Course code 02 36 6254 00
Number of ECTS points 5
Course title in the language of instruction
Signal Processing
Course title in Polish Signal Processing (Przetwarzanie sygnałów)
Course title in English
Signal Processing
Language of instruction English
Form of classes
Lecture Tutorials Laboratory Project Seminar Other Total of teaching hours during semester
Contact hours 30 30 0 60
E-learning No No No No No No
Assessment criteria (weightage) 0.40 0.60 0.00
Unit running the course Instytut Elektroniki
Course coordinator prof. dr hab. inż. Paweł Strumiłło
Course instructors
Prerequisites
The student knows fundamentals of mathematical analysis and matrix algebra
Course learning outcomes
  1. Distinguish between types of biological signals
  2. Select digitazation parameters for analog signals
  3. Calculate and interpret the Fourier spectrum of the signal
  4. Use appropriate digital filters in biomedical applications
  5. Detect components of biomedical signals in the time and frequency domain
  6. Use basic methods of statistical classification of medical data
Programme learning outcomes
  1. null
  2. null
  3. null
Programme content LECTURE: 1. Biological signals - classification 2. Measurement of biological signals, sources of interference 3. Linear systems and weave 4. Spectral analysis of signals, properties and applications 5. Sampling and quantization of signals - digital signals 6. Digital filtering of signals 7. Models of random signals, correlation analysis 8. Analog-to-digital and digital-to-analog processing 9. Sample systems and programs for biological signal analysis LABORATORY: Part I: Introduction to signal processing in Python (8 laboratory exercises) 1. Data display, board activities and functions 2. Saving and loading files: text, binary, Matlab and wave files 3. Signal display 4. Fourier transformation of signals 5. Digital filtering of signals Part II: Project on processing and analysis of biological signals
Assessment methods
The written test will verify the following skills:
1. Recognizing different types of biomedical signals
2. Selecting proper recording parameters for digital acquisition of analog signals
3. Computing and interpreting the Discrete Fourier Transform of signals
The laboratory and project will verify the skills:
4. Applying appropriate digital filters to achieve the given processing objective
5. Detecting specific components of biomedical signals in time and spectrum domain
6. Applying basic statistical methods for medical data classification

 
Grading policies A written form verifying knowledge of lecture material and a written project report from and project presentation.
Course content LECTURE: 1. Biomedical signals ? characterization and classification 2. Measurements of biological signals, sources of noise 3. Linear systems and convolution 4. Spectral analysis of signals, properties and applications 5. Sampling and quantization of signals Digital signals 6. Digital filtering of signals 7. Random models of signals models, correlation analysis 8. Analog-to-digital and digital-to-analog conversion 9. Case studies: systems and programs for analysis of biomedical signals LABORATORATORY: Part I: Introduction to Signac processing in Python (8 laboratory sessions) 1. Data visualization, array operations and functions 2. Storing/loading: text, binary, Matlab and wave files 3. Plotting signals 4. Fourier transform of signals 5. Digital filtering of signals Part II: Work on a project related to selected problems of signal processing and analysis
Basic reference materials
  1. Notatki wykładowe przekazane przez prowadzącego
Other reference materials
  1. Tomasz Zieliński , Cyfrowe przetwarzanie sygnałów. Od teorii do zastosowań, WKiŁ, 2009, ISBN: 978-83-206-1640-8
  2. The Scientist and Engineer's Guide to Digital Signal Processing by Steven W. Smith (www.dspguide.com)
Average student workload outside classroom
66
Comments
Updated on 2019-08-31 09:25:36
Archival course yes/no no