80 results about "Column vector" patented technology

A communication method includes the following. A terminal device sends a first physical uplink shared channel (PUSCH) and a second PUSCH. The terminal device corresponds to N sounding reference signal (SRS) resource sets, the first PUSCH is associated with a first SRS resource set among the N SRS resource sets, the second PUSCH is associated with a second SRS resource set among the N SRS resource sets, and the first SRS resource set is different from the second SRS resource set. The terminal device includes M antenna ports. A first precoding matrix for the first PUSCH and a second precoding matrix for the second PUSCH belong to a first precoding matrix set, a non-zero element in a column vector of each precoding matrix in the first precoding matrix set is less than or equal to M / N, and M and N are integers greater than or equal to 2.

The invention relates to a voice tag generation method and device, electronic equipment and a storage medium, and belongs to the technical field of computers, and the method comprises the steps: carrying out the feature matrix decomposition of a plurality of to-be-processed first voice feature matrixes, and obtaining a decomposed first matrix and a decomposed second matrix; performing hierarchical clustering on column vectors in the first matrix to obtain a tag tree of the first matrix; performing projection processing on the second voice according to the second matrix to obtain a voice feature representation vector of which the dimension is the same as the column number of the first matrix; querying a tag tree, and determining a feature tag of the second voice; wherein the feature tag of the second voice is determined according to the clustering tag in the tag tree mapped by the voice feature representation vector. According to the embodiment of the invention, the construction and maintenance cost of a voice feature tag generation system can be remarkably reduced, and the automation degree and robustness of tag generation are improved.

The invention discloses an RMFE-based distributed matrix multiplication implementation method. The method comprises the following steps: initializing parameters, constructing an RMFE function, splitting a to-be-processed matrix sequence into sequence vectors, sequentially carrying out phi function operation, obtaining a matrix distributed multiplication result through distributed matrix multiplication, and recovering through psi function operation to obtain a multiplication result of a to-be-processed matrix. According to the method, a distributed multiplication problem of a plurality of matrixes on a small domain / Galois ring is converted into a distributed multiplication problem of a single matrix on a large domain / Galois ring, and the calculation overhead on an expansion domain is allocated to the plurality of matrixes, so that the communication overhead required by distributed matrix multiplication is effectively reduced; the method has important application value for a high-speed communication system.

The application discloses a new correlation image recognition and machine learning method based on fuzzy monotony, and belongs to the technical field of artificial intelligence of pattern recognition and machine learning; the method is defined as FMMCA, which evaluates local fuzzy monotone correlation by comparing row vectors and column vectors of an image matrix pair by pair, then the local correlations are weighted and accumulated, and finally the global fuzzy monotone correlation between images is obtained. The application directly uses fuzzy monotone correlation analysis to replace classical correlation analysis for multi-view research, so that the problems existing in classical correlation analysis do not exist, and the fuzzy monotone method feature does not need to be measured statically by distance, but can be measured dynamically by interval change, so that the influence of some noise is offset, the performance is improved, a new fuzzy monotone machine learning method is formed, the optimization of a focus loss function is not needed, the parameters are few, the robustness is good, and the computing power is small.

The invention relates to the technical field of electrical digital data processing, and discloses a Web application automatic generation and data processing method based on spreadsheet mapping, which comprises the following steps: mapping spreadsheet cells into enumeration values to construct a type fingerprint matrix; calculating a type purity index of a column vector, identifying a vertical blocking band, and splitting the matrix into a plurality of orthogonal sub-matrixes which are not communicated with each other; calculating a type distribution difference value of adjacent row vectors in the sub-matrix to determine a horizontal logic partition line so as to define a logic data block; a Web application object model is assembled based on an arrangement mode of logic data blocks, signal crosstalk between transverse side-by-side data entities is cut off by introducing a vertical topology decoupling mechanism based on column entropy, and the technical problem that downlink feature scanning is easily interfered by orthogonal noise in a multi-table mixed arrangement scene is solved. And the deterministic structure reconstruction of the complex two-dimensional heterogeneous data under the condition of no preset template is realized.

