COURSE SYLLABUS

SIGNAL CODING

1	Course Title:	SIGNAL CODING
2	Course Code:	EEM6106
3	Type of Course:	Optional
4	Level of Course:	Third Cycle
5	Year of Study:	1
6	Semester:	2
7	ECTS Credits Allocated:	6
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Doç. Dr. Mustafa DEMİRTAŞ
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	mustafademirtas@uludag.edu.tr
18	Website:
19	Objective of the Course:	The aim of this course is to provide students with the basic principles of signal representation and coding and to develop an in-depth understanding of this field. The course aims to provide knowledge on quantization optimization by focusing on scalar and vector quantization issues. It also provides students with a broad perspective on topics such as lossless compression and entropy, Huffman-Shannon type encoders, dictionary techniques, and predictive coding. While the course progresses with topics such as optimal prediction filter design and signal space representation, it also covers advanced topics such as orthogonal and biorthogonal systems, base signals and projections, subband distribution. It provides students with a comprehensive look at transformations, filter relations and important mathematical tools used in the representation of the sign, allowing them to develop expertise in this field.
20	Contribution of the Course to Professional Development	Communication Engineering Competence. Data Compression and Coding Skills. Signal Processing Applications

Learning Outcomes:

1	To develop competence in the effective quantization of signals by gaining the knowledge necessary to understand and optimize quantization processes.;
2	By learning lossless compression methods of signals and the concept of entropy, they will have basic skills in data compression.;
3	To learn prediction-based coding methods and apply these techniques to ensure efficiency in data compression and coding processes.;

22	Course Content:

Week	Theoretical	Practical
1	Fundamentals of Signal Notation and Codes
2	Scalar and Vector Quantization
3	Quantization Optimization
4	Lossless Compression and Entropy
5	Huffman-Shannon Type Encoders
6	Dictionary Techniques
7	Predictive Coding
8	Optimum Prediction Filter Design
9	Signal Space Representation
10	Orthogonal and Bioorthogonal Systems
11	Base Marks and Projections
12	Subband Distribution
13	Conversion techniques
14	Filter Associations

23	Textbooks, References and/or Other Materials:	"Signals and Systems" - Alan V. Oppenheim, Alan S. Willsky, S. Hamid Nawab "Digital Signal Processing: Principles, Algorithms, and Applications" - John G. Proakis, Dimitris G. Manolakis "Communication Systems" - Simon Haykin, Michael Moher
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	40
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	60
Total	2	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Measurement and evaluation are carried out according to the principles of Bursa Uludağ University Postgraduate Education Regulation.
Information	For evaluation, 1 midterm and 1 final exam are held and relative evaluation is applied.

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	14	5	70
Homeworks, Performances	0	0	0
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	28	28
Others	0	0	0
Final Exams	1	40	40
Total WorkLoad			180
Total workload/ 30 hr			6
ECTS Credit of the Course			6

CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS

	PQ1	PQ2	PQ4	PQ5	PQ6
LO1	4	4	3	0	5
LO2	0	4	4	4	3
LO3	0	3	4	0	5

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High