389.170 Signal Processing 2
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2019W, VU, 3.0h, 4.5EC

Properties

  • Semester hours: 3.0
  • Credits: 4.5
  • Type: VU Lecture and Exercise

Learning outcomes

After successful completion of the course, students are able to apply fundamental concepts in the area of probability theory, random variables, and stochastic processes to solve practical engineering problems.

Subject of course

1. One random variable: Cumulative distribution function (cdf) and probability density function (pdf), discrete random variables, transformation of random variables, conditional cdf and pdf, moments, characteristic function, inequalities, conditional expectation, special distributions

2. Two random variables: Joint cdf and pdf, discrete random variables, transformation of random variables, conditional cdf and pdf, moments, correlation, covariance, statistical independence, orthogonality and uncorrelatedness, characteristic function, conditional expectation, special distributions, complex random variables, circular symmetry

3. Random vectors: cdf and pdf, discrete random vectors, transformation of random vectors, conditional cdf and pdf, mean, correlation matrix, covariance matrix, statistical independence, orthogonality and uncorrelatedness, characteristic function, conditional expectation, special distributions, complex random vectors, Karhunen-Loeve decomposition, whitening transformation, innovations representation, MMSE estimation, LMMSE estimation (Wiener filter), ML estimation

4. Random signals (stochastic processes): pdf, stationarity, second-order description (mean, autocorrelation function), cyclostationarity, power spectral density, cross-correlation function und cross-power spectral density, effects of linear systems, time averages und ergodicity, discrete-time random signals, special random signals, complex random signals and circular symmetry, whitening filter, innovations filter, Wold decomposition, AR, MA and ARMA processes, LMMSE estimation (Wiener filter), linear prediction

Teaching methods

Theory lectures on the blackboard, discussion of real-world applications, numerical demonstrations, problem-solving exercises.

Mode of examination

Written and oral

Lecturers

Institute

Course dates

DayTimeDateLocationDescription
Thu11:00 - 12:0003.10.2019 - 23.01.2020EI 4 Reithoffer HS Signal Processing 2
Fri10:30 - 12:0004.10.2019 - 17.01.2020EI 4 Reithoffer HS Signal Processing 2
Signal Processing 2 - Single appointments
DayDateTimeLocationDescription
Thu03.10.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri04.10.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu10.10.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri11.10.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu17.10.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri18.10.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu24.10.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri25.10.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu31.10.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu07.11.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri08.11.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu14.11.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu21.11.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri22.11.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu28.11.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri29.11.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu05.12.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri06.12.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2
Thu12.12.201911:00 - 12:00EI 4 Reithoffer HS Signal Processing 2
Fri13.12.201910:30 - 12:00EI 4 Reithoffer HS Signal Processing 2

Examination modalities

- active participation in exercises (solution of problems)

- three-hour written exam (4 problems)

- oral exam

Course registration

Begin End Deregistration end
03.10.2019 11:00 15.10.2019 23:59 15.10.2019 23:59

Registration modalities:

Course registration required for exercise classes.

Curricula

Literature

Detailed lecture notes for this course will be made available.

Previous knowledge

basic knowledge of mathematics (particularly probability theory and linear algebra) and of signals and system theory (convolution, Fourier analysis, z transform).

Miscellaneous

Language

English