Signal Processing for Communications: a Survival Guide

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1 Teaching Team: Signal Processing for Communications: a Survival Guide Spring Semester Office Hours Office Paolo Prandoni paolo.prandoni@epfl.ch Tue 10:00-12:00 BC364 Assistants: Nick Arvanitopoulos nick.arvanitopoulos@epfl.ch Wed 11:00-12:00 INR031 Pavlos Nikolopoulos pavlos.nikolopoulos@epfl.ch Thu 11:00-12:00 INR015 Niranjan Thanikachalam niranjan.thanikachalam@epfl.ch Fri 11:00-12:00 BC367 Runwei Zhang runwei.zhang@epfl.ch TBA BC366 Marta Martinez-Camara marta.martinez-camara@epfl.ch TBA BC320 * take advantage of office hours! Schedule: Day Time Room Content Monday 14:00-16:00 INM200 Lecture 10:00-12:00 ELG116 (group A) Exercises INF211 (group B) INF213 (group C) INR113 (group D) Tuesday 08:00-10:00 ELA2 Lecture Course Material: Textbook: Signal processing for Communications, EPFL Press, 2008, by P. Prandoni and M. Vetterli. The textbook is available for sale at normal outlets but you can also get a pdf copy online for free at Homework sets, available on the class website Occasional handouts, distributed in class and available online new this semester: video lectures and exercises available via the Coursera platform (see below) Recommended additional textbooks: Discrete-Time Signal Processing, by A. V. Oppenheim and R. W. Schafer (Prentice-Hall, 1989)

2 Online Class This semester Martin Vetterli and I are offering a Signal Processing class online on the Coursera platform. You should enroll as soon as possible (start date for the online course is the same as the EPFL class) using the link A few important points: the online course is 8 weeks long and therefore it covers only a subset of the topics we will study in class here you will not be graded based on the online homework (but the online homework is a great way to practice) the online course provides a lot of interesting additional material that I will assume you will look at the topics for the final exam here are the ones listed in this syllabus When the material covered by the online lectures is exactly the same as the one I would cover in class, we will often skip the lecture and use the classroom time primarily to answer questions and analyze some points in more detail. It is important in this case that you watch the online videos before you come to class; in fact, your advantage with respect to the students who take the course online only is that you can ask question and get a personal answer; make use of that! Also, online courses are probably the way of the future, but we re still learning how to do them so all the feedback you want to share with us is more than welcome. Lectures: Every week, there are four hours of lectures and two hours of exercise sessions, according to the schedule above; some lectures may be replaced by online videos; see the syllabus below for details. Classes will be in English. If you have any problems with the language, feel free to ask me to repeat the part that you did not understand (and, in the limit, to try and translate). You are strongly encouraged to ask questions of any kind during the lectures; lectures should be an exchange of information between the teacher and the students and it s up to you to make sure this exchange goes both ways! If you really are too shy to ask questions in class, you can always come see me or the assistants during office hours; lastly, if you really don t want to have any human contact at all, you can always write me an . Exercise Sessions: During each weekly two-hour exercise session, selected homework problems will be analyzed, explained and solved in full. It is in your interest to try and solve the assigned problems beforehand in order to gain the most from the sessions. In order to maximize the overall benefit and in order to foster interaction with the assistants, the class will be split in four groups. You will find your group assignment on the course website at the end of the first week of class. The room assigned to each group is listed above. No group changes are possible. Homework: Homework sets will be made available on a weekly basis during the semester. Homework is not collected and it does not contribute to your final grade. However, it is essential that you do your homework and that you do it by yourself. Only by thinking hard and by solving the homework problems you can gain a true understanding of the course material and prepare yourself for the exam. As stated before, every week there will be a homework session in which selected problems will be solved in full. If you have a signal processing problem that you would like to see explained in class (whether it belongs to the assigned homework or not), don t hesitate to contact me or the teaching team and let us know. Don t hesitate to come see us during office hours if you need extra help with the homework. One of the nicest things about digital signal processing is that everything that we study in class can be instantaneously translated into working algorithms; now, since everyone has a PC these days, everyone automatically owns a fully-functional DSP laboratory ready to go! (Compare this to an electronics class, where you need to go

