Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: A videoconferencing endpoint is described that uses a combination of face detection, motion detection, and upper body detection for selecting participants of a videoconference for group framing. Motion detection is used to remove fake faces as well as to detect motion in regions around detected faces during postprocessing. Upper body detection is used in conjunction with the motion detection in postprocessing to allow saving faces that have been initially detected by face detection for group framing even if the participant has turned away from the camera, allowing the endpoint to keep tracking the participants region better than would be possible based only on an unstable result coming from face detection.

Abstract: A videoconferencing endpoint is described that uses a combination of face detection, motion detection, and upper body detection for selecting participants of a videoconference for group framing. Motion detection is used to remove fake faces as well as to detect motion in regions around detected faces during postprocessing. Upper body detection is used in conjunction with the motion detection in postprocessing to allow saving faces that have been initially detected by face detection for group framing even if the participant has turned away from the camera, allowing the endpoint to keep tracking the participants region better than would be possible based only on an unstable result coming from face detection.

Abstract: A method may include receiving a source video frame captured using a wide angle lens, deriving a characteristic of a target video frame from the source video frame, generating a transitional video frame using the source video frame and the target video frame, and transmitting the transitional video frame to an endpoint.