468 results about "Network component" patented technology

An adapter layer may be used to customize a machine learning component by transforming data flowing into, out of, and / or within the machine learning component. The adapter layer may include a number of neural network components, or “adapters,” configured to perform a transformation on input data. Neural network components may be configured into adapter groups. A router component can, based on the input data, select one or more neural network components for transforming the input data. The input layer may combine the results of any such transformations to yield adapted data. Different adapter groups can include adapters of different complexity (e.g., involving different amounts of computation and / or latency). Thus, the amount of computation or latency added by an adapter layer can be reduced for simpler transformations of the input data.

A machine learning model may be configured for training using an associated learning technique. A model configured for end-to-end backpropagation may adapted for associated learning by introducing functions for projecting hidden vectors and labels to a shared representation space and for reconstructing labels from representation vectors. An associated learning loss may be calculated at each layer, with the resulting gradients backpropagated locally through that layer rather than all layers. A reconstruction loss may be calculated using each layer's output including the predicted label. Training by associated learning may be parallelized (e.g., layer by layer) to yield efficiency gains. In addition, associated learning training may be more robust to training label errors. The resulting model may be used to, for example, predict data sequences in an autoregressive manner in which subsequent portions of the output data sequence are predicted in part based on previous predicted portions of the output data sequence.

Systems, devices, and automated processes are described to provide collection of wireless network status data, such as a 5G or other mobile network, and to automatically respond to queries regarding the status of the network. Systems and automated processes may obtain status data from a plurality of network components, for example a radio unit (RU), a distributed unit (DU), and a centralized unit (CU) associated with a cell site, ingest the obtained status data, including processing the status data with a machine learning model system, store the ingested status data in a data store, receive a user query related to the network, ingest the received user query using the MLM system, retrieve a result corresponding to the ingested query from the data store using the MLM system, generate, using the MLM system, a summary of the retrieved status data, and present the generated summary via a user interface.

Embodiments manage data in a file system over a network. A plurality of file system operations in the file system may be executed based on a file system client action or a file system administrative action such that the file system may be integrated with a network. Characteristics of the plurality network components in the network infrastructure that may be associated with the file system may be determined. Tests may be generated based on the characteristics of the network components such that the tests may be executed to evaluate the network components. Results of the tests may be employed to perform further actions, including determining non-compliant network components based on the results; modifying the network infrastructure based on the non-compliant network components such that one or more of file system operations are modified based on the non-compliant network devices; executing the modified file system operations on the modified network infrastructure.

Various methods and processing systems for the effective management of network traffic and facilitating data forwarding within distributed computing environments. Network components may be configured to amalgamate the Border Gateway Protocol (BGP) with Source Network Address Translation (SNAT) or network address port translation (NAPT) technologies to facilitate dynamic notification of network address accessibility and use port index identifiers to augment the precision and resilience of routing. A distributed SNAT / NAPT virtual network function (VNF) or cloud-native network function (CNF) may collaborate with LEAF switches or server aggregation devices to refine data transmission via adaptable address mapping and forwarding and enhance network scalability and resilience.

Systems, devices, and methods for disaggregating networking components are provided. An example networking chassis includes a first disaggregated server device supported by the networking chassis that includes a first central processing unit (CPU) and a first graphics processing unit (GPU) coupled with the first CPU. The networking chassis further includes a first insertable switch module communicably coupled with the first disaggregated server device that includes first switching chipsets and a first fabric management controller coupled with the first switching chipsets. The first insertable switch module at least partially controls data transmission associated with the first disaggregated server device. The first GPU of the first disaggregated server device is isolated on the first disaggregated server device, supported on the first disaggregated server device in the absence of other GPUs, or is otherwise the only GPU on the first disaggregated server device so as to provide modularity in networking applications.

Exemplary methods and apparatus are provided for implementing a deep learning accelerator (DLA) or other neural network components within the die of a non-volatile memory (NVM) apparatus using, for example, under-the-array circuit components within the die. Some aspects disclosed herein relate to configuring the under-the-array components to implement feedforward DLA operations. Other aspects relate to backpropagation operations. Still other aspects relate to using an NAND-based on-chip copy with update function to facilitate updating synaptic weights of a neural network stored on a die. Other aspects disclosed herein relate to configuring a solid state device (SSD) controller for use with the NVM. In some aspects, the SSD controller includes flash translation layer (FTL) tables configured specifically for use with neural network data stored in the NVM.