The present application belongs to the field of communications. Provided are a channel state information processing method and apparatus, so as to further improve the channel estimation performance. The method comprises: a first communication apparatus determining some column vectors in an eigenvector matrix corresponding to a channel between a second communication apparatus and the first communication apparatus, and indicating the some column vectors to the second communication apparatus by using first information as a carrier; and the second communication apparatus performing channel estimation on a first channel on the basis of the first information. Compared with performing channel estimation on a channel by means of a second communication apparatus using a complete eigenvector matrix, performing channel estimation on a channel by using some column vectors can effectively filter out noise in the channel, reduce inaccurate information in the eigenvector matrix, further improve a signal-to-interference-plus-noise ratio of the channel, and improve the channel estimation performance.

The invention provides a large language model binarization quantification method and system based on instructive alternate optimization, and relates to the technical field of large language model deploying.The method comprises the steps that a weight matrix is divided into an important area and an unimportant area by evaluating the influence degree of all weight parameters on the performance of a large language model; carrying out binaryzation on all areas of the weight matrix, and alternately optimizing a row vector scaling factor and a column vector scaling factor by adopting a first-order row-column alternate optimization iteration mode to obtain a first-order reconstruction weight matrix; and carrying out binaryzation again on the important region of the first-order reconstruction weight matrix, carrying out optimization by adopting a first-order and second-order row-column alternating optimization iteration mode to obtain a second-order reconstruction weight matrix, and correspondingly taking the second-order reconstruction weight matrix as a weight parameter after the large language model is quantized. The quantization error of the key weight parameter can be effectively reduced, the quantization precision is high, the quantization process completely depends on the internal structure information of the large language model, and the edge deployment compatibility is high.

The application relates to the technical field of electric digital data processing, and discloses a Web application automatic generation and data processing method based on electronic table mapping, which comprises the following steps: mapping electronic table cells into enumeration values to construct a type fingerprint matrix; calculating a type purity index of a column vector and identifying a vertical blocking band, and splitting the matrix into a plurality of mutually unconnected orthogonal sub-matrices; calculating a type distribution difference value of adjacent row vectors in the sub-matrices to determine a horizontal logical segmentation line, so as to define a logical data block; and assembling a Web application object model based on an arrangement mode of the logical data block. The application introduces a vertical topology decoupling mechanism based on column entropy, cuts off signal crosstalk between horizontally arranged data entities, solves the technical problem that a horizontal feature scanning in a multi-table mixed arrangement scene is easily interfered by orthogonal noise, and realizes deterministic structure reconstruction of complex two-dimensional heterogeneous data under the condition of no preset template.

This invention provides a method for solving matrix eigenvalues ​​based on a resistive variable cell array (RVA) circuit. The method includes: setting the node conductance values ​​of the RVA array; writing the initial values ​​of each element of the matrix (A-βI) into the RVA array; inputting the current signal corresponding to the random column vector b through the bit line terminal of the RVA array; simultaneously connecting the RVA unit corresponding to β to the diagonal node of the RVA array via a switch to complete the solution of the linear equation system (A-βI)x = b, and solving and storing the infinite norm of the solution vector a; connecting the RVA unit corresponding to β to a resistance control circuit via a switching circuit to update the resistance value; repeating the above process until the traversal of β is completed, the β values ​​corresponding to all extreme values ​​of the infinite norm are the total eigenvalues ​​of the matrix. This invention employs an in-memory computation method in the analog domain, which can improve computation speed while reducing energy consumption.

Systems and methods for providing an S-box. The methods comprise: implementing a plurality of layers on a field programmable gate array, wherein each of the layers comprises a substitution sublayer and a mixing sublayer. The substitution sublayer comprises a plurality of μ-boxes that are each configured to perform a different randomly chosen non-linear bijective mapping from GF(24) to GF(24). The mixing sublayer is configured to (i) compute a plurality of product terms each representing a product of an element of a maximum distance separable matrix M and an output from one of the plurality of μ-boxes, and (ii) compute a plurality of column vector elements each comprising a sum of respective ones of the plurality of product terms.

