Embedded Systems, MSE
The Embedded Systems (EMBS) Graduate program is designed for students who wish to pursue industrial jobs in automotive, aerospace, defense, and consumer electronics, as well as for practicing engineers in the embedded systems industry who want to gain knowledge in state- of-the-art tools and theories. The core topics span embedded control, real-time operating systems, model-based design and verification, as well as implementation of embedded/autonomous systems. Applicants are expected to have a strong academic background in both computer science and electrical engineering. All EMBS candidates must be confident working at both the hardware and software levels of the stack. EMBS is a multi- disciplinary field, but we understand that not all incoming students may have the background required in certain areas. While we do not have prerequisites for admission, it is highly recommended that your undergraduate coursework covered the basics of both C/C++ and Java programming, computer architecture, operating systems, and algorithms. Work experience and practical project experience in the domain of Cyber- Physical Systems a plus.
For more information: http://www.cis.upenn.edu/prospective-students/graduate/embs.php
Curriculum
| Code | Title | Course Units |
|---|---|---|
| Core | ||
| Select 4 from the following: | 4 | |
| Computer Architecture | ||
| Software Systems | ||
| Principles of Embedded Computation | ||
| Embedded Software for Life-Critical Applications | ||
| Real-Time Embedded Systems | ||
| Theory Elective | ||
| Select 1 from the following: | 1 | |
| Software Foundations | ||
| Analysis of Algorithms | ||
| Theory of Computation | ||
| Fundamentals of Linear Algebra and Optimization | ||
| Principles of Embedded Computation | ||
| Linear Systems Theory | ||
| Intro to Linear, Nonlinear and Integer Optimization | ||
| Electromagnetic and Optics | ||
| Data Mining: Learning from Massive Datasets | ||
| Applied Regression and Analysis of Variance | ||
| Statistical Inference | ||
| Mathematical Statistics | ||
| Electives | ||
| Select 5 from the following: | 5 | |
| Analysis of Algorithms | ||
| Applied Machine Learning | ||
| Operating Systems Design and Implementation | ||
| Computer and Network Security. | ||
| Networked Systems | ||
| Internet and Web Systems | ||
| Machine Perception | ||
| Special Topics | ||
| Data Structures and Sofware Design | ||
| Algorithms and Computation | ||
| Engineering Entrepreneurship I | ||
| Linear Systems Theory | ||
| Feedback Control Design and Analysis | ||
| IoT Edge Computing | ||
| Digital Signal Processing | ||
| System-on-a-Chip Architecture | ||
| Learning in Robotics | ||
| Special Topics in Electrical and Systems Engineering | ||
| Integrated Computer-Aided Design, Manufacturing and Analysis | ||
| Designing Smart Objects for Play and Learning | ||
| Design of Mechatronic Systems | ||
| Introduction to Robotics | ||
| Innovation | ||
| Total Course Units | 10 | |
The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2020 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.