Stud.IP Uni Oldenburg
University of Oldenburg
18.05.2022 02:11:54
inf960 - Fundamental Competences in Computing Science I: Signals and Dynamical Systems (Course overview)
Department of Computing Science 6 KP
Module components Semester courses Wintersemester 2021/2022 Examination
Lecture
Exercises
Notes for the module
Reference text
This course is part of the base curriculum of the MSc program "Engineering of Socio-Technical Systems". It provides students featuring a background in psychology with fundamental competences in computer science and related subjects. This course is also intended for students with a background in computer science lacking prior knowledge in digital signal processing
Time of examination
At the end of the lecture period
Module examination
Hands-on exercises and written or oral exam
Skills to be acquired in this module
This course provides an introduction into digital signal processing. It covers the mathematical foundations necessary for understanding the impact digitization has on a continuous signal as well as the goal-directed synthesis of digital filters. As such, it lays the theoretical foundations preparing for understanding and designing applications of digital signal processing in a variety of fields relevant to the MSc EngSTS, like neurophysiological measurements, brain-computing interfaces, or embedded control. In contrast to subsequent modules of the study programme, the module itself does not aim at covering such applications, but at providing a solid grasp of the underlying principles and the fundamental constraints to digital signal processing. It is targeted at psychologists, but also at computer scientists who have not previously been exposed to a systematic mathematical treatment of the fundamentals of digital signal processing.

Professional competences:
The students:
  • Name the concepts of signal and image processing in technical systems
  • Name the methods/algorithms of preprocessing, filtering, classification, interpretation and visualisation of signals and pictures
  • Select algorithms appropriately
  • Evaluate the effectiveness of algorithms
  • Design algorithms and processing chains and evaluate their quality

Methodological competences:
The students:
  • Get used to specific subjects of signal and image processing

Social competences:
The students:
  • Present solutions for specific questions in signal and image processing

Self-competences:
The students:
  • Reflect their solutions by using methods learned in this course