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052600 VU Signal and Image Processing (2024W)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Fr 13.09.2024 09:00 bis Fr 20.09.2024 09:00
- Abmeldung bis Mo 14.10.2024 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
In the winter term, the course is primarily held online. Pre-recorded lectures are published on a weekly basis, typically on Fridays. There are no weekly in-person review sessions. Rather, individual review sessions are scheduled over the course of the semester to prepare and discuss the assignments and exams. Besides these review sessions, interactions between students and tutors are primarily via the discussion forum on Moodle.
- Dienstag 01.10. 11:30 - 13:00 Digital
- Mittwoch 02.10. 15:00 - 16:30 Digital
- Dienstag 08.10. 11:30 - 13:00 Digital
- Mittwoch 09.10. 15:00 - 16:30 Digital
- Dienstag 15.10. 11:30 - 13:00 Digital
- Mittwoch 16.10. 15:00 - 16:30 Digital
- Dienstag 22.10. 11:30 - 13:00 Digital
- Mittwoch 23.10. 15:00 - 16:30 Digital
- Dienstag 29.10. 11:30 - 13:00 Digital
- Mittwoch 30.10. 15:00 - 16:30 Digital
- Dienstag 05.11. 11:30 - 13:00 Digital
- Mittwoch 06.11. 15:00 - 16:30 Digital
- Dienstag 12.11. 11:30 - 13:00 Digital
- Mittwoch 13.11. 15:00 - 16:30 Digital
- Dienstag 19.11. 11:30 - 13:00 Digital
- Mittwoch 20.11. 15:00 - 16:30 Digital
- Dienstag 26.11. 11:30 - 13:00 Digital
- Mittwoch 27.11. 15:00 - 16:30 Digital
- Dienstag 03.12. 11:30 - 13:00 Digital
- Mittwoch 04.12. 15:00 - 16:30 Digital
- Dienstag 10.12. 11:30 - 13:00 Digital
- Mittwoch 11.12. 15:00 - 16:30 Digital
- Dienstag 17.12. 11:30 - 13:00 Digital
- Dienstag 07.01. 11:30 - 13:00 Digital
- Mittwoch 08.01. 15:00 - 16:30 Digital
- Dienstag 14.01. 11:30 - 13:00 Digital
- Mittwoch 15.01. 15:00 - 16:30 Digital
- N Dienstag 21.01. 11:30 - 13:00 Digital
- Mittwoch 22.01. 15:00 - 16:30 Digital
- Dienstag 28.01. 11:30 - 13:00 Digital
- Mittwoch 29.01. 15:00 - 16:30 Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
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.
Art der Leistungskontrolle und erlaubte Hilfsmittel
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.We take cheating very seriously! We will make use of plagiarism and code checking tools. Some examples of plagiarism that will lead to an 'X' include:* Giving code (or text/math) to another student
* Give screenshot of code (or text/math) to another student
* Copy code (or text/math) from somebody else
* Copy code (or text/math) from the internet without our explicit permission
* Create code (or text/math) for others
* Let other people (or AIs) create code (or text/math) for yourselfWe do encourage you to discuss the course content with your peers, but anything you submit must be your own work! In case of doubt, ask us and/or cite your sources!
* 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.We take cheating very seriously! We will make use of plagiarism and code checking tools. Some examples of plagiarism that will lead to an 'X' include:* Giving code (or text/math) to another student
* Give screenshot of code (or text/math) to another student
* Copy code (or text/math) from somebody else
* Copy code (or text/math) from the internet without our explicit permission
* Create code (or text/math) for others
* Let other people (or AIs) create code (or text/math) for yourselfWe do encourage you to discuss the course content with your peers, but anything you submit must be your own work! In case of doubt, ask us and/or cite your sources!
Mindestanforderungen und Beurteilungsmaßstab
Prerequisites: StEOP, PR2, MG2, THI, MOD, ADS
Recommended prerequisites: NUMThere will be three assignments (one test on mathematical prerequisistes, one pen & paper assignments and one Python coding assignment), one mid-term exam, and one final exam. The various assignments and exams count towards the final grade as follows:* Assignments: 51% (1% for math test, 25% each for pen & paper and Python exercise)
* Two feedback sheets: 4%
* Midterm: 15%
* Final: 30%Grading 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 can earn up to 10% of bonus points by answering questions on Moodle about the pre-recorded videos prior to each review session. These bonus points count towards the overall points independently of the points you achieve on the assignments and the exams, i.e., they can help you pass the course.*You need at least 10% of the points on each assignment and on each exam to pass the course.*
Recommended prerequisites: NUMThere will be three assignments (one test on mathematical prerequisistes, one pen & paper assignments and one Python coding assignment), one mid-term exam, and one final exam. The various assignments and exams count towards the final grade as follows:* Assignments: 51% (1% for math test, 25% each for pen & paper and Python exercise)
* Two feedback sheets: 4%
* Midterm: 15%
* Final: 30%Grading 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 can earn up to 10% of bonus points by answering questions on Moodle about the pre-recorded videos prior to each review session. These bonus points count towards the overall points independently of the points you achieve on the assignments and the exams, i.e., they can help you pass the course.*You need at least 10% of the points on each assignment and on each exam to pass the course.*
Prüfungsstoff
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.
Literatur
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.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 30.09.2024 07:05