14 results about "Group frequency" patented technology

Various aspects of the present disclosure relate to group frequency hopping for ambient Internet of Things (AIoT) devices. For example, a non-orthogonal multiple access mechanism for AIoT devices enabling a group of devices to simultaneously and non-orthogonally transmit different transport blocks (TBs) to a receiver device (a base station, UE, and so on). The group of UEs may perform group frequency hopping to transmit data blocks at different frequencies and time slots. A receiver device may then detect the transmissions (e.g., superimposed signals) using multi-user detection techniques.

The invention is applicable to the technical field of motor imagery classification, and provides a motor imagery classification method, device and equipment based on multi-view characteristics and a medium, the method comprises the following steps: performing group frequency spectrum filtering on an original electroencephalogram signal through a preset filter to obtain the multi-view characteristics of the original electroencephalogram signal; performing time domain convolution processing on the multi-view features to obtain multi-scale time information of the original electroencephalogram signal, and adding the multi-scale time information and the multi-view features to obtain combined features; dividing the combined features into a time window set according to a time dimension, wherein time windows in the time window set are not overlapped with each other; extracting a target time feature in each time window, and extracting a global dependency relationship of each time window edge time dimension through a Transform module; and completing classification of the motor imagery according to the global dependency relationship.

The present disclosure relates to methods and apparatuses for generating and reporting a CSI report. The method performed by a UE comprising: receiving (400) a CSI report configuration from a network node (600); determining (401) one or more frequency-domain, FD, components for the set of linear combination coefficients, determining (402) one or more time-domain, TD, components for the set of linear combination coefficients, determining (403) one or more spatial domain, SD, components for the set of linear combination coefficients, determining (404) a set of frequency- / time (FD / TD)-component pairs, each pair comprising an FD component and a TD component, commonly across a subset of spatial domain components for the set of linear combination coefficients, and, generating and transmitting or reporting (405), to the network node (600), a CSI report, the CSI report comprising an indication of the determined SD, FD and TD component(s), FD / TD-component pairs, and combination coefficients of the precoder vector or matrix.

A method may include receiving a first symbol, and encoding the first symbol based on an initial codebook including a symbol frequency and a ranking for each symbol, and a group frequency for each group of symbols. The method may include incrementing a symbol frequency of the first symbol based on receiving the first symbol, swapping a rank of a lowest ranking symbol from among symbols in the initial codebook that have a symbol frequency lower than the incremented symbol frequency of the first symbol with a rank of the first symbol, in response to the symbol frequency of the lowest ranking symbol being lower than the incremented symbol frequency of the first symbol, and storing an updated codebook in a memory accessible by an encoder and / or a decoder.

The present invention relates to a method and device for removing ultrasonic radar co-frequency interference based on grouped frequency conversion. The method includes: determining a plurality of frequency intervals according to multiple operating frequencies of a target ultrasonic radar; dividing the plurality of frequency intervals into a plurality of frequency groups based on the error of the target ultrasonic radar, and determining the allocation weight of each frequency interval according to the cumulative occurrence probability of one or more frequency intervals within each frequency group; dynamically adjusting the allocation weight of each grouped interval of its own ultrasonic radar within a preset period based on the real-time operating frequency of the target ultrasonic radar, and controlling the occurrence time of ultrasonic waves with random frequencies within each grouped interval. The present invention dynamically and randomly adjusts the operating frequency of the radar through the frequency interval and the operating frequency, thereby improving the co-frequency anti-interference ability on the premise of ensuring the radar detection accuracy.

A method may include receiving a first symbol, and encoding the first symbol based on an initial codebook including a symbol frequency and a ranking for each symbol, and a group frequency for each group of symbols. The method may include incrementing a symbol frequency of the first symbol based on receiving the first symbol, swapping a rank of a lowest ranking symbol from among symbols in the initial codebook that have a symbol frequency lower than the incremented symbol frequency of the first symbol with a rank of the first symbol, in response to the symbol frequency of the lowest ranking symbol being lower than the incremented symbol frequency of the first symbol, and storing an updated codebook in a memory accessible by an encoder and / or a decoder.

The invention discloses a method for measuring interaction strength between two Rydberg atom ensembles. The method comprises the following steps: S1, preparing a first Rydberg atom ensemble A and a second Rydberg atom ensemble B; s2, respectively measuring to obtain a single group energy level frequency shift of the first Rydberg atom ensemble A and a single group energy level frequency shift of the second Rydberg atom ensemble B; s3, measuring to obtain a combined energy level frequency shift in a coupling state; s4, the interaction intensity VAB between the first Rydberg atom ensemble A and the second Rydberg atom ensemble B is obtained through calculation. According to the method, the single-group frequency shift of the two ensembles in the isolated state and the combined frequency shift of the two ensembles in the coupled state are measured respectively, and the interaction intensity between the ensembles is indirectly calculated according to the single-group frequency shift and the combined frequency shift, so that non-destructive measurement which can be dynamically monitored in real time and has high system universality is realized.

