115 results about "Reconstruction problem" patented technology

The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human disease, but living systems more generally. The present invention provides novel gene network reconstruction algorithms that utilize naturally occurring genetic variations as a source of perturbations to elucidate the networks. The algorithms incorporate relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of these novel algorithms can be demonstrated via application to gene expression data from a segregating mouse population. The network derived from such data using the novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.

The invention provides a face super-resolution reconstruction method based on an extremely deep convolutional neural network, which comprises the following steps of 1, performing down sampling and processing on a high-resolution face image by different multiples to obtain a low-resolution face image training set; 2, performing down sampling and processing on another high-resolution face image by different multiples to obtain a low-resolution face image test set; 3, putting the training set obtained in step 1 and the test set obtained in the step 2 in the extremely deep convolutional neural network for training, learning the mapping of a residual image, and obtaining a corresponding convolutional network model; and 4, inputting a low-resolution face image that needs to be reconstructed intothe convolutional network model obtained in the step 3 to obtain a reconstructed high-resolution face image. The face super-resolution reconstruction method based on the extremely deep convolutionalneural network can better deal with the super-resolution reconstruction problem of multi-scale amplification factors.

Disclosed is a compressed sensing-based FPM algorithm. The algorithm comprises the steps of 1) utilizing an FPM platform to collect a low-resolution image ri (x, y) under different illuminations; 2) based on images obtained under illumination of different angles, actually, obtained by translating images under normal illumination in a frequency domain, establishing a constraint for the collected image ri (x, y), and based on the constraint, solving the optimization problem of an optimal problem according to a compressed sensing structure; 3) solving the optimization problem through iteration to obtain a sparse coefficient a, and multiplying the a with an over-complete dictionary to obtain a final result. According to the compressed sensing-based FPM algorithm, by taking advantage of the compressed sensing technology, mathematical abstraction is performed on an original FPM algorithm, a frequency domain iteration method is abstracted into an optimal solution solving method, the image super-resolution reconstruction problem is solved from a new angle, and the algorithm reconstruction effects can be enhanced.

The invention puts forward a finite-element reconstruction method for space weighting of auto-fluorescence imaging. The method uses a diffuse optical tomography (DOT) technique to precisely reconstruct optical parameters of main biological tissue. The method limits a possible real light-source region, and divides a whole reconstruction region into a light-source possible region and a light-source impossible region so as to improve the numerical stability and effectiveness of reconstruction problems and reduce the ill-conditioned property of BLT reconstruction problems. In order to avoid famous 'Inverse Crime' problems, the method adopts a Monte Carlo (MC) random method to simulate the process of transmitting light in the biological tissue. Then the invention puts forward a finite-element method based on a space-weighting unit and adopts a tolerant algorithm for linear constrained optimization problems to reconstruct light-source information.

This invention discloses methods and apparatuses for 3D imaging in Magnetoencephalography (MEG), Magnetocardiography (MCG), and electrical activity in any biological tissue such as neural/muscle tissue. This invention is based on Field Paradigm founded on the principle that the field intensity distribution in a 3D volume space uniquely determines the 3D density distribution of the field emission source and vice versa. Electrical neural/muscle activity in any biological tissue results in an electrical current pattern that produces a magnetic field. This magnetic field is measured in a 3D volume space that extends in all directions including substantially along the radial direction from the center of the object being imaged. Further, magnetic field intensity is measured at each point along three mutually perpendicular directions. This measured data captures all the available information and facilitates a computationally efficient closed-form solution to the 3D image reconstruction problem without the use of heuristic assumptions. This is unlike prior art where measurements are made only on a surface at a nearly constant radial distance from the center of the target object, and along a single direction. Therefore necessary, useful, and available data is ignored and not measured in prior art. Consequently, prior art does not provide a closed-form solution to the 3D image reconstruction problem and it uses heuristic assumptions. The methods and apparatuses of the present invention reconstruct a 3D image of the neural/muscle electrical current pattern in MEG, MCG, and related areas, by processing image data in either the original spatial domain or the Fourier domain.

