30 results about "Permutation matrix" patented technology

A method for constructing a permutation matrix or a check matrix, a processing device, a storage medium and a coding method. The method for constructing the permutation matrix comprises: obtaining a base matrix for the permutation matrix; and performing lifting on the base matrix to obtain the permutation matrix. The lifting on the base matrix to obtain the permutation matrix comprises: obtaining a modulus graph of the base matrix, obtaining each macro ring in the modulus graph, and for each macro ring in the modulus graph, determining the size of a short ring corresponding to the macro ring in a Tanner graph of a check matrix corresponding to the permutation matrix from an equivalent cycle value ECS of the macro ring, and determining whether at least one cycle value in the macro ring needs to be set according to the size of the short ring. The method can more efficiently obtain a required permutation matrix.

The application provides a distributed security outsourcing computing processing method based on sparse matrix transformation, and relates to the technical field of computers, and the method comprises the following steps: acquiring a first matrix and a second matrix; randomly generating a key set comprising a binary permutation matrix, a sparse triangular matrix and a random vector; performing primary encryption on the first matrix and the second matrix based on the binary permutation matrix; performing secondary encryption on the primary encrypted matrix based on the sparse triangular matrix; distributing the secondary encrypted first secondary encrypted matrix to each edge end in an integrated manner, distributing the second secondary encrypted matrix to each edge end in an even manner after dividing the second secondary encrypted matrix into column vectors according to columns, and enabling the edge end to perform a subtask calculation operation by using the received matrix and return the result to the user end; receiving the calculation results of the edge ends, and aggregating the results; verifying the reliability of the aggregated results by using the random vector, and decrypting the final calculation result. The scheme can improve the security and timeliness of outsourcing computing.

The application discloses a method for calculating equivalent resistance based on a many-to-many mode, which comprises: filling a conductance matrix of a corresponding resistance network to obtain an extended matrix based on a node to be solved of an equivalent resistance, preordering the extended matrix to determine a permutation matrix P; performing LDL T decomposition on the extended matrix to calculate a lower triangular matrix L and a diagonal matrix D required for calculating the equivalent resistance and determining a non-zero element position in a sparse matrix F; calculating a matrix element of a target matrix Z according to the non-zero element position in the sparse matrix F and the lower triangular matrix L and the diagonal matrix D; and calculating the equivalent resistance according to the target matrix Z. For this purpose, the application does not need to solve all matrix elements, but only needs to calculate matrix elements consistent with the non-zero element position distribution of F, i.e. Z, so as to effectively reduce the calculation amount. Since Z is a sparse matrix and a general dense matrix, the total calculation amount is greatly reduced due to the fewer non-zero elements of Z.

The application relates to the field of natural language processing, and discloses a cross-language entity alignment method and system based on relationship semantic enhancement, which comprises the following steps: constructing entity initial embedding and relationship embedding representation of a cross-language knowledge graph; a key neighbor screening stage; screening key neighbor entities with structural matching and semantic matching based on an entity similarity matrix and a relationship similarity matrix and in combination with a preset semantic correlation threshold; neighbor-enhanced entity embedding updating; adopting a graph attention network to fuse semantic features of target entities and key neighbors thereof, and generating context-aware entity embedding; semantic consistency alignment propagation: first-order entity feature alignment and second-order relationship structure alignment are jointly optimized through a shared permutation matrix; dynamic seed expansion iteration: high-confidence unaligned entity pairs are dynamically identified according to Manhattan distances of entity embedding, entity pairs with distances lower than a threshold are added to a seed set, and the foregoing steps are iteratively executed until convergence.

The application discloses a method for constructing an LDPC code check matrix, comprising: setting at least one of row weight λ and column weight γ of the LDPC code check matrix as a fixed value (constant); setting z of a dimension z×z of a cyclic permutation matrix CPM as a predetermined value; calculating a base matrix of the corresponding LDPC code check matrix based on LDPC code length, code rate, row weight λ and column weight γ, and the cyclic permutation matrix CPM of the dimension z×z; and obtaining the LDPC code check matrix through expansion of the base matrix. According to the technical scheme provided by the application, the code length of the LDPC code is shortened, so that the transmission of small data can be performed in less OFDM symbol (as low as 1 symbol), thereby effectively reducing the time delay and cost. The regular / quasi-regular LDPC code has a very low error code platform, and the reliability of transmission is improved.

