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