102 results about "Waveform correlation" patented technology

The invention discloses a wind power plant data synchronous correction method based on view topology and a mask graph neural network, and belongs to the field of wind power plant data processing and artificial intelligence space-time sequence prediction.The method comprises the steps that firstly, historical space-time observation data of unit nodes are obtained, and a space-time feature tensor is constructed; generating a mask matrix; extracting space and time sequence waveform correlation characteristics of a plurality of unit nodes, and constructing a global multi-view adjacency matrix; inputting the spatio-temporal feature tensor and the global multi-view adjacency matrix into a spatio-temporal diagram neural network, performing blocking and shielding operation in a message passing stage of the network, extracting full-field features, and outputting an initial deduction sequence; and obtaining a boundary physical residual error, reversely compensating the boundary physical residual error to the initial deduction sequence, and generating and outputting final correction data. According to the method, test data high-fidelity error correction under high concurrency missing rate and strong noise interference is realized, and the space-time diagram calculation memory overhead of a bottom-layer deep learning framework is remarkably reduced by designing a static topology cache mechanism.

A method includes generating an output radio frequency (RF) signal. The method includes communicating the output RF signal to an antenna module. The method includes receiving a bit stream from the antenna module. The bit stream includes data representing the output RF signal prior to transmission of an output RF waveform associated with the output RF signal at the antenna module and data representing a return RF signal associated with a return RF waveform received at the antenna module following transmission of the output RF waveform as part of a sensing operation. The method includes analyzing one or more values of the data representing the return RF signal to generate an altitude indication for a vehicle, a ground speed indication for the vehicle, or a combination thereof.

The invention provides a bolt ultrasonic axial force detection method and system based on multi-mode signal cooperative processing. The method comprises the following steps: firstly, carrying out band-pass filtering and envelope extraction on a bolt ultrasonic echo signal, and constructing a two-factor adaptive threshold value based on a noise standard deviation and a peak value proportion to realize stable peak value identification; and then screening is carried out by using a peak energy ratio and a waveform correlation coefficient, and multiple reflections and false peaks are eliminated. And aiming at the selected effective wave crest, when the rising edge energy centroid method is adopted to extract sound, the drift caused by noise and coupling change is reduced. In order to enhance the reliability, a frequency domain phase difference verification mechanism is introduced, frequency domain time delay is obtained by calculating an FFT conjugate phase difference between a current signal and a reference signal, and consistency comparison is carried out on the frequency domain time delay and a main measurement sound time result; and when the deviation exceeds the limit, abnormal processing or fusion output is triggered, so that double verification of the time domain and the frequency domain is realized. And finally, obtaining an axial force value by combining temperature compensation and a calibration model. The method improves the stability and adaptability of ultrasonic axial force measurement.

The invention discloses a grounding line selection method fusing transient energy and waveform correlation, and the method comprises the steps: extracting a high-frequency component in a zero-sequence current through employing an anti-mode aliasing effect VMD, and obtaining the energy of the high-frequency component through Hilbert transformation; then, correlation analysis is carried out on the zero-sequence current to extract a transient waveform comprehensive correlation coefficient; and forming a fault feature vector by the high-frequency component energy and the transient waveform comprehensive correlation coefficient, inputting the fault feature vector to an extreme learning machine without threshold setting, training ELM by using simulation fault data, and obtaining a fault line selection model based on the ELM to realize grounding fault line selection. According to the method, the endpoint effect and the mode aliasing phenomenon occurring in EMD can be effectively avoided, the transient current comprehensive correlation coefficient is extracted, and the line selection accuracy during high-resistance grounding is guaranteed.

A method includes receiving an output radio frequency (RF) signal. The method includes generating a digitized output RF signal representing the output RF signal. The method includes transmitting an output RF waveform associated with the output RF signal. The method includes receiving a return RF waveform based on transmission of the output RF waveform. The method includes generating a digitized return RF signal representing a return RF signal associated with the return RF waveform. The method includes serializing the digitized output RF signal and the digitized return RF signal to generate a serialized baseband signal. The method includes communicating a bit stream based on the serialized baseband signal to an RF unit. The RF unit is configured to generate an altitude measurement, a ground speed measurement, or both for a vehicle based on the serialized baseband signal.

