Nyhetsflöde

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, Aug 31 8:30-9:00 E35 introduction (chap 1) - 2 Wed, Sep 2 13:15-15:00 L51 random variable(chap 2-3) RQ1 3 Mon, Sep 7 8:20-9:00 E35 stochastic processes - 4 Wed, Sep 9 13:15-15:00 M31 ergodicity (chap 4-5) RQ2 5 Mon, Sep 14 8:20-9:00 E51 power spectrum - 6 Wed, Sep 16 13:15-15:00 M33 filtering (chap 6-8) RQ3 7 Mon, Sep 21 8:20-9:00 E52 AR, ARMA-processes - 8 Wed, Sep 23 13:15-15:00 L52 estimation (chap 9-10) RQ4 9 Mon, Sep 28 8:20-9:00 M33 optimal filtering - 10 Wed, Sep 30 13:15-15:00 M35 sampling (chap 11-12) RQ5 11 Mon, Oct 5 8:20-9:00 E31 reconstruction - 12 Wed, Oct 7 13:15-15:00 M33 repetition - Some help to find your classrooom: KTH classroom search engine

Diagnostic test The course Signal Theory is taken by many students with different prior knowledge. To be able to adapt the teaching in the lectures and tutorials, we will ask every student to participate in a diagnostic test in the first lecture week. We will reward your participation with one bonus point for part A of the exam. Otherwise, the results are not used for any kind of grading and we are not able to see individual answers. To participate, you have to provide us your e-mail address in the first two lectures or send it directly to Marie Maros. 

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

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, Aug 31 8:30-9:00 E35 introduction (chap 1) - 2 Wed, Sep 2 13:15-15:00 L51 random variable(chap 2-3) RQ1 3 Mon, Sep 7 8:20-9:00 E35 stochastic processes - 4 Wed, Sep 9 13:15-15:00 M31 ergodicity (chap 4-5) RQ2 5 Mon, Sep 14 8:20-9:00 E51 power spectrum - 6 Wed, Sep 16 13:15-15:00 M33 filtering (chap 6-8) RQ3 7 Mon, Sep 21 8:20-9:00 E52 AR, ARMA-processes - 8 Wed, Sep 23 13:15-15:00 L52 estimation (chap 9-10) RQ4 9 Mon, Sep 28 8:20-9:00 M33 optimal filtering - 10 Wed, Sep 30 13:15-15:00 M35 sampling (chap 11-12) RQ5 11 Mon, Oct 5 8:20-9:00 E31 reconstruction - 12 Wed, Oct 7 13:15-15:00 M33 repetition - Some help to find your classrooom: KTH classroom search engine

Diagnostic test The course Signal Theory is taken by many students with different prior knowledge. To be able to adapt the teaching in the lectures and tutorials, we will ask every student to participate in a diagnostic test in the first lecture week. We will reward your participation with one bonus point for part A of the exam. Otherwise, the results are not used for any kind of grading and we are not able to see individual answers. To participate, you have to provide us your e-mail address in the first two lectures or send it directly to Marie Maros. 

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

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, Aug 31 8:30-9:00 E35 introduction (chap 1) - 2 Wed, Sep 2 13:15-15:00 L51 random variable(chap 2-3) RQ1 3 Mon, Sep 7 8:20-9:00 E35 stochastic processes - 4 Wed, Sep 9 13:15-15:00 M31 ergodicity (chap 4-5) RQ2 5 Mon, Sep 14 8:20-9:00 E51 power spectrum - 6 Wed, Sep 16 13:15-15:00 M33 filtering (chap 6-8) RQ3 7 Mon, Sep 21 8:20-9:00 E52 AR, ARMA-processes - 8 Wed, Sep 23 13:15-15:00 L52 estimation (chap 9-10) RQ4 9 Mon, Sep 28 8:20-9:00 M33 optimal filtering - 10 Wed, Sep 30 13:15-15:00 M35 sampling (chap 11-12) RQ5 11 Mon, Oct 5 8:20-9:00 E31 reconstruction - 12 Wed, Oct 7 13:15-15:00 M33 repetition - Some help to find your classrooom: KTH classroom search engine

Diagnostic test The course Signal Theory is taken by many students with different prior knowledge. To be able to adapt the teaching in the lectures and tutorials, we will ask every student to participate in a diagnostic test in the first lecture week. We will reward your participation with one bonus point for part A of the exam. Otherwise, the results are not used for any kind of grading and we are not able to see individual answers. To participate, you have to provide us your e-mail address in the first two lectures or send it directly to Marie Maros. 

