132 results about "Functional networks" patented technology

The invention provides a spatial-temporal feature fusion-based dynamic graph convolution electroencephalogram depression detection method, which comprises the following steps of: firstly, segmenting a sample into fragments with the length of 1 second, and calculating power spectral density (PSD) as an input feature by adopting a Welch method; the time sequence and spatial characteristics of the EEG signals are synchronously extracted through a double-branch architecture, wherein one branch captures the long-term time sequence dependence of the EEG signals by using a GRU; and the other branch adopts an improved TSCN (separable convolution is introduced), multi-scale spatial features from fine to rough are extracted through causal convolution and expansion convolution of residual layered stacking, after double-branch features are adaptively fused based on an attention mechanism, a dynamic graph structure is constructed, functional connection evolution of brain intervals is modeled by using a graph convolution network, and a dynamic graph structure is constructed. The topological structure of the network is optimized through a back propagation process, and finally depression identification is realized through a Softmax classifier. According to the method, the time sequence modeling capability of the GRU and the multi-scale spatial analysis capability of the TSCN are fused, the representation limitation of a single model is broken through, the dynamic change of a brain function network is adaptively captured through dynamic graph convolution, the physiological interpretability is enhanced, deep complementary fusion of EEG spatial and temporal characteristics is realized, the depression recognition accuracy is remarkably improved, and the method is suitable for popularization and application. And an efficient tool is provided for auxiliary diagnosis of mental diseases.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention relates to a communication method, device and system. And the first core network element determines that a service corresponding to the terminal equipment is locally processed according to the first information, and a control plane signaling corresponding to the locally processed service is forwarded to a local function network element by the first network element and does not pass through the first core network element. The first information comprises one or more of the following items: information of the terminal equipment, information of the first network element, or information of at least one core network element. And the first core network element sends indication information to the first network element so as to indicate the service corresponding to the terminal equipment to be locally processed or indicate the information of the service locally processed by the terminal equipment. In the embodiment of the invention, the first core network element can determine whether the service corresponding to the terminal equipment is locally processed or not according to the first information, namely, a method for determining whether the service of the terminal equipment can be locally processed or not is given, so that the service of the terminal equipment can be locally processed.

The invention discloses an epilepsy abnormal brain network identification method based on a multi-scale static-dynamic fusion network, and belongs to the field of brain image analysis. The method comprises the following steps: firstly, constructing a static function connection weighted graph and a dynamic function connection graph; and fusing the static and dynamic representations by adopting a cross attention module. In order to describe a multi-scale spatial relationship, performing lexical meta-processing on brain connection according to anatomical partition and a functional network; and the local-global fusion module is used for integrating the fine granularity and the macroscopic relationship, so that the brain region with diagnostic significance is highlighted. In the training stage, cross entropy, reverse contrast loss and sparse regularization based on contrast graph adjacency matrix entropy are jointly used. The method is verified on multi-center functional magnetic resonance data, compared with other mainstream depth models, the classification accuracy, generalization and interpretability are remarkably improved, an abnormal brain region consistent with an epilepsy network can be positioned, and brain image markers with biological significance can be connected and recognized.

The invention relates to the technical field of medical data processing and nerve regulation and control, in particular to a method for synchronously activating post-stroke neuroplasticity through photoacoustic, which comprises the following steps of: 1, constructing a stroke specific brain region target map: fusing brain structure connection data and functional network data of a patient, generating a three-dimensional target map containing the high-metabolism semi-dark band coordinate set and the cross-hemisphere compensatory connection intersection; 2, calculating a time-space synchronization focusing parameter; 3, synchronous stimulation and dynamic regulation are executed, wherein the ultrasonic transducer and the laser source are controlled to emit time-space synchronous sound waves and light pulses; monitoring the blood perfusion variable quantity and the neurotransmitter concentration ratio of the target brain area in real time; when the blood perfusion variable quantity does not reach the expectation or the transmitter ratio is unbalanced, the stimulation intensity is dynamically adjusted, and the focusing coordinate is translated in the direction away from the infarction area. Through photoacoustic stimulation of time-space synchronization, the difference between the propagation speeds of sound waves and light waves is overcome, and accurate collaboration of neural restoration is achieved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a mental state of a patient based on a natural language input and determining whether a relevant subset of brain data is anomalous. One of the methods includes receiving a natural language input describing at least one aspect of a mental state or of a behavior of an individual; generating a prompt based on at least in part the natural language input; submitting the prompt to a large language model; receiving at least one functional network that influences the at least one aspect of a mental state or of a behavior; for each network of the at least one functional network, analyzing MRI data for the individual to determine whether the network is anomalous; displaying to a user each network and whether it is anomalous; and taking an action in response to the displaying.