The invention provides a network security assessment method and system based on threat intelligence, and the method comprises the steps: obtaining a threat intelligence data set and a to-be-assessed network data set, and carrying out the feature association extraction processing, thereby obtaining a target security feature set; the target security feature set covers threat and component association matching, threat and behavior influence conduction and network component protection capability features, calling a pre-training security assessment model to carry out risk situation analysis on the target security feature set, and generating a network security assessment result including a potential threat triggering probability and a threat influence propagation path, and determining a to-be-evaluated network risk level and key risk point distribution information according to the network security evaluation result, generating a network security evaluation report containing a risk positioning identifier, and outputting the network security evaluation report to the network management terminal to support security policy adjustment operation, so that the network security condition can be comprehensively and accurately evaluated, and the user experience is improved. And the pertinence and effectiveness of safety protection are improved.

Systems, devices, and automated processes are described to provide collection of wireless network status data, such as for a 5G or other mobile network, and to automatically created user interactable dashboards providing various status information regarding the operation of the network. Systems and automated processes may obtain status data from a plurality of network components, for example radio units (RU), distributed units (DU), and centralized units (CU) associated with a cell sites, determine an operational state for each of the network components based on the obtained data, aggregate the determined operational states to determine an operational state of the cell sites, generate a dashboard having an indication of the operational states, provide the dashboard to a user interface, receive a user selection from the dashboard, and provide a report detailing the determined operational state.

Disclosed are techniques for communication. In an aspect, a network component determines that a level of heterogeneity associated with a network layer of a hierarchical network layer arrangement with multiple network layers is above a threshold. In some designs, the heterogeneity level determination may be based on heterogeneity metric(s) reported by device(s) associated with the network layer. The network component may transmit, to at least one device associated with the network layer in response to the heterogeneity level determination, a data reporting instruction associated with the network layer. The network component receives data associated with the network layer from the at least one device in accordance with the data reporting instruction.

Aspects of the disclosure are directed to registration of a target object in a target object database (e.g., maintained by a network component) in association with a target object identifier and a set of attributes associated with the target object. In an aspect, the registration process may be initiated in response to physical presence detection of the target object (e.g., by one or more sensing nodes), where the presence detection can be based on radio frequency for sensing (RF-S) or non RF-S approaches. Such aspects may provide various technical advantages, such as improved target object tracking and identification.

This system provides a technical solution for managing product sales, inventory, and deliveries through QR codes and predictive analytics. It incorporates algorithms that generate and assign unique QR codes to products based on real-time sales data from farmers. These QR codes provide direct links to product information, facilitating efficient inventory management and customer engagement. The system also leverages predictive analytics to forecast product demand by analyzing historical sales data, adjust inventory levels, and optimize delivery driver assignments based on proximity to farms. The hardware supporting the system includes servers, processors, network components, and scanning devices to ensure reliable operation and data synchronization. By automating QR code generation, integrating sales data, and dynamically assigning drivers, the system improves operational efficiency, minimizes errors, and enhances the customer experience.

A system and method are provided for implementing a network component, such as a software-defined wide area network, a firewall, a router, or a load balancer. The network component can be an embedded network edge device that is implemented, e.g., in software, in circuitry, or using hardware acceleration (e.g., a data processing unit (DPU), a smart network interface card (SmartNIC), etc.). The system can include multiple dataplanes, including a primary dataplane and a shadow dataplane. A packet dispatcher relays received data packets to a primary dataplane and the shadow dataplane. The primary dataplane applies a current version of the network component to data packets, and the secondary dataplane applies a new version of the network component to identical replicas of the data packets. A control plane agent compares performance data gathered from the respective dataplanes to perform verification testing on the new version of the network component.

Methods, and systems for authentication via web components. One of the methods includes: submitting a token request, by a browser application, to a first server entity for a token from an API hosted by a second server entity; receiving, at the browser application, the token and a time to live (TTL) for the token from the first server entity, the first server entity having added an API key to the token request to produce an appended token request and forwarded the appended token request to the API at the second server entity, the API at the second server entity having authenticated the API key and returned the token and the TTL to the first server entity, and the first server entity having returned the token and the TTL to the browser application; and sending, by the browser application to the second server entity, a data request with the token.

