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Rolling Shutter-Based Underwater Optical Camera Communication (UWOCC) with Side Glow Optical Fiber (SGOF)

Nowadays, a variety of underwater activities, such as underwater surveillance, marine monitoring, etc., are becoming crucial worldwide. Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted for underwater exploration. Underwater communication via radio frequency (RF) or aco...

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Published in:Applied sciences 2024-09, Vol.14 (17), p.7840
Main Authors: Li, Jia-Fu, Chang, Yun-Han, Chen, Yung-Jie, Chow, Chi-Wai
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description Nowadays, a variety of underwater activities, such as underwater surveillance, marine monitoring, etc., are becoming crucial worldwide. Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted for underwater exploration. Underwater communication via radio frequency (RF) or acoustic wave suffers high transmission loss and limited bandwidth. In this work, we present and demonstrate a rolling shutter (RS)-based underwater optical camera communication (UWOCC) system utilizing a long short-term memory neural network (LSTM-NN) with side glow optical fiber (SGOF). SGOF is made of poly-methyl methacrylate (PMMA) SGOF. It is lightweight and flexibly bendable. Most importantly, SGOF is water resistant; hence, it can be installed in an underwater environment to provide 360° “omni-directional” uniform radial light emission around its circumference. This large FOV can fascinate the optical detection in underwater turbulent environments. The proposed LSTM-NN has the time-memorizing characteristics to enhance UWOCC signal decoding. The proposed LSTM-NN is also compared with other decoding methods in the literature, such as the PPB-NN. The experimental results demonstrated that the proposed LSTM-NN outperforms the PPB-NN in the UWOCC system. A data rate of 2.7 kbit/s can be achieved in UWOCC, satisfying the pre-forward error correction (FEC) condition (i.e., bit error rate, BER ≤ 3.8 × 10−3). We also found that thin fiber also allows performing spatial multiplexing to enhance transmission capacity.
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subjects Cameras
CMOS
Light emitting diodes
long short-term memory neural network (LSTM-NN)
machine learning
Neural networks
optical wireless communication (OWC)
Sensors
side glow optical fiber (SGOF)
Underwater exploration
Unmanned aerial vehicles
visible light communication (VLC)
Wireless communications
Zinc oxides
title Rolling Shutter-Based Underwater Optical Camera Communication (UWOCC) with Side Glow Optical Fiber (SGOF)
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