Universität Wien FIND

Bedingt durch die COVID-19-Pandemie können kurzfristige Änderungen bei Lehrveranstaltungen und Prüfungen (z.B. Absage von Vor-Ort-Lehre und Umstellung auf Online-Prüfungen) erforderlich sein. Melden Sie sich für Lehrveranstaltungen/Prüfungen über u:space an, informieren Sie sich über den aktuellen Stand auf u:find und auf der Lernplattform moodle.

Regelungen zum Lehrbetrieb vor Ort inkl. Eintrittstests finden Sie unter https://studieren.univie.ac.at/info.

Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.

052600 VU Signal and Image Processing (2021W)

Prüfungsimmanente Lehrveranstaltung
GEMISCHT

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 50 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

We will adopt a mixed / hybrid format that complements pre-recorded video lectures with in-person review sessions in the lecture hall. New video lectures and tutorials will be made available on Moodle on an ongoing basis, typically on Fridays. These videos form the basis for the review sessions, which will be held in-person in the lecture hall and simultaneously streamed on Moodle via Collaborate sessions. In the review sessions, we will review the most important concepts introduced in the videos and answer any questions you may have. Attendance of the review sessions is not mandatory but *strongly recommended* to understand the material at a depth that is required for passing the course.

Attendance of each review session in person in the lecture hall is limited to 30 students on a first-come-first-serve basis. The 3G rule applies, i.e., you must have a valid negative COVID test, a valid vaccination, or a certificate of recovery to be admitted into the lecture hall.

Mittwoch 06.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 12.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 13.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 19.10. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 20.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 27.10. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 03.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 09.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 10.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 16.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 17.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 23.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 24.11. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 30.11. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 01.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 07.12. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 14.12. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 15.12. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 11.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 12.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 18.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 19.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG
Dienstag 25.01. 11:30 - 13:00 Hörsaal 2, Währinger Straße 29 2.OG
Mittwoch 26.01. 15:00 - 16:30 Hörsaal 2, Währinger Straße 29 2.OG

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 four assignments (two pen & paper assignments and two Pythong coding exercises), 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.

Mindestanforderungen und Beurteilungsmaßstab

Prerequisites: StEOP, PR2, MG2, THI, MOD, ADS
Recommended prerequisites: NUM

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 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.

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.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 24.09.2021 15:48