55 results about "Singular matrix" patented technology

The invention discloses a patch clamp electrophysiological data processing method and a patch clamp electrophysiological data processing system. The method comprises the following steps: acquiring ion channel current data through a patch clamp microelectrode; baseline drift removal processing is carried out on the ion channel current data through an adaptive quadratic linear fitting function to obtain baseline correction current data; performing discretization processing and high-frequency filtering processing on the baseline correction current data based on a preset sampling frequency to obtain a filtering current signal sequence; performing calculation through a false nearest neighbor method to obtain an optimal embedding dimension, performing calculation based on an average displacement method to obtain optimal delay time, and performing second phase space reconstruction through the filtering current signal sequence, the optimal embedding dimension and the optimal delay time to obtain an optimal vector set and a phase space reconstruction matrix; calculating through a singular value decomposition algorithm and a Shannon formula to obtain a principal component vector set and an information entropy corresponding to each vector, judging through a preset information entropy threshold to obtain a principal vector sequence, and reconstructing through a singular matrix to obtain electrophysiological feature data.

The invention discloses a practical stability domain division method and a practical stability domain division device for a wind power integration system considering probability characteristics, and relates to the technical field of data processing. Obtaining a first probability parameter of active power change and a second probability parameter of load power change, and updating a probability parameter item based on the first probability parameter and the second probability parameter; judging whether a Hessian matrix of the differential equation set at one balance point in the balance point set is a singular matrix, and if the Hessian matrix is the singular matrix, performing bifurcation analysis on the wind power active power and the load reactive power of the target node to obtain a bifurcation curve and each bifurcation point; and dividing the practical stability domain based on each bifurcation point and the bifurcation curve, and considering the influence of the uncertainty of the wind power injection active power and the load reactive power on the practical stability domain when the practical stability domain is divided.

The invention relates to a DSP acceleration implementation method and device for indirect singular value decomposition of a two-norm constrained slender matrix. The method comprises the following steps: obtaining a to-be-orthogonalized matrix of an initial data type according to an input matrix; the method comprises the following steps: performing orthogonalization processing on a to-be-orthogonalized matrix by utilizing Jacobi rotation transformation through a DSP (Digital Signal Processor), and when a second-order norm of a column vector in the processed to-be-orthogonalized matrix meets a preset condition, converting a data type of the processed to-be-orthogonalized matrix from an initial data type into a target data type, and performing orthogonalization processing again, performing zero setting on a relaxation factor corresponding to the to-be-orthogonalized matrix after the current orthogonalization processing, and continuing the orthogonalization processing until a convergence condition is met to obtain an iterated matrix; and obtaining a left singular matrix, a right singular matrix and a singular value of the input matrix according to the iterated matrix. According to the method, the to-be-orthogonalized matrix is used for processing, the calculation amount is reduced, the iteration speed is higher, and the calculation speed is increased.

The invention relates to a matrix decomposition device and method based on a memristor cross array, which are suitable for efficient hardware implementation of singular value decomposition (SVD), the memristor cross array receives a conductance value mapped by a Grubrum matrix constructed by a to-be-decomposed matrix and a bit line input voltage converted by a bit line column vector, and outputs a corresponding current; converting the corresponding current into a voltage vector; performing normalization processing on the voltage vector to obtain a normalized vector; judging whether convergence occurs or not; performing normalization processing on the steady-state voltage vector to obtain a final output vector; and according to the final output vector, main characteristic values are extracted based on a first normalization circuit, and main singular values and corresponding singular matrixes are calculated. Compared with the prior art, the high parallel computing characteristic of the memristor cross array is utilized, efficient operation and hardware acceleration of matrix decomposition are achieved, the method has the advantages of being low in power consumption, high in speed and good in expansibility, and the method is suitable for application scenes such as artificial intelligence, signal processing and large-scale matrix operation.

The present application relates to the technical field of hydraulic system fault diagnosis, and specifically to a hydraulic system fault vibration analysis and diagnosis method, the method comprising: collecting the time series pressure sequence of the hydraulic system and the vibration amplitude sequence in each direction; constructing a singular matrix using the elements in each intrinsic mode function after modal decomposition of the vibration amplitude sequence; determining the membership degree based on the distance between any two singular vectors in the singular matrix; determining the decomposition state feature difference based on the membership degree and the pressure data difference corresponding to any singular vector and its difference vector; determining the multi-scale non-homomorphic coefficient of the intrinsic mode function based on the decomposition state feature difference under all different scale groups, which is used to screen the intrinsic mode function, and analyzing the vibration fault diagnosis result of the hydraulic system with the screened intrinsic mode function. The present application makes the fault diagnosis result of the hydraulic pump more accurate.

