80 results about "Permutation matrix" patented technology

The invention relates to the technical field of information fusion, in particular to a robust filtering method for solving uncertainty of one-step random observation time delay, which comprises the following steps of: taking multiplicative noise, one-step random time delay, data packet loss, continuous loss observation and uncertain noise variance which are dependent on a system state and a state in an observation matrix into consideration; specifically, based on a maximum and minimum robust estimation principle, a CI fusion Kalman estimator (a predictor, a filter and a smoother) and two FCI fusion Kalman estimators are provided. According to the method, an augmentation method and a virtual noise method are combined, a robustness proving method based on permutation matrixes and a Lyapunov equation is introduced, it is proved that the proposed fusion estimator has robustness by defining different permutation matrixes, and in addition, through an application example of autoregressive moving average signal processing, the robustness of the fusion estimator is improved. It is verified that the robust local and fusion signal estimation problem can be solved through the state estimation method, and therefore solid practical application support is provided for the system modeling result.

The invention provides a privacy protection decision tree model reasoning method based on partial homomorphic encryption, and relates to the technical field of data privacy protection, and the method comprises the steps: obtaining to-be-predicted sample data, carrying out the preprocessing of each participant, and converting a ciphertext permutation matrix into a secret share matrix; performing bit decomposition on the to-be-predicted sample data to obtain a decomposition matrix, selecting an input sharing mode of the decomposition matrix according to the source of the to-be-predicted sample data, and then converting the input obtained decomposition matrix into a secret sharing share form; each participant performs comparative calculation by using the secret sharing share and the threshold share, evaluates Boolean functions of all leaf nodes to obtain indication result vectors, and randomly arranges the indication result vectors by using a secret share matrix to generate comparative result shares; and each participant constructs a vector pointing to a prediction output leaf node by using the comparison result share to obtain a prediction result, and outputs the prediction result to the sample data provider according to the to-be-predicted sample data source.

The invention provides a longitudinal federated lifting tree method for privacy protection, and relates to the technical field of longitudinal federated learning. The method comprises the following steps: combining a secretly shared binary matrix and gradient information to obtain a shared matrix and secretly sharing the shared matrix; each training terminal performs row permutation on the sharing matrix based on the permutation triad and secretly shares a permutation result; the linear gain scores of the candidate split are calculated cooperatively and shared secretly, and the indication vector of the split corresponding to the maximum linear gain score is calculated; according to the candidate split number, selecting corresponding columns and indication vectors from the matrix to calculate sub-node samples and a permutation matrix of sub-nodes; and recursively executing a calculation splitting step, completing splitting from the root node to the leaf node, calculating the weight of the leaf node, and storing the weight into a preset set. Updating the binary permutation matrix of the root node of the next decision model based on the leaf node weight, and continuing calculation until all decision tree models are generated; the problem that an existing longitudinal federated gradient boosting tree protocol is low in efficiency can be solved.

The invention belongs to the technical field of underwater acoustic array signal processing, and particularly relates to an acoustic vector circular array orientation estimation method based on cross covariance matrix construction, a program, equipment and a storage medium. The method comprises the following steps: introducing a preprocessing matrix to transform the output of an acoustic vector circular array, and constructing a cross covariance matrix of sound pressure and different vibration velocity components according to the transformed vector; combining and arranging the cross covariance matrixes of the sound pressure and the different vibration velocity components to obtain a combined arrangement matrix; performing singular value decomposition on the combined permutation matrix, and reconstructing a cross covariance matrix by using a unitary matrix formed by left singular vectors of the combined permutation matrix and a diagonal matrix formed by singular values; and implementing a spatial spectrum estimation method by using the cross covariance matrix, and taking the scanning angle corresponding to the maximum value in the spatial spectrum as an orientation estimation result of the detection target. The method is suitable for a column barrier condition, and can effectively improve the dual-target resolution and orientation estimation performance of the acoustic vector circular array under a low signal-to-noise ratio condition.

Various aspects of the present disclosure relate to receiving (1502) a polar codeword comprising a set of bits and generating (1504) a permuted vector based at least in part on the polar codeword and a permutation matrix. Aspects of the disclosure may relate to determining (1506) a plurality of polar subcodes based on the permuted vector, and identifying (1508), for each polar subcode of the plurality of polar subcodes, a set of kernels, where each kernel of the set of kernels is associated with a bit pattern of a set of one or more bit patterns. Aspects of the disclosure may relate to decoding (1510) the plurality of polar subcodes using a plurality of parallel polar code component decoders, where each parallel polar code component decoder includes one or more fast decode modules to decode a corresponding kernel of the set of kernels.

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 invention discloses a high-performance decoding method for quantum low-density parity check codes, and belongs to the technical field of quantum computing. Comprising the following steps: solving a transformation matrix and a permutation matrix through a matrix decoupling strategy optimized by an off-line satisfiability modulus theory, and equivalently transforming an original check matrix into a diagonal block matrix and any sparse matrix; the method comprises the following steps: decomposing an error mode into a left error corresponding to a diagonal block matrix and a right error corresponding to any sparse matrix through an online hierarchical greedy decoding algorithm, guessing an error bit of the right error, and then calculating an error symptom corresponding to the left error based on any sparse matrix; then the error symptom is divided according to the diagonal block matrixes for parallel decoding, decoding results of all the block matrixes are combined to form a left error, and finally the left error and the right error are spliced to obtain a complete error mode. According to the method, accurate and real-time decoding of the qLDPC code can be realized, and the method is applied to the frontier fields of quantum communication, quantum calculation and the like.

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.

