1440 results about "Signal correlation" patented technology

The invention relates to a control method of a digital ultrasonic flaw detector calibrating device. The control method comprises the following steps: acquiring shape data and material parameters of a to-be-detected material, calling a matched standard test block, calculating adaptive ultrasonic frequency and flaw detector parameters, creating and solving a multi-objective optimization model, and planning a detection path of the to-be-detected material based on an optimal solution; emitting ultrasonic waves with matched frequency to implant the quantum dot mark array to carry out fluorescence-ultrasonic dual-mode signal correlation calibration; scanning according to a planned detection path, establishing a time-varying channel model to predict a current time delay variation, extracting a fundamental frequency and a harmonic component of a probe excitation pulse by adopting an autocorrelation algorithm to serve as a reference, constructing a state vector of a filter based on the predicted current time delay variation, and performing phase correction on a received signal to obtain a phase correction signal; and analyzing the defect of the to-be-detected material based on the corrected receiving signal. By integrating material parameter dynamic modeling, multi-modal signal collaborative calibration and adaptive path optimization, the detection precision and efficiency under complex working conditions are improved.

The invention relates to the technical field of audio optimization, and discloses an audio data offset detection and compensation method and system, a storage medium and equipment, and the method comprises the steps: building a timestamp synchronization mechanism with nanosecond precision, and distributing a unique time identifier for each floating-point audio sampling point; detecting time sequence offsets of input and output signals through a self-adaptive sliding window, and positioning and determining an offset value based on a cross-correlation function extreme value; a compensation strategy is dynamically selected according to different intervals where the offset absolute value is located, and meanwhile a dynamic cache management mechanism is established to support real-time offset compensation operation; constructing a time sequence relation model through inter-channel signal correlation analysis in the multi-channel system, and implementing coordinated offset compensation based on a correlation analysis result and the compensation strategy; a performance monitoring index system is established to evaluate the offset compensation effect in real time, compensation parameters are adaptively adjusted according to audio content characteristics and system performance feedback, and accurate time sequence control over audio data is achieved through the method.

The invention provides a fault positioning method and system based on multi-mode acousto-optic electric signal collaborative diagnosis, and the method comprises the steps: firstly collecting an acoustic signal sequence, an optical signal sequence and an electric signal sequence during the operation of high-voltage electrical equipment, and then carrying out the cross-mode signal level correlation processing of a multi-mode signal, and generating a multi-mode signal correlation level chain; extracting a dynamic association feature set based on the multi-modal signal association hierarchical chain, inputting the dynamic association feature set into the fault feature evolution model to generate a fault feature identification sequence and a feature space diffusion trajectory, and finally determining a fault initial region and a fault influence boundary range of the equipment according to the fault feature identification sequence and the feature space diffusion trajectory. Therefore, the accuracy and reliability of fault positioning of the high-voltage electrical equipment can be effectively improved.

A hardware sampling device (200) and related method for generating observations of arbitrary random variables from probabilistic bits are disclosed. The sampling device comprises a programming unit (250) for adjusting the mean value of probabilistic bits generated by each one of a plurality of probabilistic bit generators (271a-h), a binary selection tree structure (210) comprising a plurality of nodes, and a detection logic circuit (240) for detecting a tie condition for each depth stage of the binary selection tree (210). Each node comprises a selector circuit (213) connected between a pair of input terminals (211a-b) and an output terminal (212). The and configured to randomly admit one of the two input signals to the output terminal if a tie condition is detected, or else admit the input signal which represents a logical '1'. The position index bit related to the admitted input signal is supplied to the output terminal of the node and concatenated with a sequence of previously transmitted position index bits.

A system and a method are disclosed for performing A-IoT based communications. The method includes receiving, by an A-IoT device, an energizing signal, causing, based on the energizing signal, the A-IoT device to become activated, storing energy from the energizing signal in the A-IoT device, a duration of the energizing signal including a duration associated with activating the A-IoT device and / or a duration associated with storing the energy to satisfy a threshold amount of energy, receiving, by the A-IoT device, a first signal including a preamble, at least a portion of the preamble indicating a beginning of a payload associated with the first signal, and decoding, by the A-IoT device, at least a portion of the payload based on the preamble.