The application discloses a pedestrian re-identification-based metric loss framework improvement method and related equipment, and the method comprises the following steps: dimension reduction processing is performed on a covariance matrix fitting overall distribution of different categories of samples by using a diagonalization method; diagonal elements of the covariance matrix of different categories are stored by using a proxy matrix, and the uncertainty range of the sample is mapped into a feature space by the proxy matrix; singular value decomposition is performed on an output layer parameter matrix of a feature extraction network by using an SVD decomposition network; a left singular matrix is used to replace the original parameter matrix, the correlation of the Euclidean distance of the feature vector is not changed, the feature vector is projected into an orthogonal feature solution space, and the correlation decoupling of the latitude of the feature vector is completed; and the improvement of the metric loss framework is completed according to the extracted dimension correlation decoupled feature vector. The application reduces the calculation complexity and model complexity of the loss function, and improves the training efficiency and model performance.

The invention relates to an underwater acoustic positioning method and device, equipment and a storage medium. Comprising the following steps: acquiring position coordinates of an underwater target at a first moment according to a four-buoy positioning method; setting the position coordinate as an iteration initial value of a Levenberg-Marquardt algorithm; constructing a residual equation set by adopting a three-buoy positioning method; and calculating the position coordinates of the underwater target at the second moment according to the iterative initial value and the residual equation set by adopting a Levenberg-Marquardt algorithm. Therefore, when the Levenberg-Marquardt algorithm is adopted to calculate the position coordinates of the underwater target at the second moment, the iterative initial value of the Levenberg-Marquardt algorithm is the position coordinates of the underwater target at the first moment, and the position coordinates are obtained through four-buoy positioning. Therefore, the accuracy of the position coordinates of the underwater target at the first moment is relatively high, so that the iterative initial value of the Levenberg-Marquardt algorithm is closer to a real solution, the occurrence of a singular matrix is avoided, and the positioning accuracy of the underwater target is effectively improved.

The invention provides a spatial domain-polarization domain multi-dimensional parameter estimation method for a bistatic polarization FDA-MIMO radar, and the method comprises the steps: constructing a dual-system radar system composed of a polarization MIMO radar and a polarization FDA-MIMO radar, and transmitting a signal and receiving an echo signal through the system; calculating a covariance matrix of the echo signal and then decomposing the covariance matrix to obtain a decomposition formula comprising a signal subspace and a noise subspace; constructing a selection matrix by using the signal subspace and the rotation invariant feature of the array element, and then estimating the space angle of the target signal; and constructing a non-singular matrix by using the signal subspace, estimating a polarization parameter of the target signal, and solving a distance parameter of the target signal by combining the angle parameter and the polarization parameter. According to the invention, a polarized MIMO and polarized FDA-MIMO dual-mode working system is utilized to realize joint sensing of target space position information and polarization information, and decoupling and high-precision estimation of multi-dimensional parameters can be effectively realized.

An embodiment of the present application provides a method for channel state information feedback. The method includes: measuring N groups of frequency-domain channel state information reference signals sent by a base station to obtain channel matrices of N frequency-domain channels; performing singular value decomposition on each channel matrix to obtain a left singular matrix, a diagonal matrix containing singular values, and a right singular matrix; for each diagonal matrix, determining whether it is a characteristic diagonal matrix according to the ratio of the largest singular value in the diagonal matrix to the sum of all singular values; counting the number of characteristic diagonal matrices; if the counted number is greater than or equal to a first set value, generating first channel state information to feedback to the base station; if the counted number is less than a second set value, generating second channel state information to feedback to the base station; if the counted number is between the first and second set values, generating third channel state information to feedback to the base station. The embodiment of the present application can solve the problem of poor flexibility in channel state information feedback and improve the service quality of the network.

