227-0101-AAL Discrete-Time and Statistical Signal Processing
|Semester||Spring Semester 2020|
|Periodicity||every semester recurring course|
|Language of instruction||English|
|Comment||Enrolment ONLY for MSc students with a decree declaring this course unit as an additional admission requirement.|
Any other students (e.g. incoming exchange students, doctoral students) CANNOT enrol for this course unit.
|227-0101-AA R||Discrete-Time and Statistical Signal Processing|
Self-study course. No presence required.
The underlying lecture is offered in autumn semester.
|112s hrs||H.‑A. Loeliger|
|Abstract||The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications: discrete-time linear filters, inverse filters and equalization, DFT, discrete-time stochastic processes, elements of detection theory and estimation theory, LMMSE estimation and LMMSE filtering, LMS algorithm, Viterbi algorithm.|
|Objective||The course introduces some fundamental topics of digital signal processing with a bias towards applications in communications. The two main themes are linearity and probability. In the first part of the course, we deepen our understanding of discrete-time linear filters. In the second part of the course, we review the basics of probability theory and discrete-time stochastic processes. We then discuss some basic concepts of detection theory and estimation theory, as well as some practical methods including LMMSE estimation and LMMSE filtering, the LMS algorithm, and the Viterbi algorithm. A recurrent theme is the stable and robust "inversion" of a linear filter.|
|Content||1. Discrete-time linear systems and filters:|
state-space realizations, z-transform and spectrum,
decimation and interpolation, digital filter design,
stable realizations and robust inversion.
2. The discrete Fourier transform and its use for digital filtering.
3. The statistical perspective:
probability, random variables, discrete-time stochastic processes;
detection and estimation: MAP, ML, Bayesian MMSE, LMMSE;
Wiener filter, LMS adaptive filter, Viterbi algorithm.
|Lecture notes||Lecture Notes|
|Performance assessment information (valid until the course unit is held again)|
|Performance assessment as a semester course|
|ECTS credits||6 credits|
|Language of examination||English|
|Repetition||The performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.|
|Mode of examination||written 180 minutes|
|Written aids||Lecture Notes (not including problems and solutions) and pesonal notes (max. 4 pages). No electronic devices. (Pocket calculators will be handed out, if necessary.)|
|This information can be updated until the beginning of the semester; information on the examination timetable is binding.|
|Only public learning materials are listed.|
|No information on groups available.|
|There are no additional restrictions for the registration.|
|Electrical Engineering and Information Technology Master||Course Units for Additional Admission Requirements||E-|