This paper proposes a new dynamic bandwidth allocation (DBA) method with low latency for campus area networks (Campus-LANs) and mobile fronthaul (MFH) based on time division multiplexing passive optical network (TDM-PON). These network systems require low latency of under 100 µs and priority control. The proposed method involves only two steps for allocation. The DBA cycle value, which is proportional to the latency, can be minimized because this method is very simple and appropriate for hardware (H/W) implementation. We implemented the proposed DBA on 10G-EPON MAC SoC and evaluated the allocation results and the latency on the 10G-EPON system. Our DBA offers ultra-low latency of 60 µs with priority control.
To detect a wide range of objects with one camera at once, real-time object detection in high-definition video is required in video artificial intelligence (AI) applications for edge/terminal, such as beyond-visual-line-of-sight (BVLOS) drone flight. Although various AI inference schemes for object detection (e.g., you-only-look-once (YOLO)) have been proposed, they typically have limitations on the input image size and thus need to shrink the input high-definition image down to the limit. This makes small objects collapsed and undetectable. This paper presents our proposal technology for solving this problem and its effective implementation, where multiple object detectors cooperate to detect small and large objects in high-definition video such as full HD and 4K.
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