298 results about "Channel correlation" patented technology

The invention provides a motion intention recognition control method and system based on electroencephalogram signals, and relates to the technical field of motion intention recognition. According to the method, multi-channel electroencephalogram and peripheral physiological or motion data are collected, alignment, preprocessing and frequency band division are carried out, and space-frequency-time joint representation is constructed in combination with cross-channel association and time sequence statistics; the motion intention state probability, intensity and confidence coefficient are output through a graph time sequence decoder, and self-adaption is achieved based on feature distribution alignment; incremental updating is executed under confidence driving, and error learning is avoided in combination with an error correction mechanism; asynchronous judgment is realized through double-threshold hysteresis gating, and stable switching from unintentional state to intentional state is ensured; and finally mapping the motion intention and the intensity into a control instruction of an exoskeleton or a functional electrical stimulator under safety supervision. According to the method, a complete closed-loop link from electroencephalogram signal input to execution mechanism output is formed, and recognition precision, robustness, adaptability and safety are considered.

An apparatus may include a receiver configured to receive a reference signal using a channel, a transmitter configured to transmit a representation relating to the channel, and a processing circuit configured to determine channel information based on the reference signal, generate, using a model, the representation based on the channel information, and transfer, using the receiver or the transmitter, model information to specify the model. The model information may include an identifier for the model. The model information may include structure information for the model. The model information may include information about a type of input for the model. The model information may include information about a format of input for the model. The model information may include mapping information for mapping channel information to an input of the model. The mapping information may include information for a first subband, and information for a second subband.

The invention discloses an image fusion method based on multi-dimensional complementary features and multi-scale detail enhancement, and the method comprises the steps: calculating an edge image of a source image, and splicing the edge image with the source image in a channel dimension; enhancing feature textures and edge details by a high-frequency detail enhancement module; generating three feature maps with different scales step by step through continuous down-sampling operation; from the deepest scale, the infrared and visible light features of the hierarchy are fused through a parallel three-branch fusion module; the fused features are used for reconstructing a low-resolution fusion result, and the low-resolution fusion result is transmitted to an upper layer through up-sampling operation; and in the scale of the upper layer, the steps are repeated until a final full-resolution fusion image is generated. A parallel multi-dimensional complementary fusion network PMCFusion is set, cooperative perception and efficient integration of spatial features, channel correlation and frequency domain information are achieved through a parallel three-branch fusion module PTFM, and through cross-dimensional attention interaction, the PMCFusion can selectively enhance multi-dimensional features and achieve deep complementation.

The invention discloses a cable partial discharge signal noise separation mode identification method and system, and particularly relates to the technical field of cable partial discharge detection, in a multi-cable stacking environment, partial discharge signals are synchronously collected through multiple types of sensors such as a high-frequency current sensor, an ultrasonic sensor and an electromagnetic antenna; performing band-pass filtering, wavelet denoising and multi-channel synchronous alignment on the acquired signals to suppress background noise and retain key features of partial discharge pulses; separating the mixed signals by adopting independent component analysis; calculating a decision coefficient based on an inter-channel correlation coefficient, a signal-noise power ratio and an amplitude dynamic range, and judging whether to introduce a denoising method based on deep learning; for the waveform after noise separation, executing an amplitude re-calibration process so as to correct amplitude scaling uncertainty, and extracting multi-dimensional time domain, frequency domain and phase features; and a support vector machine and other machine learning algorithms are combined to realize automatic identification of partial discharge types.

The invention provides a channel state information feedback enhancement method and system based on bidirectional channel correlation, and the method is executed at a base station end, and comprises the steps: receiving a downlink channel state information semantic vector which is coded, compressed and quantized by a user end through a deep joint source channel; carrying out inverse quantization on the semantic vector to obtain an intermediate semantic vector; based on the intermediate semantic vector, retrieving a related historical uplink semantic vector from an uplink channel semantic knowledge base; performing fusion enhancement on the intermediate semantic vector and the retrieval result through an attention mechanism to generate an enhanced semantic vector; inputting the enhanced semantic vector into a decoder, and outputting a reconstructed downlink channel state information matrix; wherein the encoder, the enhancement module and the decoder perform end-to-end joint training optimization by minimizing a reconstruction error. According to the method, the semantics is enhanced at the receiving end by utilizing the correlation of the uplink and downlink channels, the feedback precision and the system robustness are remarkably improved, and the method is suitable for a frequency division duplex system.

