ECTS - Digital Signal Processing
Digital Signal Processing (EE306) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Digital Signal Processing | EE306 | Area Elective | 3 | 2 | 0 | 4 | 6 |
| Pre-requisite Course(s) |
|---|
| EE303 |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Demonstration, Discussion, Question and Answer, Drill and Practice. |
| Course Lecturer(s) |
|
| Course Objectives | •Understand how analog signals are represented by their discrete-time samples, and in what ways digital filtering is equivalent to analog filtering. •Master the representation of discrete-time signals in the frequency domain, using the notions of z-transform, discrete-time Fourier transform and discrete Fourier transform (DFT). •Learn the basic forms of FIR and IIR filters, and how to design filters with desired frequency responses. •Understand the implementation of the DFT in terms of the FFT, as well as some of its applications (computation of convolution sums, spectral analysis) |
| Course Learning Outcomes |
The students who succeeded in this course;
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| Course Content | Signals and signal processing, discrete-time signals and systems, discrete-time Fourier transform (DTFT) and computation of the DFT, the z-Transform, sampling of continuous-time signals, transform analysis of linear time-invariant (LTI) systems, structures for discrete-time systems, digital filter design techniques, discrete Fourier transform, app |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Discrete-Time (DT) Signals and Systems •DT signals: Sequences •DT systems: Memoryless, Linear, Time-Invariant, Causal, and Stable Systems •Frequency-Domain Representation of DT Signals and Systems | Glance this week’s topics from the lecture |
| 2 | DT Signals and Systems | Review last week and glance this week’s topics from the lecture |
| 3 | The z-Transform •Properties of the Region of Convergence (ROC) for the z-transform •The Inverse z-transform •z-transform Properties | Glance this week’s topics from the lecture |
| 4 | The z-Transform | Review last week and glance this week’s topics from the lecture Glance this week’s topics from the lecture |
| 5 | Transform Analysis of Linear Time-Invariant (LTI) Systems •The Frequency Response of LTI Systems: Ideal frequency-selective filters, Phase Distortion and Delay •System Functions: Stability, Causality, Inverse Systems, Impulse Response for Rational System Functions •Relationship between Magnitude and Phase •All-Pass Systems •Minimum-Phase Systems •Linear Systems with Generalized Linear Phase | Glance this week’s topics from the lecture |
| 6 | Transform Analysis of LTI Systems | Review last week and glance this week’s topics from the lecture |
| 7 | Structures for Discrete-Time Systems •Block Diagram Representation of Linear Constant-Coefficient Difference Equations •Signal Flow Graph •Basic Structures for IIR Systems: Direct, Cascade, and Parallel Forms | Glance this week’s topics from the lecture |
| 8 | Structures for Discrete-Time Systems •Basic Network Structures for FIR Systems | Review last week and glance this week’s topics from the lecture |
| 9 | Filter Design Techniques •Prototype Analog Filters: Butterworth, Chebyshev, and Elliptic Filters •Design of DT IIR Filters from CT Filters: Impulse Invariance method, Bilinear Transformations | Glance this week’s topics from the lecture |
| 10 | Filter Design Techniques •Design of FIR Filters by Windowing | Review last week and glance this week’s topics from the lecture |
| 11 | The Discrete Fourier Transform (DFT) •Relationship between DFT and Discrete Cosine Transform (DCT) | Glance this week’s topics from the lecture |
| 12 | The DFT | Review last week and glance this week’s topics from the lecture |
| 13 | Applications to Speech and Image Processing | Glance this week’s topics from the lecture |
| 14 | Applications to Speech and Image Processing | Glance this week’s topics from the lecture |
| 15 | Final examination period | Review topics |
| 16 | Final examination period | Review topics |
Sources
| Course Book | 1. Discrete-Time Signal Processing, Second Edition, Alan V. Oppenheim, Ronald W. Schafer and John R. Buck, Prentice Hall, 1999 |
|---|---|
| Other Sources | 2. Digital Signal Processing , A Computer Based Approach, Sanjit. K. Mitra, McGraw-Hill, 1998 |
| 3. Digital Signal Processing, Algorithms and Applications, John G. Proakis and Dimitris G.Manolakis,3rd Edition, 2000 |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | 4 | 20 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 2 | 40 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 7 | 100 |
| Percentage of Semester Work | 60 |
|---|---|
| Percentage of Final Work | 40 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| 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 | Possesses sufficient knowledge in mathematics, natural sciences, and discipline-specific topics in Electrical and Electronics Engineering; uses this theoretical and practical knowledge to solve complex engineering problems. | X | ||||
| 2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analytical and modeling methods for this purpose. | X | ||||
| 3 | Designs complex systems, processes, devices, or products under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economy, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues, depending on the nature of the design.) | X | ||||
| 4 | Selects and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications; effectively uses information technologies. | X | ||||
| 5 | Designs experiments, conducts tests, collects data, analyzes, and interprets results to investigate complex engineering problems or discipline-specific research topics. | X | ||||
| 6 | Works effectively in disciplinary and interdisciplinary teams; develops the ability to work independently. | X | ||||
| 7 | Communicates effectively in both written and verbal forms; possesses proficiency in at least one foreign language; writes effective reports, understands written reports, prepares design and production reports, delivers effective presentations, and gives and receives clear instructions. | X | ||||
| 8 | Recognizes the need for lifelong learning; accesses information, follows developments in science and technology, and continuously renews oneself. | X | ||||
| 9 | Acts in accordance with ethical principles, assumes professional and ethical responsibility, and possesses knowledge about the standards used in engineering practices. | X | ||||
| 10 | Possesses knowledge about professional practices such as project management, risk management, and change management; gains awareness of entrepreneurship and innovation; understands the principles of sustainable development. | X | ||||
| 11 | Understands the universal and societal impacts of engineering practices on health, environment, and safety; recognizes the contemporary issues reflected in the field of engineering and understands the legal implications of engineering solutions. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | 4 | 2 | 8 |
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 5 | 80 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 4 | 8 |
| Prepration of Final Exams/Final Jury | 1 | 5 | 5 |
| Total Workload | 149 | ||