The present application relates to a zero trust-based service system access control method and apparatus, a device, and a medium. The method comprises: triggering, among core network functional network elements of a network, a session management function network element or an AAA server to send a user re-authentication request to a user terminal, so that the user terminal returns current user information to the AAA server; upon the AAA server verifying that current user identity information is consistent with user identity information corresponding to a target user, determining an identity authentication success result; on the basis of the identity authentication success result and the current user identity information, the AAA server initiating a biometric authentication request to the user terminal, so that the AAA server checks whether most recent biometric information of the target user returned by the user terminal matches pre-stored biometric information of the target user, and if so, returns a biometric authentication success result to the user terminal, so as to authorize the target user to continue accessing a target service system. The present application can prevent several security issues, such as unauthorized control over authorized devices.

The invention provides a communication method and a communication device applied to the field of wireless communication. In the technical scheme provided by the invention, when a user deploys a home base station and the home base station is in a closed mode and there is a demand for allowing a visitor terminal to access the home base station, a management terminal of the home base station can send first information to a first functional network element, and the first information indicates that the visitor terminal which is allowed to access the home base station is allowed to access the first functional network element. Therefore, the first function network element can trigger the flow of updating the signing data of the visitor terminal based on the first information, and the closed access group information corresponding to the home base station is stored in the signing data of the visitor terminal, so that the visitor terminal can access the network through the home base station to obtain network service.

The invention discloses a coupling tensor decomposition method based on sparse low-rank constraint, which is used for characteristic decomposition of a multi-frequency dynamic function network connection tensor in resting state function magnetic resonance imaging data. According to the algorithm, on the basis of the traditional coupling canonical factorization (CCPD), an optimization model of sparse and low-rank constraint is constructed, and the sparse and low-rank constraint optimization model is constructed by the algorithm. On the spatial connectivity dimension, redundant function connection is reduced through an L1 sparse penalty term, and the spatial specificity of the key brain network is enhanced; and in time and frequency band dimensions, low-rank regularization constraint is adopted to improve discrimination of cross-subject time sequence characteristics. Generally speaking, the method can effectively extract connectivity characteristics with statistical significance and time states of different frequency bands from dynamic function network connection tensors of multiple frequency bands, thereby effectively identifying functional connection heterogeneity characteristics between schizophrenia patients and healthy control groups.

The invention relates to a depression risk detection method based on self-adaptive multi-scale neighborhood perception fusion space-time diagram convolution, and aims to solve the problems that traditional depression diagnosis is subjective and existing electroencephalogram detection space-time feature modeling is insufficient. The method comprises the following steps: acquiring electroencephalogram signals of a testee, and extracting frequency spectrum features through a frequency domain feature extraction module; in combination with an adjacent matrix (independent of an electrode physical distance) which is adaptively learned and normalized in training, embedded features are generated through a graph convolutional neural network; and then capturing long-time-history dependence through a dynamic feature extraction module, realizing feature multi-level fusion by means of a multi-cascade multi-scale convolution module, finally integrating local and global features through an adaptive feature fusion module, and inputting a prediction layer to output a detection result. According to the method, electroencephalogram signal time-space domain joint modeling is achieved, brain region function connection dynamic and multi-scale neural features are effectively captured, information redundancy and key feature loss are avoided, classification accuracy and generalization ability are remarkably improved, depression-related key brain region and function network features can be revealed, and the method has high interpretability and potential clinical application value.

The invention provides an automatic test system and method for a third-party high-power repeater, and relates to the technical field of automatic test equipment, and the system comprises an integrated reconfigurable passive link box, a control host, a vector signal generator and a vector signal analyzer. The integrated reconfigurable passive link box comprises a box body, a radio frequency switch matrix network, an embedded function network, a control port, an input port, an output port, an uplink interface and a downlink interface. The radio frequency switch matrix network and the embedded function network are arranged in the box body. The input port, the output port, the uplink interface and the downlink interface are arranged on the surface of the box body. The vector signal generator is connected with the input port, the vector signal analyzer is connected with the output port, and a third-party high-power repeater to be tested is arranged between the uplink interface and the downlink interface. According to the invention, the radio frequency switch matrix network and the embedded function network are packaged in the integrated reconfigurable passive link box, and the control host is utilized to carry out automatic testing on the third-party high-power repeater according to the preset process, so that frequent operation processes are avoided, and damage to instruments is reduced.

