TY  - CHAP
AU  - Szalka, Panka
AU  - Hideg, Attila
ED  - Vajk, István
ED  - Dunaev, Dmitriy
TI  - Digital twin technology: general application, challenges, and potentials in healthcare
T2  - Proceedings of the Automation and Applied Computer Science Workshop 2025
PB  - Budapest University of Technology and Economics, Department of Automation and Applied Informatics
CY  - Budapest
SN  - 9789634219989
PY  - 2025
SP  - 257
EP  - 266
PG  - 10
UR  - https://m2.mtmt.hu/api/publication/36377907
ID  - 36377907
LA  - English
DB  - MTMT
ER  - 

TY  - CONF
AU  - Szalka, Panka
AU  - Forstner, Bertalan
ED  - Yam, Yeung
ED  - Baranyi, Péter Zoltán
TI  - Fine Motor Skill Assessment Using Computer Vision
T2  - Proceedings of the International Conference on AI Transformation
PB  - Corvinus University of Budapest
C1  - Budapest
SN  - 9789635039562
PY  - 2024
SP  - 48
UR  - https://m2.mtmt.hu/api/publication/35892426
ID  - 35892426
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Szalka, Panka
AU  - Attila, Hideg
ED  - Dunaev, Dmitriy
ED  - Vajk, István
TI  - Advancing Indoor Navigation: Integrating Diverse Data Sets with the ICP Algorithm for Enhanced Mapping Precision
T2  - Proceedings of the Automation and Applied Computer Science Workshop 2024 : AACS'24
PB  - Budapesti Műszaki Egyetem (BME)
CY  - Budapest
SN  - 9789634219606
PY  - 2024
UR  - https://m2.mtmt.hu/api/publication/36158460
ID  - 36158460
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Szalka, Panka
AU  - Hideg, Attila
ED  - Szakál, Anikó
TI  - Enhanced Indoor Mapping via Integration of Lidar and HoloLens 2 Data Using Iterative Closest Point Algorithm
T2  - IEEE 22nd International Symposium on Intelligent Systems and Informatics (SISY 2024)
PB  - IEEE Hungary Section
CY  - Pula
SN  - 9798350385595
PY  - 2024
SP  - 85
EP  - 90
PG  - 6
DO  - 10.1109/SISY62279.2024.10737592
UR  - https://m2.mtmt.hu/api/publication/35643916
ID  - 35643916
N1  - IEEE CI Chapter; IEEE CS Chapter; IEEE Hungary Section; IEEE IES; IEEE SMC Chapter; RAS Joint Chapter            
            Conference code: 203937            
            Export Date: 18 December 2024            
            Funding details: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFIH            
            Funding details: 2022-2.1.1-NL-2022- 00012            
            Funding text 1: The author would like to express her thanks to Attila Hideg for his support as a scientific advisor. His expertise and guidance were instrumental in shaping and directing this study. This research was supported by the Ministry of Culture and Innovation and the National Research, Development and Innovation Office within the Cooperative Technologies National Laboratory of Hungary (grant No. 2022-2.1.1-NL-2022- 00012).
AB  - This study presents an innovative method to improve the accuracy of indoor mapping by deploying and integrating a sensor that transmits two-dimensional data with a three-dimensional mesh created by another device. The Iterative Closest Point algorithm was used to coordinate the different data sets and transform their different coordinate systems into a common indoor map. Prior to this, rigorous image cleaning and matching processes are conducted to ensure the quality of the data used. The effectiveness of this methodology has been validated through multiple tests using a Lidar scanner and a HoloLens 2 Augmented Reality device. This method not only offers potential for enhancing indoor navigation and safety protocols like emergency evacuation but also presents opportunities for advancing asset tracking and other applications. © 2024 IEEE.
LA  - English
DB  - MTMT
ER  -