Abstract: Methods, devices and systems are disclosed for inter-app communications between software applications on a mobile communications device. In one aspect, a computer-readable medium on a mobile computing device comprising an inter-application communication data structure to facilitate transitioning and distributing data between software applications in a shared app group for an operating system of the mobile computing device includes a scheme field of the data structure providing a scheme id associated with a target software app to transition to from a source software app, wherein the scheme id is listed on a scheme list stored with the source software app; and a payload field of the data structure providing data and/or an identification where to access data in a shared file system accessible to the software applications in the shared app group, wherein the payload field is encrypted.

Abstract: Methods, devices and systems are disclosed for inter-app communications between software applications on a mobile communications device. In one aspect, a computer-readable medium on a mobile computing device comprising an inter-application communication data structure to facilitate transitioning and distributing data between software applications in a shared app group for an operating system of the mobile computing device includes a scheme field of the data structure providing a scheme id associated with a target software app to transition to from a source software app, wherein the scheme id is listed on a scheme list stored with the source software app; and a payload field of the data structure providing data and/or an identification where to access data in a shared file system accessible to the software applications in the shared app group, wherein the payload field is encrypted.

Abstract: At least one combined image may be created from a plurality of images captured by a plurality of cameras. A sensor unit may receive the plurality of images from the plurality of cameras. At least one processor in communication with the sensor unit may correlate each received image with calibration data for the camera from which the image was received. The calibration data may comprise camera position data and characteristic data. The processor may combine at least two of the received images from at least two of the cameras into the at least one combined image by orienting the at least two images relative to one another based on the calibration data for the at least two cameras from which the images were received and merging the at least two aligned images into the at least one combined image.

Abstract: At least one combined image may be created from a plurality of images captured by a plurality of cameras. A sensor unit may receive the plurality of images from the plurality of cameras. At least one processor in communication with the sensor unit may correlate each received image with calibration data for the camera from which the image was received. The calibration data may comprise camera position data and characteristic data. The processor may combine at least two of the received images from at least two of the cameras into the at least one combined image by orienting the at least two images relative to one another based on the calibration data for the at least two cameras from which the images were received and merging the at least two aligned images into the at least one combined image.

Abstract: At least one combined image may be created from a plurality of images captured by a plurality of cameras. A sensor unit may receive the plurality of images from the plurality of cameras. At least one processor in communication with the sensor unit may correlate each received image with calibration data for the camera from which the image was received. The calibration data may comprise camera position data and characteristic data. The processor may combine at least two of the received images from at least two of the cameras into the at least one combined image by orienting the at least two images relative to one another based on the calibration data for the at least two cameras from which the images were received and merging the at least two aligned images into the at least one combined image.

Abstract: At least one combined image may be created from a plurality of images captured by a plurality of cameras. A sensor unit may receive the plurality of images from the plurality of cameras. At least one processor in communication with the sensor unit may correlate each received image with calibration data for the camera from which the image was received. The calibration data may comprise camera position data and characteristic data. The processor may combine at least two of the received images from at least two of the cameras into the at least one combined image by orienting the at least two images relative to one another based on the calibration data for the at least two cameras from which the images were received and merging the at least two aligned images into the at least one combined image.

Abstract: A system to identify and leverage geographically proximate client devices to reduce network traffic associated with facilitating digital collaboration sessions in a real-world environment. Embodiments enable a user to initiate a digital collaboration session and to limit participation in the digital collaboration session to client devices that are physically located within a geofence (e.g., a virtual geographic boundary). Other client devices that are located within the geofence are identified and enabled to join the digital collaboration session. participants may each use their own client device to contribute to the digital collaboration session. Participants can save individual segments of the digital collaboration session by indicating an interest immediately after the segments occur. Client devices then upload data for the segment for enhancement.

Abstract: A system to identify and leverage geographically proximate client devices to reduce network traffic associated with facilitating digital collaboration sessions in a real-world environment. Embodiments enable a user to initiate a digital collaboration session and to limit participation in the digital collaboration session to client devices that are physically located within a geofence (e.g., a virtual geographic boundary). Other client devices that are located within the geofence are identified and enabled to join the digital collaboration session. participants may each use their own client device to contribute to the digital collaboration session. Participants can save individual segments of the digital collaboration session by indicating an interest immediately after the segments occur. Client devices then upload data for the segment for enhancement.

Abstract: A system to identify and leverage geographically proximate client devices to reduce network traffic associated with facilitating digital collaboration sessions in a real-world environment. Embodiments enable a user to initiate a digital collaboration session and to limit participation in the digital collaboration session to client devices that are physically located within a geofence (e.g., a virtual geographic boundary). Other client devices that are located within the geofence are identified and enabled to join the digital collaboration session. participants may each use their own client device to contribute to the digital collaboration session. Participants can save individual segments of the digital collaboration session by indicating an interest immediately after the segments occur. Client devices then upload data for the segment for enhancement.

Abstract: At least one combined image may be created from a plurality of images captured by a plurality of cameras. A sensor unit may receive the plurality of images from the plurality of cameras. At least one processor in communication with the sensor unit may correlate each received image with calibration data for the camera from which the image was received. The calibration data may comprise camera position data and characteristic data. The processor may combine at least two of the received images from at least two of the cameras into the at least one combined image by orienting the at least two images relative to one another based on the calibration data for the at least two cameras from which the images were received and merging the at least two aligned images into the at least one combined image.

Abstract: At least one combined image may be created from a plurality of images captured by a plurality of cameras. A sensor unit may receive the plurality of images from the plurality of cameras. At least one processor in communication with the sensor unit may correlate each received image with calibration data for the camera from which the image was received. The calibration data may comprise camera position data and characteristic data. The processor may combine at least two of the received images from at least two of the cameras into the at least one combined image by orienting the at least two images relative to one another based on the calibration data for the at least two cameras from which the images were received and merging the at least two aligned images into the at least one combined image.

Abstract: At least one combined image may be created from a plurality of images captured by a plurality of cameras. A sensor unit may receive the plurality of images from the plurality of cameras. At least one processor in communication with the sensor unit may correlate each received image with calibration data for the camera from which the image was received. The calibration data may comprise camera position data and characteristic data. The processor may combine at least two of the received images from at least two of the cameras into the at least one combined image by orienting the at least two images relative to one another based on the calibration data for the at least two cameras from which the images were received and merging the at least two aligned images into the at least one combined image.