Abstract: Embodiments of the present invention provide an intuitive platform for non-contact instructions regarding resource allocation. In this way, a user may utilize one or more specific gestures which are captured, analyzed, and responded to by the system of the invention in order to initiate or complete one or more resource activities such as a resource transfer, resource transfer split, or other resource action.

Abstract: A wireless sensor device for wireless transmission of sensed medical data to a medical monitoring and/or intervention device. The sensor device has a sensor element for sensing a physical parameter related to a body of a living person or animal. A wireless RF transmitter and receiver serves to transmit data packets with the sensed physical parameter, and to also receive data packets, in accordance with a TDMA based network protocol. A processor system controls operation of the wireless RF transmitter and receiver, including that the sensor device can operate as wireless RF synchronization master or slave, and the sensor device can negotiate according to a deterministic negotiation algorithm to appoint one of a plurality of wireless RF devices connected to the TDMA based network as wireless RF synchronization master, if it is detected that no master is available.

Abstract: A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parame

Abstract: A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parame