Current: Signal Processing and Communications

Program Requirements

All EECS MEng students should expect to complete four (4) technical courses within the EECS department at the graduate level, the Fung Institute's engineering leadership curriculum, as well as a capstone project that will be hosted by the EECS department. You must select a project from the list below.

2018-2019 Capstone Projects

For the capstone projects for Master of Engineering in Electrical Engineering and Computer Science (EECS) our department believes that the students are going to have a significantly better experience if the projects are followed closely by an EECS professor throughout the academic year. To ensure this, we have asked the faculty in each area for which the Master of Engineering is offered in our department to formulate one or more project ideas that the incoming students will have to choose from.

Project 1

Title -Intelligent Collaborative Radio Networks (advisors Prof. Anant Sahai & Prof. John Wawryznek)

Description - The next generation of radio systems are going to be agile, intelligent, self-configuring, and collaborative. This project is in conjunction with the DARPA Spectrum Challenge and we will have multiple teams working on different aspects of a new software-defined radio system featuring collaborative intelligence. Different backgrounds are welcome for team members --- ranging from a FPGA-targeted digital design to networking to signal processing to human/computer interaction to machine learning and game theory. 

Project 2

Title - On-chip Biosignal Computation for Health Monitoring (advisor Prof. Rikky Muller)

Description - Low-power wearable and implantable biosensors require energy efficient computation of bio-signals for disease detection and health monitoring. This project aims to design and implement these low-power digital computations first on an FPGA and then in an ASIC digital synthesis flow. The student team will build on our prior work in seizure detection to develop learning algorithms and implement them on a digital IC. Students will gain experience in signal processing, learning, digital design and implementation.

Technical Courses

At least three of your four technical courses should be chosen from the list below. The remaining technical courses should be chosen from your own or another MEng area of concentration within the EECS Department.

Fall 2018

Spring 2019 (updated as of 10/16/2018)

  • CS 260A, User Interface Design and Development 
  • CS 280, Computer Vision
  • CS 289A, Introduction to Machine Learning
  • EECS 206B, Robotic Manipulation and Interaction
  • EE 213A, Power Electronics
  • EE 225B, Digital Image Processing
  • EECS 227AT, Optimization Models in Engineering
  • EE C227C, Convex Optimzation and Approximation
  • EE229B, Error Control Coding
  • EE 230A, Integrated-Circuits Devices
  • EE C247B Introduction to MEMS Design
  • EECS 251A, Introduction to Digital Design and Integrated Circuits 
      • Pick ONE accompanying lab section below:
        • EECS 251AL, Application Specific Integrated Circuits Laboratory
        • EECS 251BL, FPGA Design Laboratory
  • EE 290C, Advanced Topics in Circuit Design
  • EE 290P, Advanced Topics in Bioelectronics 
  • EE 290T, Advanced Topics in Signal Processing

Note: The courses listed here are not guaranteed to be offered, and the course schedule may change without notice. Refer to the UC Berkeley Course Schedule for further enrollment information.