Digital Signal Processing
PDF Lehrveranstaltungsverzeichnis English Version: DSP
Version: 2 | Letzte Änderung: 11.09.2019 11:34 | Entwurf: 0 | Status: vom verantwortlichen Dozent freigegeben
Langname | Digital Signal Processing |
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Anerkennende LModule | DSP_MaCSN, DSP_MaTIN |
Verantwortlich |
Prof. Dr. Harald Elders-Boll
Professor Fakultät IME |
Gültig ab | Wintersemester 2020/21 |
Niveau | Master |
Semester im Jahr | Wintersemester |
Dauer | Semester |
Stunden im Selbststudium | 60 |
ECTS | 5 |
Dozenten |
Prof. Dr. Harald Elders-Boll
Professor Fakultät IME |
Voraussetzungen | No formal requirements, but students will be expected to be familiar with: Basic Knowledge of Signals and Systems: Continuous-Time LTI-Systems and Convolution, Fourier-Transform Basic Knowledge of Probability and Random Variables |
Unterrichtssprache | englisch |
separate Abschlussprüfung | Ja |
John G. Proakis and Dimitris K. Manolakis. Digital Signal Processing (4th Edition). Prentice Hall, 2006. |
Alan V. Oppenheim, Ronald W. Schafer. Discrete-Time Signal Processing (3rd Edition). Prentice Hall, 2007. |
Vinay Ingle and John Proakis. Digital Signal Processing using MATLAB. Cengage Learning Engineering, 2011. |
Details | In the written exam students shall demonstrate that they are able to solve problems dealing with the design, analysis and implementation of DSP systems in soft and hardware considering computational complexity and hardware resource limitation, by using their thorough understanding of the theoretical concepts, especially frequency domain analysis, and insights gained from the practical implementation of DSP systems in software using Python and on microprocessors, such that they are able to design, select, use and apply actual and future DSP systems for various signal processing application in commercial products. |
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Mindeststandard | Mindestens 24 der möglichen 50 möglichen Gesamtpunkte aus der Klausur und den zwei Tests während des Semesters. In der Klausur können maximal 40 Punkte in den zwei Tests während des Semesters können maximal jeweils 5 in der Summe also 10 Punkte erreicht werden. |
Prüfungstyp | Klausur |
Zieltyp | Beschreibung |
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Kenntnisse | Signals, Systems and Digital Signal Processing Basic Elements of DSP Systems Classification of Signals Continuous-Time and Discrete-Time Signals Deterministic and Random Signals Even and Odd Signals Periodic and Aperiodic Signals Energy and Power of Signals Some Fundamental Signals |
Kenntnisse | Discrete-Time Linear Time-Invariant Systems Difference Equations Discrete-Time Convolution Unit-Pulse and Impulse Response Basic Systems Properties: Causality, Stability, Memory |
Kenntnisse | Ideal Sampling and Reconstruction Ideal Sampling and the Sampling Theorem Aliasing |
Kenntnisse | Fourier-Transform of Discrete-Time Signals Eigenfunctions of Discrete-Time LTI Systems Frequency response of Discrete-Time LTI Systems The Fourier-Transform of Discrete-Time Signals Ideal Continuous-Time Filters |
Kenntnisse | The z-Transform The Two-sided z-Transform Properties of the z-Transform The Inverse z-Transform Analysis of LTI Systems using the z-Transform |
Kenntnisse | Discrete Fourier-Transform Sampling the DTFT The DFT and the Inverse DFT The Fast Fourier Transform Radix-2 FFT Algorithms Linear Convolution Using the FFT Overlap-And-Add |
Kenntnisse | Design of Digital Filters Design of FIR Filters Design of IIR Filters |
Kenntnisse | Random Signals Review of Probablity and Random Variables Ensemble Averages Correlation Functions Stationary and Ergodic Processes Power Spectral Density Transmission of Random Signals over LTI Systems |
