Course EIT-ISE-112-K-7
Sensor Signal Processing (2V+2L, 5.0 LP)
Course Type
SWS | Type | Course Form | CP (Effort) | Presence-Time / Self-Study | |
---|---|---|---|---|---|
- | K | Lecture with exercise classes (V/U) | 5.0 CP | ||
2 | V | Lecture | 28 h | 47 h | |
2 | L | Project course | 28 h | 47 h | |
(2V+2L) | 5.0 CP | 56 h | 94 h |
Basedata
SWS | 2V+2L |
---|---|
CP, Effort | 5.0 CP = 150 h |
Position of the semester | 1 Sem. in WiSe |
Level | [7] Master (Advanced) |
Language | [EN] English |
Lecturers | |
Area of study | [EIT-ISE] Integrated Sensor Systems |
Livecycle-State | [NORM] Active |
Contents
- Basic methods of signal analysis and the computation of characteristic and invariant descriptors (features)
- Processing of signals from single sensors und homogeneous or heterogeneous Sensor-Arrays
- Dimensionality reduction of high-dimensional sensor data by linear and non-linear methods, e.g. by explicit selection of features
- Methods der cluster analysis
- Methods for multi-dimensional sensor data analysis: projection and visualisation, fusion
- methods for classification of sensor data: statistical pattern recognition, artificial neural networks, Methods of rule-based and fuzzy classification
- Advanced ptimization methods for parameter- or structure optimization of sensor systems
- Relations, dependencies, and optimization potential between sensor realization, electronics, and algorithmics.
- New aspects of reliable sensor systems (self-x properties)
Literature
- R. Hoffmann, Signalanalyse und Erkennung, Springer 1998, ISBN 3-540-63443-6S
- Haykin, Neural Networks A Comprehensive Foundation, Prentice Hall, 1998, ISBN 0132733501
- K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, 1990, ISBN 0122698517
- R. Duda, P. Hart, D. Stork, Pattern Classification, Wiley, 2000, ISBN 0471056693
Materials
Foliensätze, QuickCog mit Anwendungsbeispielen, Matlab/Python mit entsprechenden Toolboxen zur multisensorischen Signalverarbeitung und Klassifikation, ORANGE-System, künftig mit der DAICOX-Erweiterung zum automatisierten Entwurf intelligenter Systeme, umfangreiches Sensorlabor mit relevanten Sensorprinzipien und Demonstratoren, u. a. Magnetsensoren, Farbsensoren, akustische Sensoren, Beschleunigungssensoren, Gassensoren, Impedanzspetroskopie, Farb-, Tiefen- und Infrarotkameras, oder Emotiv-Kit (BCI) zur eigenständigen Datenaquisition, inkl. Life-Klassifikation der Studierenden sowie Lokalisierungs-, Fahrermüdigkeitserkennungs- und Lab-on-Spoon-Demonstratoren
Registration
Registration in the first lecture
Requirements for attendance (informal)
Modules:
- [EIT-ISE-105-M-2] Electrical Measurement Technique I (M, 4.0 LP)
- [EIT-ISE-106-M-5] Electrical Measurement Technique II (M, 3.0 LP)
Requirements for attendance (formal)
None
References to Course [EIT-ISE-112-K-7]
Module | Name | Context | |
---|---|---|---|
[EIT-ISE-112-M-7] | Sensor Signal Processing | P: Obligatory | 2V+2L, 5.0 LP |
[SO-08-2611-M-6] | Computation -. Specialized Seminars | WP: Obligation to choose | 2V+2L, 5.0 LP |