Inhalt

### [ 536DASCDSPU19 ] UE Digital Signal Processing

Versionsauswahl
Es ist eine neuere Version 2023W dieser LV im Curriculum Master's programme Computational Mathematics 2023W vorhanden. Workload Education level Study areas Responsible person Hours per week Coordinating university
1,5 ECTS B3 - Bachelor's programme 3. year (*)Artificial Intelligence Mario Huemer 1 hpw Johannes Kepler University Linz Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2022W
Objectives Students know and understand the basics of analog as well as discrete-time signals and systems qualitatively and mathematically and can apply them to fundamental problems. They are able to

• plot signals in time- and frequency domain,
• explain the sampling theorem,
• apply the convolution operation,
• derive the spectrum of a signal numerically with the help of the DFT and the FFT,
• describe the behavior of discrete-time LTI systems in time- and frequency domain,
• analyze and design digital FIR and IIR filters, and
• apply the correlation operation.

Students know and understand the basics of adaptive filters, and the basics of time-frequency analysis.

Subject
• Basics of Analog Systems Theory
• Discrete Time Signals Fundamentals
• Discrete Time LTI Systems Fundamentals
• Fourier Transform of Discrete Time Signals (DTFT)
• Sampling and Reconstruction
• Correlation
• Spectral Analysis of Discrete Time Signals (DFT, FFT, Short Time Fourier Transformation, Applications)
• z Transform
• Digital Filters (FIR, IIR)
• Vector Matrix Representations of Discrete Time Signals and LTI Systems
Criteria for evaluation Homework to be uploaded and evaluated
Methods
• Demonstration of examples
• Matlab examples
• Homework
Language English
Study material
• Lecture Slides
• Alan V. Oppenheim, Ronald W. Schafer, Discrete-Time Signal Processing: Pearson New International Edition, Pearson Education Limited 2014.
Changing subject? No On-site course
Maximum number of participants 35
Assignment procedure Direct assignment