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Smart City Laboratory

Okos város laboratórium
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
Subject name (Hungarian, English)
Okos város laboratórium
Smart City Laboratory
Subject code BMEVITMMB09
Subject type
Training Level
Course types and hours (weekly/semester)
Course type lecture tutorial laboratory
hours (weekly) 0 0 3
type (linked/independent) autonomous course
Assessment type félévközi érdemjegy
Credits 4
Subject coordinator
DR. Fehér Gábor
position: egyetemi docens
Responsible department
Távközlési és Mesterséges Intelligencia Tanszék
Faculty Villamosmérnöki és Informatikai Kar
Subject website
Primary curriculum type
Direct prerequisites – Strong prerequisite none
Direct prerequisites – Weak prerequisite none
Direct prerequisites – Parallel prerequisite none
Direct prerequisites – Milestone prerequisite none
Direct prerequisites – Exclusion none

Objectives

Programme
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)
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.

Learning outcomes

Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:

Knowledge

No learning outcomes recorded.

Skills

No learning outcomes recorded.

Attitudes

No learning outcomes recorded.

Autonomy and responsibility

No learning outcomes recorded.

Oktatási módszertan

- 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.  

Tanulástámogató anyagok

Online források
Online reading material for presentations (book chapters, articles)

Recommended preliminary knowledge for completing the subject

Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
nincs
General rules
Requirements: 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: - Additional possibilities: - 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.
Assessment methods
In-term assessments

No detailed assessments provided.

Weight of in-term assessments

No weights provided.

Exam-period assessments

No detailed assessments provided.

Weight of exam elements

No weights provided.

Grade calculation

No grade thresholds provided.

Attendance requirements

No attendance requirements provided.

Rules for retake and resubmission

Not provided.

Short description

Not provided.

Detailed description

Not provided.

Recommended courses

Not provided.

Workload to complete the subject

No workload breakdown provided.

Validity of subject requirements
Requirements valid from:
Requirements valid until:
Curriculum placement

No curriculum placements recorded for this subject version.