Embodiments of the present disclosure relate to devices, methods, and computer-readable media for transmit cancellation. According to an embodiment of the present disclosure, a terminal device receives a UL cancel indication from a network device. The terminal device receives, from a network device, a resource indicator associated with an uplink channel. Then, based on the time unit of the uplink channel being an SBFD time unit, the terminal device transmits the uplink channel using a first resource indicated in a first resource set by the resource indicator. The SBFD time unit is configured with frequency sub-bands for different link directions. Based on the time unit being a non-SBFD time unit, the terminal device transmits the uplink channel using a second resource indicated in a second resource set by the resource indicator. In this manner, the SRS configuration may be adapted to or coordinated with an SBFD mechanism.

Systems, apparatuses, methods, and computer-readable media are provided for power control aspects for message A (MsgA) physical uplink shared channel (PUSCH). The approach includes determining a transmission power for a PUSCH associated with a physical random access channel (PRACH) in a 2-step random access channel (RACH) mode, where the transmission power for the PUSCH is determined based on a transmission power of the associated PRACH and a power offset. The approach further includes ramping up a transmission power of the PRACH and PUSCH until a maximum transmission power is reached.

The embodiment of the invention provides a communication method and a related device. The method comprises the following steps: sending first information indicating a first group of reference channels related to a first environment parameter set to user equipment; and sending second information related to the first environment parameter set to the user equipment, wherein the second information is used for determining one or more reference channels from the first group of reference channels. In the application, a central device can inform a user equipment of which environment parameter set is related by sending the second information, and help the user equipment determine at least one reference channel from a group of reference channels related to the environment parameter set indicated by the second information. According to the technical scheme, the validity of the reference channel which comes from the user equipment and is influenced by the environment parameter set can be ensured. Furthermore, the central device can determine the information related to the first DL channel without sending the channel measurement of the first DL channel, which can solve the problem of large signaling overhead caused by sending the channel measurement.

Embodiments of the present disclosure relate to a beamforming solution for FDD MIMO communication. A first device receives a reference signal from a second device in a first channel, and determines a covariance matrix associated with the first channel. The first device determines a set of beamforming vectors from the covariance matrix based on the number of dominant angles of arrival associated with the reference signal and the number of beams associated with the set of beamforming vectors, and performs a transmission to the second device based on the set of beamforming vectors in a second channel. In this way, beamforming vectors can be calculated from the transformed channel covariance matrix with the angular spread and imperfect reciprocity considered.

In end-to-end compression, a deep neural-network based encoder can be used to encode an image. The embeddings output from the encoder are quantized and encoded with a lossless encoder. Advantageously, at least one embodiment allows improving the latent entropy coding by further reducing the redundancies in the quantized latent. To that end, at least one embodiment discloses taking into account channels importance by coding an indication of a channel activity (or significance): performing post-conditional entropy coding by computing conditional probability based on a context afterwards: using channels reordering to improve inter channel correlation: or performing RDOQ like process by optimizing the main latent for a particular image.

A channel-associated information transfer method and apparatus, and a device are related to the field of storage technologies and provide a transfer mechanism applied to channel-associated information of data of a universal flash storage (UFS). The method includes a UFS device sending a first ready-to-transfer (RTT) frame to a host, where the first RTT frame indicates to transfer first target data. The first RTT frame includes channel-associated control information. The channel-associated control information is used to control transfer of first channel-associated information of the first target data. When the host receives the first RTT frame, the host sends a first data frame to the UFS device, where the first data frame includes the first target data and the first channel-associated information. The UFS device receives the first data frame.

Provided are a method performed by a first device and a device supporting same in a wireless communication system. The method performed by a first device in a wireless communication system comprises the steps of: acquiring configuration information related to a physical sidelink feedback channel (PSFCH); transmitting a first sidelink control indicator (SCI) for scheduling a physical sidelink shared channel (PSSCH) and a second SCI to a second device through a physical sidelink control channel (PSCCH); transmitting data and the second SCI including information indicating a hybrid automatic repeat and request (HARQ) feedback enabled mode to the second device through the PSSCH; and determining a PSFDCH resource related to the PSSCH on the basis of the configuration information.

The embodiment of the invention provides a communication method and a related device. The method comprises the following steps: sending first information indicating a first group of reference channels to first user equipment; and receiving, from the first user equipment, second information indicating at least one first reference channel in the first group of reference channels, a distance between any one of the at least one first reference channel and a first DL channel of the first user equipment being less than or equal to a first threshold. In the application, the central device can determine the information related to the first DL channel without sending the channel measurement of the first DL channel, which can solve the problem of large signaling overhead caused by sending the channel measurement.

