129 results about "Gaussian elimination" patented technology

A Dynamic Digital Pre-Distortion (DDPD) system is disclosed to rapidly correct power amplifier (PA) non-linearity and memory effects. To perform pre-distortion, a DDPD engine predistorts an input signal in order to cancel PA nonlinearities as the signal is amplified by the PA. The DDPD engine is implemented as a composite of one linear filter and N-1 high order term linear filters. The bank of linear filters have programmable complex coefficients. To compute the coefficients, samples from the transmit path and a feedback path are captured, and covariance matrices A and B are computed using optimized hardware. After the covariance matrices are computed, Gaussian elimination processing may be employed to compute the coefficients. Mathematical and hardware optimizations may be employed to simplify and reduce the number of multiplication operands and other operations, which can enable the DDPD system to fit within a single chip.

The invention relates to a method for statically calibrating a body axial system of a bearing reaction wind tunnel balance, wherein a loading device is arranged on a connecting end of a supporting rod of the wind tunnel balance, and the displacement of the loading device of the wind tunnel balance is measured through a laser displacement sensor which is arranged on a rigid support; after the wind tunnel balance is loaded, the reading of the laser displacement sensor is acquired, and the displacement of the loading device of the wind tunnel balance and the displacement of various loading points are calculated; the load exerted is subjected to translation and synthesized on an origin of a coordinate system of a body axle, and the bearing reaction generated on the wind tunnel balance by a base on the origin of the coordinate system of the body axle is solved and taken as the load of the body axial system of the wind tunnel balance; and a static calibration formula of the wind tunnel balances is solved through the least square method and the Gaussian elimination method by combination of the increment of outputted voltage signals before and after loading of all the wind tunnel balances and the loads of body axial systems. The static calibration method can simplify a static calibration device of the wind tunnel balance, not only does not need to use a complex offset-type or single-vector motion mechanism but also does not need to be limited by the performance of a reference balance, and is simple to calculate due to fixed position of the body axial system of the wind tunnel balance compared with the displacement method of a measuring and loading device.

A method for recovery of lost and/or corrupted data transmitted from a transmitter device to a receiver device. The data is coded by an encoder connected to the transmitter device. The data is transmitted from the transmitter device to the receiver device via a transmission system and is decoded by means of a decoder connected to the receiver device. This is performed through application of a low density parity check method, wherein lost and/or corrupted data is restored during decoding. The decoding is performed by solving the equation system of the parity check matrix H. The parity check matrix H is brought into a triangular form by column and/or row permutations. Columns of a sub-matrix B of the matrix H which impede the triangulation process are shifted into a sub-matrix P of the matrix H so that the triangulation process can be continued until the matrix H except for the sub-matrix P has been completely brought into a triangular form. The Gaussian elimination method is applied to a part of the sub-matrix P. The selection of the column or columns of the sub-matrix B which are to be shifted into the sub-matrix P is performed on the basis of the weight of the column which corresponds to the number of non-zero-entries in the column, and/or on the basis of the weight of the rows of the sub-matrix B connected to the column.

The invention discloses a method for distributing upstream going coding vectors in a multicast network, comprising: linearity unrelatable global coding vectors are distributed from the beginning of a grogram and gradually transmitted to a source node, and decoding matrix is ensured to have full rank at a destination node; namely, the linearity unrelatable global coding vectors are distributed for all the destination nodes and then transmitted towards upstream, and local coding vectors are calculated and recorded according to a certain regulation at the coding nodes. A congregation of the global coding vectors is maintained at the source node, and all the coding vectors are respectively recorded at one port of a buffer of the coding vector. When the data is transmitted, the message sent by the source node is firstly multiplied by the global coding vectors at all the ports of the buffer, and then is transmitted towards the downstream. After receiving the message, the destination nodes transfer the decoding matrix to decode by adopting Gaussian elimination method. The invention is applicable to the method for distributing the coding vectors in topology of the known network and the unknown network, so as to increase the decoding probability of the success for receiving node in the multicast network based on the network code, reduce the size of alphabet needed by decoding and lower the complexity of the network code.

