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

    címtáras azonosítással

    vissza a tantárgylistához   nyomtatható verzió    

    Applications of Data Processing

    A tantárgy neve magyarul / Name of the subject in Hungarian: Adatfeldolgozó alkalmazások

    Last updated: 2024. január 10.

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

    MSc in Electrical Engineering

    Intelligent Embedded Systems specialization

    Course ID Semester Assessment Credit Tantárgyfélév
    VIMIMB06   2/1/0/v 5  
    3. Course coordinator and department Dr. Dabóczi Tamás,
    4. Instructors Dr. Tamás Dabóczi, Full professor, MIT
    5. Required knowledge Signals and systems
    6. Pre-requisites
    Perception and Signal processing (VIMIMA20)
    7. Objectives, learning outcomes and obtained knowledge The course presents model-based algorithms for information processing related to embedded systems.
    8. Synopsis

    Detailed topics of the lectures:

    Sample applications of intelligent data processing:
    Digital twin concept and its application possibilities. (1 week)
    The concept of predictive maintenance and its application possibilities. (1 week)
    The concept of sensorless measurement technology, analytical redundancy, their application in fault-tolerant systems or in cost-sensitive systems. (1 week)
    The concept of HIL/SIL/MIL simulation, the modeling tasks for the simulated system. (1 week)
    Modeling and identification of linear dynamic systems. Parametric and non-parametric identification. Time and frequency domain matching. (1.5 weeks)
    Modeling of nonlinear systems. Static nonlinearity, model fitting, compensation based on lookup table and interpolation in non-stored points. Nonlinear dynamic systems. (1 week)
    Stimulus signal design for identification of linear and non-linear systems. (0.5 weeks)
    Information processing:
    Filter-based methods of sensor fusion. Consideration of the sensor's finite bandwidth and transmission characteristics during fusion. (1 week)
    Inverse filtering, compensation of the frequency-dependent distortion of the measuring system in poorly conditioned cases. The concept of regularization. Application of regularization to solve ill-conditioned matrix equations. (1.5 weeks)
    Prediction, replacement of missing data based on previous samples of time series. (1 week)
    Order analysis concept and methods. (1 week)
    Pattern recognition methods. (0.5 weeks)
    Information reduction:
    Concept of model-based information reduction, compressed sensing, application possibilities. (1 week)

    Detailed topics of the practices:
    1.    Identification of linear systems. Using an identification toolbox in a simulation environment
    2.    Stimulus signal design using identification toolbox
    3.    Complementary filter design. Filter design taking into account the dynamic properties of the sensor.
    4.    Solving ill-conditioned inverse problems in a simulation environment
    5.    Estimation of quantities that cannot be measured directly (measuring techniques without sensors)
    6.    Prediction
    7.    Order analysis in a simulation environment

    9. Method of instruction

    2 hours of lecture per week, 1 hour of practice (calculation practice and computer laboratory practice).

    10. Assessment

    During the study period: 1 midterm test. The condition for obtaining the signature is the completion of the midterm test at a sufficient level.

    During the exam period: Oral exam.

    11. Recaps Midterm exam can be once retaken.
    12. Consultations On demand, according to agreed schedule.
    13. References, textbooks and resources

    Measurement and Data Science, Gábor Péceli (Editor), Cambridge Scholars Publishing

    • Chapter 3, Tamás Dabóczi, "Inverse problems and algorithms of measurement science"
    • Chapter 4, Tadeusz Dobrowiecki, "Optimized Random Multisines in Nonlinear System Characterization"
    14. Required learning hours and assignment
    Lectures and classroom practice
    Preparation for lectures
    Preparation for mid-semester tests
    Study of selected written material
    Preparation for exams
    15. Syllabus prepared by Dr. Tamás Dabóczi, Full Professor, MIT