The invention provides a double-branch spectrum quantitative analysis method Mark-GLNet based on Pearson feature marking, and global dependency modeling and local feature extraction are collaboratively optimized. The Mark mark layer quantifies wavelength importance by using a Pearson's correlation coefficient, eliminates redundant wavelengths, and inhibits low correlation noise. Refining feature input GLNet: global branches utilize sequence continuity and cross-space correlation, dynamic weights highlight keys, and redundancy is suppressed; and local branches compensate channel correlation through multi-scale convolution, and reinforce details. Double branches are cooperated through feature compensation, redundant interference is reduced, and prediction precision is improved. Through verification of four open-source near-infrared data sets, the model is simple in structure, low in parameter quantity and calculation complexity and excellent in generalization ability, and has high practicability and expandability.

In order to maintain performance during wireless communication, a transmitting electronic device may concurrently and independently communicate redundant information to a receiving electronic device. In particular, information associated with a data stream may be communicated to the receiving electronic device using one or more channels by two radios using one or more wireless local area network communication protocols. The packets transmitted by the radios may preferentially include the same information. Moreover, the transmitting electronic device may attempt to maintain the redundant communication if a performance metric associated with the one or more channels degrades. For example, the transmitting electronic device may transfer communication to a different channel or may compress the information in the packets in the one or more channels if the throughput drops below a threshold value. Furthermore, the concurrent communication may allow the transmitting electronic device determine link quality without disrupting the communication of the information.

The invention provides an epilepsy prediction system based on multivariate weighted joint recursion and a graph attention network. The epilepsy prediction system can be applied to the technical field of biomedical signal processing and artificial intelligence. The system comprises a brain function imaging module which is configured to obtain electroencephalogram signal data of a target object under the condition that the target object is authorized; the processor comprises a multivariate weighted joint recursion processing unit which is configured to obtain a phase-space trajectory vector of each channel of the electroencephalogram signal data according to the electroencephalogram signal data, construct a recursion plot according to the phase-space trajectory vector of each channel, and calculate the phase-space trajectory vector of each channel based on the recursion plots of any two channels. Obtaining a channel correlation coefficient between any two channels, and according to the channel correlation coefficient between any two channels, constructing a weighted adjacency matrix representing the brain function of the target object; and the image attention network processing unit is configured to perform spatial-temporal feature processing according to the weighted adjacency matrix and the electroencephalogram signal data and output an epileptic seizure prediction result.

Methods, systems, and devices for wireless communication are described. In some systems, a user equipment (UE) can measure a received power of one or more reference signals (such as channel state information (CSI) reference signals (CSI-RSs) or synchronization signal blocks (SSBs)) transmitted by a base station for a beam management procedure. The UE can determine, based on performing the beam management procedure, a set of channel statistics associated with a channel used by the base station to transmit the one or more reference signals. The UE can transmit, to the base station, a report including at least a received indication of the one or more reference signals and the set of channel statistics. The base station can select, based at least on the indication of the received power and the set of channel statistics, a beam and a channel for communication with the UE.

Various aspects of the present disclosure relate to detecting at least two selection-suitable cells of similar pathloss on a frequency range, each cell associated with a RF channel, and determining a potential RF interference condition between the at least two selection-suitable cells due to overlapping spectra of the associated RF channels. Aspects of the present disclosure relate to determining, for the frequency range, updated maximum supported bandwidths of the UE and transmitting an indication of at least one updated maximum supported bandwidth to a wireless network.

A method and apparatus may collect information related to dynamically allocating an optimal channel for a plurality of wireless networks. The plurality of wireless networks may be grouped together based on one or more criteria. A channel allocation may be determined based on one or more factors, including an assessment of an optimization algorithm.

A communication device includes a processor, a primary radio, a scanning radio, and a storage device. The primary radio provides wireless communication services to one or more client devices in a radio frequency (RF) environment and manages the wireless communication services to comply with a predetermined wireless network protocol. The scanning radio scans a plurality of channels provided in the RF environment in one or more frequency bands to comply with the predetermined wireless network protocol. The storage device stores calibration data associated with the plurality of channels. The predetermined wireless network protocol is a Wi-Fi protocol. The primary radio is a Wi-Fi access point (AP) chipset and the scanning radio is a Wi-Fi client chipset.