Certain aspects of the present disclosure provide techniques for selecting a first orthogonal cover code (OCC) codeword from an OCC matrix for a transmission occurring during a first transmission time interval based on a first pseudo-random codeword allocator function, such as a first pseudo-random permutation matrix, corresponding to the first transmission time interval; and sending the transmission during the first transmission time interval using the first OCC codeword.

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 relates to the technical field of digital signature, in particular to a digital signature method based on QC-MDPC and pad filling, which comprises the following steps: acquiring an object to be digitally signed; qC-MDPC is adopted as private key coding, and a private key is designed by using a parity check matrix, a reversible permutation matrix, a disturbance matrix and a hash function; designing a public key by using the parity check matrix and the reversible permutation matrix; performing hash operation on the digital signature object to obtain a message digest; carrying out random length selection on the message digests, and multiplying the randomly selected message digests by the disturbance matrix to obtain a message sequence; performing pad filling on the message sequence until a BF decoding condition and a signature condition are met, completing digital signature construction, and sending a signature to a verifier; and verifying the signature by using the message sequence and the public key. The method has the advantages of being small in public key storage space, low in encryption and decryption complexity, high in safety and high in speed, and has important significance in the field of cryptography research of quantum attack resistance.

The present invention discloses a video image behavior prediction method, system, device and storage medium, relating to the field of artificial intelligence technology. The training method includes constructing undirected graph data for each frame of image according to the labeled nodes of each frame of image; arranging the order of the labeled nodes of the undirected graph data according to the permutation matrix to obtain observed image data; inputting the sequence of observed image data based on the video time sequence into the Kalman filter prediction sub-model to obtain a sequence of predicted image data, and correcting the sequence of predicted image data according to the sequence of observed image data; inputting the sequence of predicted image data into the Kalman smoothing sub-model to obtain a sequence of smoothed image data; updating the prediction parameters and correction parameters of the prediction sub-model according to the sequence of smoothed image data and the sequence of observed image data until the prediction parameters and correction parameters converge, so as to obtain a video behavior prediction model. The present invention can train an accurate video behavior prediction model when the video image data is incomplete or the labels are chaotic.

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.

A radiation source number estimation method suitable for an unmanned aerial vehicle array direction-finding system comprises the following steps: receiving observation signals through an antenna array, and calculating a covariance matrix of the observation signals; presetting the number of information sources, constructing a smoothing matrix containing a permutation matrix, and smoothing the covariance matrix based on the smoothing matrix to obtain a smoothed covariance matrix; performing information source number estimation on the smoothed covariance matrix based on an eigenvalue entropy calculation method to obtain an information source number estimation result; and arranging the information source number estimation results in an inverted order, and judging the monotonicity of the inverted sequence after the inverted order arrangement. According to the method, a new signal source number estimation scheme in array DOA estimation is provided, and due to the fact that the number of needed signal sampling points is very small, the method is suitable for signal source number estimation of an unmanned aerial vehicle array direction finding system under the conditions that received signals are small samples, aliasing color noise exists in observation signals and coherence exists in incident signals.

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 present application discloses a method for calculating the equivalent resistance between nodes in a resistor network, which includes: determining the admittance matrix G of the resistor network; converting the admittance matrix into a non-singular matrix #imgabs0# based on the permutation matrix P; performing LDL on the non-singular matrix #imgabs1# T Decompose and obtain the lower triangular matrix L with diagonal 1 and its elimination tree, diagonal matrix D; determine the equivalent resistance R to be calculated in the resistor network ij Node i, node j, and the standard unit vector e corresponding to node i i , the standard unit vector e corresponding to node j j With the permutation matrix P and the lower triangular matrix L, establish the equation group to be solved LXi=P T e i , LX j =P T e j , X i 、X j is the solution of the system of equations to be solved; based on the elimination tree, determine the solution X of the system of equations to be solved i 、X j The non-zero position of ; based on the solution of the equation system X i 、X j The non-zero position of the equation to be solved is calculated i 、X j ; Based on the solution X of the system of equations to be solved i 、X j And the diagonal matrix D, calculate the equivalent resistance R between node i and node j ij .

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.

An embodiment of the present application provides a threat event detection method, device, equipment and medium based on traceability graph matching. The method includes: obtaining a query graph and a traceability graph; inputting the query graph and the traceability graph into a target model to obtain a first node embedding matrix corresponding to the query graph and a second node embedding matrix corresponding to the traceability graph; through the target model, performing an isomorphic distance minimization calculation on the objective function to obtain a target permutation matrix between the first node embedding matrix and the second node embedding matrix; through the target permutation matrix, permuting the second node embedding matrix into a target node embedding matrix, and determining a target isomorphic distance between the target node embedding matrix and the first node embedding matrix; when the target isomorphic distance is less than a preset warning distance threshold, based on the element position alignment relationship between the target node embedding matrix and the second node embedding matrix, outputting a threat event detection result. In this way, the accuracy of threat event detection 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 present invention proposes a secure physical layer dynamic elastic polarization code encoding method suitable for non-degraded channels. The method comprises the following steps: first, channel polarization is performed based on the original polarization code, a permutation matrix and position vector are generated based on a shared key, and a secret generation matrix is ​​obtained based on the two. Then, based on the channel polarization result, the secret generation matrix is ​​divided into two sub-matrices. The shared key is used as the input of a secure hash function, and its hash value is output. The resulting hash value is used as the frozen bit and multiplied with the sub-matrix together with the plaintext information to obtain two row vectors. Finally, the vector XOR result is taken as the ciphertext for transmission. The legitimate receiver, with the help of the shared key, uses the inverse process of the above process to recover the plaintext. The present invention uses column obfuscation and random vector insertion to destroy the codeword structure of the original polarization code, thereby improving security. The hash value of the shared key is used as the frozen bit to prevent key leakage, thereby ensuring high confidentiality.