101 results about "Matrix optimization" patented technology

The invention discloses a precoding matrix and phase shift matrix optimization method in a bidirectional MIMO communication system, and relates to the field of wireless communication. Two source nodesassist in two-way communication through intelligent reflective surfaces. In order to maximize the system sum rate, a precoding matrix of a source node and a phase shift matrix of an intelligent reflecting surface are jointly optimized. The method comprises the steps of 1) establishing a system model; and 2) optimizing the source node precoding matrix and the intelligent reflecting surface phase shift matrix to maximize the sum rate. Aiming at the bidirectional MIMO communication system based on the intelligent reflecting surface assistance, the total transmission rate of the bidirectional communication system can be improved by jointly optimizing the source node precoding matrix and the intelligent reflecting surface phase shift matrix.

A system and method of adjusting therapy delivery in an implantable cardiac stimulation device including establishing a plurality of setting combinations for at least two variable parameters of the implantable cardiac stimulation device affecting delivery of therapy. At least one aspect of a patient's physiologic performance is evaluated under individual ones of the plurality of setting combinations selected such that at least one of the two variable parameters vary among the plurality of combinations. A setting combination providing more optimal patient physiologic performance is programmed for future delivery of therapy. An external device can provide measurements indicative of cardiac performance. Measurements of cardiac performance can also be obtained by an implantable device.

The invention discloses a sensor-network-based low-energy-consumption ecological environment monitoring node deploying method and relates to the Internet of Things. The method comprises the following steps: firstly determining factors to be monitored and key points with relative factors; building an optimal mathematic monitoring model according to the monitored factors and the key points; then solving the mathematic model and finding a key point with most attributes and taking the key point as a pre-selection point; invoking an attribute optimizing method for selecting the key points with all the attributes which are close to an attribute mean value if the number of the pre-selection points is greater than 1; invoking a crop growth factor weighing method for determining monitoring points if more than two pre-selection points exist after the treatment; randomly determining that the pre-selection points processed by the crop growth factor weighing method are taken as the monitoring points by using a random algorithm; finally processing a matrix T formed by the key points and the attributes by using a matrix optimizing method of the key points and the attributes to obtain the number of the final monitoring nodes and the distribution positions of the monitoring nodes.

The invention provides a measurement matrix optimization robustness method based on MSE. The method is based on a traditional model for optimizing a measurement matrix, a regular item is added and theregular item represents the mean square error of the original image and the reconstructed image. The method comprises the following steps of assuming that a mean square error obeys standard positivedistribution; carrying out the singular value decomposition by applying a center limit theorem and an equivalent dictionary, so that the optimization model of the measurement matrix is well simplified; finally using a gradient descent algorithm, so that the optimized measurement matrix is iteratively solved, the newly proposed image measurement matrix optimization model fully applies the information of the image, the cross correlation coefficient between the measurement matrix and the sparse base is reduced, the requirement on the sparsity is reduced, and the robustness of the image compressedsensing system is improved to a certain extent. Experiments show that independence between optimized measurement matrix columns is increased, and the reconstruction of high-quality image signals is facilitated.

The invention relates to a compressed sensing measurement matrix optimization method and system based on automatic encoder network. The method comprises the steps of obtaining original images as training data and dividing the training data into a plurality of image blocks through segment cutting processing; conducting sampling on the image blocks based on the preset sampling rate and the automaticencoder network, and generating initial reconstruction images; calculating the residual error value between the initial reconstruction images and the original images based on the deep residual errornetwork; combining the residual error value with the initial reconstruction image and generating a reconstruction result, and establishing a loss function based on the reconstruction images and the image blocks, and conducting training on the parameter in the automatic encoder network through the loss function, and using the automatic encoder network parameter after training completion as the compressed sensing measurement matrix. The invention is advantageous in that through the transformation of data dimensions via the automatic encoder, the process from collecting to reconstructing of images can be simulated and realized, wherein the parameter in the collecting process is the measurement matrix, which has good reconstruction quality.

