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 | Accumulated knowledge on mathematics, science and mechatronics engineering; an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems. | X | ||||
| 2 | An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems. | X | ||||
| 3 | An ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; an ability to apply contemporary design methodologies; an ability to implement effective engineering creativity techniques in mechatronics engineering. (Realistic constraints and conditions may include economics, environment, sustainability, producibility, ethics, human health, social and political problems.) | X | ||||
| 4 | An ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; an ability to use information and communications technologies effectively. | X | ||||
| 5 | An ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies. | |||||
| 6 | An ability to work effectively on single disciplinary and multi-disciplinary teams; an ability for individual work; ability to communicate and collaborate/cooperate effectively with other disciplines and scientific/engineering domains or working areas, ability to work with other disciplines. | X | ||||
| 7 | An ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written. | X | ||||
| 8 | An ability to reach information on different subjects required by the wide spectrum of applications of mechatronics engineering, criticize, assess and improve the knowledge-base; consciousness on the necessity of improvement and sustainability as a result of life-long learning; monitoring the developments on science and technology; awareness on entrepreneurship, innovative and sustainable development and ability for continuous renovation. | |||||
| 9 | Be conscious on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself. | |||||
| 10 | A knowledge on the applications at business life such as project management, risk management and change management and competency on planning, managing and leadership activities on the development of capabilities of workers who are under his/her responsibility working around a project. | |||||
| 11 | Knowledge about the global, societal and individual effects of mechatronics engineering applications on the human health, environment and security and cultural values and problems of the era; consciousness on these issues; awareness of legal results of engineering solutions. | |||||
| 12 | Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions. | |||||
| 13 | Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planing, improving or changing the norms with a criticism. | |||||
| 14 | A competency on developing strategy, policy and application plans on the mechatronics engineering and evaluating the results in the context of qualitative 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 | 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 | ||