The invention discloses a frequency response data change feature generation method and device for transformer winding fault category identification and a storage medium. The method comprises the following steps: respectively converting normal frequency response amplitude-frequency data and frequency response amplitude-frequency data of each fault winding into respective corresponding frequency response amplitude-frequency polar coordinate graphs; calculating multiple types of geometric features of all the frequency response amplitude-frequency polar coordinate graphs, and calculating a difference value between the radius of each point in the frequency response amplitude-frequency polar coordinate graph of each fault winding and the radius of each point in the normal frequency response amplitude-frequency polar coordinate graph; the feature vector for the machine learning model is formed by the multiple types of geometric features and the radius difference values. The equipment and the storage medium are used for implementing the method. The method can provide important reference and guidance for the feature extraction problem in transformer winding fault diagnosis based on a frequency response analysis method and a machine learning method.

A method and system for generating keywords that distinguish a group of selected documents from a larger corpus. The method includes: receiving a corpus that includes a plurality of documents; selecting from the corpus, based on at least one predetermined criterion, a group of the documents; determining a first Inverse Document Frequency (IDF) score for each word in a vocabulary of the group with respect to the corpus; determining a second IDF score for each word in the vocabulary of the group with respect to the group; generating based on the first IDF score and the second IDF score, a Group Frequency Inverse Document Frequency (GFIDF) score for each word in the vocabulary of the group; and assigning based on the generated GFIDF score for each word in the vocabulary of the group, at least one keyword.

Methods and systems are disclosed for enhancing allocation and use of frequency domain and time domain resources to support uplink (UL) transmission and downlink (DL) reception by a UE across subband frequency duplex (SBF D) symbols and non-SBFD symbols when a base station dynamically switches between half-duplex TDD operation and full-duplex FDD operation. The UE may align a frequency resource configured for receiving a DL reference signal carried on a non-SBFD symbol with a DL frequency sub-band of the SBFD symbol. Frequency domain resource allocation (FD RA) may group frequency resources for transmitting DL user data based on a size of a cascade of two DL sub-bands of an SBFD symbol. A nominal size of a precoding resource block group (PRG) may also be based on the size of the cascade of the two DL sub-bands of an SBFD symbol. The UE may implement the ability to respond to time domain resources scheduled for UL and DL transmissions across SBFD and non-SBFD symbols.

The invention discloses a packet aggregation approximate query processing method under local differential privacy, and belongs to the technical field of approximate query processing and privacy protection. The method is divided into two stages of sampling rate optimization and packet aggregation approximate query processing: firstly dividing user subsets, locally disturbing part of user data through a packet distribution preserving disturbance mechanism (GDP), estimating the frequency, the mean value and the variance of each packet in an unbiased manner by a server according to the local disturbance, calculating an error comprehensive coefficient and deducing the optimal sampling rate of each packet; after remaining users are disturbed by a PCKV-GRR mechanism, adaptive sampling is carried out according to the optimal sampling rate, and the server estimates and outputs an aggregation query result based on sampling data. According to the method, grouping statistics joint unbiased estimation is realized, grouping estimation errors are balanced, the privacy leakage is avoided by firstly disturbing and then sampling, the accuracy and efficiency of grouping aggregation query are improved while the local differential privacy is met, and the method is suitable for a large-scale grouping aggregation analysis scene of sensitive data.

This invention provides a method for identifying long-context attention heads based on static parameters of a large language model. The method includes: performing rotational position encoding on the static parameters of positions within a multi-head attention layer of the large language model; dividing each attention head within the multi-head attention layer into multiple frequency components to determine the frequency kernel of the attention head at the relative distance between positions; determining the effective rank of the frequency kernel based on its energy distribution across different frequency bands; determining the frequency energy distribution score and effective rank score corresponding to the energy distribution; and determining the long-context potential score of each attention head at the relative distance based on the frequency energy distribution score and effective rank score. This allows for the selection of attention heads with the potential to identify long-context modeling before deploying the large language model. This invention can improve the inference performance of the model in long-context tasks without changing the model structure or retraining the model.

The purpose of the present invention is to improve the comprehensiveness of teacher data. This data discrimination device stores a plurality of reference data items and additional data items to create a reference data group. The data determination device comprises a distance calculation unit, a grouping unit, a frequency calculation unit, and a selection unit. The distance calculation unit calculates a reference point of the reference data group and the distance from the reference point to the plurality of reference data items and the additional data items. The grouping unit groups the plurality of reference data items and the additional data items into a plurality of groups on the basis of the distance calculated by the distance calculation unit. The frequency calculation unit calculates the frequency of the plurality of reference data items and the additional data items for each of the plurality of groups. The selection unit selects a plurality of reference data items and additional data items to be included in the reference data group such that the frequency approaches a predetermined target level.

The present invention provides a signal denoising method based on self-supervised learning, which relates to the field of signal processing, including signal frequency decomposition: using a balanced multi-level signal decomposition method BHD to decompose signal data to obtain fine-grained frequency coefficients of the signal; self-supervised representation learning: using an automatic labeling strategy to label the frequency coefficients, constructing a neural network to perform supervised representation learning on the obtained labeled data, and obtaining an embedded representation of each frequency coefficient; selective signal reconstruction, according to the high and low noise frequency in the actual scene, the embedded representation of the obtained frequency coefficients is divided into a noise group and a denoising group, and the denoising group frequency coefficients are used to reconstruct the signal, and the reconstructed signal obtained is the denoising result of the corresponding signal. The present invention does not require pure signal data in the denoising process, does not rely on human experience, and is independent of specific scenes. It has high versatility while effectively denoising.