Advanced Digital Signal Processing Techniques

Објавено: June 29, 2023
1. Course Title Advanced Digital Signal Processing Techniques
2. Code 4ФЕИТ05019
3. Study program 10-DPSM, 19-MV, 22-BE
4. Organizer of the study program (unit, institute, department) Faculty of Electrical Engineering and Information Technologies
5. Degree (first, second, third cycle) Second cycle
6. Academic year/semester I/1   7.    Number of ECTS credits 6.00
8. Lecturer Dr Dimitar Tashkovski
9. Course Prerequisites
10. Course Goals (acquired competencies):

Successful completion of the course will provide students with: familiarization with all relevant discrete transformations and their features that enable processing in the domain of transformation; ability to recognize situations in which a particular transformation shows better performance than another; introduction to fast algorithms for the implementation of discrete transformations. Also, with a successful completion of the course, the student will demonstrate knowledge and understanding of: the basics of multi-rate processing, the basic idea of ​​the filter banks, the design of filter banks, and the link between the filter banks and the wavelet transformati
11. Course Syllabus:

A brief review of signals. Definition and basic properties of discrete transformations. Orthogonal discrete transformations: DFT, Hartley’s (DHT), Karhunen-Loeve (KLT), Cosine (DCT), Lapped (LOT), Wavelet (DWT), Walsh-Hadamard (WHT). Fast Algorithms: Concept and selected examples. Some typical DSP applications in the transform domain: filtering, spectrum estimation, coding, adaptive filtering, multi-rate processing. Introduction to the basic theory of multi-rate processing: decimation, interpolation and sample-rate conversion. Two-channel filter banks: QMF filter bank, filter bank with perfect reconstruction, paranitary, biorthogonal, and linear phase filter banks. Lattice structure of filter banks with perfect reconstruction. Link of filter banks with a wavelet transform. Lifting implementation of the wavelet transform. Application of wavelet transform for analysis and signal compression.
12. Learning methods:

Lectures, supported by presentations and visualization of concepts, active participation of students, homework and project assignments
13. Total number of course hours 180
14. Distribution of course hours 3 + 3
15. Forms of teaching 15.1 Lectures-theoretical teaching 45 hours
15.2 Exercises (laboratory, practice classes), seminars, teamwork 45 hours
16. Other course activities 16.1 Projects, seminar papers 30 hours
16.2 Individual tasks 30 hours
16.3 Homework and self-learning 30 hours
17. Grading
17.1 Exams 0 points
17.2 Seminar work/project (presentation: written and oral) 50 points
17.3. Activity and participation 0 points
17.4. Final exam 50 points
18. Grading criteria (points) up to 50 points 5 (five) (F)
from 51 to 60 points 6 (six) (E)
from 61 to 70 points 7 (seven) (D)
from 71 to 80 points 8 (eight) (C)
from 81 to 90 points 9 (nine) (B)
from 91 to 100 points 10 (ten) (A)
19. Conditions for acquiring teacher’s signature and for taking final exam Regular attendance at classes
20. Forms of assessment Project assignment and final exam.
21. Language Macedonian and English
22. Method of monitoring of teaching quality Internal evaluation and surveys.
23. Literature
23.1.       Required Literature
No. Author Title Publisher Year
1. D. F. Elliot and K. Ramamohan Rao, Fast Transforms: Algorithms, Analyses, Applications, Academic Press. Orlando FL,
2. P.P. Vaidyanathan, Multirate Systems and Filters Banks Prentice Hall, 19883
23.2.       Additional Literature
No. Author Title Publisher Year
1.  G. Strang and T. Nguyen,  Wavelets and filter banks,  Wellesley-Cambridge Press