The invention discloses a multi-feature-domain semi-supervised multi-artifact detection method based on channel shielding. The method comprises the following steps: firstly, carrying out channel correlation analysis on different artifacts, recalibrating data, and marking a non-artifact channel as a shielding type; the method comprises the following steps: extracting reconstruction features from EEG data by utilizing the advantages of an AE network in the aspect of feature reconstruction, and enhancing feature representation related to artifacts by utilizing an attention mechanism; frequency domain information in the PSD is extracted in combination with a CNN network, so that construction of multiple feature domains is realized, capture of complementary information is facilitated, and the robustness of the model is improved; and finally, a label shunting mechanism in a multi-classification loss function is utilized to further reduce the influence of noise labels on training, so that the detection generalization and robustness in a complex environment are effectively improved.

The invention provides a hippophae rhamnoides fruit quality grading method based on optical near-infrared multi-channel characteristics, and relates to the technical field of optical detection and fruit and vegetable quality grading, the method comprises the following steps: constructing a near-infrared multi-channel original data set, obtaining a standardized near-infrared multi-channel spectrum signal, the method comprises the following steps of: calculating corresponding absorption intensity characteristics in each near-infrared channel, calculating corresponding channel response ratio characteristics, calculating multi-channel correlation characteristics, splicing the absorption intensity characteristics, the channel response ratio characteristics and the multi-channel correlation characteristics according to a preset sequence, and carrying out weighted fusion according to a pre-calibrated weight coefficient to obtain a multi-channel near-infrared spectrum; and obtaining a comprehensive quality evaluation index. According to the sea-buckthorn fruit quality grading method based on the optical near-infrared multi-channel characteristics, the problems that existing manual or single-channel detection is low in efficiency, poor in accuracy and destructive grading can be solved, non-destructive online automatic grading of sea-buckthorn fruits is achieved, and the grading speed, the grading accuracy and the fruit quality traceability are improved.

The application provides an epilepsy prediction system based on multi-element weighted joint recursion and graph attention network, which can be applied to the fields of biomedical signal processing and artificial intelligence technology. The system comprises a brain function imaging module configured to acquire electroencephalogram signal data of a target object under authorization of the target object; a processor comprising a multi-element weighted joint recursion processing unit configured to obtain a phase space trajectory vector of each channel of the electroencephalogram signal data according to the electroencephalogram signal data, construct a recursion graph according to the phase space trajectory vector of each channel, obtain a channel correlation coefficient between any two channels based on the recursion graph of each channel, and construct a weighted adjacency matrix representing the brain function of the target object according to the channel correlation coefficient between any two channels; and a graph attention network processing unit configured to perform spatiotemporal feature processing according to the weighted adjacency matrix and the electroencephalogram signal data and output an epilepsy seizure prediction result.

This application provides a communication method and a communication apparatus. The method may include: a first apparatus receiving first indication information from a second apparatus, the first indication information being used to instruct the first apparatus to transmit a precoding of a first reference signal, the precoding of the first reference signal being associated with channel-related information of multiple first apparatuses served by the second apparatus. In this method, the precoding of the first reference signal transmitted by a terminal device may be indicated by a network device, and the precoding is associated with channel-related information of multiple terminal devices served by the network device. Therefore, the network device can determine the precoding of the first reference signal based on the channel-related information of the multiple terminal devices served by the network device. Through the design of the precoding by the network device, the first reference signal transmitted by each terminal device can be distinguished during transmission. Based on this, pilot interference between the first reference signals of different terminal devices can be avoided, and the accuracy of channel measurement can be improved.

The application relates to the technical field of industrial Internet of Things, and discloses a multi-channel time sequence data error bound quantification method and system based on dynamic graph grouping and error distribution, which comprises the following steps: S1: acquiring multi-channel time sequence data collected at the same collection point; S2: determining a scale factor, a point-by-point maximum absolute error upper bound, normalized channel time sequence data, and normalized error bounds of all channels; S3: calculating correlation measurement values of any two channels, and constructing a channel correlation graph; S4: determining a grouping threshold value, and forming an anchor point set from selected anchor point channels; S5: constructing a cross-channel correlation prediction model, and constructing an innovation sequence; S6: determining an error sensitivity coefficient, and determining a quantified error bound; S7: obtaining normalized reconstruction values of the anchor point channels and the innovation sequence; and S8: obtaining normalized reconstruction values of non-anchor point channels, and performing inverse normalization on the normalized reconstruction values according to the scale factor to obtain reconstruction values of the channels under the original dimension. The application can limit compress and highly restore original data.

