MaCSN2012_DSP < F07_Studium

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*Verantwortlich:* Prof. Dr.-Ing. Harald Elders-Boll
---++ Modul
---+++ Anerkennbare Lehrveranstaltung (LV) 
   * [[F07_DSP]]

---+++ Organisation
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          <th colspan="2">Bezeichnung</th>
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            <td>Lang</td>
            <td>%FORMFIELD{"Bezeichnung"}%</td>
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          <tr>
            <td>MID</td>
            <td>MaCSN2012_DSP</td>
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          <tr>
            <td>MPID</td>
            <td/>
          </tr>
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      </td>
      <td> </td>
      <td>
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          <th colspan="2">Zuordnung</th>
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            <td>Studiengang</td>
            <td>%FORMFIELD{"Studiengang"}%</td>
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            <td>Studienrichtung</td>
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            <td>Wissensgebiete</td>
            <td>G_VMINT</td>
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      </td>
      <td> </td>
      <td>
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          <th colspan="2">Einordnung ins Curriculum</th>
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            <td>Fachsemester</td>
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            <td>Pflicht</td>
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            <td>Wahl</td>
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      <td> </td>
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          <th colspan="2">Version</th>
          <tr>
            <td>erstellt</td>
            <td>2012-01-05</td>
          </tr>
          <tr>
            <td>VID</td>
            <td>1</td>
          </tr>
          <tr>
            <td>gültig ab</td>
            <td>WS 2012/13</td>
          </tr>
          <tr>
            <td>gültig bis</td>
            <td/>
          </tr>
        </table>
      </td>
    </tr>
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---++++ Zeugnistext
---+++++ de
Digital Signal Processing
---+++++ en
Digital Signal Processing 


---++++ Unterrichtssprache
Deutsch oder Englisch

---+++ Modulprüfung
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    <th colspan="2">Form der Modulprüfung</th>
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      <td>sMP</td>
      <td>80% (mündliche Prüfung)</td>
    </tr>
  </table>
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    <th colspan="2">Beiträge ECTS-CP aus Wissensgebieten</th>
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      <td>Summe</td>
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*Aufwand [h]:* %FORMFIELD{"Aufwand"}%


-----
---++ Prüfungselemente
%STARTSECTION{"Vorlesung / Übung"}%
---+++ Vorlesung / Übung

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    <th colspan="2">Form Kompetenznachweis</th>
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      <td>bK</td>
      <td>2-3 eTests je 20min (je 1x wiederholbar)</td>
    </tr>
    <tr>
      <td>bÜA</td>
      <td>Präsenzübung und Selbstlernaufgaben</td>
    </tr>
  </table>
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    <th colspan="2">Beitrag zum Modulergebnis</th>
    <tr>
      <td>bK</td>
      <td>20%</td>
    </tr>
    <tr>
      <td>bÜA</td>
      <td>unbenotet</td>
    </tr>
  </table>
</sticky>

---++++ 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:<br>
   * 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:<br>
   * Discrete Fourier-Transform<br>

   * Design of Digital Filters<br>

   * Random Signals<br>

   * Advanced Sampling Techniques<br>

The course should be complemented with selected topics from the following advanced subjects:<br>
   * 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<br>
<br>


%ENDSECTION{"Vorlesung / Übung"}%

%STARTSECTION{"Praktikum"}%
---+++ Praktikum

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    <th colspan="2">Form Kompetenznachweis</th>
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      <td>bSZ</td>
      <td>Praktikum (Lab Experiments)</td>
    </tr>
  </table>
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    <th colspan="2">Beitrag zum Modulergebnis</th>
    <tr>
      <td>bSZ</td>
      <td>Voraussetzung für Modulprüfung (prerequisite for final exam)</td>
    </tr>
  </table>
</sticky>

---++++ 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:<br>
   * 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:<br>
   * Discrete Fourier-Transform<br>

   * Design of Digital Filters<br>

   * Random Signals<br>

   * Advanced Sampling Techniques<br>

The course should be complemented with selected topics from the following advanced subjects:<br>
   * 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<br>


%ENDSECTION{"Praktikum"}%


%ENDSECTION{"no_toc"}%
Topic-Revision: r9 - 19 Jul 2018, GeneratedContent
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