Abstract: The present disclosure provides a method and apparatus for calculating carbon emission response based on carbon emission flows. The method includes: calculating a total carbon flow rate of a power system from a carbon flow rate of each of carbon meter users, the carbon flow rate being obtained based on a carbon emission flow of the carbon meter user; determining whether the total carbon flow rate is greater than a carbon emission response threshold; and calculating, in response to determining that the total carbon flow rate is greater than the carbon emission response threshold, a target carbon emission reduction of one or more of the carbon meter users during a carbon emission response period based on target carbon emission response demand, and initiating a carbon emission response based on the target carbon emission reduction.

Abstract: Provided is a carbon emission flow calculation method for a regional integrated energy system.

Abstract: Systems, methods, and computer readable media to detect and track a user's eye gaze and head movement are described. In general, techniques are disclosed for identifying a user's pupil location and using this information, in conjunction with a three dimensional (3D) model of the user's head, perform gaze tracking operations. More particularly, techniques disclosed herein utilize pupil gradient information to refine an initial pupil location estimate. Once identified, the pupil's location may be combined with 3D head pose information to generate an accurate and robust gaze detection mechanism.

Abstract: Systems, methods, and computer readable media to detect and track a user's eye gaze and head movement are described. In general, techniques are disclosed for identifying a user's pupil location and using this information, in conjunction with a three dimensional (3D) model of the user's head, perform gaze tracking operations. More particularly, techniques disclosed herein utilize pupil gradient information to refine an initial pupil location estimate. Once identified, the pupil's location may be combined with 3D head pose information to generate an accurate and robust gaze detection mechanism.

Abstract: Systems, methods, and computer readable media to detect and track a user's eye gaze and head movement are described. In general, techniques are disclosed for identifying a user's pupil location and using this information, in conjunction with a three dimensional (3D) model of the user's head, perform gaze tracking operations. More particularly, techniques disclosed herein utilize pupil gradient information to refine an initial pupil location estimate. Once identified, the pupil's location may be combined with 3D head pose information to generate an accurate and robust gaze detection mechanism.

Abstract: The present invention generally relates to deploying airborne agents to capture data related to a physical environment. An exemplary device comprises one or more processors; a memory; and one or more programs that includes instructions for: receiving a user request indicative of a geographical region and a data type; in response to receiving the user request, generating a flight path based on the region; causing the airborne agent to traverse at least a portion of the region based on the generated flight path; causing the airborne agent to gather data based on the data type in the user request; processing the gathered data to obtain a set of data of interest; and providing an output based on the set of data of interest.

Abstract: Systems, methods, and computer readable media to detect and track a user's eye gaze and head movement are described. In general, techniques are disclosed for identifying a user's pupil location and using this information, in conjunction with a three dimensional (3D) model of the user's head, perform gaze tracking operations. More particularly, techniques disclosed herein utilize pupil gradient information to refine an initial pupil location estimate. Once identified, the pupil's location may be combined with 3D head pose information to generate an accurate and robust gaze detection mechanism.

Abstract: Systems, methods, and computer readable media to detect and track a user's eye gaze and head movement are described. In general, techniques are disclosed for receiving one or more stereo images of a set of pupils, wherein each of the set of pupils is part of an eye of a head, calculating a location of each of the set of pupils from the stereo images, determining a head pose based on the one or more stereo images, identifying a location of the set of pupils in the head based on the determined head pose, and identifying a gaze using the locations of each of the set of pupils.

Abstract: Systems, methods, and computer readable media to detect and track a user's eye gaze and head movement are described. In general, techniques are disclosed for identifying a user's pupil location and using this information, in conjunction with a three dimensional (3D) model of the user's head, perform gaze tracking operations. More particularly, techniques disclosed herein utilize pupil gradient information to refine an initial pupil location estimate. Once identified, the pupil's location may be combined with 3D head pose information to generate an accurate and robust gaze detection mechanism.

Abstract: The present disclosure relates generally to optical flow algorithms. Section 1 of the present disclosure describes an optical flow algorithm with real-time performance and adequate accuracy for embedded vision applications. This optical flow algorithm is based on a ridge estimator. Sections 2 and 3 describe an obstacle detection algorithm that utilizes the motion field that is output from the optical flow algorithm. Section 2 is focused on unmanned ground vehicles, whereas section 3 is focused on unmanned aerial vehicles.

Abstract: The present disclosure relates generally to optical flow algorithms. Section 1 of the present disclosure describes an optical flow algorithm with real-time performance and adequate accuracy for embedded vision applications. This optical flow algorithm is based on a ridge estimator. Sections 2 and 3 describe an obstacle detection algorithm that utilizes the motion field that is output from the optical flow algorithm. Section 2 is focused on unmanned ground vehicles, whereas section 3 is focused on unmanned aerial vehicles.