Lehrinhalte
A series of 3 lectures provides the necessary background on robust signal processing and machine learning:
1. Background on robust signal processing 2. Robust regression and robust filters for artifact cancellation 3. Robust location and covariance estimation and classification
They are followed by two lectures on selected biomedical applications, such as,
? Body-worn sensing of physiological parameters ? Optical heart rate sensing (PPG) ? Signal processing for the electrocardiogram (ECG) ? Biomedical image processing
Students then work in groups to apply robust signal processing algorithms to real-world biomedical data. Depending on the application, the data is either recorded by the students, or provided to them. The group results are presented during a 20-minute presentation. The final assessment is based on the presentation and an oral examination.
Literature
? Slides can be downloaded via Moodle.
Further reading: ? Zoubir, A. M. and Koivunen, V. and Ollila, E. and Muma, M.: Robust Statistics for Signal Processing. Cambridge University Press, 2018. ? Zoubir, A. M. and Koivunen, V. and Chackchoukh J, and Muma, M. Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts. IEEE Signal Proc. Mag. Vol. 29, No. 4, 2012, pp. 61-80. ? Huber, P. J. and Ronchetti, E. M.: Robust Statistics. Wiley Series in Probability and Statistics, 2009. ? Maronna, R. A. and Martin, R. D. and Yohai, V. J.: Robust Statistics: Theory and Methods. Wiley Series in Probability and Statistics, 2006.
Voraussetzungen
Fundamental knowledge of statistical signal processing
Online-Angebote
Moodle
A series of 3 lectures provides the necessary background on robust signal processing and machine learning:
1. Background on robust signal processing 2. Robust regression and robust filters for artifact cancellation 3. Robust location and covariance estimation and classification
They are followed by two lectures on selected biomedical applications, such as,
? Body-worn sensing of physiological parameters ? Optical heart rate sensing (PPG) ? Signal processing for the electrocardiogram (ECG) ? Biomedical image processing
Students then work in groups to apply robust signal processing algorithms to real-world biomedical data. Depending on the application, the data is either recorded by the students, or provided to them. The group results are presented during a 20-minute presentation. The final assessment is based on the presentation and an oral examination.
Literature
? Slides can be downloaded via Moodle.
Further reading: ? Zoubir, A. M. and Koivunen, V. and Ollila, E. and Muma, M.: Robust Statistics for Signal Processing. Cambridge University Press, 2018. ? Zoubir, A. M. and Koivunen, V. and Chackchoukh J, and Muma, M. Robust Estimation in Signal Processing: A Tutorial-Style Treatment of Fundamental Concepts. IEEE Signal Proc. Mag. Vol. 29, No. 4, 2012, pp. 61-80. ? Huber, P. J. and Ronchetti, E. M.: Robust Statistics. Wiley Series in Probability and Statistics, 2009. ? Maronna, R. A. and Martin, R. D. and Yohai, V. J.: Robust Statistics: Theory and Methods. Wiley Series in Probability and Statistics, 2006.
Voraussetzungen
Fundamental knowledge of statistical signal processing
Online-Angebote
Moodle
- Lehrende: Jasin Machkour
- Lehrende: Michael Muma
Semester: ST 2022