Course Manual 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 |
| Valid from | winter semester 2020/21 |
| 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. |
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 | EN Klausur |
Learning goals
| Goal type | Description |
|---|---|
| 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 |
| Knowledge | Discrete-Time Linear Time-Invariant Systems Difference Equations Discrete-Time Convolution Unit-Pulse and Impulse Response Basic Systems Properties: Causality, Stability, Memory |
| Knowledge | Ideal Sampling and Reconstruction Ideal Sampling and the Sampling Theorem Aliasing |
| Knowledge | 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 |
| Knowledge | 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 |
| Knowledge | 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 |
| Knowledge | Design of Digital Filters Design of FIR Filters Design of IIR Filters |
| Knowledge | 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 |
| Knowledge | 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 |
| Knowledge | Optimum Linear Filters Linear Prediction The Wiener Filter Orthogonality Principle FIR Wiener Filter IIR Wiener Filter |
| Knowledge | Spectrum Estimation The Periodogram Window Functions Eigenanalysis Algorithms MUSIC Algorithm ESPRIT Algorithm |
| Skills | Students understand the fundamentals of discrete-time signals and systems |
| Skills | Students can analyse the frequency content of a given signal using the appropriate Fourier-Transform and methods for spectrum estimation |
| Skills | 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 |
| Skills | 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 |
| Skills | 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 requirements
| none |
| Accompanying material |
lecture slides as pdf-files, list of problems and solutions manual as pdf-files old exams and solutions |
|---|---|
| Separate exam | Yes |
Separate exam
| Exam Type | EN Übungsaufgabe mit fachlich / methodisch eingeschränktem Fokus unter Klausurbedingungen lösen |
|---|---|
| Details | 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
| Goal type | Description |
|---|---|
| Knowledge | Review of Probablity and Random Variables Moments, Averages and Distribution Functions |
| Knowledge | Random Signals Ensemble Averages Correlation Functions Stationary and Ergodic Processes Power Spectral Density Transmission of Random Signals over LTI Systems |
| Knowledge | Sampling Sampling and coding for speech and/or audio signals |
| Skills | Analysis of random variables by means of Mean and moments Distribution |
| Skills | 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 |
| Skills | Combatting noise Remove or suppress high-frequency noise from low-pass signals |
| Skills | Abilty to trade-off different methods for digital coding of speech and audio signals |
| Skills | 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 requirements
| none |
| Accompanying material |
Instructions for lab experiments as pdf-files |
|---|---|
| Separate exam | Yes |
Separate exam
| Exam Type | EN praxisnahes Szenario bearbeiten (z.B. im Praktikum) |
|---|---|
| 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