A compression device (101) is configured so as to comprise: a matrix acquisition unit (1) that sequentially acquires partial feature matrices for each of a plurality of partial images obtained by dividing an image; and a first global matrix calculation unit (3) that calculates a first global singular value matrix that is a first global singular value matrix in which the first partial feature matrix acquired by the matrix acquisition unit (1) is decomposed into singular values and which includes the singular value of the first partial feature matrix, and a first global right singular matrix that is a first global right singular matrix that includes a right singular vector corresponding to the singular value of the first partial feature matrix. In addition, the compression device (101) is provided with: a target matrix calculation unit (4) that calculates a b-th target matrix (b = integer 2 or greater) which is the b-th matrix subject to singular value decomposition, and which includes a b-th partial feature matrix which is the b-th partial feature matrix acquired by the matrix acquisition unit (1), a (b-1)th global singular value matrix, and a (b-1)th global right singular matrix; and a second global matrix calculation unit (5) that decomposes the b-th target matrix calculated by the target matrix calculation unit (4) into singular values, calculates the singular value matrix of the b-th target matrix including the singular values of the b-th target matrix as a b-th global singular value matrix, and calculates the right singular matrix of the b-th object matrix including a right singular vector corresponding to the singular value of the b-th target matrix as a b-th global right singular matrix.

The invention relates to a tubular reactor temperature control method under the perspective of a singular partial differential equation. The method comprises the following steps: establishing a tubular reactor temperature concentration reaction rate system model described by a singular partial differential equation system; simplifying the temperature concentration reaction rate system model of the tubular reactor into a singular partial differential equation system; carrying out approximate linearization on the singular partial differential equation system through a fuzzy method, and designing an integral sliding mode surface containing a singular matrix; designing a sliding mode controller; and respectively analyzing the sliding mode motion accessibility and the index tolerance of the singular partial differential equation system based on a Lyapunov method to obtain a sliding mode controller gain, and applying the sliding mode controller gain to the sliding mode controller to complete the control of the temperature of the tubular reactor. According to the method, the defect that a traditional bivariate model cannot explicitly represent dynamic coupling of the reaction rate can be thoroughly overcome, the inhibition capability on the temperature runaway phenomenon is improved from the source, and the safety accident risk is remarkably reduced.

The application belongs to the technical field of scientific and technological information processing, and provides a method and system for determining a cause-effect relationship based on a scientific and technological information analysis knowledge graph. The method comprises the following steps: determining a first verb, a second verb and a scientific and technological information text segment to be processed, performing vector conversion to obtain a first verb embedding vector, a second verb embedding vector and a first sentence embedding vector, performing vector merging and attention operation to obtain a first transformation matrix, performing first transformation processing on the first transformation matrix to obtain a second sentence embedding vector; after performing second transformation processing on the first sentence embedding vector and the second sentence embedding vector, performing SVD decomposition processing, inputting the obtained left singular matrix into a verb cause-effect relationship prediction model, and outputting a cause-effect relationship analysis result of the first verb and the second verb. The application can focus on learning relevant key information of the first verb and the second verb, reduce information redundancy, and improve the classification accuracy of the cause-effect relationship.

The present invention provides a target detection model optimization method, apparatus, medium, and equipment. The method includes: S1, selecting a target detection model suitable for deployment on an edge device; S2, decomposing the two-dimensional matrix corresponding to the weight tensor of each convolutional layer in the target detection model into a left singular matrix, a diagonal singular value matrix, and a right singular matrix; S3, selecting the first R singular values ​​from the diagonal singular value matrix, and retaining the corresponding left singular vectors and right singular vectors; S4, reconstructing the weight tensor of the convolutional layer; S5, evaluating the target detection model to determine whether the target detection model meets the requirements of the edge device; if so, taking the current target detection model as the optimized target detection model; otherwise, adjusting R and returning to S2. In this way, the number of parameters used in the target detection model can be reduced, and while taking into account the accuracy of the model, the scale of the target detection model can be reduced so that it can be deployed on resource-constrained devices.

The compression device (101) is configured to include: a matrix acquisition unit (1) that sequentially acquires a partial feature matrix for each of a plurality of partial images into which an image is segmented; and a first global matrix calculation unit (3) that performs singular value decomposition on the first partial feature matrix acquired by the matrix acquisition unit (1), i.e., the first partial feature matrix, and calculates a first global singular value matrix containing the singular values ​​of the first partial feature matrix as the first global singular value matrix, and a first global right singular matrix containing the right singular vectors corresponding to the singular values ​​of the first partial feature matrix as the first global right singular matrix. In addition, the compression device (101) includes: an object matrix calculation unit (4) that calculates a matrix that is the object matrix of the bth singular value decomposition, which includes the bth partial feature matrix obtained by the matrix acquisition unit (1) (b is an integer of 2 or more), the (b-1)th global singular value matrix, and the (b-1)th global right singular matrix; and a second global matrix calculation unit (5) that performs singular value decomposition on the object matrix of the bth object matrix calculated by the object matrix calculation unit (4), calculates the singular value matrix of the object matrix of the bth object matrix that includes the singular values ​​of the object matrix of the bth object matrix as the global singular value matrix of the bth object matrix, and calculates the right singular matrix of the object matrix of the bth object matrix that includes the right singular vector corresponding to the singular value of the object matrix of the bth object matrix as the global right singular matrix of the bth object matrix.

