Digital Signal Processing (CMPE463) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Digital Signal Processing CMPE463 Elective Courses 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 Adequate knowledge in mathematics, science and subjects specific to the software engineering discipline; the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. X
2 The ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose. X
3 The ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design methods for this purpose.
4 The ability to develop, select and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; the ability to utilize information technologies effectively. X
5 The ability to gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline.
6 The ability to work effectively in inter/inner disciplinary teams; ability to work individually.
7 Effective oral and written communication skills in Turkish; the knowledge of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8 Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development
9 The ability to behave according to ethical principles, awareness of professional and ethical responsibility; knowledge of the standards utilized in software engineering applications.
10 Knowledge on business practices such as project management, risk management and change management; awareness about entrepreneurship, innovation; knowledge on sustainable development.
11 Knowledge on the effects of software engineering applications on the universal and social dimensions of health, environment and safety; awareness of the legal consequences of engineering solutions.
12 An ability to apply algorithmic principles, mathematical foundations, and computer science theory in the modeling and design of computer-based systems with the trade-offs involved in design choices. X
13 The ability to apply engineering approach to the development of software systems by analyzing, designing, implementing, verifying, validating and maintaining software systems.

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