Lectures - Reading assignment

Every week you are supposed to read the related chapters in the course notes and answer the reflective questions in a brief essay (less than one page). Your essays are collected before(!) the corresponding 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 (5 essays = 5 points). An essay with partially correct answers will give you 1/2 point. Another 5 bonus points can be obtained in the tutorial sessions. 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, Sep 21 130:15-152:00 Q36V34 introduction (chap 1) - 2 Tue, Sep 32 13:15-15:00 QV34 random variable(chap 2-3) RQ1.pdf 3 MonThu, Sep 94 15:15-17:00 Q36L52 stochastic processes - 4 Wed, Sep 11 80 13:15-105:00 L52V34 ergodicity (chap 4-5) RQ2.pdf 5 Mon, Sep 165 130:15-152:00 L52M33 power spectrum - 6 Wed, Sep 18 087 10:15-102:00 L52V34 filtering (chap 6-8) RQ3.pdf 7 Mon, Sep 232 130:15-152:00 L52M33 AR, ARMA-processes - 8 TueWed, Sep 24 150:15-172:00 Q36L52 estimation (chap 9-10) RQ4.pdf 9 Mon, Sep 3029 160:15-182:00 L52M33 optimal filtering - 10 Wed, Oct 2 103:15-125:00 L52Q34 sampling (chap 11-12) RQ5.pdf 11 MonTue, Oct 7 15:15-17:00 E2L51 reconstruction - 12 WedThu, Oct 9 0815:15-107:00 L52 repetition - 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. D. Yates and D. J. Goodman, "Probability and Stochastic Processess," Wiley - also a "friendly introduction" in the topic explicitly for electrical and computer engineers, contains also few chapters on basic stochastic signal processing as well as a few Matlab 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