The invention discloses a compressive spectrum sensing method. The method is applied to the spectrum hole detection in a cognitive radio system, and belongs to a novel method for carrying out spectrum sensing by a compressive sensing technology. The method carries out condition setting by aiming at observing data, the detecting efficiency in the compressive spectrum sensing is improved, when acceptable conditions are met, the estimation value is reconstructed, when the acceptable conditions are not met, the observing times are increased, and the adaptive process of the matrix observation is realized. The method realizes the integral optimization through reducing the sparseness among the observing matrix array vectors, reducing the correlation among the line vectors and combining the matrix adaptivity observation in a united way. Compared with the ordinary compressive spectrum sensing, the compressive spectrum sensing method has lower mean square errors generated during the spectrum reconstruction, and the spectrum detecting efficiency is higher under the condition of the same observing times; the observing times required by the method is fewer when the same receiving operation performance.

The invention discloses a fabric defect detection method based on sparse representation coefficient optimization. The detection method comprises self-adaptive dictionary database study, sparse coefficient matrix optimization and image reconstruction as well as generation and segmentation of a vision saliency map and specifically comprises steps as follows: an image is partitioned into blocks, self-adaptive dictionary database study is performed, and a dictionary database is obtained; a sparse representation coefficient matrix is solved with an L2-norm minimization method, and abnormal coefficient elements in the obtained matrix are optimized; a fabric image is reconstructed with adoption of the obtained dictionary database and the optimized sparse representation coefficient matrix, the fabric image and a to-be-detected image are subjected to residual error processing, and a residual error saliency map is obtained; the saliency map is segmented with a maximum entropy threshold segmentation method, and a fabric defect detection result is obtained. Randomness of fabric textural features and diversity of defect varieties are overall considered, the to-be-detected fabric image is taken as a detection reference for a dictionary database studying sample and a defect area, the method has higher detection accuracy, no defect information is required to be extracted, and the self-adaptive capability is high; the computation speed is higher, and the method is suitable for online detection.

The invention discloses an efficient privacy protection perception big data collection method based on fog computing. A hierarchical fog computing auxiliary data collection framework is designed, so that a computing task can be processed on local equipment or network edge equipment, long-distance communication with a cloud center is avoided, and effective support is provided for exploring space-time correlation of perception data. Meanwhile, through the sampling disturbance encryption method, the data privacy is protected from being damaged by eavesdroppers and active attackers, the encryptionmethod does not damage the correlation of the data, and the decryption and reconstruction operation on the encrypted sampling data is simplified. Meanwhile, through the designed fog node data processing mode and anobservation matrix optimization model, the redundant data transmission amount is greatly reduced, the spatial correlation is effectively explored, and it is guaranteed that data can bereconstructed with high precision.

A compressive sensing based Gaussian matrix optimizing method belongs to the technical field of measuring matrix optimization in compressive sensing and solves the problem of poor signal reconstruction capability of Gaussian matrix measuring matrix. The compressive sensing based Gaussian matrix optimizing method includes: subjecting row vectors to orthogonal standardization and column vectors to unitization for measuring matrix computed by the (i-1)th iteration through the ith iteration and completing optimization of Gaussian matrix by utilizing range of included angles among the column vectors, the maximum value and the minimum value of the included angles among the row vectors, the maximum value and the minimum value of row vector modules and the number of rows and columns inadaptive to Gaussian distribution as reference. The compressive sensing based Gaussian matrix optimizing method is applicable to optimization of Gaussian measuring matrix in the compressive sensing.

The invention relates to a distribution network evaluation method based on power supply partitioning features. The method comprises the following steps: determining a distribution network evaluation index system which is composed of five primary indexes and 17 secondary indexes; building membership functions of the indexes by use of a fuzzy membership degree method and based on the principle of linear interpolation; determining the weights of the evaluation indexes by use of a judgment matrix method of a quantitative calculation method; establishing a direct influence matrix, calculating a comprehensive influence matrix between the indexes, and using the comprehensive influence matrix to optimize the weights of the evaluation indexes; and substituting the calculation result of the previous step to a calculation formula of the analytic hierarchy process to get the final result of distribution network evaluation. Through the method, the comprehensive score level of evaluation samples can be given more accurately, the evaluation result is more accurate, and the distribution network plan and design decision work can be better guided.

