Curriculum
With the rapid pace of growth in Internet of Things (IoT) products and applications, there is a pressing need for engineers with unique skills in both hardware and software design. Earning a Bachelor of Science in Cyber-Physical Systems Engineering (CPSE) from the Clark School of Engineering at the Unviersity of Maryland will train future engineers in both of these fields, with specializations in networks, cybersecurity, and machine learning.
The cohort-based CPSE curriculum is designed to be completed in two years at the Biomedical Sciences and Engineering Building (BSE) at the Universities of Shady Grove campus in Rockville, MD. During their junior year, students focus on foundational knowledge to prepare them for advanced-level topics in their senior year.
CPSE Major Course Requirements
The CPSE major requires a total of 62 credits, with 60 credits completed prior to enrollment in the CPSE program. Students will take the program required courses in their junior and senior years, in addition to general elective coursework in the second semester of their senior year. The specific elective course offerings will vary each spring semester.
Program Educational Objectives (PEO's)
The program education objective of this program is to produce a well-trained workforce in the emerging technologies of internet of things. The Bachelor of Science in Cyber-Physical Systems Engineering will produce engineering graduates who:
- Use their hardware and software engineering design training and problem-solving skills to contribute professionally in an industrial, research and applications environment;
- Demonstrate initiative, leadership, teamwork, and continued professional development;
- Demonstrate understanding of the impact of their professional activities on society.
First-year CPSE Retention, Graduation Rate Average, and Current Student Enrollment
- First-year CPSE retention average over all cohorts: 77.8% (7/9 Students)
- CPSE graduation rate average: 66% (6/9 Students)
- Current Junior Cohort Enrollment: 13 Students
- Current Senior Cohort Enrollment: 5 Students
- 2024-2025 Total CPSE Enrollment: 18 Students
Student Learning Outcomes
- an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
- an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors.
- an ability to communicate effectively with a range of audiences.
- an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts.
- an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives.
- an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions.
- an ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Tracks
The General Track offers a general focus of course content with classes from each of the three tracks. While there are no specific required or elective courses for this track, the General Track requires 12 credits, which is the same as the other three tracks. Consult with an advisor for details.
Included Foundational Topics:
- Analog Circuitry
- Discrete Mathematics
- Computer Organization
- Networks & Protocols
- Microelectronics
- Introduction to Internet of Things
- Coding Languages: C, Python, Java & Verilog
Included Advanced Topics:
- Firmware Development
- Real-time Operating Systems
- Network & Hardware Security
- Embedded Systems-focused Machine Learning
- Individualized Year-Long Capstone Design Project
Course | Title | Credits | Prerequisites |
---|---|---|---|
Required Courses | |||
ENEB302 | Analog Circuits | 4 | PHYS 260/261, MATH 240, 241 OR 246 |
ENEB304 | Microelectronics and Sensors | 3 | ENEB302 |
ENEB340 | Intermediate Programming Concepts and Applications for Embedded Systems (C/C++) | 3 | |
ENEB341 | Introduction to Internet of Things | 3 | |
ENEB344 | Digital Logic Design for Embedded Systems | 4 | |
ENEB352 | Introduction to Networks and Protocols | 3 | ENEB340 |
ENEB353 | Computer Organization for Embedded Systems | 3 | |
ENEB354 | Discrete Mathematics for Information Technology | 3 | MATH141 |
ENEB355 | Algorithms in Python | 3 | ENEB340 & ENEB354 |
ENEB408A | Capstone Design Lab I | 3 | |
ENEB408B | Capstone Design Lab II | 3 | |
ENEB452 | Embedded Systems | 3 | |
ENGL393 | Technical Writing (Offered by the English Dept.) | 3 | |
Elective Courses | |||
ENEB345 | Probability and Statistical Inference | 3 | |
ENEB346 | Linear Algebra for Machine Learning Applications | 3 | MATH140 |
ENEB443 | Hardware/Software Security for Embedded Systems | 3 | ENEB454 |
ENEB444 | Operating Systems for Embedded Systems | 3 | ENEB340 & ENEB344 |
ENEB451 | Network Security | 3 | ENEB352 |
ENEB452 | Advanced Software for Connected Embedded Systems | 3 | |
ENEB453 | Web-Based Application Development | 3 | |
ENEB455 | Advanced FPGA Systems Design Using Verilog for Embedded Systems | 3 | ENEB340 & ENEB344 |
ENEB456 | Machine Learning Tools | 3 | |
ENEB457 | Foundations of Databases for Web Applications | 3 | ENEB345, 352 & 355 |
Course | Title | Credits |
---|---|---|
Required Courses | ||
ENEB455 | Advanced FPGA System Design using Verilog for Embedded Systems | 3 |
Elective Courses (select three of the following) | ||
ENEB443 | Hardware/Software Security for Embedded Systems | 3 |
ENEB451 | Network Security | 3 |
ENEB452 | Advanced Software for Connected Embedded Systems | 3 |
ENEB453 | Web-Based Application Development | 3 |
ENEB456 | Machine Learning Tools | 3 |
ENEB457 | Foundations of Databases for Web Applications | 3 |
Total Credits | 12 |
Course | Title | Credits |
---|---|---|
Required Courses | ||
ENEB456 | Machine Learning Tools (Machine Learning Tools) | 3 |
Elective Courses (select three of the following) | ||
ENEB443 | Hardware/Software Security for Embedded Systems | 3 |
ENEB451 | Network Security | 3 |
ENEB452 | Advanced Software for Connected Embedded Systems | 3 |
ENEB453 | Web-Based Application Development | 3 |
ENEB455 | Advanced FPGA Systems Design Using Verilog for Embedded Systems | 3 |
ENEB457 | Foundations of Databases for Web Applications | 3 |
Total Credits | 12 |
Course | Title | Credits |
---|---|---|
Required Courses | ||
ENEB451 | Network Security | 4 |
Elective Courses (select three of the following) | ||
ENEB443 | Hardware/Software Security for Embedded Systems | 3 |
ENEB452 | Advanced Software for Connected Embedded Systems | 3 |
ENEB453 | Web-Based Application Development | 3 |
ENEB455 | Advanced FPGA Systems Design Using Verilog for Embedded Systems | 3 |
ENEB456 | Machine Learning Tools | 3 |
ENEB457 | Foundations of Databases for Web Applications | 3 |
Total Credits | 12 |