The invention provides an RFID label positioning error correction method based on deep learning, and the method comprises the following steps: 1, deploying a multi-mode sensing network in a target region, synchronously collecting radio wave signals and environment auxiliary data of an RFID label, and constructing a space-time multi-dimensional feature data set; step 2, constructing a joint architecture hybrid model composed of a space-time Transform network and a generative adversarial network; according to the method, a space-time multi-dimensional feature data set is constructed by fusing RFID signals and environment auxiliary data through a multi-mode sensing network, adaptive parameters are dynamically generated in combination with a model-independent meta-learning algorithm so as to quickly respond to environment changes, multi-path signal correlation is captured by using a space-time Transform and generative adversarial network combined architecture, feature expression is optimized, and the robustness of the system is improved. The problems that a single signal feature is missing, model parameters cannot dynamically adapt to the environment and the anti-interference robustness of a traditional network is insufficient are effectively solved, and the effectiveness, the real-time performance and the precision of RFID label positioning in the complex environment are remarkably improved.

Aspects presented herein may improve joint communication-radar system by providing a radar reference signal with GI-FMCW waveform. Aspects presented herein may be compatible with communication-centric waveforms and thereby may apply to UEs or network entities with minimal changes if specified. In one aspect, a wireless device transmits a set of communication signals. The wireless device configure one or more parameters associated with a set of radar reference signals for radar sensing based on the set of communication signals, each radar reference signal in the set of radar reference signals including a guard interval. The wireless device transmits the set of radar reference signals associated with the one or more parameters.

Embodiments of the described technologies determine input signals, where the input signals are specific to a user of the user network. The input signals are input to a set of artificial intelligence (AI) models. In response to the input signals, the first set of AI models output a first set of AI-derived signals relating to the input signals. At least one prompt template is applied to the first set of AI-derived signals to create at least one prompt. The at least one prompt is input to at least one generative AI model. In response to the at least one prompt, the at least one generative AI model outputs at least one thought starter machine-generated by the at least one generative AI model. The at least one thought starter includes digital content configured to be distributed via the user network.

Method includes receiving a first delay time and a first timing measurement by a TMV from a first receiver at a first node. The first timing measurement is generated by comparing a first time associated with a first signal to a first local clock time. The first signal is transmitted by a transmitter at a source. The TMV receives from a second receiver at a second node in a network, a second delay time and a second timing measurement. The second timing measurement is generated by comparing a second time associated with a second signal to a second local clock time. The second signal is transmitted by the transmitter at the source. The TMV generates a correction factor time based on the first timing measurement, the first delay time, the second timing measurement, and the second delay time. The TMV transmits the correction factor time to the second node.

The embodiment of the invention provides a communication method, a communication device, a storage medium and a program product, relates to the technical field of communication, and can solve the technical problem of unreliable parameter indication in related technologies. The method is applied to a first node, and comprises the following steps: receiving a first signaling from a second node; sending a first signal to the second node based on the first signaling; wherein the first signaling is used for indicating configuration information related to sending of the first signal by the first node; the first signal includes at least one of the following: a message Msg1, a message Msg3, and a data transmission after the message Msg3.

Methods and apparatuses for user equipment (UE) initiated early sounding reference signal (SRS) triggering. A method of operating a UE includes receiving a configuration related to a SRS. The configuration indicates a list of downlink (DL) signals associated with one or more cells, SRS resources, and an association between the DL signals and the SRS resources The method further includes measuring and determining a DL signal from the DL signals and determining, from the SRS resources, an SRS resource associated with the DL signal. The method further includes transmitting an uplink (UL) signal associated with the determined SRS resource and transmitting the SRS resource based on the UL signal.

The invention relates to the technical field of biomedical signal processing, and discloses a multi-wavelength PPG signal motion artifact elimination algorithm and system. The method comprises the following steps: acquiring a multi-wavelength PPG signal by using acquisition equipment; inputting the acquired noise-containing multi-wavelength PPG signal into a filter; the filter processes an input signal and then generates a denoised PPG signal; extracting a period of the denoised signal in real time by using an autocorrelation function; inputting the denoised PPG signal and a period extracted in real time into a BSS algorithm to find an optimal noise reference signal; taking the denoised PPG signal and the optimal noise signal as a first input and a second input of adaptive filtering to eliminate motion artifacts; the motion artifacts are eliminated by using the algorithm, and finally the PPG signals with the motion artifacts eliminated are output; the method overcomes the problems of dependence on additional hardware, low correlation of noise reference signals, complex algorithm and the like in the prior art.