This invention provides a method, apparatus, and storage medium for estimating the geometric parameters of a spherical cone target. The method includes: acquiring sparse echo data of the spherical cone target; determining the value range of the parameters to be estimated based on prior information of the spherical cone target, obtaining a parameter combination set including several parameter combinations; the parameters to be estimated include the base radius and the semi-cone angle; substituting the parameter combinations in the parameter combination set into a geometric diffraction theory model to obtain the RCS data of the spherical cone target; performing two-dimensional imaging and transformation processing on the sparse echo data and the RCS data respectively to obtain a first column vector and a second column vector; constructing a parameter dictionary based on the second column vector; and inputting the first column vector and the parameter dictionary into a preset optimization algorithm for iteration to obtain the estimated target parameter values. The geometric parameter estimation method for spherical cone targets provided by this solution reduces the computational load while ensuring the accuracy of parameter estimation.

The invention discloses a median positioning method and system, electronic equipment and a storage medium, and relates to the field of data processing. The method comprises the following steps: acquiring N pieces of binary data and a single-column vector consisting of N elements 1; screening middle-bit elements on the same bit in sequence from high to low, and in a single-column vector, adjusting an element 1 corresponding to invalid data to an element 0; wherein for any invalid data, elements of subsequent bits are adjusted to be elements of the current bit in the invalid data every time one median element is screened out, and for any invalid data, elements of subsequent bits are adjusted to be elements of the current bit in the invalid data; after a median element is screened out and elements in the single-column vector are adjusted, whether the number of elements 1 in the single-column vector is 1 or not is judged, and if yes, binary data corresponding to the elements 1 are obtained and serve as median values; if not, continuing to screen the median elements of the next bit and adjusting the elements in the single-column vector until the number of the elements 1 in the single-column vector is 1 or screening of the median elements of all the bits is completed. Therefore, the median searching efficiency is improved.

The application discloses a signal recovery method and system based on a block Gauss-Seidel algorithm, a medium and equipment, and the method comprises the following steps: obtaining a noise observation vector and a perception matrix; initializing the number of iterations, an estimated signal, a residual vector and a hyperparameter; setting an iteration relative error, calculating the inner product of the column vector of the perception matrix and the estimated vector obtained in this iteration, calculating the residual vector, and selecting the maximum homogeneous residual; determining the control index set of the target block; randomly extracting the corresponding working column from the coefficient matrix to obtain a submatrix; substituting the target block of the submatrix into the iteration formula of the block Gauss-Seidel algorithm without pseudo-inverse calculation to obtain the estimated value of the next iteration, and repeating the loop iteration until the threshold condition is met to stop the iteration; and outputting the estimated sparse signal obtained in the last iteration. The application improves the convergence rate and recovery effect of signal recovery and improves the recovery performance of the sparse signal.

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 an online fault diagnosis method for a hydro-generator based on a potential interval distance of a through screw, relates to the technical field of generators, and is suitable for detecting a stator winding ground fault. Voltage collection devices are additionally arranged on a plurality of through screws of the hydro-generator, real-time induced potential is acquired, a historical data matrix is established, and a data buffer matrix is updated at interval time; the data buffer matrix is divided into a data evaluation matrix, a fundamental wave and an odd harmonic phase are extracted through Fourier decomposition, a harmonic phase matrix is constructed, a phase difference matrix is obtained by calculating normalized phase differences of adjacent columns, a quartile distance is solved after being converted into a column vector array, a dynamic quartile distance array is formed, abnormal limit values are determined by combining a box plot principle and an error level of the voltage collection device, and faults are determined by traversing the array. The method does not need additional sensing devices and traditional threshold setting, is convenient to operate, has low implementation difficulty, and can accurately and timely identify faults.

