York University

EECS 4452 Digital Signal Processing:

Theory and Applications

(Winter 2019)

The Lassonde School of Engineering
Department of Electrical Engineering & Computer Science

Content starts here

Content starts here

Instructor: Prof. Gene Cheung

Timeslot: MWF 10:30-11:30

Location: MC 112

Office Hours: MW 14:00-15:00 (LAS 2012)


  • 01/04/2019: Check Moodle for uploaded lecture slides.
  • 12/07/2018: Course homepage online.

Course Summary

This course covers the fundamentals of digital signal processing (DSP) from a signal representation perspective. Topics covered include signal space, bases, discrete-time Fourier transform, z-transform, discrete Fourier transform, multirate systems, sampling, interpolation, approximation and compression. The course also include applications of DSP to real-world signals including images. The prerequisite is EECS3451 (Signals and Systems).

Required Textbook

  • M. Vetterli, J. Kovacevic, V. Goyal, Foundations of Signal Processing, Cambridge University Press, 2014. (also available online HERE)

Supplementary Material

  • A. Oppenheim, R. Shafer, Discrete-time Signal Processing (3rd edition), Pearson, 2009.
  • S. Boyd, L. Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares,  Cambridge University Press, 2018.


  • Bi-weekly assignments (30%)
  • Midterm (30%)
  • Final (40%)

Course Outline (subject to change)

  • Week 1:  Course overview, linear algebra review
  • Week 2:  Vector space, Hilbert space.
  • Week 3:  Bases, Riesz bases, orthonormal bases
  • Week 4:  Biorthogonal bases, (frames)
  • Week 5:  Infinite-/finite-length sequences, systems
  • Week 6:  Discrete-time Fourier transform
  • Week 7:  Discrete Fourier transform
  • Week 8:  z-transform
  • Week 9:  Multirate sequences and systems
  • Week 10:  Sampling and interpolation
  • Week 11:  Approximation
  • Week 12:  Application: image processing

last modified January 4, 2019