Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/131972
Título: | Neural Network-Based Detection of OCC Signals in Lighting-Constrained Environments: A Museum Use Case | Autores/as: | Rufo, Saray Aguiar-Castillo, Lidia Rufo Torres,Julio Francisco Perez-Jimenez, Rafael |
Clasificación UNESCO: | 3325 Tecnología de las telecomunicaciones | Palabras clave: | Convolutional Neural Networks Indoor Positioning Lighting Constraints Optical Camera Communication Simulation |
Fecha de publicación: | 2024 | Publicación seriada: | Electronics (Switzerland) | Resumen: | This research presents a novel approach by applying convolutional neural networks (CNNs) to enhance optical camera communication (OCC) signal detection under challenging indoor lighting conditions. The study utilizes a smartphone app to capture images of an LED lamp that emits 25 unique optical codes at distances of up to four meters. The developed CNN model demonstrates superior accuracy and outperforms traditional methodologies, which often struggle under variable illumination. This advancement provides a robust solution for reliable OCC detection where previous methods underperform, particularly in the tourism industry, where it can be used to create a virtual museum on the Unity platform. This innovation showcases the potential of integrating the application with a virtual environment to enhance tourist experiences. It also establishes a comprehensive visible light positioning (VLP) system, marking a significant advance in using CNN for OCC technology in various lighting conditions. The findings underscore the effectiveness of CNNs in overcoming ambient lighting challenges, paving the way for new applications in museums and similar environments and laying the foundation for future OCC system improvements. | URI: | http://hdl.handle.net/10553/131972 | ISSN: | 2079-9292 | DOI: | 10.3390/electronics13101828 | Fuente: | Electronics (Switzerland)[EISSN 2079-9292],v. 13 (10), (Mayo 2024) |
Colección: | Artículos |
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