Technische Hochschule Ostwestfalen-Lippe


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Institute Industrial IT (inIT)

Information Fusion

Degree programme: Information Technology (Master, M. Sc.)
Course name: Information Fusion
Abbreviation: IFU
Semester: 2nd semester, summer
Responsible lecturer: Prof. Dr.-Ing. Volker Lohweg
Lecturers: Prof. Dr.-Ing. Volker Lohweg, Practical Exercise: M.Sc. Uwe Mönks
Language: English
Relation to curriculum: Optional course
Teaching type / hours: Lecture / 3 hours per week, Practical Exercise / 2 hours
Students' workload: 180 hours = 75 hours confrontation time (lectures, exercises, and labs) plus 105 hours additional student individual work/homework time
ECTS credits: 6 CR
Prerequisites: Mathematics for undergraduates, Signals and Systems or System Modeling and Analysis, Image Analysis or Digital Image Processing
Goals: Information Fusion identifies the concept of combining data from different information sources, such as sensors or human experts. The conceptual strategy is based on obtaining new or more certain information by data combination. In numerous applications it is not possible to capture all necessary information or features by a single sensor source. In such cases more sensors and additive expert's know-how can generate more precise data regarding different real world systems, e.g. robots, machines and equipment, data experts systems, cognitive systems and so on.
Contents: The following topics are highlighted:
  • Sensory Signal Representation
  • Fusion Models
  • Human-centric Models
  • Fusion Methods:
    • Statistical Concepts, Dempster-Shafer-Theory (Evidence Theory), Fuzzy Concepts, Neuronal Concepts.
  • Multi-Sensor-Fusion and Real World Examples
Examination: programming project with presentation (30 min), graded
Teaching media: Beamer, blackboard, charts, script Information Fusion
Literature: Bosse, Eloi; Concepts, Models, and Tools for Information Fusion, Artech House Publishers, Boston, MA, USA 2007.
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing, James C. Bezdek (Editor), James Keller, Raghu Krisnapuram, Nikhil Pal, 1999.
Shafer, Glenn; A Mathematical Theory of Evidence, Princeton University Press, 1976.
Campos, Fabio; Decision Making in Uncertain Situations: An Extension to the Mathematical Theory of Evidence,, Boca Raton, FL, USA 2006.