Disclosed are techniques for communication. In an aspect, a first UE determines information (e.g., from an external device, or via the first UE's own energy sensing) associated with scheduled uplink transmissions on a set of symbols by a second UE. The first UE performs measurements on at least some of the scheduled uplink transmissions during a first subset of the set of symbols where no DL signals are received by the first UE. The first UE receives at least one DL signal during a second subset of the set of symbols, and mitigates interference associated with the at least one DL signal based on the measurements.

Aspects of the present disclosure provide an over-the-air (OTA) interferometer that enables redirecting, by a reconfigurable intelligent surface (RIS) to a destination, a signal that impinges the RIS at each of a plurality of time slots, thereby modulating the signal. The RIS is virtually divided into a plurality of RIS portions. In each time slot, phase components to be applied to the plurality of RIS portions are determined based on one or more phase values. The phase values may include an underlying phase related to redirecting a source signal, an additional phase related to data that an input operatively connected to the RIS aims to transmit to the destination by modulating the signal being redirected, and / or a further phase related to interference patterns that can help extract the data phase at the destination. The destination may demodulate the received signals based on the signal strength measurements.

Disclosed are system and techniques for classifying events such as emergencies. A system can include a computer system to perform operations including: receiving sensor signals from a group of devices at a location, determining whether one or more of the sensor signals exceed expected threshold levels, in response to determining that the one or more of the sensor signals exceed the expected threshold levels, correlating the sensor signals, classifying the correlated sensor signals into an emergency event based on applying an artificial intelligence (AI) model to the correlated sensor signals, the AI model having been trained to classify the correlated sensor signals into a type of emergency, determine a spread of the emergency event, and determine a severity level of the emergency event, generating, based on information associated with the classified emergency event as output from the AI model, emergency response information, and returning the emergency response information.

An apparatus for a network device, the apparatus comprising at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the network device to: determine a first gain associated with a received signal, wherein the first gain characterizes a difference between a first signal to noise ratio obtainable by processing the received signal using a first technique based on interference rejection combining and a second signal to noise ratio obtainable by processing the received signal using a second technique different from the first technique, select, based on the first gain, a power control strategy out of a plurality of power control strategies for a terminal device associated with the received signal, at least temporarily apply the selected power control strategy for the terminal device.

The invention belongs to the technical field of compressor maintenance, and discloses a compressor predictive maintenance system and method based on multi-source data fusion, and the method comprises the steps: carrying out the heterogeneous data time-frequency feature analysis of compressor signal data, and generating a multi-dimensional feature spectrum; carrying out load-dependent fault signal separation to generate load decoupling fault feature data; performing environment interference factor elimination analysis to generate a pure fault feature matrix; performing cross-domain signal correlation mapping to generate a multi-source data fusion mode map; constructing a fault feature propagation link, and generating fault evolution path data; carrying out degradation trend prediction under a variable load condition, and generating fault development situation data; constructing a dynamic threshold self-adaptive early warning model, and generating fault early warning critical value data; health state comprehensive evaluation is carried out, a compressor state report is generated, and predictive maintenance implementation is executed; according to the invention, accurate recognition and prediction of the compressor fault are realized, and the equipment reliability and maintenance efficiency are improved.

The invention discloses a numerical control machine tool fault prediction and diagnosis method and system based on an AI model, and the method comprises the steps: building a vibration-temperature correlation curve with time sequence characteristics according to a vibration signal and a temperature signal, and building a current-rotating speed correlation curve with time sequence characteristics according to a current signal and a rotating speed signal; inter-axis space linkage coding is carried out on the motion axes to generate inter-axis interaction response characteristics, and the inter-axis space linkage coding comprises analysis of signal mutual influence relations among different motion axes; performing time scale linkage coding on the vibration-temperature correlation curve and the current-rotating speed correlation curve to generate time interaction response characteristics; and fusing the inter-axis interaction response features and the time interaction response features, and generating a fault prediction and diagnosis result through a pre-trained AI model. The problems of single evaluation data and insufficient multi-source signal correlation analysis in the prior art are solved, and the accuracy of the fault prediction diagnosis result of the numerical control machine tool is improved.

