Module Handbook

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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:

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