n.BA.AD.ISP.23HS (Image and Signal Processing) 
Module: Image and Signal Processing
This information was generated on: 11 May 2024
No.
n.BA.AD.ISP.23HS
Title
Image and Signal Processing
Credits
4

Description

Version: 1.0 start 01 August 2023

 

Study Programme Applied Digital Life Sciences
Regulations Applicable RPO, 29 January 2008, School of Life Sciences and Facility Management Academic Regulations, 15 Dec. 2009, Annex for the Bachelor of Applied Digital Life Sciences degree programme
Module Type  
X Compulsory Module    Elective Module    Optional Module
Planned Semester 4th Semester
Module Coordinator Norman Juchler
Telephone / E-Mail +41 (0)58 934 56 87 / norman.juchler@zhaw.ch
Lecturer(s),
Speaker(s),
Associate(s)		 Norman Juchler, Georg Spinner
Entrance Requirements Contents from the modules Data and Information, Programming, Physical Computing in Life Sciences
Learning Outcomes and Competencies Technical skills
The students
  • know the difference between analog and digital signals
  • know the meaning of the Fourier transform in signal theory
  • are able to create and interpret spectra
  • know how to handle time series and image data
  • know the effect of noise and other sources of error
  • know important filter operations and transformations
Transferable skills
The students
  • recognize the importance of signal processing in clinical applications such as wearables or medical imaging
  • are aware of the physical and information-theoretic limitations of signals
  • understand that signals represent a distorted and noisy reality
  • can critically reflect on the information content of signals (or images)
Module Content
  • Mathematical and information theoretical basics
  • Analog and digital signals
  • Sampling / sampling theorem
  • Fourier transform / spectra / wavelets
  • Basics of filtering / convolution
  • Dealing with erroneous signals: noise, distortion, blurring
  • Introduction of important filters and transformations
  • Tools in Python (NumPy, SciPy.signal, OpenCV)
Follow-up Modules Clinical Data Processing, Projektarbeit 2, Medical Image Analysis and Probabilistic Modelling
Methods of Instruction  Lecture and project work
Digital Resources Moodle
Lesson Structure / Workload  
 Contact Hours 42
 Guided Self-Study 14
 Independent Self-Study 64
 Total Workload 120
Classroom Attendance Attendance is encouraged, but not enforced with attendance list
Assessment
  • Experience grade (project work) 30%
  • Written exam at the end of the semester 70%
 
If there is a low number of participants, the lecturer may change the form of a repeat examination after consultation with the head of the study programme: e.g. an oral examination can be used to replace a written one. Please report any changes to the form of examinations by e-mail to pruefungsadmin.lsfm@zhaw.ch and Cc. Head of study programme.
Language of Instruction  English
Comments -

 

Course: Image and Signal Processing
No.
n.BA.AD.ISP.23HS.V
Title
Image and Signal Processing

Note

  • No module description is available in the system for the cut-off date of 11 May 2024.