The invention provides a global response reconstruction method and device based on an intelligent enhanced reduced-order model, and the method comprises the steps: obtaining the local structure data of a to-be-reconstructed scene, and enabling the local structure data to be obtained through the collection of the to-be-reconstructed scene based on a sensor; an orthogonal base space matrix under model order reduction is obtained, the column vector of the orthogonal base space matrix is an orthogonal base vector, and the orthogonal base vector is expanded into a low-dimensional subspace; and based on the local structure data and the orthogonal base space matrix, performing reconstruction to obtain a high-dimensional global response of the to-be-reconstructed scene matched with the local structure data. A high-dimensional and complex structure response field is projected to a low-dimensional subspace by using an orthogonal base space matrix with physical consistency, so that the high-dimensional global response of a to-be-reconstructed scene can be obtained through accurate reconstruction and deduction based on local structure data of the to-be-reconstructed scene.

The invention relates to a matrix operation method, a matrix operation device, computer equipment, a storage medium and a program product. The method comprises the steps that in response to operation instructions of a first matrix and a second matrix, matrix blocks are distributed to threads needing to be called by a heterogeneous accelerator in the heterogeneous computing system; and sending an operation instruction to the heterogeneous accelerator, and indicating each thread in the heterogeneous accelerator to operate the distributed matrix blocks in parallel. Wherein each matrix block comprises at least one first matrix row vector and at least one second matrix column vector. By adopting the method, the matrix operation efficiency can be improved.

The global relationship attention guided cross-view geo-location method belongs to the technical field of computers, uses a deep residual network as a backbone network, and uses a global relationship attention module to capture more robust image global structure information for matching. A double-branch network including a global branch and a local branch is designed to capture deep features with rich semantic information and local features with multi-scale context information, respectively. In the local branch, dilated convolution is used to increase the receptive field of the feature map, and a square ring division strategy is used to divide the feature map at four scales. For the feature maps of each branch, they are converted into column vector descriptors, and then a classifier is used to obtain the predicted category of each column vector. Finally, a cross-entropy loss function is used to measure the difference between the predicted category and the real category of the image, thereby improving the geo-location accuracy.

The invention provides a general interference suppression method and system based on adaptive window short-time Fourier transform, and the method comprises the steps: carrying out the adaptive initialization of a Gaussian window width parameter according to the amplitude standard deviation of each pulse signal, carrying out the short-time Fourier transform, obtaining a time-frequency matrix of each pulse, carrying out the vectorization, carrying out the arrangement according to columns, and obtaining a time-frequency matrix of each pulse; constructing a time frequency-pulse matrix; performing complex RPCA decomposition on the matrix, and separating a low-rank matrix and a sparse matrix; adjusting a Gaussian window width parameter according to energy distribution feedback of each column vector of the sparse matrix, and performing iterative optimization until convergence to obtain a final sparse matrix; extracting interference time-frequency column vectors, reconstructing the interference time-frequency column vectors into an interference time-frequency matrix, and performing inverse short-time Fourier transform to obtain a time-domain interference signal; and finally, subtracting the corresponding interference signal from the original echo signal. According to the method, various types of interferences can be effectively suppressed, and particularly, the suppression effect can be remarkably improved on the premise of protecting target signals when broadband interferences and complex deception interferences are handled.

The invention discloses an LDPC (Low Density Parity Check) check matrix construction method for a high-bit-rate flash memory. The construction process of a check matrix is divided into four steps of determining check matrix parameters, constructing a high-bit-rate basis matrix by a regular column weight method, constructing a shift parameter matrix by a basis matrix expansion method and filling the check matrix by taking the shift parameter matrix as a unit. Starting from establishment of check matrix parameters, setting parameters of the high-bit-rate flash memory and a corresponding matrix scale; a high-bit-rate basis matrix is generated through a regular column reconstruction method, and repeatability of a basis matrix column vector is reduced; a high-bit-rate basis matrix is used as a base, a shift parameter matrix is constructed based on a basis matrix expansion method, and short rings are reduced to improve performance; and filling the check matrix by using the cyclic shift matrix as a basic unit to obtain a final construction result. According to the method, the short loop in the check matrix can be eliminated to obtain better performance, the difference of base matrix column vectors is kept, the adverse effect of an error code flat layer on effective information is reduced, and the method also has the advantages of high applicability, low storage space occupation and the like in a flash memory.

