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

Ders Genel Tanıtım Bilgileri

Course Code:

1410002025

Ders İsmi:

Image processing

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
3	0	3	5

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Type of course:

Bölüm Seçmeli

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi Recep DURANAY

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

Within the scope of this course, it is aimed to teach advanced digital signal, image processing, pattern recognition and machine learning methods on biomedical data. The main aim of the course is to increase students' mathematical, scientific and computational analysis skills in this field. In this context, the content of the course includes the acquisition of biomedical data, evaluation of its properties, teaching the reasons and applications of the preprocessing steps (noise removal, filtering, reinforcement, size reduction, etc.), feature extraction, modeling, unsupervised and supervised learning, as well as semi-trained, community and Deep learning issues will also be discussed. In addition, Matlab and Python-based individual/group projects will be carried out on basic biomedical applications in order to increase the computational skills of the students.

Course Content:

Properties of biomedical signs and images; Transformation methods used in signal and image processing; Noise removal in signs and images; Signal and image filtering methods; Signal and image filtering methods; Linear and nonlinear dimension reduction methods; Statistical, morphological and spatial feature extraction methods; Instructed learning methods in signal and image processing; Unsupervised learning methods in signal and image processing; Semi-tutorial, community and deep learning methods.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Students will be given a theoretical background on the origin and nature of signs and images.
2 - Skills
Cognitive - Practical
3 - Competences
Communication and Social Competence
Learning Competence
1) Computer engineering students will be provided with strong mathematical and algorithmic knowledge, especially in this developing interdisciplinary field.
Field Specific Competence
1) 3) Students will try to increase their computational and scientific abilities in subjects such as pattern recognition and machine learning, as well as sign and image processing.
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Obtaining and characteristics of biomedical signs and images
2)	Analysis of statistical characteristics of signals (Moments, power, information, correlation...)
3)	Digital signal processing fundamentals, sampling, quantization
4)	Frequency analysis, Conversion methods I: DFT, DCT, STFT
5)	Transformation methods II: Wavelet transform
6)	Image processing basics
7)	Noise removal in image processing
8)	Midterm
9)	Filtering and consolidation methods
10)	Analysis of signs and images with supervised learning methods
11)	Dimension reduction and linear/non-linear transformation methods
12)	Fundamentals of pattern recognition and machine learning for biomedical signs and images
13)	Analysis of signs and images with unsupervised learning methods
14)	Analysis of signs and images with supervised learning methods
15)	Analysis of signs and images with semi-tutorial, ensemble and deep learning methods
16)	Final

Sources

Course Notes / Textbooks:	Okutman Notları
References:	Lecture Notes

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3
Program Outcomes
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3) PO 2.1) Identifying complex engineering problems
4) PO 2.2) Defining complex engineering problems
5) PO 2.3) Formulating complex engineering problems
6) PO 2.4) Ability to solve complex engineering problems
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
12) PO 4.3) Ability to use information technologies effectively.
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics

Ders - Öğrenme Kazanımı İlişkisi

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

	Program Outcomes	Level of Contribution
1)	PO 1.1) Sufficient knowledge in mathematics, science and computer engineering
2)	PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems.
3)	PO 2.1) Identifying complex engineering problems
4)	PO 2.2) Defining complex engineering problems
5)	PO 2.3) Formulating complex engineering problems
6)	PO 2.4) Ability to solve complex engineering problems
7)	PO 2.5) Ability to choose and apply appropriate analysis and modeling methods
8)	PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions.
9)	PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product
10)	PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications
11)	PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications	5
12)	PO 4.3) Ability to use information technologies effectively.
13)	PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments
14)	PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation
15)	PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection
16)	PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics
17)	PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics

Öğrenme Etkinliği ve Öğretme Yöntemleri

Homework

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Attendance	10	% 10
Homework Assignments	2	% 20
Midterms	1	% 30
Semester Final Exam	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	35	3	105
Midterms	1	2	2
Final	1	3	3
Total Workload			152