Course
DSP - Digital Signal Processing
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 |
Literature
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.
Final exam
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.Minimum standard
At least 24 of the 50 points that can be gained in total in the final exam and the two midterm tests during the semester.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.
Exam Type
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.Learning goals
Knowledge
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
Skills
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
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Lecture | 2 |
| Exercises (whole course) | 2 |
| Exercises (shared course) | 0 |
| Tutorial (voluntary) | 0 |
Special literature
keine/none
Special requirements
none
Accompanying material
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
Separate exam
Exam Type
solving exercises within limited functional / methodical scope under examination conditionsDetails
Two midterm tests with excercises dealing with the subjects from the lecture/tutorial that were covered up to that point, suich the by passing the midterm tests students demonstrate that they have the required skills to sucessfully participate in the corresponding labs and/or projects.Minimum standard
Two out of five points that can be scored in total per test.Learning goals
Knowledge
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
Skills
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
Expenditure classroom teaching
| Type | Attendance (h/Wk.) |
|---|---|
| Practical training | 1 |
| Tutorial (voluntary) | 0 |
Special literature
keine/none
Special requirements
none
Accompanying material
Instructions for lab experiments as pdf-files
Separate exam
Exam Type
working on practical scenarion (e.g. in a lab)Details
Sucessful solution of the lab problems and/or projects in small groups consisting of two students, in general. The corresponding midterm test from the lecture/tutorial needs to be passed as a prerequisite for participation in the lab.Minimum standard
Successful participation of all labs and/or the corresponding small projects. To pass the corresponding midterm test 2 out of 5 points have to be gained.
Bei Fehlern, bitte Mitteilung an die
Webredaktion der Fakultät IME
Webredaktion der Fakultät IME
© 2022 Technische Hochschule Köln