An apparatus for processing a low density parity check code and a method thereof are disclosed. The method comprises the following steps: identifying a parity check matrix; encoding the code block based on the parity check matrix; and transmitting at least a portion of the encoded code block. The parity check matrix includes an information portion and a parity check portion. The parity portion comprises: a first portion (B) comprising a plurality of first permutation matrices, a second portion (D) comprising a second permutation matrix, a third portion (E) comprising a third permutation matrix, a fourth portion (T) comprising a plurality of unit matrices arranged diagonally in the fourth portion, and a plurality of fourth permutation matrices arranged diagonally in the fourth portion, the plurality of fourth permutation matrices are arranged below the plurality of unit matrices. And the fourth part (T) is in a square matrix form. A matrix according to (E) (T-1) (B) + D corresponds to a cyclic permutation matrix that is not a unit matrix.

A method for providing assurance of data association comprises formulating a track permutation matrix of measurement statistics based on sensor measurement returns and receiving a first set of correlated tracks from a correlator module (set a), identified as originating from a target. A second set of correlated tracks from the track permutation matrix (set b) is selected, based on branching and bounding techniques. The method reframes verification of a data association framework into a linear inequality, with a first portion based on set a, and a second portion based on set b. The method performs a verification procedure to determine whether properties of set a are bounded by properties of set b. If properties of the correlated tracks within set a are bounded by, or are less than or equal to, properties of the correlated tracks within set b, then the correlated tracks are verified.

Certain example embodiments may relate to a 5G and / or 6G communication system for supporting higher data transmission rates than 4G communication systems such as LTE. A method performed by a transmitter in a communication system, which method may include: determining the number of input bits; determining a base matrix on the basis of the number of input bits; determining a lifting size (Z) on the basis of at least one of the number of input bits or the base matrix; determining a parity check matrix on the basis of at least one of the base matrix or the lifting size (Z); and performing encoding on the basis of the parity check matrix and the input bits. The size of the parity check matrix is Z×(k+1) Z, the last column block of the parity check matrix corresponds to parity bits, a Z×kZ column block of the parity check matrix corresponds to information word bits, all of modulo-Z values of differences in cyclic shift values corresponding to circulant permutation matrices constituting a Z×Z circulant matrix are different, and all of the circulant matrices corresponding to the information word bits are composed of three or more circulant permutation matrices.

Example embodiments of the present disclosure are directed to methods, devices, apparatuses and computer readable storage medium for start stop bit compressed permutation matrix In the method, a first apparatus receives, from a second apparatus, a first permutation matrix associated with at least one start / stop bit for transmission of data sequences to the first apparatus. The first permutation matrix is obtained by compressing a second permutation matrix indicating an order of the data sequences with the at least one start / stop bit. Furthermore, the first apparatus reorders the data sequences based on the first permutation matrix. Thereby, the efficiency of permutation matrix compression can be improved.

The application discloses a search method and system for a Costas sequence based on multithreading, and the method comprises the following steps: inputting the order of the Costas sequence to be searched; establishing a framework data structure of a bidirectional linked list, and generating a permutation matrix corresponding to the order through recursive iteration; generating a task for judging a check matrix corresponding to the permutation matrix, and submitting the task; submitting the task received by S2 and S3, creating a thread pool by using a thread pool technology, and performing multithread operation by combining a task stealing technology, so as to accelerate the judgment on the check matrix; and collecting the tasks, finally obtaining the number of the Costas sequences and the search time, and comparing with a traditional multithread search. The search method and system for the Costas sequence based on the multithreading realize efficient utilization of threads, improve the search speed, and provide a wider application prospect.