The invention provides a sparse covariance reconstruction and adaptive grid correction-based DOA (Direction of Arrival) estimation method, and belongs to the technical field of ocean observation. According to the method, snapshot-level signal reconstruction is changed into covariance-level sparse modeling, an alternating optimization strategy is introduced to carry out energy estimation, a Sherman-Morrison matrix lemma is fused to accurately update a covariance inverse matrix, and an adaptive grid continuous correction mechanism is introduced. According to the method, a staged optimization framework is constructed, sparsity and geometric interpretability are fused, DOA estimation precision can be remarkably improved, off-grid error limitation is broken through, robustness under low signal-to-noise ratio and signal correlation is improved, algorithm calculation complexity is remarkably reduced, real-time processing capacity is improved, and the method is suitable for an underwater complex propagation environment.

A first wireless device receives, from one or more second wireless devices, a plurality of sidelink control information (SCIs). The plurality of SCIs indicate physical sidelink shared channel (PSSCH) receptions and physical sidelink feedback channel (PSFCH) transmissions. Each of the PSFCH transmissions is associated with a respective sidelink reference signal. The first wireless device determines: a sidelink reference signal associated with a first PSFCH transmission with a smallest priority value among priority values of the PSFCH transmissions; and one or more first PSFCH transmissions, comprising the first PSFCH transmission, associated with the sidelink reference signal from the PSFCH transmission. The first wireless device transmits, in a PSFCH occasion of a sidelink slot, at least one of the one or more first PSFCH transmissions.

A wireless device monitors physical downlink control channel (PDCCH) candidates of a search space set, with a search space identifier set to zero, at monitoring occasions associated with a first reference signal. The first reference signal is determined based on a downlink control information (DCI) indicating a transmission configuration indication (TCI) state associated with a control resource set (coreset) with index zero. The TCI state indicates a second reference signal that is quasi co-located with the first reference signal.

Aspects of the disclosure are directed to prediction of a predicted material profile of the target object based on measurement information associated with sensing (RF-S) signal(s). In an aspect, one or more actions are performed in response to the predicted material profile of the target object, the one or more actions including, e.g., constructing at least one digital twin associated with the target object based on the predicted material profile of the target object, or modifying operation of a wireless communications network, or a combination thereof. Such aspects may provide various technical advantages, such as more accurate digital twin generation (e.g., digital twins may be updated to reflected material profiles of objects detected in real-world), improved network operation (e.g., permit line of sight (LOS) through objects with certain material profiles, block LOS through objects with other material profiles, etc.), and so on.

The invention provides a TLF-GV signal correlation earthquake magnitude prediction method and system, and a medium, belongs to the technical field of earthquake prediction, and aims at solving the problems that a traditional magnitude prediction model lacks physical constraints, TLF-GV features are not fully utilized, the interpretability is poor, and cooperation with a preorder technology is lacked. Comprising the following steps: extracting a multi-source feature vector of a TLF-GV signal containing a gravity component absolute amplitude, a vibration component relative amplitude and a signal phase; based on the G-R law, constructing the positive correlation physical characteristics log10 (GVCIpeak) and BF * Tanomal of the abnormal intensity and the magnitude of the TLF-GV signal; weighting the features according to a Bayesian factor BF value; fitting according to historical samples to obtain a physical empirical formula of positive correlation of GVCI peak logarithm and magnitude as a physical constraint, and constructing a loss function by taking the physical constraint as a regularization item and combining with a mean square error of a magnitude prediction task; and constructing an XGBoost model, and training according to the training set to obtain a prediction model for earthquake magnitude prediction. According to the method, quantitative prediction of earthquake magnitude is realized based on multi-source features of TLF-GV signals in combination with a physically constrained XGBoost regression model.

