052600 VU Signal and Image Processing (2023W)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from We 13.09.2023 09:00 to We 20.09.2023 09:00
- Deregistration possible until Sa 14.10.2023 23:59
Details
max. 50 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 03.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 04.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 10.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 11.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 17.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 18.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 24.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 25.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 31.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 07.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 08.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 14.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 15.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 21.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 22.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 28.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 29.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 05.12. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 06.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 12.12. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 13.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 09.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 10.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 16.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 17.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 23.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 24.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
- Tuesday 30.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
- Wednesday 31.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Information
Aims, contents and method of the course
Algorithms for data analysis are often based on the assumption of independent and identically distributed (i.i.d) data. The world, however, often violates the first "i", i.e., it generates data with a rich spatial and temporal structure such as time-series and images. Representing, understanding, and processing this structure is the domain of signal processing. As such, a firm grasp of signal processing is essential to understand structure in data and design systems that exploit this structure.In the first part of this course, we will approach signal processing from the perspective of linear time-invariant (LTI) systems, i.e., we will consider signals as outputs of LTI-systems [1]. This approach will lead us to study the discrete(-time) Fourier transform (D(T)FT) and its applications, including sampling and filter design. In the second part of the course, we will study several variants and extensions of the Fourier transform, including the Hilbert-, Discrete Cosine- and Wavelet transforms. In the third part of the course, we will take an alternative approach to signal processing and consider signals as realizations of stationary stochastic processes [2]. This will lead us to the field of stochastic spectral analysis. We will conclude the course with an introduction to information theory and compression algorithms, e.g., the Lempel-Ziv-Welch (LZW) algorithm that is used in data formats such as ZIP and TIFF.The lectures are complemented by tutorials, pen & paper exercises and coding assignments on simulated and experimental data to foster a deeper understanding of the topics covered in the lectures.
Assessment and permitted materials
There will be three assignments (one preliminary math test, one pen & paper assignment, and one Pythong coding exercise), one mid-term exam, and one final exam. The various assignments and exams count towards the final grade as follows:* Assignments: 51%
* Two feedback sheets: 4%
* Midterm: 20%
* Final: 25%In addition, you can earn up to 10% of bonus points by answering questions on Moodle about the pre-recorded videos prior to each review session.
* Two feedback sheets: 4%
* Midterm: 20%
* Final: 25%In addition, you can earn up to 10% of bonus points by answering questions on Moodle about the pre-recorded videos prior to each review session.
Minimum requirements and assessment criteria
Prerequisites: StEOP, PR2, MG2, THI, MOD, ADS
Recommended prerequisites: NUMGrading will be done according to the following scheme:1. At least 87.5%
2. At least 75.0%
3. At least 62.5%
4. At least 50.0%In addition, you need at least 10% of the points *on each assignment and on each exam* to pass the course.
Recommended prerequisites: NUMGrading will be done according to the following scheme:1. At least 87.5%
2. At least 75.0%
3. At least 62.5%
4. At least 50.0%In addition, you need at least 10% of the points *on each assignment and on each exam* to pass the course.
Examination topics
The major goals of this course include:
* Understanding the theory of signals and linear time-invariant systems.
* Becoming familiar with spectral transformations and data compression algorithms.
* Being able to implement common transformations in Python and applying them to time-series and images.
* Understanding the theory of signals and linear time-invariant systems.
* Becoming familiar with spectral transformations and data compression algorithms.
* Being able to implement common transformations in Python and applying them to time-series and images.
Reading list
1. Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing, 3rd Edition, Pearson, 2010
2. Donald B. Percival, Andrew T. Walden, Spectral Analysis for Physical Applications, Cambridge University Press, 1993
3. Rafael C. Gonzales, Richard E. Woods Digital Image Processing 4th edition, Addison-Wesley, 2018.
4. Boaz Porat, Digital Processing of Random Signals, Dover Publications, 2008.
2. Donald B. Percival, Andrew T. Walden, Spectral Analysis for Physical Applications, Cambridge University Press, 1993
3. Rafael C. Gonzales, Richard E. Woods Digital Image Processing 4th edition, Addison-Wesley, 2018.
4. Boaz Porat, Digital Processing of Random Signals, Dover Publications, 2008.
Association in the course directory
Last modified: Tu 12.09.2023 17:27