TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree Without Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
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 DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT	No
COURSE DEFINITION	Reminder 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	WEEK TOPICS 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 READING	Digital 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 METHODS	Lecture,Case Study,Practice,Presentation,Discussion,Questions/Answers,Problem Solving
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Assignment 3 30 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 exam 1 72 72 Preparation for Quiz 0 0 0 Individual or group work 2 3 6 Preparation for Final exam 1 24 24 Course hours 14 3 42 Preparation for Midterm exam 1 11 11 Laboratory (including preparation) 0 0 0 Final exam 1 96 96 Homework 3 12 36 Total Workload 287 Total Workload / 30 9,56 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No