Seminar on information theory and signal reconstruction

Seminar leaders: Dr. Torsten Enßlin, Niels Oppermann, Marco Selig

Location: Max-Planck-Institute for Astrophysics, Garching

Dates: Tuesday, 13. November & Friday, 23. November, 13:15--16:30; common lunch: meeting 12:15 at MPA reception


Tuesday, 13. November:

1) Javier Cuesta: Quantum Information Theory
2) Yi-Hao Chen: Neural Networks I
3) Thomas Simon: Fundamentals of Game Theory
4) Florian Lehner: Poker from a Game Theoretical Perspective

Friday, 23. November:

1) Philipp Seifert: Kalman Filter
2) Martin Spiessl: Neural Networks II
3) Christian Huber: TBD
4) Felix ???: TBD

The seminar is intended for participants of the lecture on information theory and signal reconstruction, the content of which will be assumed to be known by all participants.
The main seminar goal is to extend the participants' knowledge beyond the material covered in the lecture, especially with respect to concrete measurement situations, imaging, and existing algorithms.

A second goal is to practice presentations and open discussions.

Talks can be given at the blackboard, or by using computer projection and should be in English and of 15 to 30 minutes length.
Topics should be coordinated with the seminar leaders (mainly with Torsten Enßlin, Niels Oppermann, Marco Selig).
A pdf-file with slides or write-up of presented material (readable handwriting is ok) must be provided by the student beforehand.
Computer presentations must be sent in via email on the day before the seminar talk, so that they can be put on a single presentation computer.

Two or three all-afternoon seminar dates will be fixed, probably one within the semester break, and the others during the WS 2012.

A marked certificate for 3 ECTS points will be provided to any student who

Topics can be picked from the list below, or chosen according to the students own interests. The talks should

It is expected that the students prepare the talks on their own, without supervision. If difficulties with a topic arise during the preparation (e.g. suitable literature cannot be found, topic is too difficult to be presented or too trivial to be interesting) the student should change the topic of the talk to circumvent the problem. If she/he decides for a complete change of the topic, the seminar leaders should be informed via email. Literature research is part of the preparation work of the student. Teamwork and splitting of topics into several coordinated talks is welcome. Students are encouraged to have test talks with co-students beforehand.

Marks will be given according to the main criterion how informative the talk was given the lecture background. The quantity of presented material will be less important than the quality of the information transferred.

Suggestions of potential topics (variations of those and completely other topics are also welcome):

  1. Medical imaging devices (e.g. description of measurement/ data model / likelihood)

    1. Computer Tomography (CT)

    2. Positron Emission Tomography (PET)

    3. Nuclear Magnetic Resonance (NMR)

    4. Functional Magnetic Resonance Imaging (fMRI)

    5. Ultrasound imaging

    6. ...

  2. Astronomical imaging devices (e.g. description of measurement/ data model / likelihood)

    1. Optical telescopes

    2. X-ray telescopes

    3. Gamma-ray telescopes (coded mask imaging)

    4. Neutrino telescopes

    5. Ultra-high-energy arrays

    6. Single-dish radio telescopes

    7. Radio interferometer

    8. CMB satellites (WMAP/Planck)

  3. Geological probes (e.g. description of measurement/ data model / likelihood)

    1. seismic waves

    2. gravitational field measurements

    3. Cartography via Kriging and other methods

    4. ...

  4. Signal reconstruction / filter algorithms (e.g. algorithm / applications of it / implicit prior assumptions)

    1. Richardson Lucy-Algorithm

    2. Kalman Filter

    3. Compressed Sensing

    4. CLEAN Algorithm of Högbom (1947)

    5. 'Maximum Entropy Method' of Imaging (Gull & Daniell, Nature 272, 686 (1978))

    6. Image compression (jpeg)

    7. Audio compression (mp3)

    8. Noise suppressing audio filter (dolby, …)

  5. Specific signals (physics / statistical characteristics / prior models / information sources)

    1. Cosmic Microwave Background radiation

    2. Climate data

    3. Stock market prices

    4. 1/f noise (e.g in transistors)

  6. Applications of information theory (basic idea / simple cases)

    1. Communication theory (Shannon, optimal coding, …)

    2. Game theory (e.g. prisoner's dilemma, …) (talk by Thomas Simon)

    3. Weather forecasts (data assimilation)

    4. Parameter estimation (e.g. in cosmology)

    5. Loan granting decisions of banks

    6. Insurance mathematics

    7. Quantum information theory (talk by Javier Cuesta)

    8. Language processing