Module: Digital Signal Processing and Digital Filters
|Digital Signal Processing and Digital Filters||Lecture||3||Winter Semester|
|Digital Signal Processing and Digital Filters||Recitation Section (large)||1||Winter Semester|
Prof. Gerhard Bauch
Recommended Previous Knowledge:
- Mathematics 1-3
- Signals and Systems
- Fundamentals of signal and system theory as well as random processes.
- Fundamentals of spectral transforms (Fourier series, Fourier transform, Laplace transform)
The students know and understand basic algorithms of digital signal processing. They are familiar with the spectral transforms of discrete-time signals and are able to describe and analyse signals and systems in time and image domain. They know basic structures of digital filters and can identify and assess important properties including stability. They are aware of the effects caused by quantization of filter coefficients and signals. They are familiar with the basics of adaptive filters. They can perform traditional and parametric methods of spectrum estimation, also taking a limited observation window into account.
The students are able to apply methods of digital signal processing to new problems. They can choose and parameterize suitable filter striuctures. In particular, the can design adaptive filters according to the minimum mean squared error (MMSE) criterion and develop an efficient implementation, e.g. based on the LMS or RLS algorithm. Furthermore, the students are able to apply methods of spectrum estimation and to take the effects of a limited observation window into account.
The students can jointly solve specific problems.
The students are able to acquire relevant information from appropriate literature sources. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.
ECTS-Credit Points Module:
Workload in Hours:
Independent Study Time: 124, Study Time in Lecture: 56
Course: Digital Signal Processing and Digital Filters
Transforms of discrete-time signals:
Discrete-time Fourier Transform (DTFT)
Discrete Fourier-Transform (DFT), Fast Fourier Transform (FFT)
Correspondence of continuous-time and discrete-time signals, sampling, sampling theorem
Fast convolution, Overlap-Add-Method, Overlap-Save-Method
Fundamental structures and basic types of digital filters
Characterization of digital filters using pole-zero plots, important properties of digital filters
Design of linear-phase filters
Fundamentals of stochastic signal processing and adaptive filters
LMS- and RLS-algorithm
Traditional and parametric methods of spectrum estimation
K.-D. Kammeyer, K. Kroschel: Digitale Signalverarbeitung. Vieweg Teubner.
V. Oppenheim, R. W. Schafer, J. R. Buck: Zeitdiskrete Signalverarbeitung. Pearson StudiumA. V.
W. Hess: Digitale Filter. Teubner.
Oppenheim, R. W. Schafer: Digital signal processing. Prentice Hall.
S. Haykin: Adaptive flter theory.
L. B. Jackson: Digital filters and signal processing. Kluwer.
T.W. Parks, C.S. Burrus: Digital filter design. Wiley.