PDF Course Catalog Deutsche Version: DSP

Version: 2 | Last Change: 11.09.2019 11:34 | Draft: 0 | Status: vom verantwortlichen Dozent freigegeben

Long name | Digital Signal Processing |
---|---|

Approving CModule | DSP_MaCSN, DSP_MaTIN |

Responsible |
Prof. Dr. Harald Elders-Boll
Professor Fakultät IME |

Level | Master |

Semester in the year | winter semester |

Duration | Semester |

Hours in self-study | 60 |

ECTS | 5 |

Professors |
Prof. Dr. Harald Elders-Boll
Professor Fakultät IME |

Requirements | 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 |

Language | English |

Separate final exam | Yes |

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.

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.

In the final exam 40 points can be gained in total, in the two midterm test 5 points can be gained each yielding 10 points in total for the two tests.

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

Discrete-Time Linear Time-Invariant Systems

Difference Equations

Discrete-Time Convolution

Unit-Pulse and Impulse Response

Basic Systems Properties: Causality, Stability, Memory

Ideal Sampling and Reconstruction

Ideal Sampling and the Sampling Theorem

Aliasing

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

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

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

Design of Digital Filters

Design of FIR Filters

Design of IIR Filters

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

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

Optimum Linear Filters

Linear Prediction

The Wiener Filter

Orthogonality Principle

FIR Wiener Filter

IIR Wiener Filter

Spectrum Estimation

The Periodogram

Window Functions

Eigenanalysis Algorithms

MUSIC Algorithm

ESPRIT Algorithm

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

Discrete-Time Linear Time-Invariant Systems

Difference Equations

Discrete-Time Convolution

Unit-Pulse and Impulse Response

Basic Systems Properties: Causality, Stability, Memory

Ideal Sampling and Reconstruction

Ideal Sampling and the Sampling Theorem

Aliasing

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

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

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

Design of Digital Filters

Design of FIR Filters

Design of IIR Filters

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

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

Optimum Linear Filters

Linear Prediction

The Wiener Filter

Orthogonality Principle

FIR Wiener Filter

IIR Wiener Filter

Spectrum Estimation

The Periodogram

Window Functions

Eigenanalysis Algorithms

MUSIC Algorithm

ESPRIT Algorithm

Students understand the fundamentals of discrete-time signals and systems

Students can analyse the frequency content of a given signal using the appropriate Fourier-Transform and methods for spectrum estimation

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

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

Analyze effects of practical sampling

Quantization noise

Aliasing

Trade-off pros and cons of advanced implementations like noise shaping

Students can analyse the frequency content of a given signal using the appropriate Fourier-Transform and methods for spectrum estimation

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

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

Analyze effects of practical sampling

Quantization noise

Aliasing

Trade-off pros and cons of advanced implementations like noise shaping

Type | Attendance (h/Wk.) |
---|---|

Lecture | 2 |

Exercises (whole course) | 2 |

Exercises (shared course) | 0 |

Tutorial (voluntary) | 0 |

keine/none

none

lecture slides as pdf-files

list of problems and solutions manual as pdf-files

old exams and solutions

list of problems and solutions manual as pdf-files

old exams and solutions

Review of Probablity and Random Variables

Moments, Averages and Distribution Functions

Random Signals

Ensemble Averages

Correlation Functions

Stationary and Ergodic Processes

Power Spectral Density

Transmission of Random Signals over LTI Systems

Sampling

Sampling and coding for speech and/or audio signals

Moments, Averages and Distribution Functions

Random Signals

Ensemble Averages

Correlation Functions

Stationary and Ergodic Processes

Power Spectral Density

Transmission of Random Signals over LTI Systems

Sampling

Sampling and coding for speech and/or audio signals

Analysis of random variables by means of

Mean and moments

Distribution

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

Combatting noise

Remove or suppress high-frequency noise from low-pass signals

Abilty to trade-off different methods for digital coding of speech and audio signals

Determine the quatization noise and the SNR for different sampling schemes

Mean and moments

Distribution

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

Combatting noise

Remove or suppress high-frequency noise from low-pass signals

Abilty to trade-off different methods for digital coding of speech and audio signals

Determine the quatization noise and the SNR for different sampling schemes

Type | Attendance (h/Wk.) |
---|---|

Practical training | 1 |

Tutorial (voluntary) | 0 |

keine/none

none

Instructions for lab experiments as pdf-files

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