Kenntnisse | Advanced Sampling Techniques Quantization and Encoding Sampling of Bandpass Signals Sampling of Random Signals Sample Rate Conversion Sample Rate Reduction by an Integer Factor Sample Rate Increase by an Integer Factor Sample Rate Conversion by a Rational Factor Oversampling and Noise Shaping |
Kenntnisse | Optimum Linear Filters Linear Prediction The Wiener Filter Orthogonality Principle FIR Wiener Filter IIR Wiener Filter |
Kenntnisse | Spectrum Estimation The Periodogram Window Functions Eigenanalysis Algorithms MUSIC Algorithm ESPRIT Algorithm |
Fertigkeiten | Students understand the fundamentals of discrete-time signals and systems |
Fertigkeiten | Students can analyse the frequency content of a given signal using the appropriate Fourier-Transform and methods for spectrum estimation |
Fertigkeiten | Analysis of discrete-time LTI Systems 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 |
Fertigkeiten | Implementation of discrete-time LTI systems 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 |
Fertigkeiten | Analyze effects of practical sampling Quantization noise Aliasing Trade-off pros and cons of advanced implementations like noise shaping |
Typ | Präsenzzeit (h/Wo.) |
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Vorlesung | 2 |
Übungen (ganzer Kurs) | 2 |
Übungen (geteilter Kurs) | 0 |
Tutorium (freiwillig) | 0 |
keine |
Begleitmaterial |
elektronische Vortragsfolien zur Vorlesung, elektronische Übungsaufgabensammlung mit Lösungen alte Klausuren und Lösungen |
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Separate Prüfung | Ja |
Prüfungstyp | Übungsaufgabe mit fachlich / methodisch eingeschränktem Fokus unter Klausurbedingungen lösen |
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Details | Zwei semesterbegleitende Tests in Form von Aufgaben, die den bis zum jeweiligen Zeitpunkt in der Vorlesung/Übung behandelten Stoff aufgreifen und so bei Bestehen sicherstellen, dass die Grundlagen zur erfolgreichen Teilnahme an den entsprechenden Praktikumsversuchen und/oder Projekten gegeben ist. |
Mindeststandard | Mindestens 2 von maximal 5 erreichbaren Punkten pro Test. |
Zieltyp | Beschreibung |
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Kenntnisse | Review of Probablity and Random Variables Moments, Averages and Distribution Functions |
Kenntnisse | Random Signals Ensemble Averages Correlation Functions Stationary and Ergodic Processes Power Spectral Density Transmission of Random Signals over LTI Systems |
Kenntnisse | Sampling Sampling and coding for speech and/or audio signals |
Fertigkeiten | Analysis of random variables by means of Mean and moments Distribution |
Fertigkeiten | Analysis of random signals 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 |
Fertigkeiten | Combatting noise Remove or suppress high-frequency noise from low-pass signals |
Fertigkeiten | Abilty to trade-off different methods for digital coding of speech and audio signals |
Fertigkeiten | Determine the quatization noise and the SNR for different sampling schemes |
Typ | Präsenzzeit (h/Wo.) |
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Praktikum | 1 |
Tutorium (freiwillig) | 0 |
keine |
Begleitmaterial |
elektronische Beschreibung der Praktikums-Versuche |
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Separate Prüfung | Ja |
Prüfungstyp | praxisnahes Szenario bearbeiten (z.B. im Praktikum) |
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Details | Erfolgreiche Bearbeitung dfer Parktikumsversuche oder Projekte in Kleingruppen von in der Regel zwei Studierenden. Das Bestehen des entsprechenden Tests aus der Vorlesung/Übung ist Zugangsvoraussetzung um am Praktikum teilnehmen zu können. |
Mindeststandard | Erfolgreiche Teilnehme an allen Versuchen und/oder erfolgreiche Bearbeitung von kleinen Projekten. Im entsprechenden Test in der Vorlesung/Übung müssen zum Bestehen 2 von 5 möglichen Punkten erreicht werden |
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