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

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    Applied Computer Systems Laboratory 2

    A tantárgy neve magyarul / Name of the subject in Hungarian: Rendszer- és alkalmazástechnika labor 2

    Last updated: 2019. január 19.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    Electrical Engineering M.Sc.         
    Computer Based Systems Major Specialization         
    Course ID Semester Assessment Credit Tantárgyfélév
    VIAUMB03 3 0/0/3/f 4  
    3. Course coordinator and department Dr. Vajk István,
    4. Instructors

    Name:

    Title:

    Department.:

    Dr. György Orosz

    assoc. professor

    Measurement and Information Systems

    Dr. László Blázovics

    senior lecturer

    Automation and Applied Informatics

    5. Required knowledge –
    6. Pre-requisites
    Kötelező:
    Only those student can register to the course who accomplished the Development of Software Applications (VIAUMA09) course.
    (Training.Code=("5N-M7") ÉS
    TárgyEredmény( "BMEVIAUMA09" , "jegy" , _ ) >= 2
    ÉS
    TárgyEredmény( "BMEVIAUMA10" , "jegy" , _ ) >= 2
    ÉS NEM ( TárgyEredmény( "BMEVIAUM315" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIAUM315", "FELVETEL", AktualisFelev()) > 0))

    VAGY

    (Training.Code=("5NAM7") ÉS
    TárgyEredmény( "BMEVIAUMA09" , "jegy" , _ ) >= 2
    ÉS NEM ( TárgyEredmény( "BMEVIAUM315" , "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIAUM315", "FELVETEL", AktualisFelev()) > 0))

    ÉS
    NEM
    (TárgyEredmény( "BMEVIAUMB05", "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVIAUMB05", "FELVETEL", AktualisFelev()) > 0)

    A fenti forma a Neptun sajátja, ezen technikai okokból nem változtattunk.

    A kötelező előtanulmányi rend az adott szak honlapján és képzési programjában található.

    Ajánlott:
    –
    7. Objectives, learning outcomes and obtained knowledge The aim of this course is to provide students with overview of application development process in a modern C++ development environment (Qt/QML) and also in the field of neural networks.
    8. Synopsis Lab Topic
    1. Practical exercises of the git version control system. Creating remote and local repositories, branches, commits and manipulating them.
    2. Qt-C++ basics. Understanding the development process of the Qt ecosystem. Creating small example applications.
    3. Creating QML based user interfaces. Understanding and practicing the declarative UI paradigm.
    4. Complex Qt-QML exercises. Creating interactive hybrid C++-QML applications.
    5. Classification with multi-stage processing. Processing of vibration and sound signals using time- and frequency-domain methods, classification based on fuzzy systems and neural networks.
    6. Complex exercises in the topic of classification based on fuzzy systems and neural networks.
    9. Method of instruction The course consists of 6 laboratory exercises (4 hours/session)
    10. Assessment
    In lecture term:
    		6 laboratory exercises
    In examination period:
    		none
    Pre-exam:none

    The credits can be obtained by successfully participating on the laboratory exercises The final grade is calculated based on the grades of laboratory exercises, which are counted from quick tests, in class results and submitted documentation.
    11. Recaps One laboratory exercise can be repeated during the semester and the repeat period in accordance with the Code of Studies and Exams (CSE).
    12. Consultations Upon request, appointed with the laboratory instructors.
    13. References, textbooks and resources Laboratory guides on the department website.
    14. Required learning hours and assignment
    Contact hours 24
    Preparation for laboratory exercises 96
    Total
    		 120
    15. Syllabus prepared by

    Name:

    Title:

    Department.:

    Dr. Gábor Tevesz

    assoc. professor

    Automation and Applied Informatics

    Dr. László Sujbert

    assoc. professor

    Measurement and Information Systems