A kind of matrix computing device, method, system, circuit, chip and equipment, compressed format matrix can be directly calculated, so as to improve the computing efficiency of compressed format matrix.Matrix computing device includes: vector outer product processing engine and accumulator, matrix computing device is based on vector outer product and carries out the calculation of compressed format first matrix and second matrix, in the process of calculation, vector outer product processing engine carries out vector outer product calculation to the first column vector of the row coordinate reserved and the second column vector of the column coordinate reserved, then accumulator is based on the index of position coordinate, and the third element value of the same position coordinate is accumulated, to obtain the result matrix of two compressed format matrixes for calculating, relative to the method that compressed format matrix needs to be decompressed first in traditional, then matrix calculation is carried out to the decompressed matrix, the matrix computing device provided in the embodiment of the application can effectively improve the computing efficiency of compressed format matrix.

The application discloses a text object recognition method and device, which is used to improve the efficiency and accuracy of type identification of objects included in network attack events or dynamic events. The method comprises the following steps: in response to a recognition instruction, extracting a plurality of objects included in the description text of a network event; respectively extracting features of the plurality of objects to determine a feature matrix of each object; wherein each row vector in the feature matrix of any object is used to represent a feature of any object; performing feature re-extraction on each column vector of the feature matrix of any object to generate an intermediate matrix corresponding to each column vector, and performing feature enhancement on each intermediate matrix to generate a target vector corresponding to each intermediate matrix; inputting a target matrix composed of the generated plurality of target vectors into a pre-trained type prediction model to determine the type to which any object belongs.

The application provides a sorting QR decomposition method for a MIMO system, comprising: determining an initial matrix to be sorted according to a channel matrix of a MIMO system to be detected; determining column vectors to be sorted of the matrix and a column number thereof, denoted as a target number; if the target number is greater than a preset value, performing a first sorting strategy: in a first round of iteration, exchanging column orders of a column vector to be sorted with a smallest norm value and a first column vector to be sorted, and in each subsequent round of iteration, exchanging column orders of a column vector to be sorted with a second smallest norm value and a column vector to be sorted in a current round, to obtain a first target matrix; and a last column vector of the first target matrix corresponds to a highest layer traversal constellation point of an FSD algorithm. The above scheme can obtain an optimized target matrix. The application also provides an FSD-based signal detection method, which performs FSD signal detection by using the above optimized target matrix, so that the number of highest layer traversal constellation points is reduced, and the detection accuracy and reliability are still ensured.

Disclosed herein are a quantum simulation acceleration apparatus and method. The quantum simulation acceleration apparatus may configure a one-dimensional (1D) column vector [α, β] from a quantum state of an input qubit, execute a quantum gate operation on the 1D column vector using at least one of a quantum gate having a diagonal component or a quantum gate having a non-diagonal component, or a combination thereof, and trace the quantum state of the qubit changing depending on the quantum gate operation.

The invention discloses an optical fiber communication network operation anomaly detection method and device, and the method comprises the steps: constructing a three-dimensional column vector based on the completion time of each detection, the number of a detection object, and the operation anomaly probability of the detection object, and forming a first dynamic matrix; column vectors in the first dynamic matrix are arranged from large to small according to the operation anomaly probability, and a second dynamic matrix is obtained; determining the detection object of which the operation anomaly probability is greater than a first threshold value in a preset number of column vectors from the left side of the second dynamic matrix as an abnormal object; the first threshold value is set according to the material and operation environment of the optical fiber communication network equipment. According to the method, unified formalized description of the running state data can be realized, and importance distinguishing is performed on the column vectors according to the running anomaly probability values, so that the detection efficiency is improved.

