Introduction
In this module students learn stochastic methods to describe Signals and how these signals propagate through a linear system. The students can develop optimized filters based on the input signal statistics.
Lecture
- Probability, Random variables, Probability density functions, Expectation values,
- Correlation
- Time discrete stochastic processes
- Quantization
- Adaptive coding
- Wiener Filter, Introduction to Kalman-Filters
Hands-on
- Stochastic signals with various probability density functions
- Simulation of quantization effects
- Generation of test signals
- Simulation of adaptive coding
- Simulation of linear systems and stochastic input signals
Time Table
- Extent: 2 SWS Lecture, 1 SWS Exercise, 1 SWS Hands-On
- Time Table
- Lecture: No Lecture during this Semester
- Exercise: No Exercise during this Semester
- Hands-on: See Group Assignment List
Literature
- Oppenheim, A.V; Schafer, R.W, Buck, J.,R.; Zeitdiskrete Signalverarbeitung, Pearson Studium, 2004.
- Oppenheim, A.; Willsky, S., A.; Signals and Systems, Prentice Hall, 1997.
- Jayant, N.S.; Noll, P., Digital Coding of Waveforms, Prentice Hall, 1984.
- Papoulis, A.; Signalanalysis, McGraw Hill, 1977.
- Girod, B.; Rabenstein, R.; Stenger, A.; Einführung in die Systemtheorie, Teubner Verlag, 1997.