The invention provides a space-air-ground three-dimensional communication cross-network cooperative reliable transmission method, device and system. The system comprises a generalized service layer, a resource adaptation layer and a fusion network layer, the generalized service layer receives a service request initiated by a user, analyzes the service request to obtain a service attribute, and sends the service attribute to the resource adaptation layer; the resource adaptation layer performs service resource adaptation mapping by using a reinforcement learning algorithm according to the service attribute and the current network state information, generates a service deployment strategy and issues the service deployment strategy to the fusion network layer; and the fusion network layer schedules a corresponding network component according to the received service deployment strategy, and completes path selection, forwarding scheduling and coding strategy application tasks corresponding to the service request. The system has multi-protocol identification and unified packaging capabilities, and can receive multiple data formats and carry out standardized processing. According to the method, multi-path flexible scheduling, network coding and covert channel methods are integrated, and the limitation of an existing communication system in the aspects of protocol compatibility, transmission efficiency and reliability is broken through.

The invention relates to a method for automatically generating a multi-professional pipe network BIM model based on a CAD drawing of a graph neural network, comprising: pipe network CAD drawing information analysis and multi-professional pipe network BIM model generation, the pipe network CAD drawing information analysis comprising graph neural network model construction, CAD drawing analysis based on the graph neural network model, legend table analysis and multi-graph information aggregation; the multi-professional pipe network BIM model is generated based on the extracted pipe network component information, and different reconstruction strategies are adopted for different types of pipe network components. According to the method, an automatic building pipe network CAD drawing analysis technical route combining deep learning driving and knowledge rule constraint is constructed, symbol primitive semantic extraction is carried out based on a graph neural network, high-accuracy symbol and layer automatic identification is realized, a pipe network system expressed by multiple CAD drawings is converted into a graph structure, and the method is suitable for large-scale popularization and application. And entity matching and information aggregation are carried out on a plurality of drawings and a plurality of graph structures, so that an automatic, efficient and high-stability building pipe network system information model reconstruction task is realized.

A modular Computer-On-Fan-Tray-Assembly (COFTA) networking device system includes a networking device chassis housing a circuit board coupled to a plurality of non-networking components and including: a networking processing system coupled to networking ports that are accessible on the networking device chassis, a networking processing system management connector coupled to the networking processing system, a non-networking component management connector coupled to the non-networking components, and a networking software management connector. At least two COFTA systems are each connected to a different subset of the networking processing system management connector, the non-networking component management connector, and the networking software management connector. Each COFTA system provides a respective operating system that manages a different subset of the networking processing system via the networking processing system management connector, the plurality of non-networking components via the non-networking component management connector, and networking software via the networking software management connector.

According to one aspect of the present disclosure, a method of video post-processing may include receiving, by a processor, a plurality of input feature maps associated with an image. The plurality of input feature maps may be generated by a video pre-processing network. The video post-processing method may include inputting, by the processor, the plurality of input feature maps into a first depth-wise separable convolutional (DSC) network of a fast residual channel attention network (FRCAN) component. The video post-processing method may include outputting, by the processor, a first set of output feature maps from the first DSC network of the FRCAN component.

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and / or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

A system and method for the dynamic authentication revocation using privilege assurance of enterprise computer network environments using lateral movement detection and prevention. The system uses local session monitors to monitor logon sessions within a network, generating and verifying event logs and authentication records to ensure the legitimacy of authenticated user sessions and to revoke credentials when an illicit session is detected and further removing potentially compromised network components from the network, halting lateral movement in real-time.

Aspects of the disclosure are directed to ray-tracing (RT) configurations (e.g., RT parameters such as the number of reflections, refractions (transmissions through walls), diffractions, scattering type / modeling, etc., and so on) for a RT procedure associated with modeling one or more wireless channels in an environment. In an aspect, a user equipment (UE) performs the RT procedure based on a model of the environment and a respective RT configuration. The UE transmits a RT report based on the RT procedure to a network component (e.g., a digital twin (DT) entity, location management function (LMF), or sensing management function (SnMF)), which may then utilize the RT report to update a digital twin (DT) of the environment. Such aspects may provide various technical advantages, such as more efficient RT procedures, higher quality RT procedures, more accurate DT modeling, etc.

The invention provides a tailing pond abnormal data monitoring method based on an improved Transform, and belongs to the technical field of tailing pond safety monitoring. Adding a time convolutional network before the self-attention mechanism, performing fine modeling on different time sequence associations by adopting a branch structure to obtain an optimized attention mechanism, and constructing a detection model based on the optimized attention mechanism and the time convolutional network; and training the constructed detection model based on the reconstruction loss function and in combination with a maximum and minimum value strategy, calculating an abnormal score based on the trained detection model, and identifying an abnormal point in the abnormal score. By introducing a time convolutional network component to replace an absolute position coding mechanism in a traditional Transform model, complex long-term dependency in time sequence data is successfully captured, effective fusion of global time sequence information is realized, and the accuracy of anomaly detection is improved. And secondly, a self-attention mechanism module is optimized, the recognition capability of the model on an abnormal mode is enhanced, and the detection performance is further improved.

