ECTS - Digital Signal Analysis
Digital Signal Analysis (EE571) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Digital Signal Analysis | EE571 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Problem Solving, Project Design/Management. |
| Course Lecturer(s) |
|
| Course Objectives | The aim of this course is to explain mathematical methods for signals and communications and to describe spectrum estimation techniques. |
| Course Learning Outcomes |
The students who succeeded in this course;
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| Course Content | Mathematical methods for signal processing, spectrum estimation, discrete Karhunen-Loeve transform, detection of a signal in noise, multiple signal classification (MUSIC), least mean square algorithm, classification systems, Kalman filters. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Mathematical Methods for Signal Processing: Metric Spaces, Normed Vector Spaces, Eigenvectors, Singular Value Decomposition, Matrix Inverses, Pseudoinverses , Principles of Constrained Optimization | Glance this week’s topics from the lecture |
| 2 | Mathematical Methods for Signal Processing | Review last week's lecture notes |
| 3 | Mathematical Methods for Signal Processing | Review last week's lecture notes |
| 4 | Spectrum Estimation based on Nonparametric Techniques : Correlogram and Periodogram Methods; Windowed Spectrum Methods | Review last week's lecture notes |
| 5 | Spectrum Estimation based on Linear Models (Parametric Techniques) : Auto Regressive (AR), Moving Average (MA) and Auto Regressive Moving Average (ARMA) models; Yule-Walker equations and least-squares methods | Review last week's lecture notes |
| 6 | Parametric Techniques | Review last week's lecture notes |
| 7 | The Discrete Karhunen-Loeve Transform | Review last week's lecture notes |
| 8 | Matched Filter: Detection of a Signal in Additive Noise | Review last week's lecture notes |
| 9 | Multiple Signal Classification (MUSIC); Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) | Review last week's lecture notes |
| 10 | Gradient Based Adaptation: Steepest Descent Algorithm Stochastic Gradient Based Adaption: Least Mean Square (LMS) Algorithm | Review last week's lecture notes |
| 11 | Classification Systems : Classifiers, Feature Selection and Feature Generation | Review last week's lecture notes |
| 12 | Classification Systems | Review last week's lecture notes |
| 13 | Classification Systems | Review last week's lecture notes |
| 14 | Kalman Filters: Statement of the Kalman Filtering Problem; Innovation Process; Estimation of the State; Filtering; Initial Conditions; The Extended Kalman Filter | Review last week's lecture notes |
| 15 | Final examination period | Review of topics |
| 16 | Final examination period | Review of topics |
Sources
| Course Book | 1. M.D. Srinath, P.K. Rajasekaran, Introduction to Statistical Signal Processing with Applications |
|---|---|
| Other Sources | 2. Gerard Covaert, Data Analysis |
| 3. Discrete Random Signals and Statistical Signal Processing, C.W.Therrien, Prentice Hall, 1992. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 3 | 15 |
| Presentation | - | - |
| Project | 1 | 25 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 25 |
| Final Exam/Final Jury | 1 | 35 |
| Toplam | 6 | 100 |
| Percentage of Semester Work | 65 |
|---|---|
| Percentage of Final Work | 35 |
| 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 | Ability to apply knowledge on Mathematics, Science and Engineering to advanced systems. | X | ||||
| 2 | Implementing long-term research and development studies in the major fields of Electrical and Electronics Engineering. | X | ||||
| 3 | Ability to use modern engineering tools, techniques and facilities in design and other engineering applications. | X | ||||
| 4 | Graduating researchers active on innovation and entrepreneurship. | |||||
| 5 | Ability to report and present research results effectively. | |||||
| 6 | Increasing the performance on accessing information resources and on following recent developments in science and technology. | |||||
| 7 | An understanding of professional and ethical responsibility. | |||||
| 8 | Increasing the performance on effective communications in both Turkish and English. | |||||
| 9 | Increasing the performance on project management. | |||||
| 10 | Ability to work successfully at project teams in interdisciplinary fields. | |||||
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 | 4 | 56 |
| Presentation/Seminar Prepration | |||
| Project | 1 | 6 | 6 |
| Report | |||
| Homework Assignments | 3 | 5 | 15 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | |||
| Prepration of Final Exams/Final Jury | |||
| Total Workload | 125 | ||