53 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 invention relates to the technical field of wireless communication, in particular to a low-complexity signal detection method of an MIMO-OTFS system, which comprises the following steps: setting a grouping symbol number according to a channel condition, and calculating a maximum iteration number; in each iteration: calculating a filtered received signal through the channel matrix and the received signal; constructing a matrix psi, processing a structure matrix psi S of the matrix psi by adopting an RCM algorithm to obtain a permutation matrix, and calculating a reordering matrix according to the permutation matrix; performing Cholesky decomposition on the reordering matrix to obtain a banded matrix; calculating the SINR of each symbol which is not detected at present; all symbols which are not detected at present are arranged in a descending order according to the SINR size, the first S symbols in the sorting result are taken to be detected one by one and output, and the channel matrix and the received signal are updated; judging whether the maximum iteration number is reached or not, and if not, continuing the next iteration; according to the method, the time complexity is reduced while the bit error rate performance is ensured through an iterative strategy of reliability symbol detection and interference elimination.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a UE may receive, from a network node, one or more reference signals associated with measurement of a signal quality associated with a transmission channel. The UE may transmit, to the network node, a channel state information report that includes precoder information indicative of a geometric mean decomposition precoder associated with the transmission channel and includes first information associated with one or more Givens rotation matrices and second information associated with one or more permutation matrices. Numerous other aspects are described.

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.

A method, computer program product, and computer system for identifying, by a computing device, a plurality of information associated with a telemetry system. A permutation matrix may be constructed for use with the telemetry system. One or more parameters for use in the permutation matrix may be extracted based upon, at least in part, the plurality of information associated with the telemetry system. A plurality of iterations may be performed with the permutation matrix based upon, at least in part, the one or more parameters extracted for use in the permutation matrix. A final telemetry geometry may be provided for the telemetry system.

This specification provides a privacy-protecting data provision and query method and apparatus. In the data provision method, during the preparation phase, the server receives first encrypted data from the client. This first encrypted data is obtained by homomorphically encrypting an n*n-dimensional permutation matrix corresponding to a predetermined permutation rule. Based on a first plaintext sequence consisting of n plaintext data held by the server and the first encrypted data, a ciphertext sequence consisting of n first ciphertexts is obtained. This ciphertext sequence corresponds to a second plaintext sequence, which is equivalent to the plaintext sequence obtained by permuting the first plaintext sequence based on the predetermined permutation rule. During the query phase, the server receives the target location of the target data to be queried from the client. This target location is the position after performing a permutation operation on the original position of the target data based on the predetermined permutation rule. The target ciphertext of the target location is read from the ciphertext sequence and sent to the client.

The invention discloses a human-computer interaction feature mining-based prediction method, which comprises the following steps of: inputting cue words and original data, extracting original features through GPT-4, constructing an input matrix X, mapping the input matrix X to a uniform embedding layer, completely exposing an association mode among all the original features by using a double-line attention mechanism, and extracting the original features by using the correlation mode. Local and global original features are efficiently sampled through a permutation matrix and a double-branch lightweight convolutional network, correlation mining of cross-modal and / or multi-source heterogeneous features of the original features is achieved, and finally feature fusion and feature extraction are conducted through a multi-layer perceptron connected through residual errors. Classification and fitting tasks of multi-modal and multi-source heterogeneous data can be well processed, and the prediction accuracy is improved.

The invention provides a memristor-based fixed-point matrix FFT (Fast Fourier Transform) calculation method, which belongs to the technical field of signal processing and image processing, and comprises the following steps of: firstly, constructing a non-linear voltage controlled memristor model, simulating a state conversion behavior in logic operation through a dynamic regulation mechanism, and realizing the function of a basic logic gate; according to the method, an FFT algorithm based on matrix block optimization is designed, FFT calculation is decomposed into small-scale matrix operation units, calculation efficiency is optimized in combination with a Kronecker product and an arrangement matrix, FFT operation with higher point number is expanded in a recursion mode, meanwhile, a full adder and a multiplier are designed based on a memristor, efficient fixed-point number operation is achieved, and finally, the FFT calculation efficiency is improved. The method is applied to image FFT processing, and a processing result is obtained. The scheme of the invention has higher calculation efficiency and lower energy consumption while ensuring the calculation precision, can be widely applied to the fields of image processing, signal analysis, scientific calculation and the like, and has important practical value and application prospect.

