Modulhandbuch MaCSN2012_Digital_Signal_Processing

Verantwortlich: Prof. Dr.-Ing. Harald Elders-Boll

Modul

Anerkennbare Lehrveranstaltung (LV)

Organisation

Bezeichnung
Lang MaCSN2012_Digital_Signal_Processing
MID MaCSN2012_DSP
MPID
Zuordnung
Studiengang MaCSN2012
Studienrichtung G
Wissensgebiete G_VMINT
Einordnung ins Curriculum
Fachsemester 1-2
Pflicht G
Wahl
Version
erstellt 2012-01-05
VID 1
gültig ab WS 2012/13
gültig bis

Zeugnistext

de
Digital Signal Processing
en
Digital Signal Processing

Unterrichtssprache

Deutsch oder Englisch

Modulprüfung

Form der Modulprüfung
sMP 80% (mündliche Prüfung)

Beiträge ECTS-CP aus Wissensgebieten
G_VMINT 5
Summe 5

Aufwand [h]: 150

Prüfungselemente

Vorlesung / Übung

Form Kompetenznachweis
bK 2-3 eTests je 20min (je 1x wiederholbar)
bÜA Präsenzübung und Selbstlernaufgaben

Beitrag zum Modulergebnis
bK 20%
bÜA unbenotet

Spezifische Lernziele

Kenntnisse
  • Signals, Systems and Digital Signal Processing (PFK.5, PFK.6, PFK.8)
  • Discrete-Time Linear Time-Invariant Systems
    • Difference Equations (PFK.5, PFK.6)
    • Discrete-Time Convolution (PFK.5, PFK.6)
    • Unit-Pulse and Impulse Response (PFK.5, PFK.6)
    • Basic Systems Properties: Causality, Stability, Memory (PFK.5, PFK.6)
  • Ideal Sampling and Reconstruction
    • Ideal Sampling and the Sampling Theorem (PFK.6, PFK.8)
    • Aliasing (PFK.6, PFK.8)
  • Fourier-Transform of Discrete-Time Signals (PFK.6, PFK.7)
    • Frequency response of Discrete-Time LTI Systems
    • The Fourier-Transform of Discrete-Time Signals
  • The z-Transform (PFK.6, PFK.7)
    • The Two-sided z-Transform
    • Properties of the z-Transform
    • The Inverse z-Transform
    • Analysis of LTI Systems using the z-Transform
  • Discrete Fourier-Transform (PFK.6, PFK.7)
    • The DFT and the Inverse DFT
    • The Fast Fourier Transform
  • Design of Digital Filters (PFK.6, PFK.8)
    • Design of FIR Filters
    • Design of IIR Filters
  • Random Signals (PFK.5, PFK.6, PFK.7)
    • Ensemble Averages
    • Correlation Functions
    • Stationary and Ergodic Processes
    • Power Spectral Density
    • Transmission of Random Signals over LTI Systems
  • Advanced Sampling Techniques (PFK.6, PFK.7)
    • Quantization and Encoding
    • Sampling of Random Signals
    • Sample Rate Conversion
    • Oversampling and Noise Shaping
  • Optimum Linear Filters (PFK.6, PFK.8)
    • Linear Prediction
    • The Wiener Filter
    • Adaptive Filters
  • Spectrum Estimation (PFK.6, PFK.7)
    • The Periodogram
    • Eigenanalysis Algorithms
Fertigkeiten
  • Students understand the fundamentals of discrete-time signals and systems (PFK.6)
  • Students can analyse the frequency content of a given signal using the appropriate Fourier-Transform and methods for spectrum estimation (PFK.6, PFK.7)
  • Analysis of discrete-time LTI Systems (PFK.6, PFK.7)
    • Students can calculate the output signal via convolution
    • Students can determine the frequency response of a given system
    • Students can characterize a given system in the frequency domain and in the z-domain
  • Implementation of discrete-time LTI systems (PFK.1, PFK.2, PFK.6, PFK.8)
    • Students can implement the convolution sum in software
    • Students can implement different structures for IIR systems in software
    • Sudents can use the FFT to implement an FIR system
  • Analyze effects of practical sampling (PFK.6, PFK.7)
    • Quantization noise
    • Aliasing
    • Trade-off pros and cons of advanced implementations like noise shaping

Exemplarische inhaltliche Operationalisierung

The follwowing subjects can be presented quickly assuming students have had prior exposure to discrete-time systems:
  • Signals, Systems and Digital Signal Processing
  • Discrete-Time Linear Time-Invariant Systems
  • Ideal Sampling and Reconstruction
  • Fourier-Transform of Discrete-Time Signals
  • The z-Transform
The follwoing subjects should be presented in depth:
  • Discrete Fourier-Transform

  • Design of Digital Filters

  • Random Signals

  • Advanced Sampling Techniques

The course should be complemented with selected topics from the following advanced subjects:
  • Optimum Linear Filters
  • Spectrum Estimation
  • Adaptive Filters
The theory should be illustrated and put into practise by MATLAB  code of the presented methods and algorithms

Praktikum

Form Kompetenznachweis
bSZ Praktikum (Lab Experiments)

Beitrag zum Modulergebnis
bSZ Voraussetzung für Modulprüfung (prerequisite for final exam)

Spezifische Lernziele

Lerninhalte
  • Random Signals (PFK.5, PFK.6, PFK.7)
    • Ensemble Averages
    • Correlation Functions
    • Stationary and Ergodic Processes
    • Power Spectral Density
    • Transmission of Random Signals over LTI Systems
  • Sampling (PFK.6, PFK.7)
    • Sampling and coding for speech and/or audio signals
Fertigkeiten
  • Analysis of random signals (PFK.6, PFK.7, PFK.7, PFK.9)
    • Determine whether a given random signal is stationary or not
    • Analyse whether a random signal contains discrete harmonic components
      • by using the autocorrelation function
      • by using the power spectral density
  • Combatting noise
    • Remove or suppress high-frequency noise from low-pass signals (PFK.1, PFK.6, PFK.8)
  • Abilty to trade-off and implement different methods for digital coding of speech and audio signals (PFK.1, PFK.6, PFK.8)
  • Determine the quatization noise and the SNR for different sampling schemes (PFK.2, PFK.5, PFK.6, PFK.7)

Exemplarische inhaltliche Operationalisierung

The follwowing subjects can be presented quickly assuming students have had prior exposure to discrete-time systems:
  • Signals, Systems and Digital Signal Processing
  • Discrete-Time Linear Time-Invariant Systems
  • Ideal Sampling and Reconstruction
  • Fourier-Transform of Discrete-Time Signals
  • The z-Transform
The follwoing subjects should be presented in depth:
  • Discrete Fourier-Transform

  • Design of Digital Filters

  • Random Signals

  • Advanced Sampling Techniques

The course should be complemented with selected topics from the following advanced subjects:
  • Optimum Linear Filters
  • Spectrum Estimation
  • Adaptive Filters
The theory should be illustrated and put into practise by MATLAB  code of the presented methods and algorithms

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