196 results about "Linear network" patented technology

Methods and systems for detecting defects on a specimen are provided. One system includes a generative model. The generative model includes a non-linear network configured for mapping blocks of pixels of an input feature map volume into labels. The labels are indicative of one or more defect-related characteristics of the blocks. The system inputs a single test image into the generative model, which determines features of blocks of pixels in the single test image and determines labels for the blocks based on the mapping. The system detects defects on the specimen based on the determined labels.

Apparatus and methods relating to bonded interconnection of local communication networks are disclosed. Bonded communication links, including multiple constituent links that have been bonded together, are terminated to enable communications over the bonded links using bandwidth available on the constituent links. A cross-connect function determines whether received communication traffic is to be forwarded to one or more of a local communication network and the terminated bonded links. The received communication traffic is forwarded in accordance with the determination. The local communication networks may include ring networks, linear networks, or both. The bonded links and links between nodes in a local communication network are bonded DSL (Digital Subscriber Line) links in some embodiments. Bonding of DSL links in this manner can be used, for instance, to extend the reach of DSL service without sacrificing bandwidth.

The invention discloses a network theory based signature scheme which comprises the steps of parameter generation, signature generation and signature verification, wherein the parameter generation is performed mainly by using a trapdoor generating function, the signature generation is performed by using a network vector selecting function, and the signature verification is performed by using the signature length and the network judgment. According to the invention, message vectors are sequentially mapped on each vector of a network base so as to generate a signature, therefore, the signature scheme has Gaussian randomness, and can guarantee that the selected vector does not reveal any information about messages and network private keys. A network signature scheme based secure linear network encoding method effectively merges the network theory based signature scheme and random linear network encoding, therefore; and compared with the traditional secure network encoding method, the method has low-complexity signature operation, and can withstand the pollution attacks under the condition of a quantum computer, thereby increasing the security of network transmission.

A new random linear network coding scheme for reliable communications for time division duplexing channels is proposed. The setup assumes a packet erasure channel and that nodes cannot transmit and receive information simultaneously. The sender transmits coded data packets back-to-back before stopping to wait for the receiver to acknowledge (ACK) the number of degrees of freedom, if any, that are required to decode correctly the information. Provided herein is an analysis of this problem to show that there is an optimal number of coded data packets, in terms of mean completion time, to be sent before stopping to listen. This number depends on the latency, probabilities of packet erasure and ACK erasure, and the number of degrees of freedom that the receiver requires to decode the data. This scheme is optimal in terms of the mean time to complete the transmission of a fixed number of data packets. It is shown that its performance is very close to that of a full-duplex system, while transmitting a different number of coded packets can cause large degradation in performance, especially if latency is high. Also described herein is the throughput performance of the novel system and technique along with a comparison to existing half-duplex Go-back-N and Selective Repeat ARQ schemes. Numerical results, obtained for different latencies, show that the novel system and technique described herein has similar performance to the Selective Repeat in most cases and considerable performance gain when latency and packet error probability is high.

The invention relates to a distributed network application layer multicast routing method based on network coding. Directed at data distribution of a single information source multiple information sinks distributed network, multiple mutual disjointed data distribution paths are established between a source node and each destination node, each distribution path can be established on the principle of maximal access bandwidth, minimal transmission delay and lowest transmission cost. At the same time, each destination node can be used as a middle node of other destination nodes to participate in multicast path building. As for the public links of different destination nodes, stochastic linear network coding is carried out on multiplex data flows on nodes connected with the public links, avoiding transmission collision and conflict between data flows. The invention has the advantages that the multicast communication transmission capacity of a distributed network is effectively improve, the usage efficiency of network resources is enhanced, and the total cost of network transmission is lowered.

The invention discloses a fault-tolerant control method for a networked control system with time-varying delay. In view of conditions of parameter perturbation, time-varying delay, external disturbance and random failure happening to an actuator, a discrete time closed-loop nonlinear networked control system model is firstly built, a Lyapunov-Krasovskii function with the delay information is then built, a Lyapunov stability theory and a linear matrix inequality analysis method are used, sufficient condition for asymptotic stability of the nonlinear networked control system and existence of an H-infinity fault-tolerant controller are obtained, a Matlab LMI toolbox is used for solution, and a gain matrix for a non-fragile fault-tolerant controller is: K=K<->P; and conditions for optimizing the minimum disturbance rejection rate gamma is given, and the controller gain matrix K<*> optimized under the minimum disturbance rejection rate gammamin= square root of e is acquired. The condition of time-varying delay existing in the system is considered, the time-varying delay is analyzed and processed based on a free-weighting matrix method, and the conservation is reduced.