The invention relates to a parameter blind identification method of a puncturing turbo code component coder, belonging to the technical field of the code identification in a communication system. The method comprises the steps of multiplexing a puncturing turbo code information bit and a verification bit to form a puncturing convolutional code, carrying out statistics and analysis for the code length and a code word starting point of the puncturing convolutional code by virtue of a linear matrix analysis method, identifying a verification matrix of the component coder by utilizing partial Walsh-Hadamard conversion, obtaining a puncturing mode and generating a matrix further by virtue of a Gaussian elimination method, carrying out the blind identification on the parameters of the puncturing turbo code component coder, and verifying the accuracy of the identified parameters. By adopting the method, the operation complexity of identifying the component coder parameter can be alleviated, the identification efficiency and the identification reliability can be improved, and the method is applicable to the fields of the intelligent communication, information processing and the like.

The invention discloses a method and a device for detecting signals in a multi-input and multi-output system, which belongs to the field of communication. The method comprises the following steps of: carrying out channel estimation according to obtained receiving signals to obtain a channel matrix H; obtaining an inverse matrix R of an estimation error covariance matrix Q of all emitting signals to be detected according to the channel matrix H; decomposing the inverse matrix R of the estimation error covariance matrix Q; and obtaining the estimation value of all emitting signals to be detected according to the decomposing results, a conjugate matrix HH of the channel matrix H and the receiving signal vector r. The device comprises a channel estimation module, a calculating module, a decomposing module and a detection module. Through decomposing the inverse matrix R of the estimation error covariance matrix Q and adopting a Gaussian elimination method, the invention realizes the signal detection, avoids the matrix inversion during the signal detection, and reduces the calculation complexity during the signal detection.

One embodiment of the present invention provides a system that performs a procedure to solve a system of linear inequalities. During operation, the system receives a representation of the system of linear inequalities Ax≦b, wherein Ax≦b can be a linearized form of a system of nonlinear equations. Within this representation, A is an interval matrix with m rows corresponding to m inequalities, and with n columns corresponding to n variables, the vector x includes n variable components, and the vector b includes m scalar interval components. The system solves the system of linear inequalities Ax≦b by performing a Gaussian elimination process using only positive multipliers so as not to change the sense of any inequality. For a given column j in A, performing the Gaussian elimination process involves attempting to select a primary pivot row r including a primary pivot element, a_rj, which does not contain zero, and attempting to select a secondary pivot row s including a secondary pivot element, a_sj, which does not contain zero and is opposite in sign to a_rj. If r and s are successfully selected, the system uses the secondary pivot element a_sj to zero elements opposite in sign to it in the same column of A, except for the primary pivot element a_rj. The system also adds a copy s′ of the secondary pivot row s to the matrix A, thereby increasing the number of rows in the matrix A. Next, the system uses the primary pivot element are to zero elements opposite in sign to it in the same column of A, except for the copy of the secondary pivot element a_s′j in row s′.

A two-plane rotation (TPR) approach to Gaussian elimination (Jacobi) is used for computational efficiency in determining rotation parameters. A rotation processor is constructed using the TPR approach to perform singular value decomposition (SVD) on two by two matrices yielding both eigenvalues and left and right eigenvectors. The rotation processor can then be replicated and interconnected to achieve higher dimensioned matrices. For higher dimensional matrices, the rotation processors on the diagonal solve the 2×2 rotation angles, broadcast the results to off-diagonal processors, whereby all processors perform matrix rotations in parallel.

