Current: Robotics and Embedded Software

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

TitleAssistive Exoskeletons for Stroke Assessment and Recovery (advisor Prof. Ruzena Bajcsy)

DescriptionThere are over 9 million stroke survivors in the US. Intense, functional rehabilitation has been shown to dramatically improve recovery and quality of life. However poor clinical metrics and the lack of affordable robotic-assisted rehabilitation has resulted in sub-optimal outcomes due to the inability to access rehabilation centres, the fear of injury, and the associated finiancial and time burden. This multidisciplinary project looking into the development of upper limb assistive devices for assessing limb function post neurological incident and tracking recovery progress. In collaboration with UCSF, we are investigating the relationship between neurological and functional recovery. These lessons can then be used to guide rehabilitation based on patient performance.

A number of different tasks will be present in this project, ranging from customer discovery, quality function deployment, design, prototyping, manufacture, and testing with human subjects. The capstone team will join a pre-existing upper limb rehabilitiation robotics group comprising of postdocs, graduate, and undergraduate students from a diverse range of engineering and science backgrounds. While recieving this support, the capstone project will be accessed as two major go/no go milestones throughout the year, based on feedback from the stroke survivors and the physiotherapists they will be working with personally throughout the year. 


Project 2

Title - Ultra E-bike Design (advisor Prof. Seth Sanders)

Description - The team will design and build prototype ultra high performance e-bike(s). Will encompass assessment of state-of-art, then design of ultra-light hub and/or bottom bracket drive train. Technologies covered include battery system design, electric machine design, sensorless motor drive, crank torque and cadence sensing. Will aim to complete prototype during term. Ideal team will have 2 EE and 2 ME students.


Project 3

Title - Object Pose Estimation for Robot Manipulation (advisor Prof. Anca Dragan)

Description - The ability to detect objects in a secene and register their poses is crucial to manipulation capability: the robot needs to know what and where something is to be able to grasp it and pick it up. With progress in deep leaning, object detection and pose estimation techniques are rapidly advancing, but those advancements tend to happen in the computer vision community and be decoupled from robotics. This project will implement a state of the art object recognition and pose estimation system, and customize it to address the specific challenges that arise in manipulation (e.g. occlusions, more srict error thresholds) while capitalizing on the opportunities that robots bring (structured domain, active sensing, complementary sensing channels beyond images).

Project 4

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.