The invention provides a fault detection method of a nonlinear network control system based on an event triggering mechanism, and relates to the technical field of network system fault detection. Themethod comprises the following steps: firstly, establishing a T-S fuzzy model of the nonlinear network control system, setting an event triggering condition, establishing a fuzzy fault detection filter model, establishing a fault weighting system, and then establishing a fault detection system model; selecting an appropriate residual evaluation function and a detection threshold according to the fault detection system model, and detecting whether a fault of the nonlinear network control system occurs; and finally, further designing a parameter matrix and an event triggering matrix of a fault detection filter according to the stability of the fault detection system and sufficient conditions of existence of the fault detection filter. By adoption of the fault detection method of the nonlinear network control system based on the event triggering mechanism provided by the invention, the robustness to external disturbance and communication delay is greatly improved, and the limited networkresources and computing resources can be saved by the application of the event triggering mechanism.

Computer-implemented techniques for verifying ESD device connectivity in an IC include the steps of: receiving an input dataset including layout parameters corresponding to the integrated circuit; identifying ESD devices based at least in part on the input dataset; extracting devices and parasitic elements in at least a portion of the integrated circuit based at least in part on the input dataset; generating a file including connectivity information and dimensional characteristics for extracted devices and parasitic elements associated with at least ESD protection circuitry in the integrated circuit; identifying at least one ESD test based on the identified ESD devices and on connectivity to the respective ESD devices; and performing a linear network analysis for each identified ESD test based at least in part on the netlist evaluated under ESD conditions, the identified ESD devices being removed from the network analysis, the network analysis being used to ensure that current densities through respective conductive connections included in the identified ESD test are less than or equal to a first prescribed threshold, and/or voltage drops associated with the respective conductive connections are less than or equal to a second prescribed threshold.

The invention provides a network flow prediction method and device based on a cognitive network. The method comprises the following steps of: carrying out least square method processing on an input signal X(t); outputting prediction sample data Y(t); carrying out wavelet transformation on the Y(t); decomposing the Y(t) into components with different frequency compositions; carrying out wavelet transformation on a coefficient sequence {D1(k), D2(k), ...... DL(k), AL(k)} at the k moment; training the network with the component {D1(k), D2(k), ...... DL(k)} as input of an Elman network and a wavelet coefficient {D1(k+T), D2(k+T), ...... DL(k+T)} at the k+T moment as output; training the network with the component of {AL(k)} as input of a linear network and {AL(k+T)} as output; training the network with each trained wavelet component {D1(k+T), D2(k+T), ...... DL(k+T), AL(k+T)} as input of a BP network and the original flow time {f(k+T)} at the k+T moment as the network output; obtaining the prediction output; introducing an LMS (Least Mean Square) algorithm to pre-process the input sample aiming at advantages and disadvantages of the traditional flow model and prediction method; inputting the input sample to a WNN (Wavelet Neural Network) prediction model, therefore, the over-fitting problem in the traditional model is solved, and a more accurate model and prediction are provided for the network flow.

The invention discloses a robust video transmission method combined with network coding on an end system and a relay node. An unequal protection algorithm of scalable video coding, a prioritization partition method of scalable code streams, an interleaving and packaging algorithm of video streams and a video quality distortion model are designed, and the video quality distortion model is optimized and solved to obtain an optimal redundancy allocation scheme. A sending end adds proper redundancy on a compression source stream to be transmitted through random linear network coding, and the reliability of video transmission is ensured from the angle of the end system. The relay node reduces energy consumption of network bandwidth resources, and improves efficiency of the video transmission and mistake-resisting performance through the random linear network coding. The robust video transmission method ensures that a receiving end can obtain layered code streams according to the sequences of importance of the scalable code streams, improves the quality of the video transmission, and can adapt to requirements for band widths, computing power and resolution ratios of different terminals in the network.