In an aspect, a communications device obtains a residual AoA bias associated with a first AoA measurement of a RS-P transmitted from a wireless reference node to a first base station, the wireless reference node associated with a location known to the communications device, obtains a second AoA measurement associated with an uplink signal (e.g., PRACH, SRS, UL-SRS-P, etc.) transmitted from a UE to the first base station, and calibrates the second AoA measurement based on the residual AoA bias. In another aspect, a communications device obtains a residual AoD bias associated with a first AoD measurement of a RS-P transmitted from a first base station to a wireless reference node with a known location, obtains a second AoD measurement associated with a downlink signal (e.g., DL-PRS) transmitted from the first base station to a UE, and calibrates the second AoD measurement based on the residual AoD bias.

A wireless device receives configuration parameters indicating a sounding reference signal (SRS) resource set comprising a first SRS resource and a second SRS resource. The first SRS resource is associated with a first transmission configuration indicator (TCI) state that is associated with a first pathloss reference signal (PL-RS) and the second SRS resource is associated with a second TCI state that is associated with a second PL-RS. The wireless device determines a PL-RS, for an uplink transmission via the second SRS resource, is the first PL-RS based on a first index of the first SRS resource being lower than a second index of the second SRS resource. The wireless device transmits the uplink transmission via the second SRS resource based on a transmission power determined based on the determined PL-RS and the second TCI state.

Methods, systems, and devices for wireless communications are described. A remote unit (RU) may transmit, to a distributed unit (DU), an indication of a skew time parameter associated with providing compensated samples for a plurality of symbols associated with a slot. The RU may store a set of samples associated with a first subset of the plurality of symbols associated with the slot, the storing based on a processing delay associated with receiving demodulation reference signals during a portion of the first subset of the plurality of symbols associated with the slot. The RU may transmit, to the DU, a burst of compensated samples for the plurality of symbols in a transmission window that is offset in time from a beginning symbol of the slot according to the skew time parameter.

Systems, methods, and storage media for manifesting a virtual object in a virtual environment are disclosed. Exemplary embodiments may: receive, at a first physical object detector, a first signal, from a first physical object-associated element in a first physical environment; identify, at a first value identification module, based on the first signal, a first value associated with the first signal; identify, at a first virtual object identification module, based on the first value, a first virtual object; and manifest, at a first virtual environment output device, a first manifestation of the first virtual object in a first manifestation of the first virtual environment.

The invention discloses a target positioning method, device, equipment and product capable of reducing errors, and the method comprises the steps: carrying out the compensation of a receiving signal of corresponding signal receiving equipment through the relative time delay and the relative frequency difference, and obtaining an alignment signal; presetting a compensation value for representing an error, and compensating the alignment signal by using the compensation value to obtain a compensated alignment signal; calculating a correlation metric value of the compensated alignment signals of the two signal receiving devices by using a preset metric function; and judging whether the positioning candidate position is the target positioning position or not according to the correlation metric value. According to the method, the received signals of the two signal receiving devices needing correlation measurement can be preprocessed and then compensated, so that the influence caused by factors such as the position speed error and the clock synchronization error of the signal receiving devices is reduced, the error of signal correlation measurement is reduced, and the accuracy of final target positioning is improved.

Wireless communication devices, systems, and methods related to beam prediction monitoring reference signals (BPM-RSs) for monitoring the performance of artificial intelligence (AI) and / or machine learning (ML) models including associated protocols and signaling, are provided. For example, a method of wireless communication performed by a user equipment (UE) can include receiving a first beam prediction management reference signal (BPM-RS) configuration, wherein the first BPM-RS configuration indicates one or more BPM-RS resources; monitoring, based on the first BPM-RS configuration, performance of a machine learning (ML) model; and transmitting, to a network unit, a report based on the monitoring.

Methods, systems, and devices for wireless communications are described. A wireless communication device may receive a communication signal that includes multiple layers. The signal may include at least one layer that is associated with a probabilistic shaping signal. The wireless communication device may apply one or more probabilistic shaping parameters to the communication signal and obtain a preprocessed signal. The application of the one or more probabilistic shaping parameters may include performing one or more matrix decomposition operations to obtain the preprocessed signal and a preprocessed channel estimate. The preprocessed signal and the preprocessed channel estimate may include probability information associated with the probabilistic shaping signal based on the application of the one or more probabilistic shaping parameters. Accordingly, the wireless communication device may demodulate the communication signal based on the preprocessed signal information.