This invention relates to the field of mine safety monitoring technology, specifically disclosing a method for deploying mine fire monitoring sensors. The method includes: establishing a three-dimensional model; acquiring fire smoke propagation data through numerical simulation to construct an original spatiotemporal concentration matrix; extracting threshold response time feature vectors and maximum time-varying gradient feature vectors from the original spatiotemporal concentration matrix; performing nonlinear logarithmic mapping and centering on the original spatiotemporal concentration matrix, and constructing a spatial feature basis matrix through singular value decomposition; concatenating the spatial feature basis matrix, threshold response time feature vectors, and maximum time-varying gradient feature vectors, introducing feature weight penalty coefficients, and constructing an augmented feature matrix; performing orthogonal triangular decomposition with column pivot selection on the transpose of the augmented feature matrix, and determining the optimal monitoring point based on the row index of the permutation matrix, thereby achieving effective monitoring and early warning of the initial stage of a fire. This improves the ability to capture low-concentration signals in the initial stage of a fire and the accuracy of reconstructing the overall concentration field.

An apparatus receives a first permutation matrix associated with at least one start / stop bit for transmission of data sequences to the first apparatus. The permutation matrix is obtained by compressi

The application relates to a clustering method and device for misaligned multi-views, electronic equipment and a storage medium, comprising the following steps: constructing an adjacency graph for each view, and matching the graph structure of the adjacency graph of a non-reference view with the adjacency graph of a reference view through a permutation matrix; inputting each view and the adjacency graph thereof into a first-layer graph convolution network to extract a deep representation of each view; constructing a local adjacency graph of each view based on the adjacency graph and the deep representation of each view, and updating the number of neighbors of the local adjacency graph at least once every training period; inputting the deep representation and the local adjacency graph of each view into a second-layer graph convolution network to extract a clustering indication matrix of each view, training and optimizing the permutation matrix based on a unified loss function, simultaneously learning adaptive weights of the views, and obtaining a clustering result, so that a consistent clustering structure across views can be efficiently learned under the condition that samples are misaligned, and the accuracy and robustness of the misaligned multi-view clustering task are improved.

The invention provides a privacy protection homomorphic permutation method and system based on permutation factorization, and belongs to the field of computer security and cryptography. The method comprises the steps of obtaining permutation matrix representation of target permutation, and defining an ideal permutation decomposition form; searching all factor pairs meeting a depth 1 ideal decomposition form for the permutation matrix by adopting a depth 1 ideal decomposition search algorithm; traversing all factor serial numbers, and replacing the permutation matrix with a left factor in a factor pair corresponding to a serial number when iteration is performed to the factor serial number; next round of depth ideal decomposition search is carried out through iteration, and a maximum depth decomposition scheme is determined; and when the maximum decomposition depth is reached, the scheme corresponds to a permutation matrix chain: iteratively acting a permutation matrix in the permutation matrix chain obtained by decomposition on a secret state data vector to realize homomorphic permutation. According to the method, the homomorphic rotation operation frequency is reduced, the calculation load of homomorphic linear transformation is reduced, and the execution efficiency of the privacy protection calculation task is improved.

A discrete cosine transform processing device includes an adder, a register, multiple multiply-accumulate operation circuits, an accumulator, an operation control circuit and an output circuit. The adder processes first input data based on a first permutation matrix to generate second input data. The register stores an operation matrix. The operation control circuit configures the multiply-accumulate operation circuits and the accumulator based on a matrix multiplication operation corresponding to a unit dimension, such that the operation matrix and the second input data are multiplied and accumulated by the multiply-accumulate operation circuits and the accumulator to generate a first operation result. The output circuit processes the first operation result based on a second permutation matrix to generate a second operation result, wherein the first permutation matrix, the operation matrix, and the second permutation matrix are derived from decomposition of a discrete cosine transform matrix.

The invention belongs to the technical field of data encryption, and relates to a symmetric white box encryption method based on a substitution-permutation network structure. The method comprises the following steps: step 1, generating a white box assembly; step 2, encryption; and step 3, decryption. The above steps use eight algorithms, including a pre-processing matrix generation algorithm, a front external coding matrix generation algorithm, a post-processing matrix generation algorithm, a rear external coding matrix generation algorithm, a T table generation algorithm, an M matrix generation algorithm, an encryption algorithm and a decryption algorithm. According to the method, substitution and key XOR operation are converted into a pre-calculated lookup table, random secret components are introduced into the lookup table, a permutation matrix is expanded, and the structure of an encryption internal state is changed, so that an attacker is difficult to recover a key or decrypt data through a white-box attack means. Compared with an existing white-box AES scheme, the method has the advantages that compatibility with a standard decryption algorithm is kept, all existing white-box attacks can be resisted, and white-box safety is achieved.

