ENEE 624 Advanced Digital Signal ProcessingInformation SheetSpring 2003Instructor:Babis PapadopoulosAssistant ProfessorElectrical & Computer Engineering DepartmentA.V. Williams Building, Room 1423e-mail: [email protected]: (301) 405-3743office hours: Wednesdays 3–4pm, AVW 1423 (regular office hours)Tuesdays 3–4pm, AVW 2328 (group discussion)Teaching Assistant:Yongsun Hwange-mail: [email protected] hours: TBAThe staff will be very available during their office hours and you are encouraged to come by tosee us during the times indicated. While in an emergency Yongsun may be willing to meet brieflywith you outside these hours, please be sensitive to the fact that he is also taking courses, writinga thesis, etc.1Course Synopsis:ENEE 624 is a graduate-level introduction to the fundamentals deterministic and statisticalsignal processing. The concepts and techniques we will develop are extraordinarily rich versatile andpowerful, and form the basis for an enormous class of algorithms used in a remarkably diverse rangeof applications. At the same time, everyone contemplating taking ENEE 624 should understandthat this is an intense, vigorously paced, and extremely demanding subject in terms of its workload.However, assuming you come with the right background (i.e., prerequisites) and are ready to makethe serious commitment that the subject demands, it can be an extremely rewarding experience.The material in the course constitutes a common foundation for work in the areas of deterministicand statistical signal processing. Ultimately, the course is about getting you to develop new waysof thinking about signals and systems and solving problems that involve them. The development ofthe material that forms the basis of ENEE 624 has historically been driven mostly by applications.However, we emphasize that our focus on the course will not be on the applications—which formthe basis for entire courses of their own—but on the common problem solving framework thatthey share. Nevertheless, we will cite various relevant applications as we develop the material andsometimes extract simplified examples from these contexts.Prerequisites:Although, strictly speaking, prerequisites and corequisites are not enforced at the graduatelevel, ENEE 620 (or equivalent) is an official and essential corequisite for ENEE 624. In gen-eral, ENEE 624 assumes fluency, with discrete- and continuous-time linear systems, discrete- andcontinuous-time stochastic processes, and some basic linear algebra. Equally important to thespecific prerequisites, a high level of maturity, dedication, and commitment to understanding theconcepts in depth is expected of all who take the subject. In fact, we caution any undergraduatestudents enrolled in the course that it is very likely that they find the intensity and demands of thecourse unmanageable.Lectures:Mondays and Wednesdays 5:00pm–6:15pm, EGR 1202.Attendance to the lectures is assumed, but not formally recorded.Recitations:Yongsun will hold weekly recitation sections every Friday (the precise time and place of thetwo sections will be announced by Wednesday February 5). Recitations begin on Friday February7. During recitations, Yongsun will go over sample examples and additional topics related to thematerial presented in lecture. Recitations also give you an opportunity to ask clarifying questionsregarding material or concepts presented in lecture.Attendance to lectures and recitations is assumed, but not formally recorded. They both workbest when they are highly interactive, so your participation is important and strongly encouraged.Asking questions both in class and in office hours is a sign of engagement in the material, not anexpression of weakness.Required Textbooks:P. P. Vaidyanathan, Multirate Systems and Filter Banks, Prentice-Hall, 1993.M. Hayes, Statistical Digital Signal Processing and Modeling, Wiley, 1996.2Other Recommended Textbooks:S. Haykin, Adaptive Filter Theory, Prentice-Hall, 1996.S. M. Kay, Modern Spectral Estimation: Theory and Applications, Prentice-Hall, 1987.References:A list of references on detection and estimation theory, background material, and advancedtopics is appended at the end of this handout.Course Handouts:Course handouts will be typically distributed at the beginning and/or end of lectures. Yongsunwill have extra copies in case you did not pick a copy in class. A select set of these handouts willalso be available on the course web site.Problem Sets:There will be quasi-weekly problem set assignments. Each problem set will include a set ofrecommended exercises (not to be handed in) and a set of required problems (to be handed in), arandom subset of which will be graded each week. Some assignments will also include one or two,often (but not always) more challenging, optional problems. You can make use of these optionalproblems when you think you might benefit from (and you have time for) extra practice with thematerial. Although these are not to be turned in, solutions to these problems will be providedalong with the regular problems.Problem sets must be handed in by the end of the class in which they are due. Note that problemset solutions will be available at the end of the due date lecture. Consequently, it is difficult andunfair to seriously evaluate late problem sets.You are expected to do all the assigned problems. While the grade you get on your homework isonly a minor percentage of your final grade, working through (and, yes, often struggling with) thehomework is a crucial part of the learning process. As such, it will invariably have a major impacton your understanding of the material and, in turn, your exam performances and your final grade.Moderate collaboration in the form of joint problem solving with one or two of your classmates ispermitted provided that– you struggle with the problems on your own prior to interacting with classmates.– the write-up you submit is your own.In making the exams and in assigning a final grade we will assume that you have worked all theproblems.Projects:There will be two project assignments in ENEE 624 this semester. These projects will provideyou with hands-on experience regarding some the implementation aspects of some of the key algo-rithms and techniques we will consider in the course of the semester. In addition to serving as avehicle for experimentation and optimization, the projects will expose you to some of the challengesthat arise in several R&D DSP-based applications.