The present invention proposes a method for collecting and processing hand-eye calibration data of an industrial robot and a line profile sensor to avoid singularity. When calibrating the posture matrix of the hand-eye relationship matrix, in order to avoid matrix singularity in the calculation process, the industrial robot needs to drive the sensor to move linearly to at least 4 positions in different directions while keeping the terminal posture unchanged, wherein at least 4 positions cannot be coplanar and at least 3 positions cannot be colinear, and collect information from each position point to participate in the calculation. When calibrating the position matrix of the hand-eye relationship matrix, in order to avoid matrix singularity in the calculation process, the robot needs to drive the sensor to perform at least three posture changes, and collect information from each posture point to participate in the calculation. This method can not only effectively avoid the occurrence of singular matrices in the hand-eye calibration process, but also improve the calibration accuracy.

The program causes a computer to perform the following operations: perform singular value decomposition on a first Jacobian matrix to generate a second Jacobian matrix including a first matrix, a second matrix, and a first singular matrix having singular values as diagonal elements; determine whether to execute a first process when at or near a singular point or a second process when away from the singular point based on a comparison result between the singular values and a first threshold value; in the first process, generate a third Jacobian matrix by converting diagonal elements in the first singular matrix, and determine and output operation command values for a plurality of joints (JT) of the robot using the third Jacobian matrix and a movement command value of a moving part (12) of the robot (10); and in the second process, determine and output the operation command values using the first Jacobian matrix or the second Jacobian matrix and the movement command value.

The present invention discloses a method and device for dividing a practical stability domain of a wind power grid-connected system considering probability characteristics, which relates to the field of data processing technology. By performing probability modeling analysis on the uncertainties of the active power and load power of a wind farm, a first probability parameter of the active power change and a second probability parameter of the load power change are obtained. The probability parameter item is updated based on the first probability parameter and the second probability parameter. When it is determined that the value of the probability parameter item reaches a specific critical value, it is judged whether the Hessian matrix of a differential equation group at an equilibrium point in an equilibrium point set is a singular matrix. If it is a singular matrix, a bifurcation analysis is performed on the wind power active power and the load reactive power of a target node to obtain a bifurcation curve and each bifurcation point. The practical stability domain is divided based on each bifurcation point and the bifurcation curve, so that the influence of the uncertainty of the wind power injected active power and the load reactive power on the practical stability domain is taken into account when dividing the practical stability domain.

The application relates to the technical field of data processing, and discloses a multi-dimensional data intelligent management method for regional safety management software, which comprises the following steps: obtaining a left singular matrix, a right singular matrix, a diagonal line matrix and eigenvectors of the left singular matrix and the right singular matrix of a two-dimensional driving data matrix; obtaining a first kind of reservation degree, which is divided into time periods; obtaining an adjusted voting value and marking important time periods; obtaining an influence weight value, determining a first eigenvector, obtaining a reconstructed two-dimensional driving data matrix of the first eigenvector, obtaining a reconstructed influence weight value according to the reconstructed two-dimensional driving data matrix, and then obtaining a feature weight of the first eigenvector; and determining data that needs to be reserved and stored according to the feature weight, so as to realize multi-dimensional data intelligent management for the regional safety management software. The application aims to solve the problem of low multi-dimensional data intelligent management efficiency caused by excessive redundant data.

