Enabling Project-Based Learning with MATLAB, Simulink, and Target Hardware

Project-Based learning is extremely effective because students can see, hear, and touch what would otherwise be very abstract. In this webinar we will show you how MATLAB, Simulink, and the new support for low-cost target hardware can easily interface with a broad range of very affordable hardware and experiments to teach courses focused on:

• Mechatronics
• Circuit design
• Programming
• Controls
• Robotics
• Renewable energy

Users of Simulink can automatically generate stand alone applications to run in real-time on devices such as the Arduino, Raspberry Pi, and LEGO^® MINDSTORMS^® NXT without the need for either MATLAB Coder™ or Simulink Coder™. Using this new capability, we explore integrating simulation and hardware to show the following concepts:

• Reading sensors and writing to actuators
• Interactive prototyping of algorithms for control and signal processing
• Testing algorithms with physical hardware components
• Deploying real-time algorithms to stand alone hardware
• Integrating algorithms with robots and real-world systems

About the Presenter: Dr. Ye Cheng is a member of the Academic Technical Evangelist team at MathWorks helping faculty members better utilize MATLAB and Simulink for education and research. She has several years of teaching experience in senior-level mechanical engineering laboratories. Ye holds Ph.D. and M.S. degrees in mechanical and aerospace engineering, focusing on advanced 3D imaging techniques for the study of turbulence and multiphase flow.

Todd Atkins is a member of the Educational Technical Marketing team at MathWorks who is exploring how best to work with universities to help prepare the next generation of engineers and scientists. He has been on the technical staff for five years in a number of roles including support, development, and marketing.

Todd holds a B. S. and M. Eng. in electrical engineering and computer science from Massachusetts Institute of Technology. His research was in the fields of artificial intelligence and computer vision. Additionally Todd was a teaching assistant for MIT’s 6.001: Structure and Interpretation of Computer Programs course for three semesters.