Module Handbook

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Module EIT-ISE-112-M-7

Sensor Signal Processing (M, 5.0 LP)

Module Identification

Module Number Module Name CP (Effort)
EIT-ISE-112-M-7 Sensor Signal Processing 5.0 CP (150 h)

Basedata

CP, Effort 5.0 CP = 150 h
Position of the semester 1 Sem. in WiSe
Level [7] Master (Advanced)
Language [EN] English
Module Manager
König, Andreas, Prof. Dr.-Ing. (PROF | DEPT: EIT)
Lecturers
König, Andreas, Prof. Dr.-Ing. (PROF | DEPT: EIT)
Area of study [EIT-ISE] Integrated Sensor Systems
Reference course of study [EIT-88.C48-SG#2021] M.Sc. Automation & Control (A&C) [2021]
Livecycle-State [NORM] Active

Courses

Type/SWS Course Number Title Choice in
Module-Part 		Presence-Time /
Self-Study 		SL SL is
required for exa. 		PL CP Sem.
2V+2L EIT-ISE-112-K-7
Sensor Signal Processing
P 56 h 94 h
PROJ-Schein
ja PL1 5.0 WiSe
  • About [EIT-ISE-112-K-7]: Title: "Sensor Signal Processing"; Presence-Time: 56 h; Self-Study: 94 h
  • About [EIT-ISE-112-K-7]: The study achievement "[PROJ-Schein] proof of successful completion of the project(s)" must be obtained.
    • It is a prerequisite for the examination for PL1.

Examination achievement PL1

  • Form of examination: oral examination (30 Min.)
  • Examination Frequency: each semester

Evaluation of grades

The grade of the module examination is also the module grade.


Contents

From [EIT-ISE-112-K-7] Sensor Signal Processing:
  • 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)

Competencies / intended learning achievements

After completing this module you can...
  • ... explain the relevant principals and methods from the field of Computational Intelligence, in particular for the field of sensor technology.
  • ... employ selected relevant methods and their configuration in a common design environment (Matlab).
  • ... design, validate, and optimize complete application-specific systems.
  • ... adapt and extend the achieved implementation to changing needs.
  • ... explain the interdependence of system solution with available, potentially restricted implementation platforms (Sensors/Hardware).

Requirements for attendance of the module (informal)

Modules:

Requirements for attendance of the module (formal)

None

References to Module / Module Number [EIT-ISE-112-M-7]

Course of Study Section Choice/Obligation
[EIT-88.781-SG#2010] M.Sc. Electrical and Computer Engineering [2010] [Free Elective Area] Elective Subjects [W] Elective Module
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
[EIT-88.D55-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
Module-Pool Name
[EIT-AC-MSC-RCS-WP-CORE-MPOOL-7] RCS Core Electives
[EIT-EIT-MSC-TW-MPOOL-7] Technical Elective Modules Master EIT
[EIT-SIAK-DT-ENG-MPOOL] SIAK Certificate "Digital Transformation" - Modules EIT "Engineering"
[GS-CVT-EE-2022-E-MPOOL-6] Catalog Electives Electrical and Computer Engineering 2022
[GS-CVT-EE-E-MPOOL-6] Catalog Electives Electrical and Computer Engineering