Home  »  Institute of Science »  Master's of Biomedical Engineering without Thesis

COURSE UNIT TITLECOURSE UNIT CODESEMESTERTHEORY + PRACTICE (Hour)ECTS
ADVANCED TOPICS IN SIGNALS ANS SYSTEMS FOR BIOMEDICAL ENGIN BME531 ------- 3 + 0 10

TYPE OF COURSE UNITElective Course
LEVEL OF COURSE UNITMaster's Degree Without Thesis
YEAR OF STUDY-
SEMESTER-------
NUMBER OF ECTS CREDITS ALLOCATED10
NAME OF LECTURER(S)-
LEARNING OUTCOMES OF THE COURSE UNIT At the end of this course, the students;
1) Learn z-transform, Laplace transform and Fourier analysis issues.
2) Learn digital filter design.
3) Learn power spectral estimation methods.
4) Applying course units in homeworks on MATLAB or similar programs.
MODE OF DELIVERYFace to face
PRE-REQUISITES OF THE COURSENo
RECOMMENDED OPTIONAL PROGRAMME COMPONENTNo
COURSE DEFINITIONReminder of the basic concepts in signals and systems analysis and teaching the advanced topic. To consolidate the theoretical knowledge with practice in biomedical engineering.
COURSE CONTENTS
WEEKTOPICS
1st Week Fourier transform-continuous and discrete time, Discrete Fourier Transform
2nd Week Sampling theorem, analysis of linear time invariant systems.
3rd Week z- transform and Laplace transform: poles and zeros, stability and causality.
4th Week Phase effects in Fourier analysis, group delay
5th Week Time-frequency analysis: Short-time Fourier Transforms, Discrete Wavelet Transform (DWT).
6th Week Random processes: Auto- and cross-correlation functions, Power density spectra, Energy spectra.
7th Week Digital filter design: FIR and IIR filters designs.
8th Week Digital filter design: FIR and IIR filters designs.
9th Week Linear prediction :and Optimum Linear Filters : Forward and backward linear prediction, properties of linear prediction-error filter.
10th Week Linear prediction :and Optimum Linear Filters : Forward and backward linear prediction, properties of linear prediction-error filter.
11th Week Power Spectrum Estimation Methods: parametric and non-parametric methods.
12th Week Power Spectrum Estimation Methods: parametric and non-parametric methods.
13th Week Applications in biomedical signals.
14th Week Applications in biomedical signals.
RECOMENDED OR REQUIRED READINGDigital Signal Processing, Principles, Algorithms and Applications, 4th Ed. J.G.Proakis, D.G. Manolakis. Pearson Education, Prentice Hall.
Biomedical Signal Analysis: A Case Study Approach by Rangaraj M. Rangayyan, Wiley Interscience, 2001.
PLANNED LEARNING ACTIVITIES AND TEACHING METHODSLecture,Case Study,Practice,Presentation,Discussion,Questions/Answers,Problem Solving
ASSESSMENT METHODS AND CRITERIA
 QuantityPercentage(%)
Mid-term130
Assignment330
Total(%)60
Contribution of In-term Studies to Overall Grade(%)60
Contribution of Final Examination to Overall Grade(%)40
Total(%)100
ECTS WORKLOAD
Activities Number Hours Workload
Midterm exam17272
Preparation for Quiz000
Individual or group work236
Preparation for Final exam12424
Course hours14342
Preparation for Midterm exam11111
Laboratory (including preparation)000
Final exam19696
Homework31236
Total Workload287
Total Workload / 309,56
ECTS Credits of the Course10
LANGUAGE OF INSTRUCTION
WORK PLACEMENT(S)No
  

KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
LO1LO2LO3LO4
K1  X   X   X  
K2  X   X   X  
K3        X
K4  X   X   X  
K5       
K6       
K7       
K8