The invention relates to the technical field of medical informatization and medical data processing, and discloses an airway stenosis treatment data standardized storage method. An airway three-dimensional medical image is converted into a topological graph, an adjacency relation matrix, a node attribute matrix and an edge attribute matrix are constructed in sequence, multi-dimensional structure features of each node are calculated, standard sorting is executed to generate a permutation matrix, and then a unified standardized structure coding vector is formed through rearrangement and vectorization splicing. A standardized storage record is generated in combination with patient identification and time, fingerprint verification is carried out, and the problems that in traditional clinical data storage, structural information is difficult to completely retain and retrieve, and context is prone to being lost are solved. According to the method, the structure can be recovered without secondary reconstruction through matrix and vectorization processing, the sequence difference of different case nodes is eliminated through unified sorting, high-dimensional coding after vectorization saves storage space and is compatible with a vector retrieval library, and meanwhile, the integrity and safety of data are ensured through fingerprint verification.

A computer architecture for matrix-matrix multiplication, where an output C equals matrix B multiplied by matrix A, comprises: a systolic array of processing elements configured to: load stationary values from a permutated matrix A, which is based on a matrix A having columns with indexes 0 to N-1 and the permutated matrix A is formed by shifting and rotating each column, by the column index; load, in a first direction, each row of values from a matrix B; multiply the stationary value from the permutated matrix A with the value from the matrix B to produce a product; accumulate the product with a partial sum input received from another processing element; and output a partial sum output resulting from the accumulation; wherein the processing elements are configured to transfer the values from the matrix B to an adjacent row via a diagonal connection with respect to the first direction.

The invention provides an incremental multi-view clustering method and system based on anchor point alignment, and belongs to the technical field of data analysis mining, and the method comprises the following steps: incrementally receiving a multi-view data stream, and uniformly mapping each view data to a public feature space by using a Gaussian kernel function; initializing a consensus anchor map and an anchor point feature matrix for the first view; for the tth view which arrives subsequently, taking a historical consensus matrix as guidance, solving a joint optimization problem, and synchronously learning an anchor map matrix, an anchor point feature matrix and a permutation matrix for alignment of the tth view; the joint optimization problem minimizes the sum of a data reconstruction error, an anchor image alignment error and a key semantic reconstruction error so as to ensure cross-view semantic coherence; according to the method, the consensus is solved and updated by adopting an alternating optimization strategy, and finally the consensus anchor graph is clustered, so that efficient and accurate clustering of flow type multi-view data is realized, and the method has linear time complexity and excellent clustering performance.

This invention proposes a reconfigurable metasurface beamforming design method based on CGAN and Gumbel-Sinkhorn. This method constructs a conditional generative adversarial network (GAN) as the inverse design network. The generator takes the target beam pattern as a conditional input to generate the corresponding encoding matrix. The Gumbel-Sinkhorn operator is introduced to discretize this continuous matrix, transforming it into an approximately doubly random permutation matrix. The encoding is sorted and selected by multiplying this permutation matrix with a predefined structure vector (SV), outputting a discrete 0 / 1 metasurface encoding array. A noisy forward prediction network is trained to predict the beam pattern corresponding to a given encoding array. In the training of the inverse design network, the discretized encoding matrix output by the generator is input into the noisy-trained forward prediction network. The mean square error between the predicted beam pattern and the target beam pattern is calculated as the target loss term to optimize the network parameters and accurately learn the complex mapping relationship from the beam pattern to the encoding array.

The invention relates to the technical field of artificial intelligence, and particularly discloses an inference attack-oriented federated learning privacy protection method and system, and the method comprises the steps: all clients secretly negotiate a unified base permutation matrix before the starting of federated learning training; generating respective private permutation matrixes, and calculating correction factors based on the basic permutation matrixes and the private permutation matrixes; in the first round of training, the client uses a private permutation matrix to carry out structure disorder processing on a local gradient, and compresses the correction factor and embeds the correction factor into a frequency domain of the disorder gradient; extracting and recovering the correction factor based on the received protected gradient; and aligning the gradients with out-of-order structures sent by the clients to the unified sequence based on the recovered correction factors, and carrying out aggregation. According to the method, a double replacement mechanism is introduced, the gradient structure is disrupted on the premise that all original values and positions are reserved, and it is ensured that honest and curious servers and external eavesdroppers cannot interpret local updating.