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

There are two parallel tutorials where you will work in groups. Before each tutorial you will be randomly assigned to a group of 4 student. The intention of the random grouping is to introduce you to as many of your study fellows as possible so that you might find those with whom you can study best. In each tutorial you will first have a brief recapitulation and discussion of the course content followed by problem solving first in groups and then altogether with the TA.

In five tutorials you will get the opportunity to obtain 1 bonus point for part A of the final exam (5 problems = 5 points). After your group worked on the assigned problem the TA asks you to hand in the solution you found in the group. If your group solution shows that a reasonable approach (not necessarily correct) then every group member gets 1 bonus point. If the solution shows that you jointly worked on the problem, then you get 1/2 bonus point. The date and problem which will be worth a bonus point will not be revealed before. Another 5+1 bonus points can be obtained in answering reading assignments and participate in the diagnostic test. The intention of this is to motivate your participation in the tutorial.

TutorialDate TimeRoomsExercises on tutorials 1 Fri, Sep 4 13:15-15:00 Q24, V12 1.1, 1.14, 2.2, 2.6, 2.11

Tutorial1solultionQ24

2 Wed, Sep 9 08:15-10:00 B22, B23 2.4, 2.8, 2.10

3 Fri, Sep 11 13:15-15:00 K53, M24 3.1, 3.2, Old Exam

4 Wed, Sep 16 08:15-10:00 B22, B23 4.1, 4.8, 4.9b, 4.10b, 4.11a

5 Fri, Sep 18 13:15-15:00 M23, M37 5.1, 5.16, 6.2, 6.11

6 Wed, Sep 23 08:15-10:00 B22, B23 7.1, 7.5, 7.7

7 Fri, Sep 25 13:15-15:00 M24, M23 8.1, 8.2, 9.5

8 Wed, Sep 30 08:15-10:00 B22, B23 9.1, 9.7, 9.18 9 Fri, Oct 2 13:15-15:00 M23, M37 10.1, 10.3, 11.1, 11.4 10 Wed, Oct 7 08:15-10:00 L43, L44 12.1, 12.4, 12.17 11 Fri, Oct 9 13:15-15:00 B22, B23 recap chapter 10-12  12 Wed, Oct 14 13:15-15:00 L41, L42 repetition (old exams)  In the tutorial we are going to solve as many problems as the time allows. We recommend you that you prepare yourself for the tutorial, e.g., look at the problem and ask concrete questions during the tutorial.

Recommended homework exercises To deepen your problem solving skill, we also recommend you to solve individually or in groups other relevant exercises:

ChapterRecommended homework exercisesExercises on tutorials 1 1.3, 1.4, 1.5, 1.8, 1.14, 1.15 1.1, 1.14 2 2.4, 2.7, 2.9, 2.12 2.2, 2.6, 2.8, 2.11, 2.10, 2.13 3 No additional exercises 3.1, 3.2 4 4.2, 4.3, 4.5, 4.12 4.1, 4.8, 4.9b, 4.10b, 4.11a 5 5.2, 5.4, 5.7, 5.8, 5.14 5.1, 5.16 6 6.3, 6.5, 6.6, 6.10, 6.13, 6.15 6.2, 6.11 7 7.2, 7.3, 7.4 7.1, 7.5, 7.7 8 No additional exercises 8.1, 8.2 9 9.2, 9.3, 9.10, 9.11, 9.15 9.1, 9.5, 9.7, 9.18 10 10.2, 10.4, 10.7 10.1, 10.3 11 11.5, 11.6, 11.7 11.1, 11.4 12 12.10, 12.12, 12.17, 12.18 12.1, 12.4, 12.17

Course wiki To enhance the engagement in the subject, every student is invited to work with the course wiki. Your engagement will be rewarded with 1 extra bonus point. Therefore, you either

* add a course wiki entry with at least 50   words   on a related course related topic (alternative   explanation, extension, example,…) or you
* do a major revision of a course wiki entry.
To obtain the bonus point send an e-mail to the teacher and TA pointing on your contribution. For a major revision, you need to explain your revision (it has to be a "major" revision). Every student can get at most 1 bonus point during the course but you are welcome to work more with the course wiki. You can gain a bonus point until the second last lecture.

It is recommended to limit the visibility to students, teachers and assistants of the course.

