Digitale Signalverarbeitung: Filterung und Spektralanalyse mit MATLAB®- Übungen (German Edition) [Karl-Dirk Kammeyer, Kristian Kroschel] on Amazon. com. Prof. Dr.-Ing. Karl-Dirk Kammeyer (Former Head of Department) Digitale Signalverarbeitung – Filterung und Spektralanalyse mit MATLAB®-Übungen BibT EX. Digitale Signalverarbeitung: Filterung und Spektralanalyse mit MATLAB- Übungen. By Karl Dirk Kammeyer, Kristian Kroschel.
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Webmaster06 Aug Professional Competence Theoretical Knowledge The students know and understand basic algorithms of digital signal processing. They can control their level of knowledge during the lecture period by solving tutorial problems, software tools, clicker system.
They are familiar with the basics of adaptive filters. Written exam Workload in Hours: They are aware of the effects caused by quantization of filter coefficients and signals.
The students know and understand basic algorithms of digital signal processing. Most important for… Prospective Students Students. Digital filters and signal processing.
They can choose and parameterize suitable filter striuctures. Gerhard Bauch Admission Requirements: The students are able to apply methods of digital signal processing to new problems.
Furthermore, the students are able to apply methods of spectrum estimation and to take the effects of a limited observation window into account. Characterization of digital filters using pole-zero plots, important properties of digital filters. Mathematics Signals and Systems Fundamentals of signal and system theory as well as random processes. 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.
Personal Competence Social Competence The students can jointly solve specific problems. Capabilities The students are able to apply methods of digital signal processing to new problems.
They know basic structures of digital filters and can identify and assess important properties including stability. In particular, the can design adaptive filters according to the minimum mean squared error MMSE criterion and develop an efficient implementation, e.
They can perform traditional and parametric methods of spectrum estimation, also taking a sjgnalverarbeitung observation window into account.
Transforms of discrete-time signals: None Recommended Previous Knowledge: Autonomy The students are able to acquire relevant information from appropriate literature sources. Subnavigation Back to Students Organisational details about your studies Exams-dates-modul descriptions Fundamentals of spectral transforms Fourier series, Fourier transform, Laplace transform Educational Objectives: The students are able to acquire relevant information from appropriate literature sources.