A method and base station for enabling beamforming, wherein the method for enabling beamforming comprising: establishing a downlink beam optimization model according to an integrated channel error model and a downlink model; obtaining an optimal beamforming matrix optimization goal according to the established downlink beam optimization model; respectively obtaining the channel matrixes of the service cell in which the base station is located and the cells exclusive of the service cell in the cooperative cluster in which the base station is located and the nonideal parameters respectively corresponding to each channel matrix; obtaining a beamforming matrix for the base station according to the obtained channel matrixes and the nonideal parameters respectively corresponding to each channel matrix and the optimal beamforming matrix optimization goal, the base station transmitting downlink data according to the obtained beamforming matrix. The beamforming method and base station effectively suppress TDD system channel error, eliminate complex iteration calculation steps, and can effectively suppress inter-cell interference.

The invention discloses an adaptive anti-interference method for a satellite navigation system based on 2-norm multi-target optimization, and the method comprises the following steps: 1, constructing a direction vector and a constraint condition; 2, simplifying the constraint condition; 3, constructing a multi-target optimization anti-interference criterion; 4, solving the optimal solution to the multi-target optimization anti-interference criterion. According to the invention, a 2-norm matrix optimization function which enables the antenna gain in all directions to be constant is added to a single optimization function of a conventional blind anti-interference algorithm, thereby enabling an original optimization problem to be converted into multi-target optimization from single-target optimization, enabling the antenna gains in all directions to tend to be constant, and solving the problems that a directional diagram null steering opening is large in width in the blind anti-interference algorithm and the gains in non-interference directions are not flat.

The invention discloses a satellite-borne AIS collision signal separation method based on adaptive moment estimation. The method specifically comprises the steps of sampling received AIS collision signals to obtain N ways of receiving signals, and carrying out digital down conversion processing to obtain an observation signal matrix; preprocessing the observation signal matrix, and carrying out collision removal processing through combination of mutual information maximization and maximum adaptive moment estimation and a signal separation algorithm, thereby obtaining N ways of separated signals; carrying out frame header burst detection, frequency offset estimation, symbol timing synchronization and whitening filtering processing on the N ways of separated signals; and carrying out decoding processing through utilization of a Viterbi algorithm, thereby obtaining valid data frames. According to the method, the maximum adaptive moment estimation is introduced into the mutual informationmaximization algorithm, the estimation precision of a separation matrix is improved, moreover, the time required for matrix optimization and collision removal is reduced, the timeliness is relativelyhigh, and the method is relatively applicable to a satellite-borne AIS receiving system.

The invention discloses a data high-quality compression method fusing dictionary training and observation matrix optimization, deep mining of correlation between a dictionary and an observation matrixis realized through joint optimization of a sparse dictionary and the observation matrix, and the problem of actual sensor data acquisition is mainly solved. The method comprises the steps that a two-dimensional dictionary training method based on K-SVD idea is constructed to realize sparse representation of space-time data in IIoT; column coherence of a sensing matrix is minimized by optimizingcorresponding observation matrixes in space and time domains, so that a trained sparse dictionary is matched; and finally, a joint optimization method is provided, and trade-off consideration is performed between dictionary training and observation matrix optimization, so that the reconstruction error reaches a theoretical minimum value. Compared with an existing data compression scheme, the HQDCmethod provided by the invention can efficiently work in a real IIoT scene; meanwhile, the HQDC method is remarkably superior to other data compression schemes in reconstruction precision.

The invention discloses an observation matrix optimization method which can reduce storage space of a light field and greatly enhance the quality of the reconstructed light field. The method comprises the steps that (1) the affecting process of an observation matrix based on a compression perception light field photography technology is explored; (2) a structured observation matrix P is inferred according to the special structure of a camera; (3) dictionary training is performed from a sample library by utilizing a sparse dictionary training algorithm so that a dictionary D is obtained; and (4) an optimized solving algorithm is designed according to inconsistency of the observation matrix in compression perception and the dictionary with combination of the structured observation matrix P so that an optimized observation matrix P is obtained.