Note: Do not plagiate! Say it in own words and provide sufficient references to the sources!

There are two parallel tutorials where you will work in groups. Before each tutorial you will be randomly assigned to a group of 4 student. The intention of the random grouping is to introduce you to as many of your study fellows as possible so that you might find those with whom you can study best. In each tutorial you will first have a brief recapitulation and discussion of the course content followed by problem solving first in groups and then altogether with the TA.

In five tutorials you will get the opportunity to obtain 1 bonus point for part A of the final exam (5 problems = 5 points). After your group worked on the assigned problem the TA asks you to hand in the solution you found in the group. If your group solution shows that a reasonable approach (not necessarily correct) then every group member gets 1 bonus point. If the solution shows that you jointly worked on the problem, then you get 1/2 bonus point. The date and problem which will be worth a bonus point will not be revealed before. Another 5+1 bonus points can be obtained in answering reading assignments and participate in the diagnostic test. The intention of this is to motivate your participation in the tutorial.

TutorialDate TimeRoomsExercises on tutorials 1 Fri, Sep 4 13:15-15:00 Q24, V12 1.1, 1.14, 2.2, 2.6, 2.11

Tutorial1solultionQ24

2 Wed, Sep 9 08:15-10:00 B22, B23 2.4, 2.8, 2.10

Tutorial2solution

3 Fri, Sep 11 13:15-15:00 K53, M24 3.1, 3.2, Old Exam

4 Wed, Sep 16 08:15-10:00 B22, B23 4.1, 4.8, 4.9b, 4.10b, 4.11a

5 Fri, Sep 18 13:15-15:00 M23, M37 5.1, 5.16, 6.2, 6.11

6 Wed, Sep 23 08:15-10:00 B22, B23 7.1, 7.5, 7.7

7 Fri, Sep 25 13:15-15:00 M24, M23 8.1, 8.2, 9.5

8 Wed, Sep 30 08:15-10:00 B22, B23 9.1, 9.7, 9.18 9 Fri, Oct 2 13:15-15:00 M23, M37 10.1, 10.3, 11.1, 11.4 10 Wed, Oct 7 08:15-10:00 L43, L44 12.1, 12.4, 12.17 11 Fri, Oct 9 13:15-15:00 B22, B23 recap chapter 10-12  12 Wed, Oct 14 13:15-15:00 L41, L42 repetition (old exams)  In the tutorial we are going to solve as many problems as the time allows. We recommend you that you prepare yourself for the tutorial, e.g., look at the problem and ask concrete questions during the tutorial.

Recommended homework exercises To deepen your problem solving skill, we also recommend you to solve individually or in groups other relevant exercises:

ChapterRecommended homework exercisesExercises on tutorials 1 1.3, 1.4, 1.5, 1.8, 1.14, 1.15 1.1, 1.14 2 2.4, 2.7, 2.9, 2.12 2.2, 2.6, 2.8, 2.11, 2.10, 2.13 3 No additional exercises 3.1, 3.2 4 4.2, 4.3, 4.5, 4.12 4.1, 4.8, 4.9b, 4.10b, 4.11a 5 5.2, 5.4, 5.7, 5.8, 5.14 5.1, 5.16 6 6.3, 6.5, 6.6, 6.10, 6.13, 6.15 6.2, 6.11 7 7.2, 7.3, 7.4 7.1, 7.5, 7.7 8 No additional exercises 8.1, 8.2 9 9.2, 9.3, 9.10, 9.11, 9.15 9.1, 9.5, 9.7, 9.18 10 10.2, 10.4, 10.7 10.1, 10.3 11 11.5, 11.6, 11.7 11.1, 11.4 12 12.10, 12.12, 12.17, 12.18 12.1, 12.4, 12.17

Course wiki To enhance the engagement in the subject, every student is invited to work with the course wiki. Your engagement will be rewarded with 1 extra bonus point. Therefore, you either

* add a course wiki entry with at least 50   words   on a related course related topic (alternative   explanation, extension, example,…) or you
* do a major revision of a course wiki entry.
To obtain the bonus point send an e-mail to the teacher and TA pointing on your contribution. For a major revision, you need to explain your revision (it has to be a "major" revision). Every student can get at most 1 bonus point during the course but you are welcome to work more with the course wiki. You can gain a bonus point until the second last lecture.

It is recommended to limit the visibility to students, teachers and assistants of the course.

Note: Do not plagiate! Say it in own words and provide sufficient references to the sources!