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