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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.
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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.


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