This invention discloses a multi-channel electromagnetic induction detection method and system based on uniform magnetic field constraint. A uniform magnetic field is constructed within the detection area of ​​the structure under test. Electromagnetic induction response signals are acquired through multiple channels under the constraint of the uniform magnetic field, and the spatial coordinates of each acquisition channel are marked. The raw electromagnetic induction signals acquired by each channel are preprocessed, including noise reduction filtering, envelope extraction, and amplitude normalization. A deep learning model is then used to extract structural features from the preprocessed multi-channel electromagnetic response signals. Based on the spatial position information of each acquisition channel, magnetic field uniformity parameters, and response characteristics, a channel correlation weight matrix is ​​constructed to describe the spatial consistency and physical coupling relationships between channels. A multi-channel joint optimization model is constructed, and the equivalent electromagnetic parameter distribution inside the structure under test is reconstructed through iterative solution. This invention improves the reliability, repeatability, and engineering applicability of electromagnetic induction detection results.

A station (STA) receives a first frame via a first channel and sets a first network allocation vector (NAV) associated with the first channel to a non-zero value based on the first frame. The STA transmits a second frame on a channel including the first channel and a second channel, and when the second frame is transmitted, the STA ignores the non-zero value of the first NAV under the condition that a second NAV associated with the second channel is zero.

This disclosure provides methods, components, devices and systems for non-primary channel access (NPCA) conditions for joint operation with coordinated access point (AP) procedures. Some aspects more specifically relate to preventing an NPCA-enabled station from switching to the secondary primary channel during a coordinated transmission messaging sequence. In some examples, a first AP may transmit, to the NPCA-enabled station, a first frame that indicates one or more parameters associated with a determination of whether to transition from the first primary channel to a secondary primary channel. Based on receiving the indication of the one or more parameters, the NPCA-enabled station may determine not to switch to the secondary primary channel, and may complete the coordinated transmission messaging sequence.

In an electronic device and an operating method thereof according to an embodiment, the electronic device may include a communication circuitry supporting short-range wireless communication and neighbor awareness network (NAN) communication; a memory storing at least one computer program; and at least one processor. The at least one computer program may include instructions that cause, when executed by the at least one processor individually or collectively, the electronic device to connect to a first access point (AP) through a non-dynamic frequency selection (non-DFS) channel, transmit, to an external electronic device, a first service discovery frame including information related to a DFS channel and information indicating a maximum bandwidth supportable by the electronic device, in a process of discovering the external electronic device to be connected via an NAN data path (NDP), connect to a second AP supporting the DFS through the DFS channel while maintaining a connection between the first AP and the electronic device, upon identifying, based on a second service discovery frame received from the external electronic device, that the external electronic device supports the DFS and a maximum bandwidth supportable by the external electronic device is greater than a bandwidth of the non-DFS channel, establish the NDP with the external electronic device, based on the first service discovery frame and / or the second service discovery frame, and perform NAN communication with the external electronic device, based on first schedule information related to performing the NAN communication through a frequency band of the non-DFS channel and a frequency band of the DFS channel. The first schedule information may enable the external electronic device and the electronic device to perform the NAN communication through the frequency band of the non-DFS channel and the frequency band of the DFS channel during a first period between discovery windows.

A method is disclosed of selecting users for multi user multiple-input multiple-output (MU-MIMO) communication from an initial set of potential users. The method comprises repeating the following steps in successive iterations until a stopping criterion is met: determining a channel correlation metric for each user in the set of potential users, reducing the set of potential users by exclusion of a user based on the channel correlation metric, and calculating a performance metric of the set of potential users. The method also comprises selecting users corresponding to one of the sets of potential users, wherein the selection is based on a comparison of the calculated performance metrics of the sets of potential users. The channel correlation metric for a user may comprise one or more of: a channel filter norm for the user, a channel norm for the user, a channel gain for the user, pair-wise correlations between the user and one or more other users of the set of potential users, and a channel eigenvalue for the user. The performance metric may comprise one or more of: a sum-rate, a per-user-rate, an average error rate, a maximum error rate, a per-user error rate, and a sum-correlation. Corresponding apparatus, network node and computer program product are also disclosed.