The application discloses an integrated diffraction neural network and processor for matrix calculation, the diffraction neural network comprising: an input module configured to encode row vectors or column vectors of an input matrix into input optical signals, the input optical signals encoded by different row vectors or column vectors having different wavelengths; the diffractive optical transformation module is configured to transform optical mapping of the operator matrix, and the diffractive optical transformation module is used for receiving input optical signals, applying the same optical transformation to the input optical signals with different wavelengths and converting the input optical signals into output optical signals; and the output module is used for receiving and separating the output optical signals with different wavelengths and guiding the output optical signals to a detector for detecting the signals. As the diffraction type optical conversion module applies the same optical conversion to the input optical signals with different wavelengths, conversion operator matrixes are consistent for the input optical signals with different wavelengths within a certain bandwidth, and the optical conversion efficiency is improved. And the input signal light with different wavelengths can obtain a result of executing corresponding matrix calculation of the input matrix and the transformation operator matrix through the modulation unit. The input module, the diffraction type optical transformation module and the output module in the integrated diffraction neural network for matrix calculation are easy to integrate, and the minimum integration area of the integrated diffraction neural network for matrix calculation can be reduced. The matrix calculation process is a passive process, and the system energy consumption of large-scale matrix calculation is remarkably reduced.

A communication method and apparatus, used for improving channel estimation accuracy. The method comprises the following steps: sending indication information of a first basis and indication information of a first number, the first number being the number of first weight coefficient vectors, and the first weight coefficient vectors being used for performing weighted merging on the first number of column vectors in the first basis; receiving indication information of a first weight coefficient vector, and a first weight coefficient, the first weight coefficient vectors being determined according to a channel state information-reference signal (CSI-RS) measurement, the first basis, and the first number, and the first weight coefficient being used for indicating the maximum weight coefficient obtained according to the CSI-RS measurement; determining channel information according to the first basis, the first weight coefficient vectors, the first weight coefficient, and a second weight coefficient vector, the second weight coefficient vector being used for performing weighted merging on a second number of column vectors in the first basis, and the second weight coefficient vector being determined according to an SRS measurement, the first basis, and the second number of column vectors.

Systems and methods are disclosed for port selection for a wireless communication system. In one embodiment, a method performed by a Radio Access Network (RAN) node comprises dividing a channel matrix of one subcarrier of a Multiple-Input-Multiple-Output (MIMO) channel between an antenna array of the RAN node and a particular User Equipment (UE) into sub-matrices. The method further comprises forming a re-ordered channel matrix as a concatenation of the sub-matrices and forming a port-sorting matrix based on eigen vector matrices and eigen value matrices obtained via Eigen Value Decompositions (EVDs) performed on channel covariance matrices for the sub-matrices. The method further comprises applying the port-sorting matrix to the re-ordered channel matrix, re-ordering column vectors in the port-sorted channel matrix based on eigen values obtained from the EVDs, and applying grouping of the re-ordered, port-sorted channel matrix and port sorting accordingly to obtain a final port-sorted channel matrix.

Embodiments of the present application provide a data encoding and decoding method, an error correction method, a memory controller, a chip and a device, wherein the encoding method comprises: determining data to be written into a memory, and mapping the data into a plurality of data symbols; encoding the plurality of data symbols according to an extended encoding matrix to obtain a plurality of check symbols; wherein the extended encoding matrix is obtained based on a standard encoding matrix transformation, the extended encoding matrix does not have the same column vector under the target data size symbol, and the column vector in the extended encoding matrix is different from the unit column vector in the unit matrix corresponding to the check symbol; and writing the plurality of data symbols and the plurality of check symbols as an encoding result into the memory; wherein the plurality of check symbols are used to correct the encoding result when the encoding result is read from the memory. The embodiments of the present application can improve the coverage of the check symbol to the data symbol, provide support for improving the symbol quantity that can be corrected by the error correction algorithm, and improve the error correction capability.

The invention provides a method and a system for accelerating operation of each layer in a machine learning model. The method comprises the following steps: receiving rows or columns of a weight matrix and an input vector; transforming rows or columns of the weight matrix and the input vector using an embedded function; performing a search to identify transformed rows or columns in the weight matrix that would obtain a high post-activation value; measuring a similarity between rows or columns of the weight matrix returned by the search and the associated input vector using a kernel function to determine a post-activation value; determining an output matrix, including: when a value vector is a column vector of a unit matrix, making the output matrix equal to the post-activation value, otherwise, when a value vector is not a column vector of a unit matrix, combining the post-activation value with an associated value vector; and outputting the output matrix.