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.

2017-2018 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)

DescriptionThe 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 -HD Processor Design (advisor Jan M. Rabaey)

Description - Computing with HD vectors, referred to as “hypervectors,” is a brain-inspired alternative to computing with numbers providing excellent energy-efficiency and error tolerance. This project aims to propose a general hardware architecture for HD-based classification tasks.   The main goal is to design an efficient and reconfigurable HD encoder that can be reused across a set of cognitive tasks. The architecture should be described using synthesizable Verilog and tested toward an ASIC or FPGA design flow.

Project 3

Title - Benchmarking for De Novo Genome Assembly (advisor Prof. Tom Courtade)

Description - Long-read assembly technologies are paving the way for de novo assembly of reference-quality genomes. Several assemblers have been released over the past year, but their respective advantages and disadvantages are largely unquantified. This project will develop a benchmarking dataset on which available assemblers will be evaluated, thus allowing for apples-to-apples comparisons. No prior knowledge of DNA assembly is required, but programming experience and a willingness to work with real datasets and beta tools are needed.

 

Project 4

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

Description - Low-power wearable and implantable biosensors require energy efficient computation of biosignals 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 gain experience in digital design, implementation and test.

Project 5

Title - Data Science for Analysis of Large Scale Mobility Patterns (advisor Prof. Alexandre Bayen)

Description - The explosion of smartphones has led the majority of motorists to drive "under the influence of apps". The proliferation of navigational apps and services designed to help drivers avoid traffic congestion result in a shift in the types of routes people take. Increased traffic in residential areas has provoked community backlash against the apps and the companies that provide them. Cities are now launching a war against these apps and trying to resist these new types of traffic jams caused by navigation apps. This project will use machine learning and data analytics to model these phenomena and propose new solutions, in the context of connected and automated vehicles.

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 2017

Spring 2018 (updated as of 10/20/17)

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.