14:00-15:00 (LAS 2012)
- 01/04/2019: Check Moodle for uploaded lecture
- 12/07/2018: Course homepage online.
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).
- M. Vetterli, J. Kovacevic, V. Goyal, Foundations of Signal Processing,
Cambridge University Press, 2014. (also available online HERE)
- 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
- Bi-weekly assignments (30%)
- Midterm (30%)
- Final (40%)
Outline (subject to change)
- Week 1: Course overview, linear algebra
- Week 2: Vector space, Hilbert space.
- Week 3: Bases, Riesz bases, orthonormal
- Week 4: Biorthogonal bases, (frames)
- Week 5: Infinite-/finite-length
- 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