The present invention discloses a method for designing discrete constant modulus quadrature phase coding for multiple targets. The method comprises the following steps: S1: determining the number of waveform signals M and the number of code elements N of each waveform of a multi-input multi-output radar, and randomly generating training samples of a size of M×N; S2: calculating the distance of a range gate and the waveform correlation function, and setting a loss function based on the distance and correlation function; S3: inputting the training samples into an established phase coding network for training, performing modulo-1 and discretization processing on the network output to obtain discrete constant modulus phase coding; S4: calculating the coding loss value based on the loss function, and updating the network parameters based on the loss value; S5: proceeding to S6 upon completion of training, otherwise returning to S3; S6: obtaining a trained network, inputting the data to be coded into the trained network, and obtaining a discrete constant modulus quadrature phase coded signal. This method enables the multi-input multi-output radar to operate normally in multi-target scenarios, and the output is a discrete code, which is more suitable for practical applications.

A wireless device may modulate a first portion of data of a transport block associated with a joint communication and sensing (JCS) waveform according to a first modulation scheme of a set of modulation schemes for sensing. The wireless device may modulate a second portion of data of the transport block according to a second modulation scheme. The wireless device may map the modulated first portion of data to a first set of resource elements associated with sensing and may map the second portion of data to a second set of resource elements associated with data communications. The wireless device may transmit the transport block including the mapped first and second portions of data via the JCS waveform. In some cases, the wireless device may transmit the transport block according to one or more parameters of a configuration for the JCS waveform.

The invention discloses a signal identification and measurement method based on 5GNR SSB time-frequency feature matching, and aims to solve the problems that in the prior art, a traditional 5GNR module chip is high in complexity and high in cost, and a simple analog detection scheme is prone to noise interference and cannot effectively distinguish LTE and 5G signals. The method comprises the following steps: extracting an energy envelope contour of a digital signal sample, adaptively calculating a detection threshold based on a dynamic range of the energy envelope contour, identifying candidate signal envelopes, and carrying out LTE interference pre-judgment; after the candidate envelopes are paired, verifying an effective envelope group through matching of multi-dimensional time-frequency characteristics including time domain periodicity, frequency domain form, waveform correlation and the like, and executing periodic suspicion rejection to eliminate interference; and finally, calculating the 5GNR signal intensity based on the effective envelope group. According to the invention, high-accuracy identification, high-efficiency measurement and strong anti-interference performance of the 5GNR SSB signal can be realized, and the hardware cost is reduced.

A system includes one or more microphones arranged to capture an audio stream, a transceiver arranged to exchange wireless data signals with mobile communications devices in a vicinity of the one or more microphones, a network element communicatively coupled to the transceiver, and data processing means communicatively coupled to the one or more microphones and the network element. The data processing means are configured to perform operations including comparing data indicative of a waveform within the audio stream with data transmitted within the network element, identifying a communication channel associated with the waveform and over which data is exchanged between the network element and a first mobile communications device via the transceiver based on the comparison, and modifying data exchanged between the network element and the first mobile communications device over the identified communication channel to control an audio output of the first mobile communications device.

This invention relates to the field of smart city lighting facility operation and maintenance, and discloses a method and system for assessing the health status and predicting faults in street light networks. The method includes the following steps: First, energy domain and information domain data of street light nodes are collected. A dynamic topology map with isomorphic information and energy domains is constructed based on electrical waveform correlation and channel characteristic similarity. Node features are reconstructed using a graph attention network, and passive monitoring residuals are calculated. Based on the dual-threshold judgment results of the residuals, when a node falls into a suspected grayscale range, an active micro-perturbation excitation mechanism is automatically triggered, breaking the steady state by issuing a fine-tuning light command. Subsequently, the degree of anomaly in the circuit physical layer is quantified by comparing the integral deviation between the actual physical response and the ideal response based on the transfer function. Finally, the passive residuals and active deviations are dynamically fused to output a full lifecycle health index. This invention solves the problem of difficulty in identifying early, weak faults under complex noise conditions, improving the accuracy of predictive maintenance of street light networks.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may perform a crest factor reduction on a radio frequency (RF) waveform in multiple passes, wherein the multiple passes each include: a clipping operation to reduce a peak-to-average power ratio (PAPR) associated with the RF waveform according to a threshold; and a filtering operation to remove at least a portion of out-of-band emission from the clipped RF waveform. The UE may transmit the RF waveform over a wireless channel, wherein the RF waveform transmitted over the wireless channel satisfies a spectral mask. Numerous other aspects are described.

