BIOMEDICAL ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
EEE4501	Digital Signal Processing	Fall Spring	3	2	4	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi ZAFER İŞCAN
Recommended Optional Program Components:	None
Course Objectives:	The course will provide the students with the understanding of how to analyze and manipulate digital signals, and the fundamental programming knowledge and experience to do so.

Learning Outcomes

The students who have succeeded in this course;
1. Identify if a system is a Linear Time-Invariant (LTI) System.
2. Describe signals and systems using Linear Constant Coefficient Difference Equations.
3. Define Fourier representations of signals and systems.
4. Demonstrate how to find the Z-transform of a LTI system.
5. Describe the relationship between poles, zeros, and stability.
6. Describe the Sampling Theorem and how this relates to Aliasing and Folding.
7. Define multirate signal processing concepts.
8. Demostrate skills to design, analyze, and implement digital filters in Matlab.
9. Determine the frequency response of FIR and IIR filters.
10. Determine the spectrum of a signal using the DFT, FFT and spectrogram.
11. Demonstrate DCT of a signal and ceptrum analysis.

Course Content

Introduction to Matlab; Discrete-Time Signals and Systems; Linear Constant Coefficient Difference Equations, Fourier Transform; Z-Transform; LTI Discrete-Time Systems in the Transform Domain; Digital Processing of Continuous-Time Signals, Sampling; Multirate Signal Processing; Laplace Transform; Digital Filter Design; Linear Prediction, Discrete Fourier Transform; Discrete Cosine Transform; Cepstrum Analysis

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Meeting, Discussion of the Course, Introduction to MATLAB
2)	Introduction to Digital Signal Processing Discrete-Time Signals and Systems	[Mitra] Chapter 1 [Mitra] Chapter 2 [OppenheimSchafer] Chapter 2.0-2.6
3)	Linear Constant Coefficient Difference Equations Fourier Transform	[Mitra] Chapter 2.7-2.9, 3.1-3.6 [OppenheimSchafer] Chapter 2.5-2.9
4)	Z-Transform	[Mitra] Chapter 6 [OppenheimSchafer] Chapter 3
5)	LTI Discrete-Time Systems in the Transform Domain	[Mitra] Chapter 7 [OppenheimSchafer] Chapter 5
6)	Digital Processing of Continuous-Time Signals Sampling	[Mitra] Chapter 4 [OppenheimSchafer] Chapter 4.1-4.6
7)	Multirate Signal Processing	[Mitra] Chapter 13 [OppenheimSchafer] Chapter 4.7-4.9
8)	Laplace Transform	Slides Various resources
9)	Midterm Exam. Discussion and solutions of the questions.
10)	Digital Filter Design	[Mitra] Chapter 8, 9, 10 [OppenheimSchafer] Chapter 7
11)	Linear Prediction	Slides Various resources
12)	Discrete Fourier Transform	[Mitra] Chapter 5.2-5.10 [OppenheimSchafer] Chapter 8
13)	Discrete Cosine Transform	[Mitra] Chapter 5.11 [OppenheimSchafer] Chapter 8.8
14)	Cepstrum Analysis Overview and Wrap-up	[OppenheimSchafer] Chapter 13
15)	Course Project Presentations and Demonstrations

Sources

Course Notes / Textbooks:	Discrete-Time Signal Processing, Alan V. Oppenheim and Ronald W. Schafer, Third Edition, Pearson, 2010.
References:	Digital Signal Processing: A Computer-based Approach, Sanjit K.Mitra, Third Edition, McGraw-Hill 2006.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	14	% 5
Homework Assignments	5	% 5
Project	1	% 20
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Application	10	3	30
Study Hours Out of Class	17	3	51
Presentations / Seminar	1	1	1
Project	6	4	24
Quizzes	2	0	0
Midterms	1	2	2
Final	1	2	2
Total Workload			152

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge of subjects specific to mathematics (analysis, linear, algebra, differential equations, statistics), science (physics, chemistry, biology) and related engineering discipline, and the ability to use theoretical and applied knowledge in these fields in complex engineering problems.
2)	Identify, formulate, and solve complex Biomedical Engineering problems; select and apply proper modeling and analysis methods for this purpose
3)	Design complex Biomedical systems, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose.
4)	Devise, select, and use modern techniques and tools needed for solving complex problems in Biomedical Engineering practice; employ information technologies effectively.
5)	Design and conduct numerical or physical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Biomedical Engineering.
6)	Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Biomedical Engineering-related problems.
7)	Ability to communicate effectively in Turkish, oral and written, to have gained the level of English language knowledge (European Language Portfolio B1 general level) to follow the innovations in the field of Biomedical Engineering; gain the ability to write and understand written reports effectively, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself.
9)	Having knowledge for the importance of acting in accordance with the ethical principles of biomedical engineering and the awareness of professional responsibility and ethical responsibility and the standards used in biomedical engineering applications
10)	Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development.
11)	Acquire knowledge about the effects of practices of Biomedical Engineering on health, environment, security in universal and social scope, and the contemporary problems of Biomedical Engineering; is aware of the legal consequences of Mechatronics engineering solutions.