A non-linear modeling solving method based on matrix index electromagnetic transient simulation comprises the steps that first, under a state analysis frame, electromagnetic transient simulation models of a linear network and a grid-connected non-linear element are established, and then through subsystem interconnection relation, an overall electromagnetic transient simulation model of an electric power system to be studied is formed; after relevant simulation parameters such as simulation step sizes and convergence precision are set, a simulation program is started; in each simulation step size, a fixed point iterative method is used for solving a non-linear equation comprising a matrix index function, and the results are used as a state variable of the current moment; an output vector yn + 1 is obtained through an output equation, output files are written in, and a step size is pushed forward in a simulation mode; and iteration is carried out in sequence until simulation is over. The great numerical precision and the rigidity processing capacity of a matrix index integration method are kept, general modeling and simulation capacity is achieved on the non-linear performance of an electric power system element, and the application range in the electric power system electromagnetic transient simulation field of the matrix index integration method is expanded.

The invention discloses a joint source-network coding-based gradable video stream transmission method, which is used for providing a reliable transmission mechanism for gradable video streams in the wireless environment. The method comprises the following steps of: (1) dividing the gradable video streams into subcode streams with different priorities; (2) coding the subcode streams with different priorities by using LDPC codes with different coding rates to realize unequal error protection; and (3) performing random linear network coding on the coded LDPC code words in the transmission process. The degradable video streams are divided into the subcode streams with the different priorities by adopting network coding so as to ensure the effective use of the bandwidth resource and the quality of the decoding streams is maximized; meanwhile, important data is protected by more redundant information, and the throughput of the transmission system and the error recovery capacity of the transmission system are improved, so that the gradable code streams are reliably transmitted in the wireless environment.

The invention belongs to the network encoding based data distribution technical field, in particular to a data distribution method with the combination of interlaced coding and network encoding. The novel network coding data distribution method provided by the invention is that the coding is synchronously made on a spatial domain and a time domain: the spatial domain adopts linear network coding which can reach the network capacity, and the time domain adopts linear error coding and interlaced coding. The network coding method provided by the invention has the function of forward correction to the noise error produced on the chain during the transmission compared with the general network coding.

The invention discloses a data transmission optimization method based on linear network coding. The method comprises the steps that 1, data information needing to be transmitted is grouped in a sending source, and the grouped data is coded through a linear network coding technique; 2, the grouped data is forwarded to a relay vehicle to be subjected to a recoding operation and forwarded; 3, if a next-hop vehicle is not a destination vehicle, the step 2 is repeated; 4, when the destination vehicle receives the data, linear correlation calculation is firstly conducted, and if the data does not have the linear correlation, the data is received; 5, a decoding operation is conducted on the received data to obtain original data through recovery, and if decoding cannot be conducted, the condition is fed back to the last-hop vehicle; and 6, if the destination vehicle cannot obtain the original data through decoding, the condition continues to be fed back to the last-hop relay vehicle until the original data is obtained through decoding. According to the method, the packet loss rate generated in the data transmission process can be effectively decreased, and the data retransmission times are decreased.

The embodiment of the invention provides an expression recognition method and device, electronic equipment and a storage medium. The expression recognition model comprises a convolutional neural network model, a full connection network model and a bilinear network model. The method comprises steps of in the expression recognition process, preprocessing the to-be-identified image to obtain a face image and a key point coordinate vector; carrying out operation on the face image through a convolutional neural network model to output a first feature vector, performing operation on the key point coordinate vector through the full connection network model to output a second feature vector, performing operation on the first feature vector and the second feature vector through the bilinear networkmodel to obtain second-order information, and further obtaining an expression recognition result according to the second-order information. In the process, the prior expression information containedin the face key points is considered, the robustness to postures and illumination is good, and the expression recognition accuracy is improved. Furthermore, when the expression intensity is low, the expression can be correctly identified.

A technology is provided for improving computer network throughput. Data located in memory of a processing device may be identified. The data packets located in the memory may be sent through a tunneling interface to encapsulate the data packets using a tunneling protocol on a first computing device. Alternatively, the data packets can be sent through a split proxy interface system. The data packets received in the interface may also be encoded using random linear network coding (RLNC) to form encoded packets, using a processor. Further, the encoded packets may be sent across a packet network to a second computing device.

