Develops the knowledge of control system concepts and applies them to electromechanical systems. Systems are characterized and modeled using linear systems methods, focused with a controls perspective. Impulse responses, step responses, and transfer functions are reviewed. Principles of stability and damping are developed and applied to the specification and design of open and closed loop compensators to deliver specific input-output performance. Laboratory exercises are designed to illustrate concepts, reinforce analysis and design skills, and develop instrumentation techniques associated with the lecture topics. Student must register for BOTH the Lecture and Laboratory components of this course.

Machine learning has applications in a wide variety of fields ranging from medicine and finance to telecommunications and autonomous self-driving vehicles. This course introduces machine learning and gives you the knowledge to understand and apply machine learning to solve problems in a variety of application areas. The course covers neural net structures, deep learning, support vector machines, training and testing methods, clustering, classification, and prediction with applications across a variety of fields. The focus will be on developing a foundation from which a variety of machine learning methods can be applied. Students may not take and receive credit for this course if they have already taken TCET-620.

Special topics are experimental courses offered per semester.

Independent study.

Machine learning has applications in a wide variety of fields ranging from medicine and finance to telecommunications and autonomous self-driving vehicles. This course introduces machine learning and gives you the knowledge to understand and apply machine learning to solve problems in a variety of application areas. The course covers neural net structures, deep learning, support vector machines, training and testing methods, clustering, classification, and prediction with applications across a variety of fields. The focus will be on developing a foundation from which a variety of machine learning methods can be applied. Students may not take and receive credit for this course if they have already taken EEET-520.

This course continues research work started in TCET-788 Thesis Planning after completion of that initial research and documentation. The MSTET graduate thesis is a document that describes and presents the results of scholarly research in the field of telecommunications. The results of a MSTET graduate thesis provide new knowledge, processes, software or other assets that advance the state of the art of telecommunications, even in a modest way. (Department consent required)

The MSTET graduate project describes and presents the results of scholarly research in the field of telecommunications. The results of a MSTET graduate project provide new knowledge, processes, software, or other assets that advance the state of the art of telecommunications or organize or implement existing knowledge in a unique and useful way. Department permission is required.