ECE 301 (001) Linear Systems (Spring 2023). Past: [S'22] [S'21]

Lecture section: MW 4:30–5:45 pm, In-Person @ 1021 EB2

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: Mushfiqur Rahman, Devadharshini Ayyappan, and Nate Sullivan

Course forum: ECE 301 on Piazza

Homework submission (due at 4:30 pm before class): Gradescope (for the first time, log in Gradescope via Moodle or use Entry Code V5J6P5)

Office hours: See Google Calendar

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. The Spring 2023 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 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 or R. Students are expected to be able to write computer programs in C and Matlab and apply mathematical tools from ECE 220 and calculus.

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

Reference books:

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 2023)

Tentative Class Schedule:

Class # Date Topic Lecture notes Reading Assignment HW Assignment
1 1/9 Intr, Math review Slides Ch0 VT 1.2.3–4
VT Ch4, 1.5.2, 1.6, 1.8		 HW1 (due 1/12)
2 1/11 CT & DT signals; Complex exp Slides Ch1 OW 1.1–4 HW2 (due 1/18)
1/16 MLK holiday (no class)
3 1/18 Periodicity of discrete signals HW3 (due 1/25)
4 1/23 Impulse & step functions OW 1.1–4
5 1/25 System examples & simple system properties OW 1.5–6 HW4 (due 2/1)
6 1/30 Time-invariance, linearity OW 1.6
7 2/1 DT system response, DT convolution Slides Ch2A OW 2.1–2 HW5 (due 2/8)
8 2/6 DT convolution (cont'd) OW 2.2
9 2/8 CT convolution HW6 (due 2/15)
10 2/13 LTI properties Slides Ch2B OW 2.3
11 2/15 Linear algebra review Slides ML VT Ch5–6;
Scheffe App 1
12 2/20 Vector space ISLR 10.2,
Murphy 12.2
2/22 Midterm 1 (exam paper) HW7 (due 3/1)
13 2/27 Eigenanalysis Scheffe Ch1
14 3/1 Principal component analysis (PCA) HW8 (due 3/8)
Matlab code
yalefaces.zip
15 3/6 Linear regression DL Ch9
16 3/8 Convolutional neural network (CNN) HW9 (due 3/22)
Spring break
17 3/20 CNN (cont'd); Fourier transform Slides CTFT OW 4.1–2
(OW 3.2–4)
18 3/22 Fourier transform (cont'd) OW 3.2–4 HW10 (due 3/29)
19 3/27 Sinc, rect, and properties
Convolution and multiplication		 OW 4.2–3; 4.4–5
20 3/29 DTFT and its properties Slides DTFT OW 5.1-6
21 4/3 DTFT and its properties (cont'd)
4/5 Midterm 2 (exam paper) HW11 (due 4/12)
22 4/10 DFT; Relations among CTFT, DTFT, DFT Slides DFT Johnson 5.7-9
23 4/12 Sampling theorem, Nyquist frequency OW 7.1-3 HW12 (due 4/19)
24 4/17 Sampling theorem (cont'd)
Bilateral Laplace transform, pole-zero plots, ROC		 Slides Laplace OW 9.1-3
25 4/19 CT LTI system characterization, connections to freq. response OW 9.4, 7, 8 Worked-out examples
26 4/25 Z-transform, pole-zero plots, ROC
DT LTI system characterization, connections to freq. response		 OW 10.1-2
10.4-5		 Prescheduled last HW due
(Last HW due on 4/19)
5/1 Closed-Book Comprehensive Final Exam from 3:30 to 6 pm in 1021 EB2. Six problems, two from the last part of the course. Cheatsheets allowed. Calculator not allowed.