prikaz prve stranice dokumenta Senzor pokreta kao digitalna brava
  Download
PDF 2.6 MB
master's thesis
Senzor pokreta kao digitalna brava
2023. urn:nbn:hr:200:198659
Pavičić, Marko
Josip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
Department of Communications
Chair of Electronics and Microelectronics

Cite this document

Pavičić, M. (2023). Senzor pokreta kao digitalna brava (Master's thesis). Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek. Retrieved from https://urn.nsk.hr/urn:nbn:hr:200:198659

Pavičić, Marko. "Senzor pokreta kao digitalna brava." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2023. https://urn.nsk.hr/urn:nbn:hr:200:198659

Pavičić, Marko. "Senzor pokreta kao digitalna brava." Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, 2023. https://urn.nsk.hr/urn:nbn:hr:200:198659

Pavičić, M. (2023). 'Senzor pokreta kao digitalna brava', Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, accessed 10 December 2024, https://urn.nsk.hr/urn:nbn:hr:200:198659

Pavičić M. Senzor pokreta kao digitalna brava [Master's thesis]. Osijek: Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek; 2023 [cited 2024 December 10] Available at: https://urn.nsk.hr/urn:nbn:hr:200:198659

M. Pavičić, "Senzor pokreta kao digitalna brava", Master's thesis, Josip Juraj Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Osijek, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:200:198659

Please login to the repository to save this object to your list.
Metadata
TitleSenzor pokreta kao digitalna brava
Title (english)Gesture sensor as digital lock
AuthorMarko Pavičić
MentorDavor Vinko (mentor)
Committee memberDavor Vinko (predsjednik povjerenstva)
Committee memberKrešimir Grgić (član povjerenstva)
Committee memberLuka Filipović (član povjerenstva)
GranterJosip Juraj Strossmayer University of Osijek
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek
(Department of Communications)
(Chair of Electronics and Microelectronics)
Osijek
Defense date and country2023-09-18, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Artificial Intelligence
Abstract
U ovom diplomskom radu je provedeno istraživanje i implementacija digitalne brave korištenjem senzora pokreta na Arduino Nano 33 BLE Sense razvojnoj pločici. Rješenje se sastoji od tri ključna dijela. Prvo, podaci o gestama su prikupljani putem žiroskopa i akcelerometra te su spremljeni u CSV datoteke. Zatim je korištena TensorFlow biblioteka za strojno učenje kako bi se trenirao model za prepoznavanje gesti koristeći prikupljene podatke. Nakon treniranja, generirana je datoteka zaglavlja koja sadrži trenirani model. Konačno, trenirani model se koristi za prepoznavanje gesti te se uspostavlja komunikacija s web-stranicom putem Bluetooth-a kako bi se otvorila vrata ovisno o prepoznatoj gesti. Rješenje je uspješno prikupljalo podatke o gestama, treniralo model za prepoznavanje gesti te omogućavalo otvaranje vrata na temelju prepoznate geste. Kvaliteta prepoznavanja gesti je evaluirana kroz testiranje sustava s različitim kombinacijama gesti i pragovima detekcije sve dok nije postignuta zadovoljavajuća točnost.
Abstract (english)
In this master's thesis, the research and implementation of a digital lock using a motion sensor on the Arduino Nano 33 BLE Sense development board was carried out. The solution consists of three key parts. First, gesture data was collected via gyroscope and accelerometer and saved in CSV files. The TensorFlow machine learning library was then used to train a gesture recognition model using the collected data. After training, a header file containing the trained model is generated. Finally, the trained model is used for gesture recognition and communicates with a website via Bluetooth to open the door depending on the recognized gesture. The solution successfully collected data on gestures, trained a model for gesture recognition and enabled door opening based on the recognized gesture. The quality of gesture recognition was evaluated by testing the system with different combinations of gestures and detection thresholds until satisfactory accuracy was achieved.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:200:198659
Study programmeTitle: Graduate University Study Programme in Electrical Engineering; specializations in: Communications and Informatics, Power Engineering
Course: Communications and Informatics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike (magistar/magistra inženjer/inženjerka elektrotehnike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2023-10-09 08:56:00