Turnus of offer:
each year, can be started in winter or summer semester
Course of studies, specific field and terms:
- Master MES 2020 (advanced module), computer science / electrical engineering, Arbitrary semester
- Master Entrepreneurship in Digital Technologies 2020 (advanced module), technology field computer science, Arbitrary semester
- Master Computer Science 2019 (optional subject), advanced module, Arbitrary semester
- Master Biophysics 2019 (advanced module), advanced curriculum, 1st and 2nd semester
- Master IT-Security 2019 (advanced module), Elective Computer Science, 1st or 2nd semester
- Master MES 2014 (advanced module), computer science / electrical engineering, 1st and/or 2nd semester
- Master Entrepreneurship in Digital Technologies 2014 (advanced module), technology field computer science, 2nd and/or 3rd semester
- Master Computer Science 2014 (advanced module), advanced curriculum, 2nd and/or 3rd semester
Classes and lectures:
- CS5194 T: Lab course (project work, 3 SWS)
- - CS4220 T: Pattern Recognition (lecture with exercises, 3 SWS)
- - CS5275 T: Selected Topics of Signal Analysis and Enhancement (lecture with exercises, 3 SWS)
- 90 Hours in-classroom work
- 150 Hours private studies
- 60 Hours group work
- 20 Hours written report
- 40 Hours exam preparation
Contents of teaching:
- Introduction to statistical signal analysis
- Principles of feature extraction and pattern recognition
- Linear optimum filters
- Adaptive filters
- Spectrum analysis
- Basic concepts of multirate signal processing
- Applications in speech and image processing
- Realization of signal processing tasks for typical application scenarios in teamwork
- Students are able to explain the basic elements of stochastic signal processing and optimum filtering.
- They are able to describe and apply linear estimation theory.
- Students are able to describe the concepts of adaptive signal processing.
- They are able to explain theconcepts of feature extraction and pattern recognition.
- They are able to analyze and design multirate systems.
- Students are able to explain various practical applications of signal processing algorithms.
- They are able to create and implement signal processing systems on their own and in teamwork.
Responsible for this module:
- : See description of module parts
- German and English skills required
Examination prerequisites can be defined at the beginning of the semester. If preliminary work is defined, it must have been completed and positively evaluated before the first examination.
Admission requirements for taking the module:
Admission requirements for participation in module examination(s):
- Successful completion of the project assignment, seminar presentation and exercise assignments as specified at the beginning of the semester.
- CS4510-L1: Signal Analysis, oral exam, 100% of module grade.
(Consists of CS4220 T, CS5275 T, CS5194 T)
Letzte Änderung: 17.8.2022