EE at UC Merced

Welcome to the EE 021 Midterm/Final Showcase

The Students listed here have demonstrated excellence in their programming skills, as judged by their instructional staff, in their project.

If/when provided, the link to the GitHub repository of the project will be added.

This list is not in order of ranking.

EE 021: Fall 2025

Final Projects:

  1. Kevin Florentino Morelos and Riley Clarke: For effectively integrating MATLAB, App Designer, and Simulink, while helping others in the class too.
  2. Brandon Sanchez: For demonstrating superior programming skills in MIPS.
  3. Jackson Blunt: For achieving 97.13% accuracy on the Digit Recognizer (MNIST dataset) task on Kaggle. He is on the leaderboard here.

Midterm Projects:

  1. Andrea Canedo: For designing Object Detection using Machine Learning through Google Teachable Machine
  2. Christine Chang: For designing Video-based Lane Detection
  3. Akshat Mehrotra: For designing a Program to predict the probability of a Football catch using Computer Vision
  4. Brandon Sanchez: For designing a GUI for plotting IV curves
  5. Trevor Sen: For designing a Graphical RPG game

Honorable Mentions:

  1. Jackson Blunt: For designing and building an Automated sun-tracking solar Panel
  2. Kyle Lin: For designing and building a project for Humidity Sensing

EE 021: Fall 2024

Final Projects:

  1. Miriam Martinez Pena, Jake Perkins, and Roberto Macias: For a crisp and accurate MATLAB GUI for pyoscilloscope
  2. Ramiz Haddad and Matthew Lansing: For Python-based image classification using the MNIST dataset
  3. Ken Guo: For carefully designing a UI for an oscilloscope using C++
  4. Honorable mentions: John Kim, Violet Chen, and Andrew Nuisud for taking on MIPS programming

Midterm Projects:

  1. John Kim: For designing pycrypt — An app to design and test encryption and decryption algorithms in Python
  2. Brayan Nunez: For designing pylant — An app to learn more about various plants and calculate the amount of water needed to grow certain crops
  3. Thomas Williams: For designing a radar proof-of-concept and visualizing its performance