Digital Filters
| Title | ΨΗΦΙΑΚΑ ΦΙΛΤΡΑ / Digital Filters |
| Code | ΗΛ0401 |
| Faculty | Engineering |
| School | Electrical and Computer Engineering |
| Cycle / Level | 1st / Undergraduate |
| Teaching Period | Spring |
| Coordinator | Panagiotis Petrantonakis |
| Common | No |
| Status | Active |
| Course ID | 20000562 |
| Academic Year | 2023 – 2024 |
| Class Period | Spring |
| Faculty Instructors | |
| Weekly Hours | 4 |
| Class ID | 600242846
|
Course Type 2016-2020
- Scientific Area
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600242846
Language of Instruction
- Greek (Instruction, Examination)
Learning Outcomes
With the successful completion of the course students will be able to design adaptive linear filters to remove undesired parts of a signal like noise distortion, as well as the conditions for correct operation (convergence).
General Competences
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
Course Content (Syllabus)
Adaptive filters converging to optimal Wiener filter coefficients, Steepest descent convergence to
optimal Wiener, Adaptive Least Mean Squares, Block LMS and FFT
acceleration, Linear prediction Levinson-Durbin, Lattice filters,
Linear and Recursive Least Squares, Kalman filters.
Keywords
Adaptive linear filters, Lattice filters, linear prediction, Kalman filters
Educational Material Types
- Slide presentations
- Interactive excersises
- Book
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
Description
Interactive demonstration of algorithms and results via MATLAB.
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 26 | ✓ | |||
| Laboratory Work | 13 | ✓ | |||
| Reading Assigment | 39 | ✓ | |||
| Written assigments | 15 | ✓ | |||
| Total | 93 |
Student Assessment
Description
Optional 5 homework assignments add up to 2.5 bonus points. Students must achieve at least 5 passing grade on the final examination in order to benefit from the homework bonus.
Student Assessment methods
- Written Assignment (Formative)
- Written Exam with Problem Solving (Formative)
- Computer implementation
Bibliography
Course Bibliography (Eudoxus)
Βασικές Τεχνικές Ψηφιακής Επεξεργασίας Σημάτων ISBN:978-960-418-016-5, Γ. ΜΟΥΣΤΑΚΙΔΗ, ΕΚΔΟΣΕΙΣ Α. ΤΖΙΟΛΑ & ΥΙΟΙ Α.Ε.,
ΘΕΣ/ΝΙΚΗ, 18548755
Additional bibliography for study
Adaptive Filter Theory, 4e, ISBN: 978-0130901262, Simon Haykin, Prentice Hall, 2001
Last Update
25-09-2023
