Digital Signal Analysis (EE571) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Digital Signal Analysis EE571 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type N/A
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 Coordinator
Course Lecturer(s)
  • Asst. Prof. Dr. Hakan Tora
  • Instructor Dr. Tolga Sönmez
Course Assistants
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;
  • Able to apply mathematical methods to problems of signal and communication, design, implement and apply spectrum estimation and evaluate their performance, use a combination of theory and software implementations to solve signal and communication problems, identify applications in which it would be possible to use different digital signal analysis approaches, design and implement classification systems, analyze the accuracy and determine advantages and disadvantages of each method, use computer tools (such as Matlab) in developing and testing stochastic DSP algorithms, and complete a term project.
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.
2 Implementing long-term research and development studies in major areas of Electrical and Electronics Engineering.
3 Ability to use modern engineering tools, techniques and facilities in design and other engineering applications.
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