The invention relates to the technical field of intelligent manufacturing and industrial engineering, and provides a production line layout optimization method based on a constraint perception Transform and a soft permutation matrix, and the method comprises the steps: obtaining the equipment set information of a production line and a preset process constraint set, and converting the equipment set information into equipment embedding features; inputting the equipment embedded features into a constraint perception Transform model, extracting global dependency features through an encoder of the constraint perception Transform model, and generating position distribution information of each piece of equipment on all layout positions based on the global dependency features; according to the position distribution information, generating a soft permutation matrix through differentiable Sinkhorn iteration processing; based on the soft permutation matrix, determining a discrete distribution relation between the equipment and the layout position through a Hungary algorithm, and generating a hard layout scheme according to the discrete distribution relation; and calculating the violation degree of each process constraint according to the soft permutation matrix and generating constraint loss, and forming a multi-task loss function by the constraint loss and the layout quality prediction loss.

The application provides a privacy-protected longitudinal federated boosting tree method and relates to the technical field of longitudinal federated learning; the method comprises the following steps: combining secret sharing binary matrix and gradient information to obtain a shared matrix and secret sharing; each training terminal performs row permutation on the shared matrix based on a permutation triple and secret shares the permutation result; linear gain scores of candidate splits are calculated and secret shared, and an indication vector corresponding to the maximum linear gain score is calculated; according to the number of candidate splits, corresponding columns are selected from the matrix, and the indication vector is used to calculate the sample and the permutation matrix of the child node; the splitting step is recursively executed to complete the splitting from the root node to the leaf node, the weight of the leaf node is calculated and stored in a preset set. The binary permutation matrix of the root node of the next decision model is updated based on the weight of the leaf node, and the calculation is continued until all decision tree models are generated; the method can solve the problem of low efficiency of the existing longitudinal federated gradient boosting tree protocol.

The application discloses a mobile multi-agent knowledge migration method based on a permutation strategy network and relates to the technical field of multi-agent reinforcement learning. The method comprises the following steps: embedding a permutation invariance strategy network and a permutation homomorphism strategy network into a super network framework, dynamically generating input layer and output layer weight matrices through the super network, and establishing a dynamic adaptation relationship between a joint state-action space and the size of an agent and environmental changes; introducing permutation matrix characteristics to realize decoupling of agent sequence independence and task target responsiveness, and optimizing strategy network parameters through a centralized training-distributed execution architecture; constructing a knowledge migration model containing permutation invariance and homomorphism constraints; and realizing efficient migration of strategies between similar domain tasks. The application solves the problem of low efficiency of knowledge migration caused by changes in the size of an agent and dimension explosion of a joint state-action space in a dynamic complex environment.

This invention provides a fixed-point matrix FFT calculation method based on memristors, belonging to the fields of signal processing and image processing technology. First, a nonlinear voltage-controlled memristor model is constructed, simulating state transition behavior in logic operations through a dynamic adjustment mechanism to realize the function of basic logic gates. Second, an FFT algorithm based on matrix block optimization is designed, decomposing the FFT calculation into small-scale matrix operation units and optimizing computational efficiency by combining Kronecker product and permutation matrix. This is then recursively extended to higher-point FFT operations. Simultaneously, a full adder and multiplier are designed based on memristors to achieve efficient fixed-point operations. Finally, the method is applied to image FFT processing to obtain the processing results. This invention achieves higher computational efficiency and lower energy consumption while maintaining computational accuracy, and can be widely applied in image processing, signal analysis, scientific computing, and other fields, possessing significant practical value and application prospects.

The application discloses an encoding method and a storage system, relates to the technical field of encoding, and comprises the following steps: a plurality of maximum distance separable codes are obtained by encoding a plurality of to-be-encoded code words, so that the check bits of the maximum distance separable codes comprise data blocks of system bits, the failure of a storage node corresponding to the system bits can be repaired by data of a storage node of the check bits, the symmetric elements on both sides of the main diagonal of a permutation matrix are overlapped with each other, the data blocks of the check bits of the maximum distance separable codes are updated, the failure of a storage node corresponding to the check bits can be repaired by data of a storage node corresponding to other check bits, in the repairing process, only part of the data blocks in the k storage nodes need to be downloaded to repair the data of the failed node, the data blocks in the k storage nodes do not need to be downloaded to repair the data of the failed node, the number of the data blocks required for repairing is greatly reduced, and the repairing bandwidth is reduced.

