- 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)
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 | |
Lecturers | |
Area of study | [EIT-ISE] Integrated Sensor Systems |
Reference course of study | [EIT-88.?-SG#2021] M.Sc. Automation and 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
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 (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 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] | Elective Subjects | [W] Elective Module |
[EIT-88.?-SG#2021] M.Sc. Electrical and Computer Engineering [2021] | Technical Elective Modules | [W] Elective Module |
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] | Elective Subjects | [W] Elective Module |
[EIT-88.?-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] | Elective Subjects | [W] Elective Module |
Module-Pool | Name | |
[EIT-AUT-RCS-WP-MPOOL-7] | RCS Core Electives | |
[GS-CVT-EE-E-MPOOL-6] | Catalog Electives Electrical and Computer Engineering |