An apparatus is configured to communicate with multiple network components in a service-based architecture and comprises a memory and a processor. The memory stores multiple configuration commands. The processor is configured to generate a request to establish a communication session between a first network component, a second network component, and a third network component. The first network component exchanges connectivity signals with the second network component and the third network component. The second network component performs first plurality of session operations in response to the connectivity signals exchanged with the first network component. The second network component reports to the third network component that the first session operations are performed. The processor is further configured to, in response to determining that the communication session between the first network component and the second network component is lost, maintain the communication session between the first network component and the third network component.

A method for enhancing security of data, code, and / or network components by introducing a layer of integrity management that adapts to changing conditions within a network. The method may include use of a processor to perform intrusion analysis using an intrusion detection system and / or prevention system, behavioral analysis by accessing user logs to see if behavior is within a normal range, and / or geolocation analysis to see if the piece of data, section of code, and / or network component are within their usual surroundings. When there is a concern of a data breach, leak, and / or hack, the processor may initiate a response that detects other pieces of data, sections of code, and / or network components in proximity, initiates a self-destruct mechanism, triggers hardware to perform outside of operational specifications, and contains the spread of the data breach, leak, and / or hack through shutdown procedures, isolating affected systems, and / or alerting security personnel.

A system comprises one or more devices that implement network functions (NFs). The NFs comprise a network data analytics function (NWDAF) configured to provide first analytics to a policy control function (PCF). In addition, the NFs also comprise the PCF configured to: receive the first analytics from the NWDAF; generate a first policy rule based on the first analytics; and forward the generated first policy rule to a network component to process the first policy rule at the network component.

A reduced capability (redcap) user equipment (UE) is configured to receive a positioning reference signal (PRS) configuration from a network component and perform one or more positioning operations based at least in part on the PRS configuration. A base station is configured to receive, from a location management function (LMF) of a network, a positioning reference signal (PRS) configuration for a reduced capability user equipment (redcap UE) and transmit PRS to the redcap UE based on the PRS configuration.

Systems and methods are provided for increasing space for external connectors on network components mounted on a rack or cabinet. According to one implementation, a Network Element (NE) assembly includes a blade module having a relatively flat form factor. The blade module, for instance, includes at least a front section, a rear section, a left-side section, and a right-side section. When the blade module is mounted in a rack, the front section thereof is arranged at or near a front portion of the rack and the rear section thereof is arranged at or near a back portion of the rack. Furthermore, the NE assembly further includes a first side-plane connector module configured to be coupled with the left-side section or right-side section of the blade module.

System and methods for performing emergency communications availability determinations are disclosed. Network components may be configured to determine the activity status of public safety access points (PSAPs) in a network, the areas the PSAPs are associated with, and their particular function relative to those areas. This information may be provided to user equipment (UEs) that may use this information along with signal data to determine a particular graphical element to present on an interface that indicates the level of emergency communications services available to the UE at the present time.

Disclosed are techniques for wireless communication. In an aspect, a user equipment (UE) or network component may select a measurement window type corresponding to the positioning reference signal (PRS) processing window (PPW) or a measurement gap (MG) for measurement processing of each instance of a positioning frequency layer (PFL) for a positioning session of the UE based on one or more selection criteria.

The invention discloses a multi-model compression deployment method and system based on pruning, a medium and equipment, and belongs to the technical field of artificial intelligence model compression and edge computation.The method comprises the steps that the network layer topological relation of each model to be deployed is extracted, and an internal dependency graph is constructed; iteratively calculating node importance scores, cutting off nodes and edges which have the lowest importance and are not on a critical path in each round, synchronously deleting a network layer and reconstructing a computational graph until the model volume reaches a target threshold value, and obtaining each first model; constructing a cross-model dependency graph of which the edge weight is determined by the data sharing rate and the interaction frequency by taking each first model as a node and the data flow direction between the models as an edge; and screening candidate edges with weights higher than a threshold value, and if the function overlapping degree of the corresponding model pair exceeds a preset threshold value, merging overlapped network components to complete model compression and deployment. By implementing the method, the problem that a plurality of models cannot be efficiently deployed at the edge end of the transformer substation due to limitation of computing power and memory in the prior art can be solved.