Jedes Modul umfasst 3 ECTS. Sie wählen insgesamt 10 Module/30 ECTS in den folgenden Modulkategorien:
- 12-15 ECTS in Technisch-wissenschaftlichen Modulen (TSM)
TSM-Module vermitteln Ihnen profilspezifische Fachkompetenz und ergänzen die dezentralen Vertiefungsmodule. - 9-12 ECTS in Erweiterten theoretischen Grundlagen (FTP)
FTP-Module behandeln theoretische Grundlagen wie die höhere Mathematik, Physik, Informationstheorie, Chemie usw. Sie erweitern Ihre abstrakte, wissenschaftliche Tiefe und tragen dazu bei, den für die Innovation wichtigen Bogen zwischen Abstraktion und Anwendung spannen zu können. - 6-9 ECTS in Kontextmodulen (CM)
CM-Module vermitteln Ihnen Zusatzkompetenzen aus Bereichen wie Technologiemanagement, Betriebswirtschaft, Kommunikation, Projektmanagement, Patentrecht, Vertragsrecht usw.
In der Modulbeschreibung (siehe: Herunterladen der vollständigen Modulbeschreibung) finden Sie die kompletten Sprachangaben je Modul, unterteilt in die folgenden Kategorien:
- Unterricht
- Dokumentation
- Prüfung
The goal of this module is to introduce the students to the powerful world of statistical digital signal processing. While at the bachelor level digital signal processing is most often taught with deterministic signals, in the real world most interesting signals are stochastic in nature. Hence in more advanced applications, such as prediction or noise removal, the theories presented in this module are essential.
The basic digital signal processing, linear algebra and probability theory necessary to understand the module are brushed-up at the beginning. Then stochastic processes are introduced which allows the proper formulation of the optimal filtering and spectral estimation problem later on. After an in-depth treatment of the optimal filtering and estimation problem, adaptive filters are introduced which are a popular choice for many advanced statistical digital signal processing problems.
Eintrittskompetenzen
Understanding of the following concepts at the Bachelor of Science level
• Calculus
• Linear algebra
• Probability/Statistics
• Digital signal processing
Lernziele
• The student becomes familiar with stochastic signals and systems
• The student understands and can apply the different methods for signal modeling
• The student has an in-depth understanding of Wiener filtering and knows how a discrete Kalman filter can be used to solve a stochastic filtering problem
• The student understands and can apply the different methods for spectrum estimation
• The student knows the most common adaptive filters and is able to select the proper one for the application at hand
Modulinhalt
The module starts with a review of basic digital signal processing, linear algebra and probability theory. It then introduces some concepts about stochastic processes, which are necessary to understand the following applications of statistical signal processing. Then the module discusses several different ways of signal modeling which can be used for parametric methods later on. Then one of the core topics is presented, which is the optimal linear mean square error estimation of a signal which is corrupted by additive noise. The module then presents a chapter about the very important topic of spectral estimation and finally concludes with the application of the learned theory for designing adaptive filters.
The available 14 weeks are organized as follows:
• 2 weeks: Background (review of digital signal processing and linear algebra)
• 3 weeks. Discrete-time random processes (including a review of probability)
• 2 weeks: Signal modeling
• 3 weeks: Wiener filtering (including the discrete Kalman Filter)
• 2 weeks: Spectrum estimation
• 2 weeks: Adaptive filtering
Lehr- und Lernmethoden
• A three hour session each week for 14 weeks
• The first hour is a homework review session where the homework is discussed. The homework is “paper and pencil” homework and small Matlab programming assignments
• The next two hours are lecture hours, where new theory is introduced
Bibliografie
“Statistical Digital Signal Processing and Modeling” by Monson H. Hayes
Vollständige Modulbeschreibung herunterladen
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