Experimental Investigation of Neuron Based Motion Detection in Internet of Things using Optical Camera Communications

S. Teli, S. Zvánovec, Zabih Ghassemlooy
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Abstract

This paper experimentally investigates the performance of visible light based optical camera communications (OCC) link with motion detection (MD) for the optical Internet of things applications. This efficient MD can be considered another functionality of OCC in addition to traditional features of vision, illumination data communications and sensing. The experiments were conducted in an indoor static downlink OCC system employing a mobile phone front camera is employed as the receiver and an $8\times 8$ red, green, and blue (RGB) light-emitting diode array as the transmitter. The motion is detected by observing the user's finger movement in the form of centroid through the OCC link via a camera. The experiment results demonstrate that, the proposed scheme can detect all considered motions accurately with acceptable bit error rate (BER) performances at a transmission distance of up to 80 cm. We show a BER of $1.7\times 10^{-3}$ below the forward error correction limit of $3.8\times 10^{-3}$ over a transmission distance of up to 1 m. The proposed neuron based MD combined together with OCC can be considered an efficient system, which provides illumination, communications, and motion detection in a convenient smart home environment.
基于光学摄像机通信的物联网神经元运动检测实验研究
实验研究了光学物联网应用中基于可见光的光学摄像机通信(OCC)链路与运动检测(MD)链路的性能。除了视觉、照明、数据通信和传感等传统功能外,这种高效的MD可以被认为是OCC的另一种功能。实验在室内静态下行OCC系统中进行,采用手机前置摄像头作为接收机,8 × 8$红、绿、蓝(RGB)发光二极管阵列作为发射器。通过摄像头通过OCC链接观察用户手指以质心的形式运动来检测动作。实验结果表明,在传输距离为80 cm的情况下,该方案能够以可接受的误码率(BER)准确检测所有考虑的运动。在传输距离为1 m的情况下,误码率为$1.7\乘以10^ -3}$,低于前向纠错极限$3.8\乘以10^ -3}$。本文提出的基于神经元的MD与OCC相结合,可以被认为是一个高效的系统,在方便的智能家居环境中提供照明、通信和运动检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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