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Interference Cancellation and Proper Thresholding Using Deep Learning Method in Optical Camera Communication

机译:在光学相机通信中使用深度学习方法的干扰取消和适当的阈值

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During data collection from the images using rolling shutter effect in the optical camera communication, interference from the surrounding light source reduce the system performances. On the other hand, thresholding problem creates after getting the normalized intensity from the image when number of sources appear in the camera’s field of view. Therefore, we applied deep learning approach for removing the interfering light sources and use synchronous thresholding method for data correction. We also observed the performance of signal-error-rate of the system in different condition in Python environment.
机译:在使用滚动快门效果的图像中的数据收集期间,在光学摄像机通信中,周围光源的干扰会降低系统性能。 另一方面,当在相机的视野中出现的源位出时,从图像中获取归一化强度后,阈值问题会创建。 因此,我们应用了消除干扰光源的深度学习方法,并使用同步阈值方法进行数据校正。 我们还观察到在Python环境中不同条件下系统的信号误差率的性能。

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