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Lecture Information: Signal Processing Methods (Winter Semester 2024/2025)

Welcome to the Signal Processing Methods course, offered as part of the Master’s degree program in Electrical Engineering and Information Technology (ETIT) during the Winter Semester 2024/2025. This module is also available to students enrolled in the Mechatronics and Information Technology and Medical Engineering programs. The course is classified as an elective module for all three programs.

The course consists of a combination of lectures and tutorials, with 2 hours per week dedicated to each. The lectures will provide a thorough theoretical foundation in signal processing methods, while the tutorials will offer practical exercises and applications to reinforce the material covered in the lectures.

Students will have acquired several key competencies after successfully completing this course. They will be able to select appropriate estimation methods based on both theoretical properties and practical considerations, ensuring that they can tackle a range of signal-processing challenges. Additionally, students can derive estimators for specific problems and weigh the pros and cons of various data decomposition methods, applying them to given scenarios and interpreting the results effectively.

The course also provides in-depth knowledge of time-frequency analysis methods, enabling students to understand both their advantages and limitations. Students will develop the skills to interpret time-frequency representations of signals and will learn how to choose appropriate analysis and synthesis windows or wavelets, depending on the context. Furthermore, students will be capable of determining time-frequency transforms of given signals, solidifying their understanding of these crucial signal processing techniques.

We look forward to guiding you through this engaging and challenging course, providing you with both the theoretical and practical tools necessary for advanced signal processing.

This module introduces students to advanced signal processing methods that are widely employed in practice. The three main topic areas are:

1. Parameter estimation
2.  Decomposition of data into components and modes
3.  Time-frequency analysis

The following topics are treated:

• Best linear unbiased estimator
• Maximum likelihood estimation
• General Bayesian estimators
• Linear Bayesian estimators
• Principal component analysis
• Independent component analysis
• Dynamic and empirical mode decomposition
• Hilbert spaces and frames
• Short-time Fourier transform
• Wavelets
• Analytic signals
• Wigner-Ville-Distribution
• Huang-Hilbert transform

Illustrating examples from diverse application areas are discussed.

The examination in this module consists of a graded written examination of 180 minutes.

• Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory.
  Author: Kay
  ISBN: 9788131728994, 8131728994
  Publisher: Pearson Education
  Language: English

• Generalized Principal Component Analysis.
  Authors: René Vidal , Yi Ma , S.S. Sastry
  eBook ISBN: 978-0-387-87811-9

• Independent component analysis: algorithms and applications.
  Authors: A. Hyvärinen, E. Oja
  Neural Networks, Volume 13, Issues 4–5, 2000.
  ISSN: 0893-6080

• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.
  Authors: Norden E. Huang et al.
  Published by:Royal Society
  Online ISSN:1471-2946

• Wavelet Transforms and Their Applications.
  Authors: Lokenath Debnath , Firdous Ahmad Shah
  Publisher: Birkhäuser Boston, MA
  eBook ISBN: 978-0-8176-8418-1

Evaluation im Wintersemester 2021/2022 (Online-Vorlesung)

For further information, please contact Abdullah Al-Hammadi.