508results about "Using reradiation" patented technology

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 real-time monitoring and early warning method for deformation of a foundation pit support based on oblique photography, and relates to the technical field of photogrammetry and deformation measurement, high-resolution images of a support structure are acquired in a multi-angle manner through a multi-stage route, and a high-density three-dimensional point cloud reference model is constructed after preprocessing; then periodically acquiring a monitoring point cloud, and realizing high-precision space alignment with the reference model through control point coarse registration and iterative nearest point fine registration; and finally, calculating the three-dimensional coordinate deviation of each point through point cloud matching, extracting the full-surface deformation, and generating a deformation cloud picture and a statistical report. According to the method, the technical spanning from discrete point monitoring to full-field continuous monitoring and from low-dimensional data to true three-dimensional vectorization deformation analysis is realized, and the comprehensiveness and accuracy of deformation monitoring are improved.

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.

An information technology system generally includes a cloud-based management platform with a micro-services architecture deploying a set of adaptive intelligence facilities that can be configured to automate a set of capabilities of the platform related to at least one of the value chain network entities and the features of the platform and a set of data storage facilities that can be configured to store data collected and handled by the platform. The data can be related to at least one of the value chain network entities and the features of the platform. A set of monitoring facilities can be configured to monitor the value chain network entities. The platform can be configured to host a set of applications for directing an enterprise to manage the value chain network entities from a point of origin of a product of the enterprise to a point of customer use.

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 dynamic positioning precision optimization system based on a self-learning fusion model, and relates to the technical field of wireless positioning. Comprising the steps that an acquisition and synchronization module synchronously acquires AOA, RSSI and TOA signals from a plurality of anchor points and generates a synchronous multi-source signal flow; the discrimination module extracts multi-dimensional instability characteristics such as fluctuation entropy, angle jitter and delay abrupt change rate of the signal flow, and classifies the current channel environment into predefined modes such as high occlusion, non-line-of-sight error or stable line-of-sight; the mapping module calls a corresponding weight distribution strategy according to the modal identifier to generate a real-time fusion weight vector; the adaptive positioning module couples the weight vector to an unscented Kalman filter observation model, dynamically adjusts the weight of each signal source in state updating, and outputs an estimated position and a positioning confidence coefficient; the optimization module monitors the positioning confidence and iteratively optimizes the weight strategy online based on the deviation of the positioning confidence from the target value. The method effectively improves the positioning precision and robustness in a complex dynamic scene.

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.

An unmanned aerial vehicle autonomous electric power facility inspection method based on three-dimensional semantic driving relates to the technical field of unmanned aerial vehicle cruise control, and comprises the following steps: constructing a three-dimensional semantic representation containing key components under a unified world coordinate system, and generating a three-dimensional semantic point cloud with component category labels in real time in combination with airborne visual perception; and the inspection requirements in the operation and maintenance regulation are analyzed into an ordered component-level task sequence, and on the basis, the unmanned aerial vehicle autonomously plans an observation pose and a local flight path according to the three-dimensional semantic distribution of the target component, so that the adaptive approaching and coverage of the key component are realized. According to the invention, operation and maintenance semantics can be precisely followed, fine-grained sensing of key parts is realized, an inspection path is enabled to be close to a concern point, environment adaptability is realized, the inspection efficiency is effectively improved, the cost is reduced, and the problems that current electric power facility unmanned aerial vehicle inspection depends on a preset mode, the operation and maintenance semantics are difficult to meet, and key part sensing and adaptability are lacked are solved. And the inspection tour effect is not good enough.

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 an indoor positioning method based on a Bluetooth RSSI (Received Signal Strength Indicator), which comprises the following steps: after a positioning tag receives RSSI data broadcasted by Bluetooth beacons, screening the beacon with the strongest signal and calculating the distance; current data is directly adopted in first positioning, and translation processing is performed in combination with historical positions in non-first positioning so as to smooth a result. And mapping the position points to path points through a road network matching algorithm, preliminarily positioning the path points based on shortest path planning, and outputting a final positioning result through a filtering rule. According to the invention, the problem of positioning fluttering caused by fluttering of the Bluetooth RSSI signal, the problem of back-and-forth jumping of actual positioning caused by instability of the Bluetooth RSSI signal and the problem of signal abnormity caused by signal interference caused by metal, a wall body or a human body are effectively solved.

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 sound source localization and distance measurement method, device and equipment based on a microphone array and a storage medium, and relates to the technical field of acoustics, and the method comprises the steps: collecting a frequency sweeping signal played by a sound source through the microphone array, obtaining the audio data of each channel, obtaining a cross-correlation sequence through generalized cross-correlation and phase transformation weighting processing, and obtaining a distance measurement result; constructing a current to-be-processed set, processing other sequences again by taking the first sequence as a reference to obtain a new sequence group, discarding the reference sequence, taking the new group as a to-be-processed set, repeating the iteration step until only one to-be-processed sequence is left in the set, and performing up-sampling on the sequences to obtain a to-be-processed set; extracting a first main peak position to obtain a time delay estimation value of a decimal sampling level so as to determine an incident angle of a sound source relative to an array normal, performing time delay alignment on each cross-correlation sequence, constructing a single-channel beam forming signal based on an alignment result, and determining a second main peak position so as to determine a sound source distance, the efficiency of sound source localization and distance measurement is improved.

