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Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems, methods, and devices for collaborative positioning. A wireless transmit / receive unit (WTRU) is configured to perform and report positioning measurements including SL collaborative measurements. The WTRU serving as an anchor WTRU may select a set of member WTRUs for distributing the PRS measurement configurations based on the inter-WTRU distance measured over a SL. The selected subset of member WTRUs each provide measurement report to the anchor WTRU. The anchor WTRU reports to the LMF the measurements received from the member WTRUs and the associated SL distance measurements.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention discloses a method for generating an autonomous inspection orthoimage of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle surveying and mapping and autonomous navigation. Global optimization of an air route is realized through a path optimization strategy fused by a genetic algorithm in combination with grid characteristics of an inspection area and image parameter constraints, and candidate waypoints are used as nodes; redundant waypoints are screened through leg smoothness factors, route complexity is reduced, a genetic algorithm takes flight height and waypoint spacing as constraints, a fitness function containing flight distance, turning times and overlapping rate is constructed, a global optimal path is found through population iteration, multi-target requirements are optimized and balanced, and it is ensured that the route meets the image acquisition precision requirement and also meets the requirement of image acquisition. The flight distance can be shortened, the turning frequency is reduced, the cruising ability of the unmanned aerial vehicle is adapted, efficient propelling of the inspection task is guaranteed, meanwhile, waypoint coordinates output through simulation directly adapt to a flight control system, and it is guaranteed that actual flight parameters are consistent with planning parameters.

The invention is suitable for the technical field of flying dust monitoring, and provides a road construction site all-weather flying dust online monitoring method and system, and the method comprises the following steps: collecting flying dust data and wind power information through a sensor; the particulate matter concentration is judged, and the particulate matter concentration exceeding the standard and the corresponding coordinate position, wind speed data and wind direction data are screened out; determining a flying dust source area according to the coordinate position, the wind speed data and the wind direction data; a road transportation monitoring video is called, and the area where the transportation vehicle is located is determined according to the time information and the road transportation monitoring video; a road construction map is called, and the area where the transport vehicle is located is marked in the road construction map; and comparing and analyzing the flying dust source area with a road construction map, determining a potential flying dust source position, and sending the potential flying dust source position to an inspection terminal. According to the invention, the flying dust pollution source can be accurately tracked, and the inspection personnel can quickly position the potential flying dust pollution source according to the information.

A first user equipment of a wireless communication system is provided. After a second user equipment of the wireless communication system has transmitted a first signal, being a reference or control or data signal, to the first user equipment via a sidelink between the second and first user equipment, the first user equipment is configured to receive said reference or control or data signal as a second signal from the second user equipment via the sidelink. The first user equipment is configured to determine positioning information depending on difference information, such that the difference information has information on difference(s) between the first signal, transmitted by the second user equipment, and the second signal, received by the first user equipment. The positioning information has information on a distance and / or a distance change between the first and the second user equipment and / or a position of the first user equipment and / or the second user equipment and / or an angle which depends on the position of the first and the second user equipment.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Access control system (1) for buildings (2) with a stationary unit (20) and a mobile unit (10), wherein the mobile unit (10) has a mobile transceiver (11) which is designed to transmit wirelessly and encrypted signals (S) for authentication and authorization of the mobile unit (10) with respect to the stationary unit (20) and for control of the stationary unit (20) by the mobile unit (10) to a stationary transceiver (21) of the stationary unit (20), wherein the stationary unit (20) comprises a control device (22) which is designed to determine a respective position (51 - 57) of the mobile unit (10) relative to the stationary unit (20) from the signals (S) transmitted from the mobile transceiver (11) to the stationary transceiver (21) at predetermined time intervals, to determine a movement path (50) of the mobile unit (10) from the chronologically successively determined positions (51 - 57) of the mobile unit (10) and to determine from the movement path (50) and a predetermined target position a probability with which a function is to be triggered when the mobile unit (10) approaches the target position, wherein the control device (22) is further configured to compare the probability with a predetermined threshold value and to trigger the function if the probability exceeds the threshold value.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The following relates generally to light detection and ranging (LIDAR) and artificial intelligence (AI). In some embodiments, a system: receives LIDAR data generated from a LIDAR camera; measures a plurality of dimensions of a room of the home based upon processor analysis of the LIDAR data; builds a 3D model of the room based upon the measured plurality of dimensions; receives an indication of a proposed change to the room; modifies the 3D model to include the proposed change to the room; and displays a representation of the modified 3D model.

Access control system (1) for buildings (2) with a stationary unit (20) and a mobile unit (10), wherein the mobile unit (10) has a mobile transceiver (11) which is designed to transmit wirelessly and encrypted signals (S) for authentication and authorization of the mobile unit (10) with respect to the stationary unit (20) and for control of the stationary unit (20) by the mobile unit (10) to a stationary transceiver (21) of the stationary unit (20), wherein the stationary unit (20) comprises a control device (22) which is designed to determine a position of the mobile unit (10) relative to the stationary unit (20) from the signals (S) transmitted from the mobile transceiver (11) to the stationary transceiver (21) and to control a function if a control command for triggering the function is transmitted to the stationary unit (20) by a mobile unit (10) authenticated and authorized by the stationary unit (20) and / or the position of the mobile unit (10) is within a predetermined access area (30), and wherein the control device (22) is designed to determine an angle (ϕ) from which the signals (S) were received from the signals (S) transmitted from the mobile transceiver (11) to the stationary transceiver (21), to determine a measured distance (rg) between the stationary transceiver (21) and the mobile transceiver (11) from a signal (S) received at the stationary transceiver (21) from the mobile transceiver (11) and from the measured distance (rg) and a vertical distance (rv) stored as a correction value in the control device (22) between the stationary transceiver (21) and the mobile transceiver (11), to determine a horizontal distance between the stationary transceiver (21) and the mobile transceiver (11) in the polar coordinate system lying in the horizontal plane as a distance (r) from which the signals (S) were received, so that the position of the mobile unit (10) can be specified as polar coordinates of a polar coordinate system lying in a horizontal plane, in whose coordinate origin the stationary transceiver (21) is arranged.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.