The present invention relates to the field of distribution network fault detection, and specifically to a fast distribution network fault location method based on artificial intelligence. This method establishes a double-layer star topology to effectively connect the dispatching center with substations and distribution substations, uses multi-modal sensors to collect primary and secondary distribution network data, and performs fault prediction through a federated learning framework; combines graph convolutional networks to analyze topological relevance, generates a set of candidate fault distribution substations, and determines the fault type through cosine similarity verification and current waveform correlation analysis; uses traveling wave ranging technology to accurately locate the fault distribution network point. When an unknown fault is detected, the system triggers an incremental learning mechanism to update the global model parameters and continuously optimize the fault diagnosis ability. Based on the above method, fast and accurate distribution network fault detection and location are finally realized.

The application provides a few-channel switching direction finding method based on blind source separation, and belongs to the technical field of emergency search and rescue direction finding. The method comprises the following steps: constructing a corresponding original signal matrix of each array element switching, and performing pretreatment to obtain a corresponding standard signal matrix; performing blind source separation on each standard signal matrix to obtain a corresponding separation matrix and a separation signal matrix; constructing a corresponding array manifold matrix of each array element switching, and calculating all waveform correlation coefficients; using all waveform correlation coefficients, completing signal alignment of all array manifold matrices, and using a reference matrix to obtain a calibration array manifold matrix corresponding to each array element switching; and using all calibration array manifold matrices to perform direction of arrival estimation on each rescue target. The application can effectively estimate the noise of a few-channel direction finding system, and improve the direction finding precision, angle resolution capability and robustness of the few-channel direction finding system.

Systems and methods that deliver a continuous partial nerve conduction block are described. A waveform generator can configure one or more direct current (DC) waveforms to provide a continuous partial nerve conduction block. One or more electrodes can deliver the one or more DC waveforms to provide the partial block to the neural structure. Feedback can be provided to the waveform generator related to the partial block. The feedback includes monitoring a property associated with the partial block and altering a parameter associated with the one or more direct current waveforms in response to the property associated with the partial block.

This disclosure provides systems, apparatus, devices, and methods, including computer programs encoded on a storage medium, for a UE to report power headroom associated with dynamic waveform selection for uplink multi-panel transmissions. The UE may compute and report a UE-specific power headroom report (PHR) for one or more combinations of waveforms from multiple antenna panels or a panel-specific PHR for one or more waveforms for each panel. The UE (102) receives (410) signaling from a network entity (104) for configuring at least one power headroom report (PHR) to support a dynamic waveform indication in an uplink multi-panel transmission. The UE (102) sends (1012), to the network entity (104), at least one PHR comprising a UE-specific PHR or a panel-specific PHR. The UE-specific PHR is associated with at least one waveform combination of the dynamic waveform indication, and the panel-specific PHR is associated with at least one waveform of the dynamic waveform indication.

The application discloses a kind of preferred method, device, electronic equipment and storage medium of seismic data, wherein the method comprises: obtaining target well curve corresponding to target mine, and determining seismic waveform data corresponding to the target mine;Determine at least one to be selected stratum corresponding to target area, determine correlation coefficient sequence based on the to-be-selected stratum, target well curve and seismic waveform data;Wherein, the correlation coefficient sequence includes well curve correlation coefficient sequence and seismic waveform correlation coefficient sequence;Determine data correlation parameter based on the correlation coefficient sequence, and determine target seismic data based on the correlation parameter.The above technical solution is based on well curve and seismic waveform data to determine the correlation coefficient sequence corresponding to different strata, and then determine the target seismic data based on the correlation coefficient sequence, which realizes the accurate evaluation of seismic data and improves the accuracy of target seismic data.

Systems and methods that deliver a continuous partial nerve conduction block are described. A waveform generator can configure one or more direct current (DC) waveforms to provide a continuous partial nerve conduction block. One or more electrodes can deliver the one or more DC waveforms to provide the partial block to the neural structure. Feedback can be provided to the waveform generator related to the partial block. The feedback includes monitoring a property associated with the partial block and altering a parameter associated with the one or more direct current waveforms in response to the property associated with the partial block.

