ECTS - Digital Signal Processing

Digital Signal Processing (CMPE463) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Digital Signal Processing CMPE463 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Technical Elective Courses
Course Level Bachelor’s Degree (First Cycle)
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives The objective of this course is to introduce basic concepts and different approaches Digital Signal Processing. To introduce students to a working and hands-on knowledge of digital signal processing algorithms and filters.
Course Learning Outcomes The students who succeeded in this course;
  • Design and implement digital signal processing algorithms and filters for a given problem.
  • Decide on and apply suitable digital signal processing technique(s) to a given problem.
Course Content Discrete-time domain and frequency domain representation of signals and systems; sampling and reconstruction; DFT, FFT, z - transform, filter design techniques; finite word length effects; 2-D filtering; applications of DSP; programming of some DSP processors.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 Introduction : Signals , Systems and Signal Processing; Classification and Representation of Signals in discrete time domain; Linear, Time-Invariant Systems Chapters 1-2 (main text)
2 Difference Equations; Frequency Response Ch 1-2
3 Sampling and Reconstruction The discrete Fourier transform. ( DFT ) Ch 9
4 Sampling and Reconstruction The discrete Fourier transform. ( DFT ) Ch 9
5 Fast Fourier transform. ( FFT ) z- transform Frequency and time-domain response of filters. Ch 3-8
6 Fast Fourier transform. ( FFT ) z- transform Frequency and time-domain response of filters. Ch.3-8
7 Fast Fourier transform. ( FFT ) z- transform Frequency and time-domain response of filters. Ch 3-8
8 Fast Fourier transform. ( FFT ) z- transform Frequency and time-domain response of filters. Ch 3-8
9 Digital filter design, FIR and IIR filters. Digital filter structures. Finite word length effects. Chapter 8-9-10 (From other sources 1)
10 Digital filter design, FIR and IIR filters. Digital filter structures. Finite word length effects. Chapter 8-9-10 (From other sources 1)
11 Digital filter design, FIR and IIR filters. Digital filter structures. Finite word length effects. Chapter 8-9-10 (From other sources 1)
12 2- Dimensional Filtering Applications of DSP DSP Processors Chapter 15 (From other sources 1)
13 2- Dimensional Filtering Applications of DSP DSP Processors Chapter 15 (From other sources 1)
14 2- Dimensional Filtering Applications of DSP DSP Processors Chapter 15 (From other sources 1)

Sources

Course Book 1. John G. Prokis and Dimitris G. Manolakis, “Digital Signal Processing : Principle, Algorithms and Applications” Prentice Hall Inc., Englewood Cliffs, NJ (USA), 3rd Ed., 1996.
Other Sources 2. 1. S. K. Mitra, “Digital Signal Processing : A Computer-Based Approach” Mc Graw Hill Co. Inc., NY (USA), 1998.
3. 2. P. Lapsley, J. Bier and E.A. Lee ‘’ DSP Processor Fundamentals : Architectures and Features ‘’ IEEE Press, New York( USA ), 1997
4. 3. Lawrence R. Rabiner and Bernard Gold “Theory and Application of Digital Signal Processing” Prentice Hall, NJ (USA), 1975.
5. 4. C. Sidney Burrus, Computer-Based Exercises for Signal Processing Using Matlab, Prentice Hall, 1994. Matlab for Students, Prentice Hall, 1994. (for various formats).
6. 5. R.G.Lyons, “Understanding Digital Signal Processing (2nd Edition)”, Prentice-Hall, 2004.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments 2 20
Presentation - -
Project - -
Report - -
Seminar - -
Midterms Exams/Midterms Jury 2 40
Final Exam/Final Jury 1 40
Toplam 5 100
Percentage of Semester Work 60
Percentage of Final Work 40
Total 100

Course Category

Core Courses
Major Area Courses
Supportive Courses X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Gains adequate knowledge in mathematics, science, and subjects specific to the software engineering discipline; acquires the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. X
2 Gains the ability to identify, define, formulate, and solve complex engineering problems; selects and applies proper analysis and modeling techniques for this purpose. X
3 Develops the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose.
4 Demonstrates the ability to select, and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; uses information technologies effectively. X
5 Develops the ability to design experiments, gather data, analyze, and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline.
6 Demonstrates the ability to work effectively both individually and in disciplinary and interdisciplinary teams in fields related to software engineering.
7 Demonstrates the ability to communicate effectively in Turkish, both orally and in writing; to write effective reports and understand written reports, to prepare design and production reports, to deliver effective presentations, and to give and receive clear and understandable instructions.
8 Gains knowledge of at least one foreign language; acquires the ability to write effective reports and understand written reports, prepare design and production reports, deliver effective presentations, and give and receive clear and understandable instructions.
9 Acquires an awareness of the necessity of lifelong learning; the ability to access information, follow developments in science and technology, and continuously improve oneself.
10 Acts in accordance with ethical principles and possesses knowledge of professional and ethical responsibilities.
11 Knows the standards used in software engineering practices.
12 Knows about business practices such as project management, risk management and change management.
13 Gains awareness about entrepreneurship and innovation.
14 Gains knowledge on sustainable development.
15 Has knowledge about the universal and societal impacts of software engineering practices on health, environment, and safety, as well as the contemporary issues reflected in the field of engineering.
16 Acquires awareness of the legal consequences of engineering solutions.
17 Applies knowledge and skills in identifying user needs, developing user-focused solutions and improving user experience. X
18 Gains the ability to apply engineering approaches in the development of software systems by carrying out analysis, design, implementation, verification, validation, and maintenance processes.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 14 2 28
Presentation/Seminar Prepration
Project
Report
Homework Assignments 2 5 10
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 2 10 20
Prepration of Final Exams/Final Jury 1 20 20
Total Workload 126