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About the Course

This course introduces students to key concepts in electrical and computer engineering (ECE). In particular students will learn the fundamental role played by sinusoidal and complex exponential signals\ for connecting the time and frequency domains. They will also learn properties of signal processing systems, such as linearity and time invariance.

By the end of the course the successful ECE 201 student will achieve a number of desired learning outcomes:

The prerequisite for this course is: A grade of C or better in Math 113. The requirements of the course are:

  1. A willingness to work hard and to think independently.
  2. A commitment to put a time, outside of class, reading and working problems.
  3. A desire to learn and to not be afraid to ask questions.
More information may be found on the syllabus web page here and in the PDF of the syllabus, which may be obtained by clicking here.


About Monson H. Hayes

Dr. Hayes is a Professor of Electrical and Computer Engeering at George Mason University and Chair of the Department. Dr. Hayes received his Sc.D. in Electrical Engineering and Computer Science from M.I.T. in 1981 and then joined the faculty in the School of Electrical and Computer Engineering at Georgia Tech where he is currently Professor Emeritus. From 2006 until 2011, he was an Assocaite Chair for the School of ECE and Associate Director of Georgia Tech Savannah. In March of 2011, he became a Distinguished Foreign Professor at Chung-Ang University in Seoul, Korea. In the Fall of 2014, Dr. Hayes joined the faculty at George Mason University.

 Dr. Hayes has become internationally recognized for his contributions to the field of digital signal processing. He has published more than 180 articles in journals and conference proceedings, and is the author of two textbooks, Statistical Digital Signal Processing and Modeling (Wiley, 1996), and Schaum’s Outline on Digital Signal Processing (McGraw-Hill, 1999). His research interests include DSP algorithms, signal modeling, image and video processing, face recognition, and DSP education. His current projects include face recognition for personalization, lane tracking for driver awareness, pattern recognition, and deep learning.