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.

LectureDate TimeRoomTopic (reading assignment)Essay 1 Mon, AugSep 27 13:15-15:00 Q36 introduction (chap 1) none 2 Thue, AugSep 30 13:15-15:00 VQ34 stochastic processes (chap 2-3) QU1.pdf 3 Mon, Sep 3 089 15:15-107:00 Q346 ergodicity (chap 4) QU2.pdf 4 Wed, Sep 5 1511 8:15-170:00 V3L52 power spectrum (chap 5) QU3.pdf 5 WedMon, Sep 12 086 13:15-105:00 Q33L52 filtering (chap 6-8) QU4.pdf 6 ThuWed, Sep 13 108 08:15-120:00 Q36L52 AR, ARMA-processes (chap 8) QU5.pdf 7 WedMon, Sep 1923 153:15-175:00 Q36L52 estimation (chap 9) QU6.pdf 8 FriTue, Sep 214 105:15-127:00 M33Q36 optimal filtering (chap 10) QU7.pdf 9 WedMon, Sep 26 0830 16:15-108:00 Q31L52 sampling (chap 11) QU8.pdf 10 Fri, Sep 10 Wed, Oct 28 10:0015-12:00 Q34L52 reconstruction (chap 12) QU9.pdf 11 WedMon, Oct 37 105:15-127:00 Q34E2 sampling and reconstruction none 12 ThuWed, Oct 4 139 08:15-150:00 Q34L52 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