Graduate-level introduction to signal processing with emphasis on vector space methods and adaptive signal processing.
The course prerequisites are undergraduate level probability and DSP. The linear algebra content will be self-contained, though taught at a somewhat fast pace.
Tuesday and Thursday, 12:30–1:50pm, In Person
Instructor: Zhi-Pei Liang, z-liang [at] illinois [dot] edu
Teaching Assistant: Parisa Karimi, parisa2 [at] illinois [dot] edu
Parisa's office hours: Tuesdays and Wednesdays 5-6 pm, online.
Zoom meeting information: Join Zoom Meeting Meeting ID: 883 653 4419 Password: 338988
Lecture 1 slides are uploaded!
HW1 is uploaded! HW1 Solution
HW2 is uploaded! HW2 Solution
HW3 is uploaded! HW3 Solution
HW4 is uploaded! HW4 Solution
HW5 is uploaded! HW5 Solution
HW6 is uploaded! HW6 Solution
HW7 is uploaded! HW7 Solution
HW8 is uploaded! HW8 Solution
HW9 is uploaded! HW9 Solution
HW10 is uploaded! HW10 Solution
J.G. Proakis and D.G. Manolakis, Digital Signal Processing. Principles, Algorithms and Applications, 4th Ed., Prentice-Hall, 2007.
Vetterli, Kovačević, Goyal, Foundations of Signal Processing, Cambridge University Press, August 2014; here
A.V. Oppenheim, R.W. Shafer, and J. R. Buck, Discrete-Time Signal Processing, Prentice-Hall, 1999.
A. C. Singer, Powerpoint Notes. here
Handouts
Week 1:
Overview of digital signal processing and its applications
Review of basic signal processing concepts
Week 2:
All-pass systems
Minimum-phase systems
Group delay
Week 3:
Filter structures
Optimal design of FIR filters
Week 4:
Multichannel sampling
Week 5-6:
Digital rate conversion
Digital filter banks
Midterm 1
Week 7:
Time-frequency analysis: Window Fourier transform
Week 8-9:
Wavelet transform
Week 10:
Linear predictive modeling
Week 11:
ARMA processes
Week 12-13:
Adaptive filters
Midterm 2
Week 14:
Fall break
Week 15-16:
Principal component analysis (PCA)
Independent component analysis (ICA)
Cannonical component analysis (CCA)
Application examples
Final exam