The invention provides a method for quickly solving a nodal impedance matrix of a power system, and relates to the field of analytical computation of the power system. The method mainly comprises the following steps of inputting data of a nodal admittance matrix Y; establishing an augmented matrix B by the nodal admittance matrix Y and an identity matrix E together; normalizing the augmented matrix B and carrying out a Gauss-Jordan elimination method on the augmented matrix B for n times; obtaining an inverse matrix Z. At present, traditional methods for solving the nodal impedance matrix comprise an LDU (Logic Data Unit) triangular decomposition method and a Gauss elimination method, and compared with the two traditional methods, the novel method for quickly solving the nodal impedance matrix by utilizing the Gauss-Jordan elimination method, provided by the invention, has the advantages that the principle is simple and easy to understand, the computation time is reduced, the programming is convenient, and the like; compared with the traditional LDU triangular decomposition method and the Gauss elimination method, by utilizing the method for verifying systems such as an IEEE-57 node, an IEEE-118 node and an IEEE-300 node, the computation speeds can be respectively increased by about 25%-50%.

Check matrix of check code in low density is divided into three layers. Each layer is created by circular shifting first row. Check matrix built based on structure in three layers is transformed to corresponding generator matrix through Gaussian elimination. Generator matrix is utilized to generate code words of encoder, and check matrix is utilized to decode procedure of decoder. The invented method reduces hardware complexity of encoder and decoder.

The invention belongs to the field of power system analysis and computing and discloses a method for solving a node impedance matrix of an electric system on the basis of a Gaussian elimination method of a sparse symmetric matrix technology. The method mainly comprises the following steps that a node admittance matrix Y is formed; the matrix Y and a matrix En form an augmented matrix Bn=[YEn]; elimination is carried out on the matrix Bn according to the spare symmetry to obtain Bn(n-1)'=[Y(n-1)'En(n-1)']; according to Y(n-1)'Zn=En(n-1)', sparseness and symmetry, elements above and on the left of a diagonal element Znn of a matrix Zn are solved; a matrix Y(k-1)' is obtained according to the Y(n-1)'; elements above and on the left of a diagonal element Zkk of the matrix Zk are obtained according to Y(k-1)'Zk=Ek(k-1)', sparseness and symmetry. By the utilization of the symmetric sparseness, all invalid computation of the previous generation process is avoided, and computation of about 50% of nonzero elements is reduced; by the utilization of the characteristics of the E matrix element structure and the sparseness of upper triangle elements, the elements of the matrix Zk are obtained in a back substitution mode according to a symmetry mode, and back substitution computation is greatly accelerated. The method can check IEEE-30, -57 and -118 node systems and the like, and the computation speed for the IEEE-118 node system can be improved by 96-97% compared with a traditional Gaussian elimination method and an LDU triangular decomposition method.

The invention discloses a railway wind disaster prevention prediction method in the technical fields of railway disaster prevention alarming and monitoring. The method comprises the following steps of: acquiring wind speed and wind direction data of a current moment measuring point; calculating the first-order difference, the second-order difference and the third-order difference of the wind speed and wind direction data of the current moment measuring point; retrieving the wind speed and wind direction data, the first-order difference, the second-order difference and the third-order difference of three continuous moments before the current moment and establishing an identification equation of a wind speed and wind direction model; establishing a quaternion first-order equation set according to the identification equation of the wind speed and wind direction model and acquiring the model parameters of the wind speed and the wind direction through a Gaussian elimination method; updating the model parameters according to an average sliding coefficient; and calculating the wind speed and wind direction data of the next moment according to the updated model parameters. The invention improves the prediction accuracy and the accuracy of a prediction result.

The invention discloses a coding resource adaptive scheduling algorithm in a multicast network, comprising: dividing the subdomain of network topology which is needed to be processed by coding multicast, and then according to the different demands of a destination node, adopting scheduling algorithm for scheduling and configuring network resource especially coding vector resource in real time; after all information transmitted by an upstream link circuit is collected by the destination node, extracting coding vector of data header to form decoding matrix; and adopting Gaussian elimination method for decoding, and obtaining the needed primary data. The method can lead network coding resource to be configured according to the need, ensures a target end to receive the needed coding information at the most rapid speed, and decodes in time. In addition, nodes are allowed to take part in or exit from multicast at any time, so that distributed network coding multicast can be realized.