The invention provides an MIMO transmission power distribution optimizing method based on robustness design. Firstly, convexity-concavity of a matrix function is utilized, a power distribution matrix is optimized to be a diagonal matrix and the diagonal matrix is simplified to be a scalar function. Consequently, the scalar function is combined to perform optimization on MIMO transmission power distribution. During optimization of MIMO transmission power distribution, the optimal solution without limited conditions is found out and then adjusted in a fine mode. The algorithm convergence and low operation quantity are ensured. Compared with an ordinary optimal solution obtaining algorithm, the MIMO transmission power distribution optimizing method based on the robustness design can obtain the optimal solution with little operation cost. In addition, as a reference value of power distribution is fixed in the first step, the problem of algorithm divergence does not exist.

The invention discloses an isolated DC microgrid self-adaptive droop control method, which comprises three parts including application of a three-node DC microgrid, droop control and application of adiscrete distribution algorithm. The isolated DC microgrid self-adaptive droop control method provided by the invention exchanges voltage and current information with neighbor nodes under the action of a synchronous clock at each control node, iteratively evaluates an average voltage of the entire grid through adopting a local distributed algorithm, dynamically searches for target virtual resistance satisfying requirements of current sharing and voltage regulation, and realizes self-adaptive droop control of variable virtual resistance. Due to the absence of a central controller, the robustness and flexibility of control are increased. Meanwhile, measures for reducing or eliminating influences of communication delay are proposed from the aspects of communication media, communication protocols, system sampling intervals, network matrix optimization and the like, and the reliability of the strategies are improved.

The invention discloses a precoding and drive current joint optimization method for a four-color visible light communication system, which aims at minimizing sum mean square error (MSE) of signal estimation in a corresponding light channel while lighting quality requirements are met. Compared with the existing three-color visible light communication precoding matrix optimization problem, the four-color visible light communication system adds a light communication wavelength division multiplexing channel, and an ability of adjusting indoor lighting quality is also provided. In view of the optimization problem, nonlinear color rendering index constraints are firstly converted into linear light mixing ratio constraints via linear light mixing equations, and optimization solution is carried out on the MSE minimal value through introducing semidefinite relaxation algorithm.

The invention discloses a downlink beam forming method of a VLC (Visible Light Communication) MU-MISO (Multiple Users-Multiple Input-Single Output) system, and belongs to the technical field of visible light communication. According to the method, firstly, the optimization solution is carried out on a beam shaping matrix optimization model by a bi-level iterative method, thus obtaining an optimized beam shaping matrix; and then carrying out downlink beam forming according to the beam shaping matrix. The invention also provides a beam shaping matrix optimization model taking a maximum reachable rate as an optimization target. The invention also discloses a downlink beam forming device of the VLC (Visible Light Communication) MU-MISO (Multiple Users-Multiple Input-Single Output) system and the VLC (Visible Light Communication) MU-MISO (Multiple Users-Multiple Input-Single Output) system. Compared with the prior art, the method, the device and the system have the advantages of low hardware demand and the reachable rate of the system can be further improved.

The invention discloses a test optimization selecting method considering key failures. The method includes the steps of obtaining a failure-test relevance matrix, establishing an optimization model, solving the optimization model and the like. According to the method, from the angle of system security, the importance of the key failures is analyzed; then based on the relevance matrix, with the test price minimization as the optimization target, with the failure detection rate and isolation rate and the key failure detection rate and isolation rate as the constraints, the test optimization selecting model considering the key failures is established; finally, solution is conducted by means of a binary particle swarm algorithm based on mass center improvement and inertia weight self-adaptiveadjustment. The severe threats caused by key failure detection omission to device security can be effectively eliminated.

