ADVANCED SIGNAL PROCESSING
| Title | ΠΡΟΗΓΜΕΝΗ ΕΠΕΞΕΡΓΑΣΙΑ ΣΗΜΑΤΟΣ / ADVANCED SIGNAL PROCESSING |
| Code | DM15 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Nikolaos Laskaris |
| Common | No |
| Status | Active |
| Course ID | 40002422 |
| Academic Year | 2017 – 2018 |
| Class Period | Spring |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Class ID | 600111838
|
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/1/600111838
- eLearning (Moodle): http://pileas.csd.auth.gr
Language of Instruction
- Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Digital Signal Processing. Pattern recognition. Computational Intelligence. MATLAB
Learning Outcomes
Gaining a deeper understanding of signal processing techniques. Review of current trends and modern application areas.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Generate new research ideas
- Be critical and self-critical
Course Content (Syllabus)
Wavelets and Multiscale Signal Processing. Adaptive filters. Independent component analysis. Nonlinear time series analysis. Manifold Learning Theory. Sparse coding. Applications in Biosignal analysis.
Keywords
signal analysis, advanced techniques
Educational Material Types
- Notes
- Slide presentations
- Book
- matlab software
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 30 | 1 | ✓ | ||
| Laboratory Work | 45 | 1.5 | ✓ | ||
| Reading Assigment | 60 | 2 | ✓ | ||
| Project | 90 | 3 | ✓ | ||
| Total | 225 | 7.5 |
Student Assessment
Description
In the final mark contibute : 1) the deliverable of a student-specific project (50%)
2) the performance in the analysis and presentation of a journal-article from the recent bibliography (30%)
3) written exams (20%)
Student Assessment methods
- Written Exam with Short Answer Questions (Summative)
- Performance / Staging (Summative)
- Report (Summative)
Bibliography
Additional bibliography for study
S. Mallat, ''A Wavelet tour of signal processing''.
A. Hyvarinen, J. Karhunen, E.Oja., ''Independent Component Analysis''.
H. Abarbanel, ''Analysis of observed Chaotic Data''.
M. Kirby, ''Geometric Data analysis''.
and A collection of tutorial articles.
Last Update
03-10-2018