The invention discloses a personalized MCI electrical stimulation intervention method based on a dynamic brain network, and belongs to the technical field of electrical stimulation target determination. Individual heterogeneity of a brain function network of a patient is considered, specific frequency band selection is carried out on electroencephalogram data sets of a normal group and a cognitive impairment group, an effective data set is constructed, and the effective data set is determined. The method comprises the following steps: respectively constructing average dynamic brain network connection of total test times of a normal group and a cognitive impairment group based on an adaptive method of dynamic time-varying weight optimization of reaction time, and then determining a main abnormal frequency band through the difference of the average dynamic brain network connection of the two groups; and then target points are determined for the core nodes connected with the average dynamic brain network of the main abnormal frequency band, and compared with traditional single-frequency-band analysis, the application proposes that the effective data set is constructed by the specific frequency band to determine the main abnormal frequency band; in addition, stimulation targets are positioned according to the core nodes connected with the average dynamic brain network of the abnormal frequency band, and the reliability of target selection is improved.

The embodiment of the invention provides a method and a device for determining a service strategy and a communication system. The method comprises the following steps: a first function network element acquires user information of a first service; determining policy information of the first service according to the user information of the first service; the policy information of the first service comprises execution range information of the first service and service quality information of the first service; and the first function network element sends the policy information of the first service to the second function network element. According to the method, the first function network element determines the strategy information of the service from the service dimension, the service quality of the service can be provided, the execution range of the service can also be provided, and any new service of the communication network service can be supported as well. Therefore, the method can effectively meet and support strategy requirements of various services.

The invention provides a brain network construction and analysis method based on multivariable analysis. The method is mainly used for multi-view analysis of a brain structure and a functional network. The technical problem to be solved is that information loss is caused by neglecting necessary multivariate relationships among brain region nodes when univariate analysis is carried out on a brain function network and a brain structure network. According to the method, the canonical correlation analysis method based on data driving is used for constructing the single-mode brain network, the problem that a traditional univariate analysis method neglects necessary multivariate relations is avoided, the complex relation between the brain structure and functions can be reflected more accurately, and a more reliable basis is provided for deep research of a brain working mechanism.

The invention relates to the technical field of brain function network analysis, in particular to a post-stroke aphasia patient fMRI focal recognition method based on deep learning. The method comprises the following steps of: acquiring a region consistency value for geometric structures of matched local regions of two adjacent fMRI images in an fMRI image sequence, similarity of gray distribution and an image matching degree, and dividing a same time window by using the region consistency value; and obtaining a functional connection coefficient according to the difference of the region consistent values of the fMRI images in two adjacent time windows and the difference of the geometric structure features and the gray level distribution features of the local regions, determining the convolution kernel size of deep learning of the fMRI images in the time windows by using the functional connection coefficient, and performing focal recognition on the fMRI images in the time windows. According to the method, the function connection coefficient presenting the cooperation degree of the brain region activity modes in the adjacent time windows is analyzed, the convolution kernel size of the time windows is determined in a self-adaptive mode, and the recognition effect on the local focus of the patient is improved.

Disclosed in the present disclosure is a multi-functional network tester, including a central control module, a power module, and one or more of a network cable length measurement module, a line-finding module, a switch detection module, and a network cable line-alignment module, in which the multi-functional network tester further includes an optical power module or a multimeter module, the network cable length measurement module, the line-finding module, the switch detection module and / or the network cable line-alignment module are connected to the central control module respectively, the optical power module or the multimeter module is connected to the central control module, and the power module is used for power supply.

The invention provides a brain function network causal analysis method based on phase-space reconstruction and unified GCA, and relates to the field of functional brain network analys.The method comprises the steps that fMRI data are collected and preprocessed, and a time sequence of interested nodes is extracted from the preprocessed fMRI data; for extracting time sequences X and Y of any two to-be-analyzed interested nodes, constructing a variable time delay unified Granger causal model based on phase space reconstruction; and traversing all to-be-analyzed node pairs of interest, calculating the causal direction and strength between each pair of nodes to construct a whole-brain directed causal connection matrix, and performing network metric attribute analysis. According to the method, phase-space reconstruction is taken as a core, a causal analysis framework is provided by unifying GCA, and end-to-end modeling is realized. The final target is to generate a high-fidelity fMRI data model, so that the causal connection relationship is closer to a brain real neural mechanism, and the reliability and the application value of functional brain network research are improved.