The invention discloses an electronic voting casual shuffling method based on semi-trusted hardware, trusted execution environments of a first participant and a second participant are mutually verified through a remote authentication protocol, and after authentication is passed, the trusted execution environments of the first participant and the second participant negotiate to generate a shared random seed; constructing a permutation matrix based on the random seeds and the trusted execution environments of the two participants, generating a secret share of the permutation matrix, and sending the secret share to the corresponding participants according to a distribution rule of copy secret sharing; the voter generates a secret share of voting data by using the trusted execution environment of each participant, and sends the secret share to the corresponding participant according to a distribution rule of copy secret sharing; and the two participants respectively use the locally held voting data and the secret share of the permutation matrix to carry out casual shuffling calculation, and the secret share of the position permutation voting data is obtained and disclosed, so that any third party can obtain a voting result after casual shuffling through the secret share of the position permutation voting data.

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 provides a model fusion system based on explainable information quantitative evaluation, which is used for fusing multiple trained models and comprises a weight alignment predictor, which is used for processing weight spaces of two models to be fused to obtain a weight alignment relationship between the two models, wherein the weight alignment relationship is expressed as a set of permutation matrices, and each element in the permutation matrix corresponds to a neuron at a level for describing a one-to-one correspondence of the two models in the neuron; a model evaluation unit, which is used for calculating weight contribution degrees of each parameter in each model weight set in the weight space according to the weight alignment relationship between the two models; and a model fusion unit, which is used for calculating fusion proportions of each layer weight of each model based on the feature contribution degrees of each parameter in each model weight, and fusing the weights of the two models based on the fusion proportions.

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.

A computer-implemented method for simulation of a given quantum circuit, the method comprising separating the quantum circuit into layers of quantum gates of a common gate class; decomposing each of the layers of quantum gates of the common gate class into a sequence of base operations, the base operations being selected from a diagonal matrix operation based on unitary diagonal operations applied in parallel to qubits of the quantum circuit, a permutation matrix operation expressible as a matrix with exactly one entry of 1 in every row and column, with the rest being zero, and a Hadamard matrix operation based on Hadamard gates applied in parallel to qubits of the quantum circuit that transforms a plurality of the qubits between two orthogonal bases; and evolving a quantum state vector according to the layers decomposed into the sequence of base operations.

The application provides a method and device for overcoming period jump in full-wave inversion, electronic equipment and storage medium. The method comprises the following steps: obtaining observation data and synthetic data; constructing a distance matrix between the observation data and the synthetic data based on the observation data and the synthetic data; determining a permutation matrix based on the distance matrix; determining the total deviation of the observation data and the synthetic data based on the distance matrix and the permutation matrix; updating the model by taking the total deviation as a target function; repeating the above steps until the model converges; and enabling the target function corresponding to the model to be in a convex domain where the global minimum value is located, thereby overcoming the problem of period jump.

The application discloses a special accelerator for a hierarchical greedy decoding algorithm, and belongs to the technical field of quantum computation, and comprises a transformation unit, a decoding core and a permutation unit; the transformation unit is used for receiving original error symptoms, and multiplying the original error symptoms with a transformation matrix through sparse matrix vector multiplication to generate transformed error symptoms; the decoding core is used for executing a hierarchical greedy decoding algorithm, dividing error patterns into left errors and right errors, and performing calculation including guessing right errors and decoding left errors in parallel based on the transformed error symptoms to obtain new values of the error patterns; and the permutation unit is used for receiving the new values of the error patterns, multiplying the new values of the error patterns with a permutation matrix through sparse matrix vector multiplication to calculate final error patterns. The application can realize real-time qLDPC decoding and significantly reduce decoding delay.

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 invention discloses a coding method and a storage system, and relates to the technical field of coding, and the coding method comprises the steps: carrying out the coding of a plurality of to-be-coded code words, obtaining a plurality of maximum distance separable codes, enabling the check bits of the maximum distance separable codes to comprise data blocks of system bits, it is ensured that when a storage node corresponding to a system bit fails, a data block of a check bit with a maximum distance and a separable code can be updated through data restoration of the storage node of the check bit and mutual carrying of symmetrical elements on the two sides of a main diagonal line of a permutation matrix; it is ensured that the failure of the storage nodes corresponding to the check bits can be repaired through the data of the storage nodes corresponding to other check bits, the data of the failure nodes can be repaired only by downloading part of data blocks in the k storage nodes in the repairing process, and the data of the failure nodes does not need to be repaired by downloading all the data blocks in the k storage nodes; and the number of data blocks required for repairing is greatly reduced, so that the repairing bandwidth is reduced.

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 relates to a knowledge reasoning method based on federal learning, and belongs to the field of artificial intelligence. The method comprises the following steps: S1, constructing a server and a client, and initializing an entity table, an existence matrix and a permutation matrix; s2, initializing a student model and a teacher model of the client; s3, the client performs local training and updates the teacher model; s4, the client performs local training and updates the student model; s5, the server selects some clients to upload entity embedding matrixes of the student model, calculates semantic similarity between the clients and aggregates the entity embedding matrixes; and S6, the client updates the entity embedding matrix of the student model based on the learnable weight matrix and the local entity embedding 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.