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

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    Smart City Laboratory

    A tantárgy neve magyarul / Name of the subject in Hungarian: Okos város laboratórium

    Last updated: 2024. március 3.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics     		MSc, Electric Engineer, Smart City secondary specialization
    Course ID Semester Assessment Credit Tantárgyfélév
    VITMMB09   0/0/3/f 4  
    3. Course coordinator and department Dr. Fehér Gábor,
    4. Instructors Dr. Gábor Fehér, associate professor, TMIT
    Dr. Attila Vidács, associate professor, TMIT
    Dr. Rolland Vida, associate professor, TMIT
    6. Pre-requisites
    Kötelező:
    NEM
    (TárgyEredmény( "BMEVITMMB04", "jegy" , _ ) >= 2
    VAGY
    TárgyEredmény("BMEVITMMB04", "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.

    7. Objectives, learning outcomes and obtained knowledge The primary goal of this course is to offer a comprehensive exploration into the diverse array of hardware and software architectural components crucial for realizing the vision of a Smart City. Participants will delve into the intricate building blocks that underpin this concept, gaining insights into their functionality and interconnectivity. Through practical exercises, students will not only learn to design and execute system-level measurements but also to critically evaluate real-world case studies.
    8. Synopsis
    Detailed topics for exercises and labs:
     
    1. Familiarisation with traffic simulators for Intelligent Transport Systems (presence measurement)
    2. Solving a traffic simulation problem related to intelligent transport systems (online measurement)

    3. Building and deploying IoT sensor hardware related to smart city. Integration of sensor, microcontroller and radio. Sending and receiving data (presence measurement)
    4. Using a smart city IoT platform. Processing and displaying data from the sensor (online measurement).

    5. Image recognition, image processing tasks with street camera images (presence measurement)
    6. Analysis and processing of smart city related camera images using deep learning (online measurement)

    7. Analysis of smart city sensor data (meters, environmental data) using artificial intelligence. Estimation and forecasting (presence measurement)
    8. Processing smart city related data series with artificial intelligence (online measurement)

    9. Using a self-driving car simulator (CARLA), processing signals from sensors. Lidar, radar, IMU and raw depth and segmented camera images. Introduction to sensors and simulation management (presence measurement)
    10. Simulation exercise using sensors for self-driving car (online measurement)
    9. Method of instruction
    - Laboratory measurements consist of both in-person and online formats, with an equal distribution of 5 sessions each within a 4-hour period. Guidance and support will be available for online labs, allowing students to complete assignments using their own computer, while instructors will supply all necessary resources. At the beginning of the semester, students can sign up for in-person measurements, when multiple opportunities are available for a given lab session.
     
    - Prior to attending in-person measurements, students are expected to prepare using electronic measurement instructions and guides. Preparation will be evaluated through test questions derived from these materials at the beginning of each session. The preparation for the online measurements are tested at the same time. If a student answers 2 or more test questions incorrectly, a repeated measurement is required.
       
    - During the lab sessions, students must complete the mandatory tasks outlined in the test instructions. Each laboratory measurement necessitates the submission of an electronic report by the student. For online measurements, students are required to present their results at the subsequent in-person session. In this case, if the report justifies it, the instructors may waive the presentation. Grades for lab measurements will be determined based on the submitted report.
     
    10. Assessment
    During term time:
    - Recognition for the semester: completion of 10 measurements with at least a satisfactory grade on each assessment. The mid-semester grade is the arithmetic mean of the grades given for the measurements, using the usual rounding rules.
     
    During the examination period: -
    11. Recaps
    - Each student is given two make-up assessments, which can be taken by the end of the make-up period at the latest, in agreement with the responsible teacher and the measurement supervisor.
     
    - There are no make-ups during the examination period.
    12. Consultations On request, by prior arrangement with the lecturer.
    13. References, textbooks and resources Online reading material for presentations (book chapters, articles)
    14. Required learning hours and assignment
    Kontakt óra42
    Félévközi készülés órákra78
    Felkészülés zárthelyire0
    Házi feladat elkészítése0
    Kijelölt írásos tananyag elsajátítása
    Vizsgafelkészülés0
    Összesen120
    15. Syllabus prepared by Dr. Gábor Fehér, associate professor, TMIT
    Dr. Attila Vidács, associate professor, TMIT
    Dr. Rolland Vida, associate professor, TMIT