Abstract: Apparatus, method and storage medium associated with UAV assisted emergency responses are disclosed herein. In embodiments, an UAV may comprise a flight controller to control at least one or more engines of the UAV to navigate the UAV to condition road traffic for an emergency vehicle on emergency en route to a destination, wherein to condition road traffic, the flight controller is to receive navigation data of the emergency vehicle, and in response, control at least the one or more engines to navigate the UAV in advance of the emergency vehicle, to alert road traffics ahead of the emergency vehicle of pending transit of the emergency vehicle. Other embodiments may be disclosed or claimed.

Abstract: Embodiments of a system and method for method for automatically determining pet owner interaction with a pet are generally described herein. A method may include obtaining sensor data including pet behavior metrics from a sensor, aggregating the sensor data, and interpreting the aggregated sensor data to determine an engagement factor of a pet owner to a pet. The method may include outputting a report based on the engagement factor or the aggregated sensor data.

Abstract: Apparatus, method and storage medium associated with UAV assisted emergency responses are disclosed herein. In embodiments, an UAV may comprise a flight controller to control at least one or more engines of the UAV to navigate the UAV to condition road traffic for an emergency vehicle on emergency en route to a destination, wherein to condition road traffic, the flight controller is to receive navigation data of the emergency vehicle, and in response, control at least the one or more engines to navigate the UAV in advance of the emergency vehicle, to alert road traffics ahead of the emergency vehicle of pending transit of the emergency vehicle.

Abstract: Embodiments of a system and method for method for automatically determining pet owner interaction with a pet are generally described herein. A method may include obtaining sensor data including pet behavior metrics from a sensor, aggregating the sensor data, and interpreting the aggregated sensor data to determine an engagement factor of a pet owner to a pet. The method may include outputting a report based on the engagement factor or the aggregated sensor data.

Abstract: Various techniques for performing camera control techniques based on brainwave data from an electroencephalography (EEG) headset are disclosed herein. In an example, a computing system operates to receive brainwave data representing brainwave activity, process the brainwave data, and transmit a command to a camera device based on the processed brainwave data. For example, the brainwave data may correlate to raw brainwave signals (from gamma, beta, alpha, theta, or delta brainwaves), or composite brainwave signals (from multiple brainwaves, representative of attention, meditation, or like states). As a result, the computing system may transmit a command to a camera device to capture an image, a burst of images, start/stop video recording, or like commands for image and video operations, based on a human user's detected brainwave state. Further processing, detection, and training methods for brainwave control of camera operations are also disclosed.

Abstract: Various techniques for performing camera control techniques based on brainwave data from an electroencephalography (EEG) headset are disclosed herein. In an example, a computing system operates to receive brainwave data representing brainwave activity, process the brainwave data, and transmit a command to a camera device based on the processed brainwave data. For example, the brainwave data may correlate to raw brainwave signals (from gamma, beta, alpha, theta, or delta brainwaves), or composite brainwave signals (from multiple brainwaves, representative of attention, meditation, or like states). As a result, the computing system may transmit a command to a camera device to capture an image, a burst of images, start/stop video recording, or like commands for image and video operations, based on a human user's detected brainwave state. Further processing, detection, and training methods for brainwave control of camera operations are also disclosed.

Abstract: A system and method for monitoring an aircraft during taxing on ground around an airport may include control of an engines-off taxiing system. The engines-off taxiing system may include a wireless handheld device having an aircraft stop pushbutton. The wireless handheld device may be carried by a wing-walker, and the associated aircraft stop pushbutton may be manually actuated by the wing-walker.

Abstract: In sonic examples, an obstacle detection system is configured to generate and display a graphical user interface (GUI) that includes an overhead image of an area in which a vehicle is positioned, a graphical representation of the vehicle, and graphical representations of one or more obstacles, The graphical representations of the one or more obstacles and vehicle can be arranged relative to the overhead image to indicate determined real-world positions of the one or more obstacles and vehicle, respectively, relative to other features shown in the overhead (e.g., airport structure or other buildings).

Abstract: A system is configured to generate and display information regarding a strike zone of an aircraft. In some examples, a system is configured to generate and display an image of an environment around an aircraft together with a graphical indication of a strike zone of the aircraft, where the indication is scaled to reflect the strike zone at a distance range of one or more detected objects.

Abstract: In sonic examples, an obstacle detection system is configured to generate and display a graphical user interface (GUI) that includes an overhead image of an area in which a vehicle is positioned, a graphical representation of the vehicle, and graphical representations of one or more obstacles, The graphical representations of the one or more obstacles and vehicle can be arranged relative to the overhead image to indicate determined real-world positions of the one or more obstacles and vehicle, respectively, relative to other features shown in the overhead (e.g., airport structure or other buildings).