Prospective: 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)

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 Vision Correcting Displays (advisor Prof. Brian Barsky)

Description -Vision problems such as near-sightedness, far-sightedness, as well as others, are due to optical aberrations in the human eye. These conditions are prevalent, and the number of people who have these hardships is growing rapidly. Correcting optical aberrations in the human eye is traditionally done optically using eyeglasses, contact lenses, or refractive surgeries; these are sometime not convenient or not always available to everyone. Furthermore, higher order aberrations are not correctable with eyeglasses. This research is investigating a novel approach which involves a new computation based aberration-correcting light field display: by incorporating the person’’s own optical aberrations into the computation, content shown on the display is modified such that the viewer will be able to see the display in sharp focus without using corrective eyewear. Our research involves the analysis of image formation models; through the retinal light field projection, it is possible to compensate for the optical blurring on the target image by a process of prefiltering with the inverse blur. As part of this project, we are building a light field display prototype that supports our desired inverse light field prefiltering. We are working towards extending the capability to correct for higher order aberrations. This latter aspect is particularly exciting since it would enable people for whom it is not possible to see displays in sharp focus using eyeglasses to be able to do so using no corrective eyewear. This is a broad project that incorporates many different aspects. A variety of different backgrounds for students is welcome.  Students are free to choose what is the most interesting part for them.

Project 3

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.

Project 4

Title - Impact of App Useage in Large Scale Mobility (advisor Prof. Alex Bayen)

Description - The goal of this project is to use large scale traffic data (GPS from smartphones, CDRs from cellular access providers, video data, loop data and other sources of data), at scale, to infer mobility patterns in large scale cities. With classical machine learning and optimization techniques, the team will provide analysis of the impact of app useage on congestion. The team will analyze how better routing decisions can be made to improve congestion. The analysis will rely on game theoretic analysis of selfish behavior and convex optimization. The team will have the opportunity to work with two frameworks, Flow (a platform unifying rrlab and SUMO on AWS), and the Connected Corridors, which enables one to run the microsimulator Aimsun on AWS as well. 

Project 5

Title - Building a Software-Defined Radio (advisor Prof. Borivoje Nikolic)

Description This project is looking for 3-4 teams that will design building blocks for a software-defined radio (SDR). The template for the project will be developed in the Fall'18 offering of the EE290C class. The building blocks will have added value by demonstrating a complete working radio prototype. Example building blocks include a carrier and symbol synchronization, FFT and equalization, universal error-correction decoder (for turbo, LDPC, polar codes), and interfaces to the processor system.

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

  • TBD

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.