Course overview
The aim of this course is to foster and promote knowledge of more advanced digital signal processing methodologies and skills/competencies in analysis of practical problems. The course teaches the theoretical background of digital signal processing methodologies verified by series of computer projects and applications of real problems.
What you will learn
- Multirate digital signal processing applications in various practical applications and least-square methods for designing a variety of systems.
- Develop solutions for practical problems with a combination of advanced signal processing methodologies.
- Evaluate the performance of system modeling and estimation/prediction methods in modern applications
Meet your instructor
Takis KasparisCourse content
- Session 1: Multirate digital signal processing and applications
- Session 2: Least-square methods for system modeling and filter design
- Session 3: Forward and backward linear prediction
- Session 4: AR, MA and ARMA modeling
- Session 5: Inverse systems and deconvolution
- Session 6: Weiner filters
- Session 7: Power spectrum estimation.
- Session 8: Introduction to adaptive filters
Teaching methodology
Presentations, group discussions, case studies.
Assessment
- Midterm exam (25%) Will include combination of numerical exercises and open-ended theoretical questions.
- Computer Projects (35%)
- Numerical and theoretical exercises (10%).
- Final written exam (30%) Will include combination of numerical exercises and open-ended theoretical questions