The present application relates to the technical field of encoding and decoding verification, and in particular, to an LDPC simulation verification platform and verification method. The verification platform comprises a sequence, an input unit, a to-be-tested device, an output unit, a reference model, and a scoreboard. The LDPC simulation verification platform is developed based on the UVM verification methodology, which is convenient for transplantation and redevelopment; through the verification matrix optimization program developed based on the MATLAB, an optimal verification matrix can begenerated for different NAND Flash particle characteristics; through the LDPC encoder / decoder reference model developed based on the C++, the expected data of each computing node in each codeword operation cycle can be accurately output, and by comparison with the resistance transistor logic code computed result, the problem location can be quickly locked; and by integrating the programming language interface component, real-time comparison between the C++ reference model and the resistance transistor logic code result and the error reporting can be implemented, and verification efficiency canbe improved.

The invention discloses a bidimensional compressed sensing image acquisition and reconstruction method based on discrete cosine transformation (DCT) and discrete Fourier transformation (DFT), belongs to the technical field of designs of measurement matrixes and optimization of reconstruction matrixes in the compressed sensing process and provides a method for firstly determining the measurement matrix and a sparse matrix and then optimizing the reconstruction matrix. In a measurement stage, the 0-1 sparse matrix is adopted; in a reconstruction stage, a Gaussian matrix is adopted; and therefore, an after-optimization method capable of easily implementing hardware and guaranteeing a signal reconstruction effect can be realized. The method comprises the following steps of: performing row vector orthogonal normalization and column vector unitization on the reconstruction matrix obtained by the (i-1)th iteration calculation through ith iteration, optimizing the reconstruction matrix on the basis of maximum values of absolute values of relevant coefficients among row and column vectors, the convergence stability of row vector modules and the number of rows and the number of columns which obey the Gaussian distribution, and finishing the after-optimization on measurement data subjected to one-dimensional and two-dimensional sparse transformation and the measurement matrixes by calculating a transitional matrix and a proximity matrix. The method lays a foundation for the compressed sensing from theoretical research to industrialization.

The invention discloses a satellite gravity field inversion method. The method comprises the following steps of: obtaining gravity, gravity vector and gravity gradient tensor data and a corresponding kernel matrix through isoparametric transformation; generating a corresponding depth weighting matrix; constructing a target function of multi-component joint inversion; and finally obtaining an inversion result. Compared with a traditional spherical shell unit, the dggrid used in the method can improve the reuse capability of a kernel function and reduce the memory occupation of a kernel matrix. Meanwhile, the characteristic that the dggrid has consistent adjacency is utilized, and center lines on three opposite sides of the dggrid serve as the roughness matrix construction direction, so that the purposes of optimizing the construction of a roughness matrix and simplifying the calculation of the smoothness coefficient of the roughness matrix are achieved. Besides, in the inversion optimization solution process, by using a depth weighting matrix irrelevant to the component attenuation characteristic, the multi-gravity / gravity vector / gravity gradient tensor component joint inversion setting is optimized, the convergence speed is accelerated, and the inversion accuracy is improved.

The invention discloses a spatial modulation method using stacked Alamouti coding mapping. The spatial constellation in the spatial modulation method using the stacked Alamouti coding mapping is defined as containing all possible combinations of activated antennas; and corresponding symbols in stacked codes are activated according to a specific SC codeword in the spatial constellation to form a SA-SM transmission signal for transmission. The invention is suitable for any even number of transmitting antennas, and is suitable for any number of activated antennas between 1 and n<T>; when the number of the activated antennas is the same, the SA-SM scheme provided by the invention carries more symbols than a spatial modulation orthogonal space-time coding scheme, therefore, higher spectral efficiency can be obtained; the SA-SM scheme provided by the invention has a never disappeared determinant feature without any parameter or matrix optimization, and the disappeared determinant feature canensure that the SA-SM scheme obtains second-order emission diversity; and finally, the SA-SM scheme provided by the invention has a block orthogonal structure on the coding structure, therefore, a low-complexity QRDM detection method can be adopted to perform decoding, which has very low decoding complexity.

