Lecture section: MW 4:30–5:45 pm, In-Person @ 1025 EB2. Attendance is required. Recordings will be available on Piazza.

Discussion section (optional): F 1:10–2 pm, Online Synchronous (Zoom link can be found on Piazza). Recordings will be available on Piazza.

Instructor: Dr. Chau-Wai Wong

Teaching assistants: Peiran Wang, Eesha Atif, and Longwei Yang

Course forum: ECE 301 on Piazza

Homework submission (due at 4:30 pm before class): Gradescope (for first-time users, please use Entry Code 4JKZD4.)

Office hours: Th 9:30–10 am (Wong @ Zoom); MW 12–1 pm (TA @ Open space outside 2116 EB2)

Exam dates: Midterm 1: 2/19 (in class); Midterm 2: 4/2 (in class); Final: 4/30 (3:30-6 pm, 1025 EB2)

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 covered include: characterization of continuous- and discrete-time systems, sampling theorem, Fourier transforms, Laplace transform, and z-transform.

Topics: Characterization of continuous- and discrete-time systems, sampling theorem, Fourier transforms, Laplace transform, and z-transform. This offering will also give an concise introduction to artificial intelligence (AI) / machine learning (ML), covering basic topics such as the convolutional neural network (CNN), linear regression, and principal component analysis (PCA).

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); 411 (Machine Learning)

Course structure & Grading: The course consists of two mandatory 75-min lectures with pop-up quizzes and one optional 50-min discussion section per week. A teaching assistant will lead the discussion section, covering practice problems and answering questions from students. There will be weekly homework assignments (35%) that contains both written problems and programming problems, two midterm exams (2*20%), and one final exam (25%). Programming will be in Matlab, and optionally, in Python. Students are expected to be able to write computer programs in C and Matlab and apply mathematical tools from ECE 220 and calculus. Failure to submit four (4) homework assignments without justification will result in a failing grade.

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. [ECE 220 Textbook]
• M. P. Deisenroth, A. A. Faisal, and C. S. Ong, Mathematics for Machine Learning, 2020. [Online]
• S. H. Chan, Introduction to Probability for Data Science, 2021. [Online]
• [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 (Spring 2025)