To provide a device, method, and program that contribute to accurately performing multi-user detection while reducing a computation amount.SOLUTION: A wireless receiving device decomposes a channel matrix defined between transmission antennas of one or more wireless transmitting devices and reception antennas coupled to the wireless receiving device into a left singular matrix U, a singular value matrix Σ, and a right singular matrix V through singular value, generates a diagonal matrix Σ' by using the singular value matrix Σ and an arbitrary diagonal matrix A, generates a replica vector x^ by using a received signal vector y' obtained by applying reception beamforming to the received signal from each of the reception antennas by using the left singular matrix U and a linear filter W generated using the right singular matrix V and the diagonal matrix Σ', and executes a nonlinear signal detection algorithm by using the received signal vector y' and the replica vector x^.SELECTED DRAWING: Figure 1

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 present application is applicable to the field of image processing technology, and provides an image processing method, device, electronic device and medium, the method comprising: obtaining attribute information and index matrix of a target image; performing singular value decomposition on the index matrix to obtain a left singular matrix, a right singular matrix and a diagonal matrix corresponding to the target image; wherein the values ​​of each element on the diagonal of the diagonal matrix are all singular values ​​of the index matrix; determining a singular value quantity threshold according to the attribute information; performing a zeroing operation on some elements of the diagonal matrix, the left singular matrix and the right singular matrix according to the singular value quantity threshold to obtain an approximate matrix for representing compressed data of the target image. Compared with the prior art, the method improves the compression efficiency by performing singular value decomposition on the index matrix of the target image. At the same time, the singular value quantity threshold matching the image can be accurately determined according to the attribute information of the image, so as to ensure the image quality when compressing the target object.

According to an aspect, there is provided an apparatus configured to perform the following. The apparatus obtains an approximate effective channel matrix for a radio channel between the apparatus, acting as a transmitter, and a receiver. The apparatus calculates an eigenvalue decomposition, EVD, of a matrix product of the approximate effective channel matrix and a conjugate transpose of the approximate effective channel matrix and determines, based on the EVD, a left singular matrix of a singular value decomposition, SVD, of the approximate effective channel matrix and a diagonal matrix of singular values of the SVD of the approximate effective channel matrix. The apparatus calculates eigenvectors of the approximate effective channel matrix based on the approximate effective channel matrix and the left singular matrix of the SVD.

Provided in the embodiments of the present application are a data transmission method and a communication apparatus. The method comprises: acquiring first data, the first data having M dimensions; determining dimension information corresponding to each dimension amongst P dimensions, the dimension information corresponding to each dimension being used for determining the dimensional size of a non-singular matrix corresponding to each dimension, and the P dimensions being from amongst the M dimensions; on the basis of the first data and the dimension information corresponding to each dimension, determining the non-singular matrix corresponding to each dimension and second data; and transmitting the non-singular matrix corresponding to each dimension and the second data. Thus, on the basis of dimension information corresponding to each dimension, a first apparatus can determine the dimensional size of a non-singular matrix corresponding to each dimension without performing higher-order singular value decomposition on high-dimensional data, thus remarkably reducing the computational complexity of compression of first data.

The invention provides a distributed power model updating method and device based on model incremental training and electronic equipment. According to the implementation scheme, when local power incremental data of distributed power nodes are insufficient, singular value decomposition is carried out on a local model parameter matrix of a local power model in the distributed power nodes, and a left singular matrix, a diagonal matrix and a right singular matrix are obtained; respectively extracting corresponding column vectors from the left singular matrix and the right singular matrix based on main singular values in the diagonal matrix to obtain an important parameter matrix; and determining a non-important parameter matrix based on a difference matrix between the local model parameter matrix and the important parameter matrix, and performing low-rank decomposition on the non-important parameter matrix to obtain a local first low-rank parameter matrix and a local second low-rank parameter matrix, thereby performing global model updating and updating the local power model of each power node. By adopting the method, the model training speed and precision can be improved, and the model is prevented from drifting.

The invention provides a method and system for reducing model fine tuning parameter redundancy based on parameter subspace similarity, and the method comprises the steps: carrying out the efficient parameter fine tuning of downstream domain data through an instruction fine tuning base model, and obtaining a fine-tuned low-rank parameter; setting a parameter redundancy measurement step length and a search space to obtain a group of low-rank parameter lists with components of different degrees removed; singular value decomposition is used to obtain a left singular matrix, and the Grassmann distance between the left singular matrix and the initial base model is calculated; and selecting a low-rank parameter with the maximum Grassmann distance, and combining the parameters to obtain a fine tuning parameter actually used in the reasoning stage. According to the method, components which are most similar to base model parameter subspaces in fine tuning parameters are reserved, so that components which are similar to base model distribution are reserved while interference fine tuning parameter components are reduced; the problem of disastrous forgetting and distribution mismatching of the base model and the downstream task can be reduced while the fine tuning parameter can learn effective downstream knowledge to the greatest extent.

