Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

    címtáras azonosítással

    vissza a tantárgylistához   nyomtatható verzió    

    Signal Processing Fundamentals

    A tantárgy neve magyarul / Name of the subject in Hungarian: A jelfeldolgozás alapjai

    Last updated: 2018. augusztus 6.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics

    Course Datasheet and requirements

    Course ID Semester Assessment Credit Tantárgyfélév
    VIHIM009 2 4/0/0/f 4  
    3. Course coordinator and department Dr. Levendovszky János,
    4. Instructors

    Janos Levendovszky, Dsc



    5. Required knowledge Calculus
    7. Objectives, learning outcomes and obtained knowledge

    The course is concerned with the foundations of signal processing with special emphasis of the representations of signals in different domains. The adaptive part helps the students solve adaptive identification and equalization tasks. After successfully completing the course the students are capable of solving various signal processing tasks arising in different applications.

    8. Synopsis
    • Representation of analog signals in time/frequency/s domains.
    • A/D conversion (sampling and quantization)
    • Nonlinear quantization, Lloyd-Max algorithm,
    • Digital signals
    • Description of Linear Time Invariant Systems in time doian
    • DFT
    • FFT alagorthms
    • Z transform
    • Description of Linear Time Invariant Systems in z domain
    • Digital filtering
    • Foundations of adaptive signal processing (LMS algorithm, Robbins-Monroe stochastic approximation, model degree selection)
    • Application to adaptive system identification
    • Application to adaptive equalization
    • Application to time series prediction
    9. Method of instruction Lectures
    10. Assessment

    a.       One mid-term test .

    b.       Condition for the signature is passing the test (scoring  above 40% ).

    11. Recaps There are two possibilities to repeat the mid-term test.
    12. Consultations

    1 before mid-term test, more upon request

    13. References, textbooks and resources
    • J.G. Proakis, D.G. Manolakis: „Digital Signal Processing", Prentice Hall, 1996, ISBN 0-13394338-9
    • S. Haykin „Adaptive filters" ,Prentice Hall, 1996
    14. Required learning hours and assignment
    Kontakt óra56
    Félévközi készülés órákra34
    Felkészülés zárthelyire60
    Házi feladat elkészítése 
    Kijelölt írásos tananyag elsajátítása 
    15. Syllabus prepared by

    János Levendovszky, DSc


    Dept. of Networked Systems and Services