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İzmir Ekonomi Üniversitesi

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    GRADUATE SCHOOL

    M.SC. in Computer Engineering (Without Thesis)

    EEE 542 | Course Introduction and Application Information

    Course Name
    Detection and Estimation Theory
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    EEE 542
    Fall/Spring
    3
    0
    3
    7.5

    Prerequisites
    None
    Course Language
    English
    Course Type
    Elective
    Course Level
    Second Cycle
    Mode of Delivery -
    Teaching Methods and Techniques of the Course -
    National Occupation Classification -
    Course Coordinator -
    Course Lecturer(s)
    Assistant(s) -
    Course Objectives This course aims to provide a graduate-level introduction to detection and estimation theory. Course content includes the topics such as Gauss-Markov processes and stochastic differential equations, Bayes estimation theory, maximum likelihood, linear minimum deviation, minimum-squares estimation, properties of estimators, error analysis, state prediction for linear systems, Kalman-Bucy and Wiener filters, leveling and pre-estimation methods, nonlinear estimation, filtering applications, communications, control, system identification and biomedical engineering applications.
    Learning Outcomes

    The students who succeeded in this course;

    • learn how to use and discuss the maximum likelihood, maximum a posteriori probability and least-squares estimates of a parameter,
    • learn how to perform Karhunen-Loeve expansion,
    • learn how to apply Wiener filter and Kalman filter to solve linear estimation problems,
    • be able to evaluate performance of decision making and estimation systems,
    • be able to design and implement various detection and estimation algorithms using Matlab simulation software.
    Course Description Gauss-Markov processes and stochastic differential equations, Bayes estimation theory, maximum likelihood, linear minimum deviation, minimum-squares estimation, properties of estimators, error analysis, state prediction for linear systems, Kalman-Bucy and Wiener filters, leveling and pre-estimation methods, nonlinear estimation, filtering applications, communications, control, system identification and biomedical engineering applications.

     



    Course Category

    		Core Courses
    Major Area Courses
    Supportive Courses
    Media and Management Skills Courses
    Transferable Skill Courses

     

    WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

    Week Subjects Related Preparation Learning Outcome
    1 Introduction, Probability, Random Vectors, Vector Spaces Lecture notes
    2 Detection Theory, Decision Theory, and Hypothesis Testing Lecture notes
    3 Detection Theory, Decision Theory, and Hypothesis Testing Lecture notes
    4 Detection Theory, Decision Theory, and Hypothesis Testing Lecture notes
    5 Parameter Estimation Lecture notes
    6 Maximum Likelihood Estimation Lecture notes
    7 Stochastic Processes and Systems Lecture notes
    8 Midterm
    9 Karhunen-Loeve and Sampled Signal Expansions Lecture notes
    10 Detection and Estimation from Waveform Observations Lecture notes
    11 Wiener and Kalman Filtering Lecture notes
    12 Advanced Topics Lecture notes
    13 In-class Presentations
    14 In-class Presentations
    15 In-class Presentations
    16 Review of the Semester  

     

    Course Notes/Textbooks Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, Steven Kay, 1993. ISBN 0-13-345711-7 Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, Steven Kay, 1998. ISBN 0-13-504135-X
    Suggested Readings/Materials Related Research Papers

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    5
    30
    Presentation / Jury
    Project
    1
    30
    Seminar / Workshop
    Oral Exams
    Midterm
    Final Exam
    1
    40
    Total

    Weighting of Semester Activities on the Final Grade
    6
    60
    Weighting of End-of-Semester Activities on the Final Grade
    1
    40
    Total

    ECTS / WORKLOAD TABLE

    Semester Activities Number Duration (Hours) Workload
    Theoretical Course Hours
    (Including exam week: 16 x total hours)
    16
    3
    48
    Laboratory / Application Hours
    (Including exam week: '.16.' x total hours)
    16
    0
    Study Hours Out of Class
    15
    4
    60
    Field Work
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    5
    10
    50
    Presentation / Jury
    0
    Project
    1
    45
    45
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    0
    Final Exam
    1
    22
    22
        Total
    225

     

    COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

    #
    		 PC Sub Program Competencies/Outcomes
    * Contribution Level
    1
    2
    3
    4
    5
    1 Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.
    -
    -
    -
    X
    -
    2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
    -
    -
    X
    -
    -
    3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.
    -
    -
    -
    X
    -
    4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    -
    X
    5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
    -
    -
    -
    -
    X
    6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.
    -
    -
    -
    X
    -
    7 Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.
    -
    -
    -
    X
    -
    8 Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.
    -
    -
    X
    -
    -
    9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
    -
    -
    X
    -
    -
    10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    X
    -
    -
    11 Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.
    -
    -
    X
    -
    -
    12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.
    -
    X
    -
    -
    -

    *1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest

     

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