The invention belongs to the field of wireless communication, and discloses a pre-coding method for a multi-user MIMO-OTFS system. The method comprises the following steps: establishing an optimization problem according to a pre-coding matrix, a complete channel matrix, a system noise variance, a user weight and a total transmitting power constraint in a multi-user MIMO-OTFS communication model; a complete channel matrix in the optimization problem is approximated to be an LoS path approximation channel matrix, a matrix SVD is obtained, precoding matrix design is carried out based on a right singular matrix in an SVD decomposition result, and a directional component and a power component are included; substituting the designed precoding matrix into an optimization problem, constructing a sub-optimization problem about the power component and the complex gain of each sub-channel, optimizing the transmitting power of each sub-channel, and outputting the optimal power component; and constructing a final precoding matrix according to the direction component and the optimal power component. According to the method, efficient utilization of system resources can be realized, and the calculation complexity can be greatly reduced while the WSR performance is ensured.

The invention discloses an imaging satellite task synthesis method and system based on maximum weight group search, a product, equipment and a storage medium, belongs to the technical field of imaging satellite task planning, and solves the problem that target points as many as possible can not be synthesized in each observation in the prior art. And the technical problem of influence of different orbits, breadths and data delivery requirements on task synthesis in a satellite constellation can be flexibly solved. Extracting parameter information of the visible imaging window; constructing a composite matrix; and judging whether the composite matrixes are singular matrixes or not, if so, outputting all the established composite strips, and ending the process. And if not, establishing a synthesis strip of the maximum weight group, deleting the tasks in the synthesis strip of the maximum weight group and the tasks conflicting with the tasks, updating the synthesis matrix, and then re-judging whether the synthesis matrix is a singular matrix or not. The imaging satellite task synthesis method is used for synthesizing target points as many as possible in each observation, and can flexibly cope with different synthesis targets.

The invention provides a sea clutter suppression method based on K-means clustering, and the method comprises the steps: carrying out the preprocessing of a radar echo signal, and carrying out the singular value decomposition of a fast and slow time dimension pulse pressure data matrix; performing singular value spectrum distribution analysis on the singular value matrix to obtain a singular value, performing spatial correlation analysis on the right singular matrix to obtain a spatial correlation coefficient, and performing average Doppler estimation on the left singular matrix to obtain an autocorrelation estimation value; constructing a three-dimensional space of {a singular value, a spatial correlation coefficient and an autocorrelation estimation value}; three clustering centers are randomly set, and K-means clustering is carried out to complete classification formed by clutter, target and noise signals; and eliminating clutter subspaces, and reconstructing a fast and slow time dimension pulse pressure data matrix to complete clutter suppression.

The present invention discloses a patch clamp electrophysiological data processing method and system, which obtains ion channel current data by collecting patch clamp microelectrodes; removes baseline drift from the ion channel current data by an adaptive quadratic linear fitting function to obtain baseline-corrected current data; discretizes and performs high-frequency filtering on the baseline-corrected current data based on a preset sampling frequency to obtain a filtered current signal sequence; calculates an optimal embedding dimension by a false nearest neighbor method, calculates an optimal delay time based on a mean shift method, and performs second phase space reconstruction using the filtered current signal sequence, the optimal embedding dimension, and the optimal delay time to obtain an optimal vector set and a phase space reconstruction matrix; calculates a principal component vector set and the information entropy corresponding to each vector by a singular value decomposition algorithm and the Shannon formula, obtains a principal vector sequence by discrimination using a preset information entropy threshold, and obtains electrophysiological characteristic data by singular matrix reconstruction.

The present invention discloses a generalized Schur decomposition delay estimation method for GNSS array tensor modeling. Specifically, the method comprises the following steps: first, calculating the HOSVD of the signal tensor, compressing the core tensor and singular matrix, performing a generalized Schur decomposition on the two frontal slices of the core tensor, and then upper triangulating the matrix of the frontal slice of the core tensor; using LSKRF to decompose the matrix and solve the factor matrix, and then using the factor matrix to find the corresponding Loss of Sight (LOS) signal components; then, calculating the signal amplitude based on the structure of the signal tensor; interpolating to obtain the post-correlation interpolation function of the correlator and the LOS signal, and estimating the LOS delay based on the interpolation function peak search. The present invention achieves high-precision and low-computational-complexity delay estimation of GNSS signals, with higher robustness and lower computational complexity.