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Lectures - Reading assignment
Before almost each lecture you are supposed to read the related chapter in the course notes and answer the reflective questions in a brief essay (less than one page). Your essays are collected before(!) each lecture. The essays are not mandatory, but if you successfully answer all questions, you obtain 1 bonus point for part A of the final exam (9 essay = 9 points). An essay with partially correct answers will give you 1/2 point. The bonus points are valid for the next exam and first re-exam. For the answers you should not copy text from a textbook. Group work is also not allowed, but feel free to discuss with your fellows. The reports will be checked against plagiarism. The intention of this task is to give you an incentive to study the material in parallel to the course.
| Lecture | Date | Time | Room | Topic (reading assignment) | Essay |
|---|---|---|---|---|---|
| 1 | Mon, Aug 27 | 13:15-15:00 | Q36 | introduction (chap 1) | none |
| 2 | Thu, Aug 30 | 13:15-15:00 | V34 | stochastic processes (chap 2-3) | QU1.pdf |
| 3 | Mon, Sep 3 | 08:15-10:00 | Q34 | ergodicity (chap 4) | QU2.pdf |
| 4 | Wed, Sep 5 | 15:15-17:00 | V32 | power spectrum (chap 5) | QU3.pdf |
| 5 | Wed, Sep 12 | 08:15-10:00 | Q33 | filtering (chap 6-8) | QU4.pdf |
| 6 | Thu, Sep 13 | 10:15-12:00 | Q36 | AR, ARMA-processes (chap 8) | QU5.pdf |
| 7 | Wed, Sep 19 | 15:15-17:00 | Q36 | estimation (chap 9) | QU6.pdf |
| 8 | Fri, Sep 21 | 10:15-12:00 | M33 | optimal filtering (chap 10) | QU7.pdf |
| 9 | Wed, Sep 26 | 08:15-10:00 | Q31 | sampling (chap 11) | QU8.pdf |
| 10 | Fri, Sep 28 | 10:00-12:00 | Q34 | reconstruction (chap 12) | QU9.pdf |
| 11 | Wed, Oct 3 | 10:15-12:00 | Q34 | sampling and reconstruction | none |
| 12 | Thu, Oct 4 | 13:15-15:00 | Q34 | repetition | none |
Some help to find your classrooom: KTH classroom search engine
Additional reading
The course notes are an excellent collection of the topics considered in the course. However, you may look for additional literature to complement or deepen your studies. Since there is unfortunately no book which is good for all topics, here list of selected textbooks:
- D. G. Manolakis and V. K. Ingle, "Applied Digital Signal Processing," Cambridge University Press - good complement to the course notes with Matlab examples and exercises, covers also more basic stuff
- M. H. Hayes, "Statistical Digital Signal Processing and Modeling," Wiley
- also good complement to the course notes with Matlab examples and exercises, covers also more advanced signal processing material - H. Stark and J. W. Woods, "Probability, Statistics, and Random Processes for Engineers," Pearson - easy introduction in probability theory for engineers explaining the basic concepts including examples
- R. M. Gray and L. D. Davisson, "An Introduction to Statistical Signal Processing," Cambridge University Press - little bit more advanced introduction in probability theory for engineers, includes a chapter on second order theory