The invention relates to an encoding and decoding method suitable for a 3D TLC NAND flash memory. The efficient and reliable reading and writing of 3D TLC NAND flash memory data under the condition of dynamically adjusting a code rate are realized. According to the constructed 3D TLC NAND flash memory channel model, a first basis matrix is constructed based on a finite field, and the first basis matrix comprises a plurality of sub-matrixes; the positions and the number of sub-matrixes in the first basis matrix are adjusted and expanded, a second basis matrix is obtained, the second basis matrix comprises a plurality of sub-matrixes and a row matrix composed of the sub-matrixes, the number of rows of the row matrix in the second basis matrix is dynamically adjusted according to a preset RBER threshold value, and the adjusted second basis matrix is obtained; performing cyclic permutation matrix expansion on elements in a sub-matrix in the adjusted second basis matrix to obtain a check matrix; performing matrix multiplication according to the information bit of the to-be-coded codeword and the check matrix, calculating to obtain a check bit, and generating to obtain a coded codeword according to the check bit and the information bit; and decoding the coding codeword according to the check matrix to finally obtain a correct information bit.

The invention relates to an unaligned multi-view clustering method and device, electronic equipment and a storage medium, and the method comprises the steps: constructing an adjacent graph for each view, and enabling the graph structure of the adjacent graph of a non-reference view to be matched with the adjacent graph of a reference view through a permutation matrix; inputting each view and the adjacent graph thereof into a first-layer graph convolutional network, and extracting a depth representation of each view; constructing a local adjacency graph of each view based on the adjacency graph of each view and the depth representation, and updating the number of neighbors of the local adjacency graph at an interval of at least one training period; and inputting the depth representation of each view and the local adjacency graph into a second-layer graph convolutional network, extracting a clustering indication matrix of each view, training and optimizing a permutation matrix based on a unified loss function, and learning adaptive weights of the views at the same time to obtain a clustering result. The cross-view consistent clustering structure is efficiently learned, and the accuracy and robustness of the misaligned multi-view clustering task are improved.

This invention relates to the field of medical informatics and medical data processing technology, and discloses a standardized storage method for airway stenosis treatment data. By converting a three-dimensional medical image of the airway into a topological graph, and sequentially constructing an adjacency matrix, a node attribute matrix, and an edge attribute matrix, the multidimensional structural features of each node are calculated, and a standardized permutation matrix is ​​generated through normalized sorting. Then, a unified standardized structural encoding vector is formed through rearrangement and vectorization concatenation. Standardized storage records are generated by combining patient identification and time, and fingerprint verification is performed. This solves the problems of incomplete preservation, difficulty in retrieval, and easy loss of context in traditional clinical data storage. This method restores the structure without secondary reconstruction through matrix and vectorization processing; unified sorting eliminates differences in node order among different cases; the high-dimensional encoding after vectorization saves storage space and is compatible with vector retrieval libraries; and fingerprint verification ensures data integrity and security.

This invention discloses a traffic flow prediction method based on graph serialization and bidirectional state space. First, a spatiotemporal graph of traffic flow is constructed. Topological features of nodes are extracted using a graph attention network, and a double-random permutation matrix reflecting the optimal traversal path is generated through a differentiable relaxation sorting operator, adaptively mapping the two-dimensional graph structure data to one-dimensional serialized features. Then, a bidirectional gated state space model is constructed, and the serialized features are recursively scanned from both the forward and reverse directions using discretized state space parameters, and a hybrid hidden state is obtained through fusion using gated units. Finally, the transpose of the permutation matrix is ​​used to perform an inverse transformation on the hybrid features to restore the spatial structure, and the future traffic flow is output through a prediction head. This invention effectively solves the problem that traditional state space models struggle to handle graph structure data, achieving end-to-end joint optimization of the graph traversal strategy and the prediction model, significantly improving the accuracy and efficiency of long-sequence traffic prediction.