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Principles and Applications of Microcontrollers - Mechatronics (1)

Principles and Applications of Sensors - Mechatronics (2)

Robotics

 Applications of Matlab on Engineering

Introductory Applied Machine Learning

Course overview

  • Principles and Applications of Microcontrollers - Mechatronics (1)

This course introduces microcontrollers and aims to inspire freshmen in the field of mechatronic systems.

This course is divided into two modules: (1) Arduino module and, (2) AVR ATmega328P microcontroller module.

The content of this course includes microcontroller architecture, instruction sets, assembly languages, I/O, interrupts, transfers, timers, etc.

This course also includes a number of hands-on learning, as well as a final project to familiarize students with the application of these techniques.

  • Principles and Applications of Sensors - Mechatronics (2)

This course aims to provide students with an understanding of the principles and applications of sensing (measurement), including:

  • Ability to analyze and design sensor control systems

  • Ability to implement bio-mechatronic systems

  • Critical thinking and problem solving

  • Communication, teamwork and dedication

  • Robotics

This course is taught in an international joint with Dr. Chi Thai from the University of Georgia.

The course goal is to provide students with an advanced practicum in Embedded Robotics wherein the students will learn about the programming of embedded controllers, the interfacing of sensors, the actuation of servo motors, inter-computer serial communications (RS-232 and ZigBee), and the control of autonomous as well as remotely piloted systems. The student will be programming using a high-level integrated environment called RoboPlus, in addition to also practicing lower-level programming using the C/C++ language. These concepts and methodologies will be demonstrated in class with sample codes and the students can expand on these ideas further with a series of robotic projects (of increasing complexity) throughout the semester such as car robots, simple bipedal robots (with 7-8 degrees of freedom) and humanoid robots (with 18 degrees of freedom). Possible projects can be about master-slave robots, search and rescue robot teams, mobile wireless sensor networks, humanoid robot balance control, intruder (motion) detection, image recognition and object tracking, and humanoid robot negotiating stairs with varying tread depths. Upon completion of this course, students should have an integrated hardware/software understanding of embedded robotic systems, both autonomous or remotely piloted.

  • Engineering Application of MATLAB

This course is designed to give students an understanding of how MATLAB is used, making it a good research tool.

In this course, students will solve engineering problems with MATLAB, and be familiar with the initial, intermediate use and programming of MATLAB.

  • Introductory Applied Machine Learning

This course is an introductory course designed to introduce major machine learning algorithms, providing an overview of the various approaches. The course content covers basic machine learning concepts and their use of algorithms. This course will include a practical project, with the expectation of applying the theory learned. Course topics include:

  • Linear regression

  • PCA and PCR

  • Overfitting

  • Support vector machine

  • Decision tree

  • Artificial neural network

  • K-mean

  • KNN

  • Linear discriminant analysis and general discriminant analysis

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