Time & Location: MW 4:30–5:45 PM, Online Synchronized

Instructor: Dr. Chau-Wai Wong

Teaching Assistants: Ms. Jisoo Choi & Ms. Shweta Meena

Discussion Forum: ECE 301 on Piazza

Homework submission (due before class): Gradescope (or access it via Moodle)

Office hours: Thursday 4:30–5:30 PM (Wong), Sunday 2–3 PM (Wong); Monday 1–3 PM (Meena/Choi)

Course Description: This course covers the fundamental concepts in signal processing, with a focus on linear time-invariant systems. Signal processing has found its applications in many disciplines such as communications, controls, machine learning, bioengineering, security/privacy, and circuits. Having a good grasp of both intuitions and mathematics of signal processing theories can greatly benefit a student’s future role as an engineer.

Topics: Characterization of continuous- and discrete-time systems, sampling theorem, Fourier transforms, Laplace transform, and z-transform. In the Spring 2021 offering, we will also cover basic machine learning tools such as the principal component analysis (PCA), the linear regression, and the convolutional neural network (CNN).

Prerequisites: ECE 211 and ECE 220.

Followup ECE courses: 421 (Signal Processing); 402 (Communications); 407 & 470 (Computer Networking); 308 & 436 (Controls); 456 (Mechatronics); 451 (Power System); 492-45 (Machine Learning)

Course structure & Grading: The course consists of two 75-minute real-time Zoom lectures per week. There will be weekly homework assignments (40%) that contains both written problems and programming problems, two midterm exams (2*20%), and one final project (20%). Programming will be in Matlab, and optionally, in Python or R.

Textbooks: [OW] A. V. Oppenheim and A. S. Willsky, Signals and Systems, Prentice Hall, 2nd edition.

Reference Books:

• [VT] Y. Viniotis and H.J. Trussell, Mathematics: The Language of Electrical and Computer Engineering, Ed. 3, 2013.
• [DL] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, 2016. [Online]
• K. P. Murphy, Machine Learning: A Probabilistic Perspective, 2012. [Access via library]
• [ISLR] G. James, D. Witten, T. Hastie and R. Tibshirani, Introduction to Statistical Learning with Applications in R, Springer, 2013. [Online]
• H. Scheffe, The Analysis of Variance, Wiley, 1959. [Course Reserves]
• J. L. Devore, Probability and Statistics for Engineering and the Sciences, Ed. 9, 2015.

Some scanned sections/chapters of the books listed above can be found at NC State Course Reserves.

Course Syllabus for ECE 301 Section 001