Abstract: State-of-the-art object detection algorithms (e.g., EfficientDet, Faster-RCNN, and YOLO) and their variants are integrated into an end-to-end IoT cloud-based system deployment. The system can (1) receive real-time video streaming (RTSP/RTMP/SDP) from most CCTV systems and devices with cameras, (2) extract frames from the video streams, (3) feed the frames to multiple AI models simultaneously, and (4) compute a probability for the presence of fire and/or smoke. Based on custom risk thresholds, the outputs can be compiled programmatically into a video clip that is sent to a user automatically for real-time alerts.

Abstract: The use of microresonators with sharp corners (rectangular and square-shaped) can be limited by severe energy loss at the corners. The effect of incorporating fillet design at sharp corners (rounding of corners) of such single mode optical microresonators is described. The effect on quality factor, free spectral range (“FSR”), and energy loss for varying values of fillet radii are quantified and compared with standard circular microring resonator. It is shown that the selection of optimum fillet radius for sharp-cornered microresonators provide higher quality factor than that of the conventional circular resonators.

Abstract: The use of microresonators with sharp corners (rectangular and square-shaped) can be limited by severe energy loss at the corners. The effect of incorporating fillet design at sharp corners (rounding of corners) of such single mode optical microresonators is described. The effect on quality factor, free spectral range (“FSR”), and energy loss for varying values of fillet radii are quantified and compared with standard circular microring resonator. It is shown that the selection of optimum fillet radius for sharp-cornered microresonators provide higher quality factor than that of the conventional circular resonators.