The invention provides a communication method and device, and the method comprises the steps that a first network element sends a network element discovery message to a network storage function network element, the network element discovery message is used for discovering an analysis function network element, the network element discovery message comprises a first interoperation identifier, and the first interoperation identifier comprises a first manufacturer identifier list; the first network element receives information of one or more analysis function network elements from the network storage function network element; wherein the identification of the manufacturer of the analysis function network element is one in the first manufacturer identification list. By adopting the method, the network storage function network element can use the model provided by the model training function network element corresponding to the first interoperation identifier for the analysis function network element discovered by the first network element, so that the first network element can select the analysis function network element as the target analysis function network element; therefore, the service can be better provided for the service consumption network element.

The invention relates to a brain anomaly network positioning method and system based on functional connection network mapping in the technical field of neural image data processing. The brain abnormal network positioning method comprises the following steps: calculating a whole brain function network diagram connected with each abnormal site by using resting state function connection data of large-scale health subjects based on a plurality of dispersed abnormal sites reported in previous literatures; then superposing the function network diagrams to obtain a network probability graph; and finally, filtering the probability graph through a threshold value to obtain a final core anomaly network graph. According to the method, the inherent functional connection architecture of the brain is used as a reference system, abnormal sites which seem to be uncorrelated in different researches are successfully traced and unified to a common and stable functional network, and compared with a single brain region marker, the generated network-level biomarker integrates more source evidences, so that the network-level biomarker has the advantages that the network-level biomarker can be widely applied to the field of biomarkers of the brain region. Therefore, the problems of result heterogeneity and inconsistency in brain abnormality discovery are solved.

This application provides a printed circuit board assembly and an electronic device. The printed circuit board assembly includes: a printed circuit board, where the printed circuit board includes a functional network, and the functional network is configured to provide an electrical function; a conductive part, where the conductive part is connected to the printed circuit board and is electrically connected in parallel to the functional network; and a cover-shaped structure, where the cover-shaped structure covers the conductive part and is connected to the printed circuit board, and the cover-shaped structure is configured to prevent the conductive part located in the cover-shaped structure from contacting a conductor outside the cover-shaped structure. The foregoing technical solutions can reduce impedance of a circuit board, improve a current utilization, and further improve mechanical reliability of the circuit board.

The disclosed approach uses imaging data from a scan of a brain of a subject to generate a metric indicative of a characteristic of one or more functional networks in the brain. The metric, or data based on the metric, may be provided to a machine learning classifier to generate a prediction related to a psychological condition such as depression.

The application relates to the technical field of wireless communication, in particular to a service providing method and device, a computer device and a storage medium. The method comprises the following steps: receiving an initial service request sent by a service demander; performing address inverse transformation on a target FQDN to obtain a service network address; replacing the target FQDN in the initial service request with the service network address to obtain a target service request; and sending the target service request to a service provider, so that the service provider provides services to the service demander according to a functional network element corresponding to the service network address. The application realizes the sending of the service network address under the condition that only the SEPP network element performs address inverse transformation on the target FQDN to obtain the service network address, without the functional adjustment of other network elements, so that the communication mechanism does not need to additionally construct a DNS or a database, the management and maintenance cost of maintaining the mapping relationship between the domain network FQDN and the service network address is reduced, and the risk of service transmission interruption is also reduced.

The present invention proposes a muscle dynamic synergy analysis method based on dynamic community detection. First, the surface electromyographic signals of the human body during movement are collected to construct a time-varying electromyographic functional network; then, the muscle community clustering of the single-layer electromyographic functional network is detected to obtain the muscle communities of each layer, and the muscle communities are arranged in the order of corresponding time snapshots to form a community set; a resolution parameter is set to adjust the sensitivity of each layer of the electromyographic functional network to the community, and a coupling parameter is set to adjust the coupling degree between layers; then, the modularity is optimized with maximizing the multi-layer modularity as an indicator; finally, the clusters of strongly interconnected nodes in the electromyographic functional network are divided into different modules, and the node clusters within the same module are regarded as muscle groups with strong synergy at this moment. As the muscle synergy changes dynamically over time, this change process is revealed through dynamic community detection, thereby realizing dynamic decoding of muscle synergy in movement changes.