The invention discloses a universal matrix optimization method for accelerating erasure correcting code encoding and decoding processes. The method comprises the steps of: during encoding and decoding, decomposing a check matrix into a plurality of mutually independent sub-matrixes and a residual sub-matrix to enable encoding and decoding operation to be partially executed in parallel; and in addition, adjusting a matrix calculation sequence to reduce data block calculation frequency involved in the encoding and decoding processes so as to reduce the time cost for calculation. The method improves the performance of the erasure correcting code encoding and decoding processes, especially the performance of operation in a multi-core processor. The encoding and decoding processes realized by using the method can utilize potential parallel encoding capability, make full use of parallel processing capability of the multi-core processor and shorten the calculation time.

The invention discloses a method for optimizing a pilot frequency sequence based on an observation matrix in an uplink SCMA system, and relates to a method for optimizing the pilot frequency sequencebased on the observation matrix. The method aims to solve the problems of high pilot cost, low detection precision and complex reconstruction of the existing method in sparse channel detection. The method comprises the following steps: 1, establishing an observation matrix model consisting of pilot sequences; 2, setting the pilot frequency set size of the registered user as N, and setting the signal-to-noise ratio threshold value as [gamma]; if the SNR of the receiving end is less than or equal to [gamma], executing step 3; if the receiving end SNR is greater than [gamma], executing the step 4; 3, calculating a block sparse observation matrix formed by a pilot frequency sequence set in the observation matrix model; and 4, calculating a block sparse observation matrix formed by the pilot frequency sequence set in the observation matrix model. The method is applied to the field of pilot frequency design of SCMA.

The invention discloses a parameter estimation method for a time-frequency aliasing frequency hopping signal, and the method comprises the steps: converting a time-frequency aliasing signal received by a single channel into a time-frequency domain through short-time Fourier transform, and obtaining a time-frequency domain signal; performing background noise removal and signal distortion feature processing on the time-frequency domain signal by using a matrix optimization algorithm based on sparse linear regression; utilizing a parameter estimation algorithm based on secondary envelope optimization to remove partial interference signal features and abnormal points of the time-frequency domain signal, and extracting an average time-frequency ridge line of the optimized time-frequency domainsignal to perform smoothing processing; performing inflection point detection on the average time-frequency ridge line, and performing time-frequency domain mapping on the detected inflection point tocomplete frequency hopping period estimation of the frequency hopping signal; and on the basis of frequency hopping period estimation, completing frequency point estimation of the frequency hopping signal based on an optimization method of Hough transform. According to the invention, complete recovery of frequency hopping signals and high-precision estimation of parameters can be realized for thescene of aliasing of each source signal in the time domain and the frequency domain.

The invention discloses a differential spatial modulation method and device based on antenna grouping and a storage medium. The method comprises the following steps: respectively activating corresponding symbols in an STBC coding matrix according to two specific antenna activation matrixes, forming a TA-DSM transmission signal, and transmitting the TA-DSM transmission signal. Due to the fact thata transmitting antenna grouping mode is adopted and an Alamouti coding mode is adopted in an STBC matrix; the method is suitable for any even number of transmitting antennas; under the condition thatthe number of transmitting antennas is the same, more antenna activation matrixes are provided, so that higher spectral efficiency is obtained; the method has a never disappeared determinant characteristic without any parameter or matrix optimization. According to the invention, the coding structure has an orthogonal structure, so that a low-complexity decoding algorithm can be adopted, and the decoding complexity is very low. Simulation results show that under different system configurations, compared with other existing differential spatial modulation schemes capable of obtaining the transmit diversity, the invention has better error code performance.

The invention discloses an unmanned aerial vehicle (UAV)-carried RIS auxiliary communication system capacity optimization method and device, and the method comprises the steps: taking a UAV-carried RIS as a relay, and constructing a UAV-carried RIS auxiliary communication system; converting a system capacity optimization problem of the UAV carrying the RIS auxiliary communication system into a UAV trajectory and RIS beam forming matrix optimization problem with user rate guarantee and UAV energy consumption constraint; based on a deep reinforcement learning algorithm, an optimal UAV trajectory and an optimal RIS beamforming matrix are obtained, and system capacity maximization is achieved under the condition that the minimum target data rate of a user and UAV energy consumption constraints are met. According to the method, the track and phase shift problems are solved, and meanwhile, the system capacity can be maximized under the energy consumption constraint.