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.

A single-station positioning method based on Doppler frequency shifts and time difference information, and a system and an electronic device. The method comprises: acquiring an actual period of a pilot signal sent by an unmanned aerial vehicle (S1); on the basis of the actual period of the pilot signal sent by the unmanned aerial vehicle, determining two adjacent target signals sent by the unmanned aerial vehicle in a target area, and acquiring actual center frequencies of target signal information, an actual time difference of arrival between the two target signals at a receiving base station, actual angles of arrival, and actual Doppler frequency shifts of the unmanned aerial vehicle (S2); importing an actual range of the target area, the actual period of the pilot signal, the actual center frequencies of the target signals, the actual time difference and the actual angles of arrival into variables in a preset theoretical Doppler frequency shift model, so as to obtain a theoretical Doppler frequency shift value table, wherein the table comprises different theoretical Doppler frequency shift values corresponding to different distances from the unmanned aerial vehicle to the receiving base station (S3); performing error metric calculation processing on the actual Doppler frequency shifts and each theoretical Doppler frequency shift value, so as to obtain a theoretical Doppler frequency shift value corresponding to the minimum error metric (S4); and on the basis of the theoretical Doppler frequency shift value corresponding to the minimum error metric, determining the positions at which the unmanned aerial vehicle sends the target signals (S5). The method can ensure the accuracy of single-station positioning in a complex scenario.

The invention discloses a shield tunneling machine attitude deviation measurement method based on laser-vision deep learning heterogeneous fusion, and the method comprises the steps: employing double sensors of a laser target and a vision camera, carrying out the deployment of the sensors, and collecting data, which comprises laser data and vision data; preprocessing the data, wherein the preprocessing comprises laser data preprocessing and visual data preprocessing; the laser data preprocessing comprises laser data coordinate conversion, laser target attitude deviation calculation and noise filtering; the visual data preprocessing comprises image preprocessing, mark point detection, camera calibration and world coordinate calculation, and visual measurement attitude deviation calculation; a deep learning heterogeneous fusion model is constructed, the deep learning heterogeneous fusion model comprises a laser absolute feature branch, a visual spatial-temporal feature branch, an attention fusion layer and an output layer, and the model is trained; and inputting the preprocessed real-time data into the trained deep learning heterogeneous fusion model, and carrying out dynamic calibration on an output result.

The present disclosure provides a method in a network node for carrier phase measurements in a communication network. The method includes: obtaining a carrier phase measurement update rate for a UE moving through the network, based on a carrier frequency and a velocity of the UE; and sending the carrier phase measurement update rate to the UE in a carrier phase measurement update rate configuration.

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 relates to a system for determining the compaction of a flowable, solidifiable building material, in particular concrete, during the production of a structural element with the building material, wherein the flowable building material is introduced into a formwork system (10), comprising: at least three sensors (S1, S2, S3) to determine an amplitude (A S1 , A S2 , A S3 ) a vibration introduced into the flowable building material by a vibration unit (VE), wherein the sensors (S1, S2, S3) are arranged on the formwork system (10), at least one computing unit that is set up to perform calculations based on the amplitudes (A) determined by the at least three sensors (S1, S2, S3). S1 , A S2 , A S3) to calculate possible positions (D1, D2, D3) of the vibration unit (VE) relative to the respective sensors (S1, S2, S3) and, based on this, to triangulate the current position of the vibration unit (VE) relative to the formwork system (10), a documentation unit for documenting the dwell time of the vibration unit (VE) in the respective current positions of the vibration unit (VE) relative to the formwork system (10), the computing unit determines a calculated compaction value in the respective current positions based on the respective current positions of the vibration unit (VE) and the documented dwell time in the respective current positions.

The invention discloses a power transmission line tree obstacle distance measurement method based on binocular vision and deep learning fusion, and relates to the technical field of electric power, and the method comprises the steps: collecting a binocular image, carrying out the calibration and stereo correction, and carrying out the single-scale Retinex enhancement processing of the corrected image; and performing power transmission line and tree obstacle region segmentation on the enhanced image based on a DeepLabv3 + model in combination with a coordinate attention mechanism, and performing semantic-guided parallax estimation and optimization on the enhanced image based on a segmentation result to obtain an optimized parallax map. According to the method, a multi-level feature map is extracted through Res2Net-50, target area positioning is enhanced in combination with a coordinate attention mechanism, an ASPP module is used for fusing multi-scale context information, the segmentation precision of a power transmission line and a tree barrier is improved, a Kalman filter for adaptively adjusting observation noise variance is used for processing multi-frame distance measurement data, and the accuracy of distance measurement is improved. And the stability and the reliability of distance measurement are improved.

In one aspect, a method includes receiving, in a first wireless device, a data transmission including at least one orthogonal frequency division multiplexing (OFDM) packet from a second wireless device; processing the at least one OFDM packet to recover data and providing the data to a host processor of the first wireless device; encoding, in the first wireless device, at least one decoded symbol of the at least one OFDM packet using forward error correction coding; processing the encoded at least one decoded symbol to recover at least one corresponding transmission symbol of the at least one OFDM packet; and determining a distance estimate between the first wireless device and the second wireless device based at least in part on the corresponding transmitted symbols.