand and Abnormal Situations
Sponsored by BP
The purpose of this project was to create more automated and reliable systems for the detection of sand production and abnormal situations at BP’s offshore oil facilities. This was done using real-time data collected by ClampOn’s non-intrusive ultrasonic acoustic sensors attached to oil flowlines in conjunction with machine learning models, physics-based models and a graphical user interface. Sand particles in oil flow can cause pipeline erosion, leading to leaks and necessitating costly repairs. This project’s machine learning models aimed to process acoustic data from seafloor and topside sensors to identify sand and abnormal events in real time while integrating into BP’s existing monitoring interface to provide operators and engineers with clear alerts and trend data to improve reliability and reduce downtime.
Team members: Luke Liggett, Ifeoluwa Adeleke, Matthew Guerrero, Victor De Oliveira Venancio, Luke Dvorak
Humanoid Robot
Sponsored by Ferguson Control Systems
This project focused on developing a modular humanoid robot platform that could be repaired, modified and expanded without need to disassemble the entire system – something that may cause significant offline repair downtime in most other tightly-integrated robot systems. Their design breaks the robot into interchangeable modules such as arms, hands, torso segments and sensor units that attach through standardized mechanical interfaces and communicate through a shared power and data backbone, meaning that individual components can be quickly swapped out for maintenance, upgrades and hardware testing. The system at the focus of this project is being designed to support brain-computer interface research, making it a practical and versatile platform for studies in robotics, rehabilitation and motor control.
Team members: Josie Graves, Ben Ta, Sunay Panda, Nina Vu, Jeff Kanja, Nick Lloyd