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Curriculum

Modul CS3100-KP08, CS3100SJ14

Signal Processing (SignalV14)

Duration:


1 Semester
Turnus of offer:


each winter semester
Credit points:


8
Course of studies, specific field and terms:
  • Master CLS 2023 (compulsory), mathematics, 1st semester
  • Bachelor Robotics and Autonomous Systems 2020 (compulsory), Robotics and Autonomous Systems, 5th semester
  • Bachelor Computer Science 2019 (optional subject), major subject informatics, Arbitrary semester
  • Bachelor Computer Science 2019 (compulsory), Canonical Specialization Bioinformatics and Systems Biology, 5th semester
  • Bachelor MES 2020 (compulsory), computer science, 5th semester
  • Bachelor Media Informatics 2020 (optional subject), computer science, 5th or 6th semester
  • Bachelor Medical Informatics 2019 (optional subject), computer science, 4th to 6th semester
  • Bachelor Computer Science 2014 (compulsory), specialization field robotics and automation, 5th semester
  • Bachelor Computer Science 2014 (compulsory), specialization field bioinformatics, 5th semester
  • Bachelor Computer Science 2016 (compulsory), Canonical Specialization Bioinformatics, 5th semester
  • Bachelor Computer Science 2016 (optional subject), major subject informatics, Arbitrary semester
  • Bachelor Computer Science 2016 (compulsory), Canonical Specialization Web and Data Science, 5th semester
  • Master CLS 2016 (compulsory), mathematics, 1st semester
  • Bachelor Robotics and Autonomous Systems 2016 (compulsory), Robotics and Autonomous Systems, 5th semester
  • Bachelor IT-Security 2016 (optional subject), computer science, Arbitrary semester
  • Bachelor Biophysics 2016 (compulsory), computer science, 5th semester
  • Bachelor Medical Informatics 2014 (compulsory), computer science, 5th semester
  • Bachelor MES 2014 (compulsory), computer science, 5th semester
  • Bachelor Media Informatics 2014 (optional subject), computer science, 5th or 6th semester
  • Bachelor Computer Science 2014 (optional subject), central topics of computer science, 5th semester
Classes and lectures:
  • Image Processing (lecture, 2 SWS)
  • Image Processing (exercise, 1 SWS)
  • Signal Processing (exercise, 1 SWS)
  • Signal Processing (lecture, 2 SWS)
Workload:
  • 90 Hours in-classroom work
  • 110 Hours private studies
  • 40 Hours exam preparation
Contents of teaching:
  • Linear time-invariant systems
  • Impulse response
  • Convolution
  • Fourier transform
  • Transfer function
  • Correlation and energy density of deterministic signals
  • Sampling
  • Discrete-time signals and systems
  • Discrete-time Fourier transform
  • z-Transform
  • FIR and IIR filters
  • Block diagrams
  • FIR filter design
  • Discrete Fourier transform (DFT)
  • Fast Fourier transform (FFT)
  • Characterization and processing of random signals
  • Introduction, interest of visual information
  • 2D Sampling
  • Image enhancement
  • Edge detection
  • Multiresolution concepts: Gaussian and Laplacian Pyramid, wavelets
  • Principles of image compression
  • Segmentation
  • Morphological image processing
  • Students work self-actingly and independently with regard to the roles of GSP of the University of Lübeck.
Qualification-goals/Competencies:
  • Students are able to explain the fundamentals of linear system theory.
  • They are able to define and competently explain the essential elements of signal processing mathematically.
  • They will have a command of mathematical methods for the description and analysis of continuous-time and discrete-time signals and systems.
  • They are able to design digital filters and know various structures for their implementation.
  • They are able to explain the basic techniques for describing and processing of random signals.
  • They will have basic knowledge of two-dimensional system theory.
  • They are able to describe the main techniques for image analysis and image enhancement.
  • They are able to apply the learned principles in practice.
Grading through:
  • written exam
Responsible for this module:
Teachers:
Literature:
  • A. Mertins: Signaltheorie: Grundlagen der Signalbeschreibung, Filterbänke, Wavelets, Zeit-Frequenz-Analyse, Parameter- und Signalschätzung - Springer-Vieweg, 3. Auflage, 2013
  • A. K. Jain: Fundamentals of Digital Image Processing - Prentice Hall, 1989
  • Rafael C. Gonzalez, Richard E. Woods: Digital Image Processing - Prentice Hall 2003
Language:
  • offered only in German
Notes:

Prerequisites for attending the module:
- None

Prerequisites for the exam:
- Successful completion of homework assignments during the semester (at least 50% of max. points).

Module exam:
- CS3100-L1: Signal Processing, written exam, 90 min, 100% of module grade

Letzte Änderung:
17.2.2022