3 out and buy resistors, transistors and capacitors, plus all the other hardware...) To emphasize this fact, some homework problems will require you to write some actual code. Normally, the easiest way to do so is to use Matlab 1 ; Matlab is an interpreted language, it s designed at its core as a linear algebra package (which is great for DSP) and provides you with all sort of pre-canned visualization tools. Nothing prevents you from using the numerical package of your choice, though, or even from writing your code in a lower-level language such as C or even FORTRAN. The important issue here is that you understand that all we study in class can (and should) be implemented on a general-purpose architecture. Prerequisites: Although the course is mostly self-contained, I will assume that you re coming here with a solid working knowledge of calculus and linear algebra. We will spend a little time reviewing the most important concepts but please make sure you brush up on your math and your vector spaces. Similarly, do not let your notions of system theory and probability theory fade out since they will make your life much easier once we start studying filters and random signals. On the first day of class, I will hand out a questionnaire that should allow you to self-diagnose any problem areas you may have. Exams: There will be a midterm exam on Tuesday, April 9, which will last two hours. Midterm topics are everything we covered in weeks one to six inclusive (see syllabus). The date of the final exam will be notified by the Service Academique at the end of the semester. Both the midterm and the final exam are closed-book examinations. However, you will be allowed to bring with you two A4 sheets of handwritten notes, front and back: no photocopies please. Calculators and all other electronic devices are not allowed (yes, just like during takeoff and landing). Grading: The midterm is graded on a scale of 100 points and counts as a bonus for the final grade. If your score on the midterm is less than 50 points, there is no bonus. From 50 to 74 points the bonus is 0.5. From 75 points to 100, the bonus is 1 full point. The final exam is also graded on a scale of 100 points, which are mapped as such: Points 0 to 9 10 to to to to to to to to to 100 Grade Example: if your midterm score is 62/100 and your final score is 73/100, your final grade will be bonus = 5.5. Usual Warnings: Sorry to state the obvious, but: no exceptions to the present guidelines will be made, regardless of your personal situation. For all events which you think may grant special consideration, the one and only address is the Service Academique, not me nor the assistants. Also: attendance per se does not impact the final grade; in other words: I don t care if you never show up but, if you do choose to come to class, please pay attention, participate, take notes and (it sounds silly to even have to say it) do not talk, read newspapers, play with your iphone etc. If you d rather be doing something else, I promise I won t miss you. if you are sick on the day of the midterm your bonus will simply be zero. I m sure you ll make up for it no problem during the final. do not let me catch you cheating during the exam. 1 or, of course, its free counterpart FreeMat ( or Octave (

4 And Finally: I really hope you will enjoy the class and I am looking forward to comments and suggestions in order to improve the material and to make it more and more interesting. All constructive criticism is more than welcome. Don t hesitate to actively participate during the lectures with questions and remarks. If something isn t clear, please say so. Play with Matlab as much as you can in order to get a feeling for the practical side of signal processing. If you are passionate about the subject and want to develop a project, contact me. If you are already engaged in a project which involves signal processing, let me know about it. Above all, I think signal processing is a lot of fun and I hope that, at the end of the semester, you will agree with that. Syllabus The table below is the tentative syllabus for the whole class. Things may change according to necessity, so interpret this as a broad guideline. Classes marked as "online class" require you to watch the appropriate online Modules instead of coming to class. We may revise this approach if needs be. Week 1 Mon 18 Feb 10:00-12:00 introduction to signal processing; math review Tue 19 Feb 08:00-10:00 online class: watch Modules 1, 2 and 3 Handouts: syllabus; homework #1 Week 2 Mon 25 Feb 10:00-12:00 exercise session Tue 26 Feb 08:00-10:00 online class: watch Modules 4.1 to 4.3 on Fourier analysis plus module on examples Handouts: real-valued transforms; homework #2 Week 3 Mon 5 Mar 10:00-12:00 exercise session Tue 6 Mar 08:00-10:00 online class: watch Modules 4.4 to 4.9 on Fourier analysis Handouts: homework #3 Week 4 Mon 11 Mar 10:00-12:00 exercise session Tue 12 Mar 08:00-10:00 online class: watch Modules 5.1 to 5.6 on linear systems Handouts: homework #4

5 Week 5 Mon 18 Mar 10:00-12:00 exercise session Tue 19 Mar 08:00-10:00 online class: watch Modules 5.7 to 5.12 on linear systems Handouts: homework #5 Week 6 Mon 25 Mar 10:00-12:00 exercise session 14:00-16:00 Complements on filter design Tue 26 Mar 08:00-10:00 pre-midterm review; Q&A session Week 7 Mon 8 Apr 10:00-12:00 exercise session 14:00-16:00 stochastic signal processing Tue 9 Apr 08:00-10:00 MIDTERM Handouts: homework #6 Week 8 Mon 15 Apr 10:00-12:00 exercise session 14:00-16:00 midterm: solution and discussion Tue 16 Apr 08:00-10:00 online class: watch Module 6 on interpolation and sampling Handouts: homework #7

6 Week 9 Mon 22 Apr 10:00-12:00 exercise session Tue 23 Apr 08:00-10:00 online class: watch Module 7 on quantization Week 10 Mon 29 Apr 10:00-12:00 exercise session 14:00-16:00 multirate signal processing Tue 30 Apr 08:00-10:00 oversampling Handouts: homework #8 Week 11 Mon 6 May 10:00-12:00 exercise session 14:00-16:00 Q&A Tue 7 May 08:00-10:00 online class: watch Module 8 on image processing Handouts: image processing primer Week 12 Mon 13 May 10:00-12:00 exercise session Tue 14 May 08:00-10:00 online class: watch Module 9 on communication systems Handouts: homework #9 Week 13 Mon 20 May 10:00-12:00 holiday no class Tue 21 May 08:00-10:00 ginal Q&A Week 14 Mon 27 May 10:00-12:00 exercise session 14:00-16:00 guest Lecture: dr Christof Faller on Audio Signal Processing Tue 28 May 08:00-10:00 guest Lecture: dr Loic Baboulaz on the efacsimile Project Wed 3 July 08:15-11:15 final exam (INM202, INM200)

Spring 2015 Natural Science I: Quarks to Cosmos CORE-UA 209. SYLLABUS and COURSE INFORMATION.

Spring 2015 Natural Science I: Quarks to Cosmos CORE-UA 209 Professor Peter Nemethy SYLLABUS and COURSE INFORMATION. Office: 707 Meyer Telephone: 8-7747 ( external 212 998 7747 ) e-mail: peter.nemethy@nyu.edu