A blind identification method for an (n, 1 and m) convolutional code with error codes belongs to the technical field of blind identification of channel codes and comprises the following steps: after to-be-identified data is read by a computer, an identification matrix is established; row simplification is performed to the matrix through the method of gaussian elimination; the code length and the starting point are identified according to the simplification results; n paths of data are combined in pairs; the multinomials of a (2,1 and m) convolutional code are identified through a method of solving an equation set by a Hadamard matrix; and the result obtained through identification is updated to a final generator matrix according to the relation of the multinomials. The method has the advantages that storage of high-order Hadamard is simplified, the storage capacity of data in the operation process of a computer is reduced, the processing procedure of obtaining multinomials of the (n, 1 and m) convolutional code through the (2, 1 and m) convolutional code is introduced in detail, a parallel computing part is added, the treatment process of a program is accelerated, and the fault tolerance is good.

A method for blind identification of an (n, k and m) system convolutional code belongs to the technical field of blind identification of channel codes and comprises the following steps: after to-be-identified data is read by a computer, an identification matrix with the row number of 180 and the line number of 170 is established; row simplification is performed to the matrix through the method of gaussian elimination; for the simplified matrix, the code length, the information bit length and the data starting point are identified according to the arrangement rule of elements 0 and elements 1 in the main diagonal of the matrix, and then an appearing regular matrix is found according to the positions of obtained regular elements; the regular matrix is extracted from the simplified identification matrix to obtain a check matrix; a generator matrix is obtained through extraction according to the relation between the check matrix and the generator matrix. A specific implementation process of blind identification of the (n, k and m) system convolutional code is given in the method, so that programming operation can be realized advantageously, the identification speed is high, and the method is simple. The identification rate can reach 100% under the condition that no error code exists in the (n, k and m) system convolutional code.

Hardware acceleration for batched sparse (BATS) codes is enabled. Hardware implementation of some timing-critical procedures can effectively offload computationally intensive overheads, for example, finite field arithmetic, Gaussian elimination, and belief propagation (BP) calculations, and this can be done without direct mapping of software codes to a hardware implementation. Suitable acceleration hardware may include pipelined multipliers configured to multiply input data with coefficients of a matrix associated with a random linear network code in a pipelined manner, addition components configured to add multiplier output to feedback data, and switches to direct data flows to and from memory components such that valid result data is not overwritten and such that feedback data corresponds to most recent valid result data. Acceleration hardware components (e.g., number and configuration) may be dynamically adjusted to modify BATS code parameters and adapt to changing network conditions.

The invention discloses a power system state estimation method and system. The method comprises the following steps: acquiring telemetering and telesignaling data of primary equipment of a power system in real time; constructing a power system node branch model; determining non-zero element distribution of the active jacobian matrix according to the node branch model, and determining non-zero element distribution of the active information matrix; performing node optimization numbering based on active information matrix non-zero element distribution; calculating an active Jacobian matrix and areactive Jacobian matrix according to the new node sequence to obtain a new active information matrix and a reactive information matrix, and performing factorization by adopting a Gaussian eliminationmethod to obtain an active information matrix factor table and a reactive information matrix factor table; and performing power system state estimation based on the active information matrix factor table and the reactive information matrix factor table. When the method is used for solving the information matrix and estimating the state of the power system, the memory operation in the solving process can be reduced, the calculation load is reduced, and the calculation efficiency of power grid state estimation is improved.

The invention discloses a lower triangular Cholesky decomposition-based ambiguity correlation-lowering method and belongs to the satellite navigation and positioning technical field. According to themethod, LDL<T> decomposition is performed on an ambiguity variance-covariance matrix, so that a unit lower triangular matrix L and diagonal matrix D can be obtained; Gaussian elimination and conditional variances are adopted to exchange two integer transformation processes, so that the correlation of the off-diagonal elements of the lower triangular matrix L can be lowered, and therefore, elementsin the diagonal matrix D are sorted in an ascending order as much as possible; and finally, ambiguity search can be carried out to realize ellipsoid shape transformation, and the number of redundantinteger candidate nodes contained in a search space can be decreased, search time can be reduced, and ambiguity resolution efficiency can be improved.