The invention discloses a contact graph routing algorithm based on random linear network coding in deep space communication. The contact graph routing algorithm comprises the following steps: a sourcenode processes a data packet to be sent by adopting random linear network coding; the source node selects a plurality of link disjoint paths according to the predicted contact graph and transmits theencoded packets on the plurality of paths. the received data packet is directly forwarded at the intermediate node and re-encoded before forwarding; the destination node receives the data packets anddecodes them, and feeds back the packets which cannot be decoded successfully to the last hop node set, the last hop node set sends the encoded packets which are helpful to the destination node decoding, so that the destination node can decode the packets quickly and reduce the transmission delay. The invention adopts random linear network coding to send the original data, and controls the redundant data packets in the network through the link state to ensure that the destination node can be decoded while reducing the waste of network resources and reducing the decoding waiting time delay ofthe node.

The invention discloses a random-linear-network-code (RLNC)-based back-off frequency spectrum prediction method in a cognitive radio (CR) network. With utilization of frequency spectrum predictability caused by RLNC introduction into a main user channel of cognitive radio and a prediction effect on a frequency spectrum by a hidden markov model, a back-off prediction method is introduced to carry out frequency spectrum state prediction at a next time slot. With the method, defects that frequency spectrum state is switched frequently and the spectrum hole can not be sensed easily in the traditional CR network and the reliability is low of the existing frequency spectrum prediction algorithm can be overcome. With introduction of back-off prediction method, the frequency spectrum state prediction at a next time slot is carried out by using the frequency spectrum predictability caused by RLNC introduction into the cognitive radio network and the prediction effect on the frequency spectrum by the hidden markov model, so that frequency spectrum prediction reliability is improved.

Linear networks are divided into a plurality of regeneration repeating sections which have nodes having regeneration repeaters arranged therein at both ends, devices such as linear repeaters or OADMs are arranged at the nodes positioned in the regeneration repeating sections, a plurality of assumed paths assumed as a result of arranging the devices are extracted in the regeneration repeating sections, and transmission permission/inhibition is determined for each assumed path, and after that, the determined transmission permission/inhibition is displayed to allow a user to perform settings again.

The invention discloses an intelligent NIPS (Network Intrusion Prevention System) framework for quantifying a neural network based on a mobile agent (MA) and a learning vector. The NIPS framework comprises a data preprocessing unit, a construction classifier unit, an expert system unit and a knowledge base, wherein the data preprocessing unit is used for collecting network data streams and selecting an input sample and a test sample for the neural network from the collected network data streams; the construction classifier unit is used for making use of an input and learning sample MA-LVQ (Mobile Agent-Learning Vector Quantization) neural network classifier and performing class test to form a knowledge base; the expert system unit is used for interacting with the knowledge base according to a known security policy to compare and classify actions provided by the data streams and action descriptions in the knowledge base so as to determine an output result; and the knowledge base comprises a normal action description and an abnormal action description and is updated by interacting through the expert system unit. By adopting the NIPS framework, a better classifying effect can be achieved by a linear network, and the stronger limit on linear separability of data required by the linear network can be avoided effectively under the action of a competition layer; and the NIPS framework is more practicable and extensive.

QKD system networks ( 50, 200, 300 ) and methods of communicating between end-users (P 1 , P 2 ) over same are disclosed. An example QKD system network includes a first QKD station (A 1 ) and a second QKD station (A 2 ) with a relay station ( 58 ) in between. The relay station includes a single third QKD station (B) and an optical switch ( 55 ). The optical switch allows the third QKD station to alternately communicate with the first and second QKD stations so as to establish a common key between the first and second QKD stations. The end-users are coupled to respective QKD stations A 1 and A 2 . A secret key (S) is shared between P 1 and P 2 by QKD station B being able to independently form keys with A 1 and A 2 . This basic system, represented as P 1 -A 1 -B-A 2 -P 2 , can be expanded into more complex linear networks, such as P 1 -A 1 -B 1 -A 2 -B 2 -P 2 with B 1 and A 2 making up the relays. The basic QKD system network can also be expanded into multi-dimensions.