The Vision Controller VC4 enables the rapid development and deployment of vision applications. The VC4 comprises a powerful vision PC, PLC interfaces, and ...

Nasution, M.R.A.; Herfandi, H.; Sitanggang, O.S.; Nguyen, H.; Jang, Y.M. Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning. Sensors 2024, 24, 702. https://doi.org/10.3390/s24020702

Nasution, Muhammad Rangga Aziz, Herfandi Herfandi, Ones Sanjerico Sitanggang, Huy Nguyen, and Yeong Min Jang. 2024. "Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning" Sensors 24, no. 2: 702. https://doi.org/10.3390/s24020702

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Nasution, Muhammad Rangga Aziz, Herfandi Herfandi, Ones Sanjerico Sitanggang, Huy Nguyen, and Yeong Min Jang. 2024. "Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning" Sensors 24, no. 2: 702. https://doi.org/10.3390/s24020702

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

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Abstract: In recent years, optical camera communication (OCC) has garnered attention as a research focus. OCC uses optical light to transmit data by scattering the light in various directions. Although this can be advantageous with multiple transmitter scenarios, there are situations in which only a single transmitter is permitted to communicate. Therefore, this method is proposed to fulfill the latter requirement using 2D object size to calculate the proximity of the objects through an AI object detection model. This approach enables prioritization among transmitters based on the transmitter proximity to the receiver for communication, facilitating alternating communication with multiple transmitters. The image processing employed when receiving the signals from transmitters enables communication to be performed without the need to modify the camera parameters. During the implementation, the distance between the transmitter and receiver varied between 1.0 and 5.0 m, and the system demonstrated a maximum data rate of 3.945 kbps with a minimum BER of 4.2 × 10 − 3 . Additionally, the system achieved high accuracy from the refined YOLOv8 detection algorithm, reaching 0.98 mAP at a 0.50 IoU. Keywords: optical camera communication; proximity; multiple transmitters; object detection

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Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

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Nasution, M.R.A.; Herfandi, H.; Sitanggang, O.S.; Nguyen, H.; Jang, Y.M. Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning. Sensors 2024, 24, 702. https://doi.org/10.3390/s24020702

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Nasution MRA, Herfandi H, Sitanggang OS, Nguyen H, Jang YM. Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning. Sensors. 2024; 24(2):702. https://doi.org/10.3390/s24020702

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Nasution MRA, Herfandi H, Sitanggang OS, Nguyen H, Jang YM. Proximity-Based Optical Camera Communication with Multiple Transmitters Using Deep Learning. Sensors. 2024; 24(2):702. https://doi.org/10.3390/s24020702

20241014 — Let's imagine that we have CW laser with coherence length = 1 mm. I put two mirrors along the optical axis in such way that each of them ...