The invention discloses a reliable video transmission method based on network coding to mainly solve the problem of lower video reconstruction quality of a receiving end caused by packet loss or error codes in a general video transmission method. The reliable video transmission method based on network coding comprises the following steps of: adding corresponding redundant information to a layered code stream used for performing the scalable video coding for each picture group of original video information, and packing the layered code stream to complete code rate distribution; performing the random linear network coding on data packets subjected to code rate distribution by using a designed global coding matrix with a nonstringent lower triangular structure; and transmitting coded information to the receiving end through a transmission channel, and decoding a network coding code stream at the receiving end by using a Gaussian elimination method, and restoring the original video information. The reliable video transmission method based on network coding has the advantages of improving the problem of lower video reconstruction quality of the receiving end caused by packet loss and time delay in the transmission process, and realizing the unequal error protection of an H. 264/SVC (scalable video coding) code stream and the high-reliability video multicast.

The method includes following steps: the check matrix of low density check code is mapped into a geometrical body; the check matrix is built into four layers by using different threading method; each layer relates to a threading method in geometrical body; the check matrix built with four layer structure is converted into generator matrix by using Gaussian elimination method or triangularization method; the generator matrix is used in generating code word of code device. The check matrix is used in decode procedure of decoder.

The invention discloses a compensation method for IQ imbalance existing in an OFDM system receiver. An IQ compensation module of the OFDM system receiver effectively compensates the IQ imbalance on the basis of obtaining channel parameter estimation of the IQ imbalance. The Gaussian elimination compensation method replaces a frequency domain LS compensation method, so that the computation complexity is significantly reduced (the frequency domain LS compensation method needs to carry out matrix inversion, so that the complexity is high.). The SNR loss of the method is lower than that of the frequency domain LS compensation method.

The invention discloses an erasure correcting decoding method and system of an LDPC code. The method includes the steps that (S1) known information in a codon is put into a verification equation set to obtain residual verification equation sets; (S2) equations with only one unknown variable are found in the residual verification equation sets, and the unknown variables are solved and put into the residual verification equation set to be updated; (S3) the step (S2) is repeated, if all unknown variables are recovered, decoding is successful, and if not all the variables are recovered, the step (S4) is carried out; (S4) sub equation sets in the residual verification equation sets are searched for, if only one unknown variable occurs in the sub equation sets once and other unknown variables occur even number times, the sub equation sets are added, so that the unknown variable which occurs only once is solved, the residual verification equation sets are updated, and the step (S3) is carried out until all the unknown variables are recovered. According to the erasure correcting decoding method and system of the LDPC code, guessing and Gaussian elimination do not need to be carried out, and decoding performance of the LDPC code can be improved when the LDPC code is transmitted on a deletion channel.

The invention provides a method for decoding a fountain code. The method comprises: when a receiving terminal receives an nth generation vector Gn and code vector Tn that are transmitted by a transmitting terminal, a generation matrix G<(j)> with j as an order and a matrix formed by generation vectors Gn are processed by using a gaussian elimination method to obtain an intermediate matrix G'; if the order of the G increases by being compared with the order of the G<(j)>, a relation, expressed by a formula: G<(j+1)>=G', is met; a coding matrix T(j) and a matrix formed by coding vectors Tn are processed by using a gaussian elimination method to obtain coding matrix T<(j+1)>; and the steps are repeated until the order j of the generation matrix is equal to the number K of sending data blocks and an information matrix is T<(K)> and decoding is completed. With the decoding method, the decoding delay can be reduced; and a low redundant frame proportion can be kept. On the basis of the decoding method, the invention also provides an underwater acoustic communication transmission method of a fountain code. With the transmission method, the influence on the channel utilization rate by the feedback information transmission time can be eliminated and the channel utilization rate can be improved.