A method of characterizing particles in flow cytometry is described. The method includes detecting a plurality of pulse waveforms resulting from particles passing through an interrogation zone in a flow cytometer. The plurality of pulse waveforms include gaussian pulse waveforms and non-gaussian pulse waveforms. The method includes discriminating the non-gaussian pulse waveforms from the gaussian pulse waveforms, selecting data points from the non-gaussian pulse waveforms, and extracting parameters based on the data points selected from the non-gaussian pulse waveforms. The method includes characterizing particles associated with the non-gaussian pulse waveforms based on the parameters.

This invention discloses a deep learning-based method and system for pairing gravitational waves using gravitational lensing. The method includes: acquiring time-series strain data from a gravitational wave detector; performing frequency domain whitening on the data and converting it back to the time domain, and extracting fixed-length whitened strain segments; inputting the data into a twin encoder; selecting several other events with the highest similarity for each gravitational wave event based on the cosine similarity between feature vectors to form a candidate pairing set; calculating the waveform cross-correlation using a generalized cross-correlation-phase transform algorithm to obtain a refined similarity score and an estimate of the relative time delay between event pairs; constructing a weighted graph, and obtaining the final pairing result by solving for the maximum weight matching of the graph. In this invention, the twin encoder is used to reduce the dimensionality of high-dimensional time-domain data, and a coarse-screening strategy avoids full pairwise comparisons of the entire catalog, greatly improving the search speed on large-scale datasets.

A sensing system for cooking content includes at least one sensor and one or more processors. The at least one sensor is configured to sense an acoustic response of a cooking content in a cooking vessel. One or more processors being configured to transform the acoustic response to a frequency band, compare the frequency band with a cooking state database so as to correlate to a corresponding kind of the cooking content, analyze a wavelength distribution of the frequency band to correlate to a wavelength level; and determine a state of the cooking content by correlating the wavelength level with a corresponding waveform of the cooking state database. A method for sensing cooking content is also provided by the present disclosure.

To provide a vibration stimulation device that allows a frailty prevention effect to be expected.SOLUTION: A vibration stimulation device includes a vibration imparting device 3 for imparting vibration to the body of a user and an input part to which a music signal output from a sound source is input. The vibration imparting device 3 is operated by a vibration waveform obtained by adding a vibration waveform (hereinafter referred to as a basic waveform) for promoting a frailty prevention effect to a vibration waveform (hereinafter referred to as a music waveform) based on the music signal input to the input part. Thereby, if the vibration stimulation device is used in a state that music related to the music waveform is reproduced, for example, the user can receive the vibration by the vibration waveform for promoting the frailty prevention effect, that is, the basic waveform, while enjoying the music.SELECTED DRAWING: Figure 1

The present invention provides a driving waveform determination method, a recording medium, a liquid ejection device, and a driving waveform determination system, which determine the waveform of a driving pulse applied to a driving element of a liquid ejection head while reducing the burden on the user in terms of time and cost. The driving waveform determination method determines the waveform of a driving pulse applied to a driving element provided in a liquid ejection head that ejects liquid, and includes: a first step of determining a candidate waveform of the driving pulse; a second step of notifying a user of candidate information related to the candidate waveform; a third step of receiving an instruction from the user based on the candidate information; and a fourth step of determining the waveform of the driving pulse based on the instruction.

A configuration to allow for scheduling communication with a UE having associated characteristics based on a timing indication. The apparatus receives, from a UE, a timing indication associated with a bandwidth, a subcarrier spacing, or a waveform. The timing indication identifying a delay duration based on a processing timeline for the UE. The apparatus receives from the UE, or transmits to the UE, a first data channel having the bandwidth, the subcarrier spacing, or the waveform scheduled based on the timing indication. The first data channel is transmitted or received at a first time based on the timing indication.