The invention discloses a public network domain name shunting method and a communication system. Comprising the steps that an uplink classifier user plane function network element receives a DNS analysis request message initiated by user equipment, the message comprises a public network domain name to be analyzed, and a target IP address of the message is a first IP address; whether the public network domain name meets a DNS redirection rule is judged, if yes, the request message is sent to an intranet DNS server corresponding to the public network domain name, and a DNS analysis response message fed back by the intranet DNS server is received; and converting the analysis result from the second IP address to a virtual IP address according to a target IP address conversion rule, and sending a DNS analysis response message of which the analysis result is the virtual IP address to the user equipment. According to the method and the device, the technical problem that the common public network service cannot be normally accessed due to the fact that the same IP address is returned to two DNS analysis requests of the public network service shunted by the internal network and the common public network service by a content distribution network service provider is solved.

The invention relates to a preparation method and system of a functional cement-based material, relates to the technical field of civil construction materials, and solves the problem that the stability, economical efficiency and multifunctional cooperation of the material are difficult to consider due to lack of function-structure-process system regulation and control in the two methods at present. The method comprises the following steps: adding cement particles with a preset initial particle size after acquiring a starting signal of a granulator, starting the granulator to rotate, spraying water according to a preset water-cement ratio, monitoring the particle size and the water content of the particles in real time, and dynamically adjusting the rotating speed to reach the standard; after granulation is completed, a nozzle is switched to spray a functional material, the thickness and uniformity of a coating are monitored, and parameters are optimized to reach the standard; then cold pressing and quality evaluation are carried out, curing is carried out after the standard is reached, and finally the functional concrete is obtained. The cement-based material has the advantages that a continuous functional network is efficiently formed, various functions are given to the cement-based material, and manufacturability and performance are both considered.

The present invention belongs to the field of information processing of medical or health data and images, and particularly relates to a research method and application of the rich club of the brain functional network for juvenile myoclonic epilepsy. By calculating the Pearson correlation coefficient, the brain functional networks of two groups of subjects are constructed, and the rich connections, feeder connections and local connections of the brain networks of the JME patient group and the normal control group are calculated. The differences in the rich-club organizational structures of the brain networks of the two groups of subjects are analyzed to facilitate the discovery of abnormal rich club tissues and further understand the pathophysiological mechanism of JME.

The present invention discloses a smoothed group information-guided independent component analysis method for brain functional network analysis, which belongs to the technical field of independent component analysis of brain images. The present invention includes performing independent component analysis to obtain components at the group level as reference signals; calculating voxel features to construct a graph regularization term; using voxel features and reference signals as guidance, using multi-objective functions to perform iterative solutions to estimate the independent components of individual subjects; and calculating the time series corresponding to each component in the individual subject based on the extracted components. The present invention overcomes the limitation of the group information-guided independent component analysis method currently widely used in the field of brain functional network extraction that does not optimize the smoothness of the extracted components, and obtains a more accurate brain functional network. Voxel features are introduced as a guide in the process of constructing the objective function, which enhances the spatial smoothness and functional correlation of the results, and can help the new method learn a network that is more in line with the actual working mechanism of the brain.

A method for identifying brain-state dependent functional areas of unitary pooled activity (FAUPAs) using a statistical model that does not require a priori knowledge of the activity-induced ideal response signal time course is provided. A system for identifying a functional network in a brain of a living object includes a FAUPA identifier configured to identify FAUPAs by analyzing a plurality of images of the brain over a predetermined period. The plurality of images include a plurality of voxels, and the FAUPA identifier analyzes each voxel of the plurality of voxels in relation to one or more surrounding voxels of each voxel until each voxel of the plurality of voxels is evaluated. A brain network identification module configured to construct the functional network based on the identified FAUPAs that are functionally connected. A display module configured to display images of the brain depicting the FAUPAs included in the functional network.

The invention discloses a sensing method and device, a sensing function network element, first equipment and second equipment, and belongs to the technical field of communication, and the sensing method comprises the steps that the sensing function network element receives a first sensing service request message from the first equipment, the first sensing service request message is used for requesting to execute a target sensing service; the perception function network element executes a positioning perception service for a first terminal according to the first perception service request message so as to acquire position information of the first terminal, the first terminal is a sensing object of the target sensing service or a sensing device of the target sensing service or an auxiliary device of the target sensing service.

The embodiment of the invention provides a method and a device for determining a perception service strategy and a communication system. The method comprises the following steps: a first function network element acquires identification information of a perception service; determining policy information of the sensing service according to the identification information of the sensing service; the policy information of the sensing service comprises service quality information of the sensing service; and the first functional network element sends the policy information of the sensing service to the second functional network element. According to the method, the first function network element can determine the strategy information of the perception service from the service dimension to provide the service quality of the perception service, and the method is also suitable for an evolved communication network, so that the strategy requirement of the perception service can be effectively met and supported.