İzmir Ekonomi Üniversitesi
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  • GRADUATE SCHOOL

    M.SC. in Computer Engineering (With Thesis)

    EEE 562 | Course Introduction and Application Information

    Course Name
    Real-Time Signal Processing
    Code
    Semester
    Theory
    (hour/week)
    Application/Lab
    (hour/week)
    Local Credits
    ECTS
    EEE 562
    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 introduces algorithms and applications of real-time signal processing. Course topics include hardware and software aspects of embedded DSP systems, interaction between hardware and software, real-time principles and trade-offs in algorithm design and implementation. Students will learn to use digital signal processors such as the TMS320C6x (with Matlab/Simulink) to implement some real-time audio, image and video processing algorithms.
    Learning Outcomes

    The students who succeeded in this course;

    • know how to use and discuss the basic techniques/algorithms of the field,
    • have knowledge of the advantages and limitations of different real-time signal processing algorithms,
    • be able to evaluate potential applications of real-time signal processing techniques,
    • be able to design various signal processing algorithms using Matlab and signal processing toolbox, and implement them on the real-time DSP platform.
    Course Description Hardware and software aspects of embedded DSP systems, interaction between hardware and software, real-time principles and trade-offs in algorithm design and implementation.

     



    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 to Real-Time DSP Systems Welch, Wright and Morrow, Real-Time Digital Signal Processing from MATLAB® to C with the TMS320C6x DSPs, CRC Press, 2nd ed., 2011 (Ch. 1)
    2 Review: Discrete-Time Signals and Systems; Sampling and Quantization Welch, Wright and Morrow, Real-Time Digital Signal Processing from MATLAB® to C with the TMS320C6x DSPs, CRC Press, 2nd ed., 2011 (Ch. 2)
    3 Fundamentals of Analog to Digital and Digital to Analog Converters Lecture Notes
    4 Basic Operation of DSPs: DSP Memory Architecture, I/O, Interrupt Data Processing; Real-Time Operation, Worst-Case Timing Analysis Lecture Notes
    5 Basic Operation of DSPs: DSP Memory Architecture, I/O, Interrupt Data Processing; Real-Time Operation, Worst-Case Timing Analysis Lecture NotesWelch, Wright and Morrow, Real-Time Digital Signal Processing from MATLAB® to C with the TMS320C6x DSPs, CRC Press, 2nd ed., 2011 (Ch. 3)
    6 Review of FIR Filter Design Techniques and Tools; FIR Filter Structures and Implementation; Realization of Real-Time FIR Filter Design on the DSP Platform
    7 Review of IIR Filter Design Techniques and Tools; IIR Filter Structures and Implementation; Realization of Real-Time IIR Filter Design on the DSP Platform Welch, Wright and Morrow, Real-Time Digital Signal Processing from MATLAB® to C with the TMS320C6x DSPs, CRC Press, 2nd ed., 2011 (Ch. 4)
    8 Midterm Lecture Notes
    9 Efficient Code Development: Code Optimization, Effect of Data Types and Memory Map Welch, Wright and Morrow, Real-Time Digital Signal Processing from MATLAB® to C with the TMS320C6x DSPs, CRC Press, 2nd ed., 2011 (Ch. 6, 7)
    10 Frame-Based DSP, Using Direct Memory Access, Digital Filters Using Frames Welch, Wright and Morrow, Real-Time Digital Signal Processing from MATLAB® to C with the TMS320C6x DSPs, CRC Press, 2nd ed., 2011 (Ch. 8)
    11 Fast Fourier Transform Computation, Development and Realization of the Efficient FFT Algorithms on the DSP Platform, FFT Applications Welch, Wright and Morrow, Real-Time Digital Signal Processing from MATLAB® to C with the TMS320C6x DSPs, CRC Press, 2nd ed., 2011 (Ch. 9)
    12 Spectrum Estimation and Analysis, Real-time DSP Implementation Lecture Notes
    13 Adaptive Filtering Basics, Least-Mean-Square (LMS) Algorithm, Real-time DSP Implementation Lecture Notes
    14 Real-time DSP Applications in Audio, Image and Video Processing Lecture Notes
    15 Real-time DSP Applications in Audio, Image and Video Processing
    16 Review of the Semester  

     

    Course Notes/Textbooks The textbook referenced above and course slides
    Suggested Readings/Materials Related Research Papers

     

    EVALUATION SYSTEM

    Semester Activities Number Weigthing
    Participation
    Laboratory / Application
    Field Work
    6
    60
    Quizzes / Studio Critiques
    Portfolio
    Homework / Assignments
    Presentation / Jury
    Project
    2
    40
    Seminar / Workshop
    Oral Exams
    Midterm
    Final Exam
    Total

    Weighting of Semester Activities on the Final Grade
    8
    100
    Weighting of End-of-Semester Activities on the Final Grade
    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
    2
    32
    Study Hours Out of Class
    15
    4
    60
    Field Work
    6
    0
    Quizzes / Studio Critiques
    0
    Portfolio
    0
    Homework / Assignments
    0
    Presentation / Jury
    0
    Project
    2
    42
    84
    Seminar / Workshop
    0
    Oral Exam
    0
    Midterms
    0
    Final Exam
    0
        Total
    224

     

    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.
    -
    -
    -
    -
    -
    2 Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.
    -
    -
    -
    -
    -
    3 Uses scientific methods to complete and apply information from uncertain, limited or incomplete data; can combine and use information from different disciplines.
    -
    -
    -
    -
    -
    4 Is informed about new and upcoming applications in the field and learns them whenever necessary.
    -
    -
    -
    -
    -
    5 Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.
    -
    -
    -
    -
    -
    6 Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs
    -
    -
    -
    -
    -
    7 Designs and implements studies based on theory, experiments and modelling; analyses and resolves the complex problems that arise in this process.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -
    9 Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.
    -
    -
    -
    -
    -
    10 Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.
    -
    -
    -
    -
    -
    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.
    -
    -
    -
    -
    -
    12 Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.
    -
    -
    -
    -
    -

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

     


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