The application discloses a cable fault positioning method based on double-end pulse matching and relates to the technical field of discharge fault monitoring. The application effectively distinguishes reflection events from independent events by performing waveform cross-correlation analysis, amplitude and time stamp deviation determination on pulse events in the same HFCT, and the width of a shielding window is obtained by using reflection interval and candidate basic interval statistical fusion, so that false reflection pulses are marked as invalid events or are re-determined, thereby eliminating the interference of reflection pulses on independent event counting. Further, through time regularization alignment of double-end independent events, Hungarian algorithm minimum weight matching and Mahalanobis distance secondary matching, it is ensured that the same discharge event is correctly matched at both ends, and false positioning points caused by reflection are avoided. Finally, the method can output a discharge repetition rate based on true independent event statistics and accurately position a discharge point.

This application discloses a node data communication system and method based on a chained industrial Ethernet, relating to the field of data communication technology. It calculates the waveform correlation coefficient between real-time torque-time waveform data and standard torque-time waveform data, calculates the lag time between their peak points, compares the waveform correlation coefficient with a first preset threshold, and compares the absolute value of the lag time with a second preset threshold. Based on the comparison result, a waveform quality label is generated. The waveform quality label includes a first label, a second label, and a third label. In response to the waveform quality label being the third label, a high-priority communication window request is sent to the master station. After receiving the high-priority communication window authorized by the master station, the real-time torque-time waveform data cached in the real-time data caching module is packaged into a third reporting data frame group, and the third reporting data frame group is sent to the master station within the high-priority communication window. This application improves the efficiency of node data communication.

The invention relates to the technical field of data processing, in particular to a prefabricated data center fault alarm method and system based on an algorithm model. The method comprises the following steps: acquiring multi-dimensional real-time operation data of target equipment and neighborhood associated equipment in a prefabricated data center, and constructing equipment space topology; calculating a normalized space potential energy weight based on the rated power, the physical distance and the airflow impedance coefficient, and determining a dynamic coupling conduction coefficient in combination with a Pearson's correlation coefficient and waveform cross-correlation lag time; calculating the external interference synthesis field intensity based on the abnormal deviation and the conduction coefficient; and by using an LSTM predicted value, introducing an adaptive interference correction gain to subtract the field intensity influence to obtain an essential residual error, and realizing fault alarm of the prefabricated data center. According to the method, the accuracy of fault alarm is improved by constructing equipment space topology and potential energy weight, calculating a dynamic coupling conduction coefficient, evaluating external interference synthesis field intensity and introducing self-adaptive correction gain and eliminating interference.

The invention discloses a gravitational lens gravitational wave pairing method and system based on deep learning, and the method comprises the steps: obtaining time sequence strain data of a gravitational wave detector, carrying out the frequency domain whitening processing of the data, converting the data back to a time domain, and carrying out the interception to obtain a whitening strain fragment with a fixed length; inputting into a twin encoder; selecting a plurality of other events with the highest similarity for each gravitational wave event based on the cosine similarity between the feature vectors to form a candidate pairing set; a generalized cross-correlation-phase transformation algorithm is adopted to calculate waveform cross-correlation, and refined similarity scores and relative time delay estimation between event pairs are obtained; and constructing a weighted graph, and obtaining a final pairing result by solving the maximum weight matching of the graph. According to the method, dimension reduction is carried out on the high-dimensional time domain data by using the twin encoder, and full-amount pairwise comparison on directories is avoided through a coarse screening strategy, so that the search speed on a large-scale data set is greatly improved.

Techniques are provided for generating secure radio frequency (RF) sensing waveforms to reduce the risk of a man-in-the-middle (MITM) attack for positioning, sensing and communications. An example method for transmitting a secure radio frequency sensing waveform includes transmitting a first set of parameters including timing information or frequency information associated with a radio frequency sensing waveform, transmitting the radio frequency sensing waveform, and transmitting a second set of parameters including scrambling information associated with the radio frequency sensing waveform.

According to an embodiment, a preamble detection circuit includes a slicer that normalizes a demodulated Amplitude Shift Keying (ASK) signal to generate a binary waveform, a correlator that performs correlation with a reference preamble sequence, and decision logic that determines if a valid preamble is detected. The circuit enables a primary demodulation chain to remain in a low-power state until a valid preamble is identified, providing efficient preamble detection for wireless power transfer systems while minimizing power consumption during communication inactivity. The circuit's configurable parameters allow optimization for different operating conditions and communication protocols.