The invention, which belongs to the communication technology, especially relates to a compression-sensing-based direction-of-arrival (DOA) estimation method for an intelligent antenna. According to a sparse characteristic of a target signal in a natural space in an airspace, a CS-theory-based DOA estimation model under single sampling is constructed; on the basis of the constructed DOA estimation model, CS-theory-based DOA estimation is realized, thereby obtaining a DOA of the target signal; and with an orthogonal matching pursuit algorithm, a reconstruction problem of a s spatial sparse signal in the CS-theory-based DOA estimation is solved, and iteration is carried out by multiple times and thus a residual error between an estimation signal and an original signal is reduced continuously, so that the estimation signal is close to the original signal and DOA estimation is completed. The method has the following beneficial effects: relevant information can be estimated accurately based on a little sampling information; and thus novel DOA estimation method is provided.

A dynamic reconstruction method considering distributed power supply and soft switch access for a power distribution network comprises the steps of building a source-load stochastic model, and performing stochastic power flow analysis on the power distribution network, wherein source-load stochastic model comprises a wind power generation system stochastic model, a solar photovoltaic power generation system stochastic model and a load stochastic model, and the stochastic power flow analysis of the power distribution network is based on a cumulant method; building an intelligent soft switch running model and a dynamic network reconstruction model, wherein the intelligent soft switch running model is expressed by capacity constraint of an intelligent soft switch converter, and the dynamic network reconstruction mathematical model comprises a target function and a constraint condition; and solving the built models by employing a genetic algorithm. The network reconstruction problem of thepower distribution network containing a distributed power supply and an intelligent soft switch is solved, a load among feeders of the power distribution network is balanced, the integral power flowdistribution of the system is improved, the overload is eliminated, the voltage level is improved, the electric energy quality and the power supply reliability are improved, the network loss is effectively reduced, and the running economy of the power distribution network is improved.

The invention discloses a CT (Computed Tomography) image reconstruction method based on a variational inequality at a sparse sampling angle. The CT image reconstruction method comprises the following steps: (1) carrying out equal-angle interval projection scanning within an angle range of 0-180 degrees by aiming at a fan-beam CT to obtain spare projection data y, wherein an angle interval in a projection direction is between 2 and 6 degrees; (2) calculating a projection matrix A through an X-ray source, a detector and the position information of an object to be reconstructed; (3) according to the projection data y obtained in the step (1) and the projection matrix A obtained in the step (2), simultaneously introducing the sparsity and the non-negativity of an image gradient to serve as priori knowledge to obtain a reconstruction model of an image reconstruction problem at the sparse sampling angle; (4) converting the reconstruction model in the step (3) into a variational inequality form; and (5) solving the variational inequality in the step (4) to obtain a reconstructed image. On the premise that image reconstruction quality is guaranteed, reconstruction convergence speed can be quickened, and single-iteration time is shortened.

The invention provides a reconstructible distributed software bus comprising a bus library module, a bus node agent module, a bus monitor module and a bus configuration service module; the bus library module is an API interface allowing a node to user bus service; the bus node agent module is a network agent of the node on the software bus; each host is deployed with one embodiment of the bus node agent module, thus realizing data exchange between the node of the local host and nodes of other hosts; the bus monitor module is used for monitoring a node composition relation, an issue subscription relation and data exchange state; the bus configuration service module is used for editing parameters of each node in the bus. In a reconstruction system, software integration and assembly flexible recombination and reconstruction problems of a multi-layer software/assembly and a cross-platform heterogeneous system can be solved by the reconstructible distributed software bus; the reconstructible distributed software bus has no center self-organizing net, a transmission strategy is controllable, communication nodes can migrate, and the software/assembly can be recombined.

The invention provides a KPCA-based fused magnesium furnace fault diagnosis method for fault separation and reconstruction. The method comprises the following steps: acquiring historical normal data of a fused magnesium furnace, historical fault data when the fused magnesium furnace generates a fault, and test data when a fault occurs in online monitoring; performing preprocessing on the historical normal data of the fused magnesium furnace and the historical fault data; performing high-dimension mapping and then PCA decomposition on the historical normal data of the fused magnesium furnace by use of a nucleus principal component analytical method; and performing diagnosing and fault reconstruction on the test fault data when the fault occurs in the online monitoring by use of a fault load vector set, determining a fault type, and recovering the fault data to corresponding normal data so that the fault is eliminated. According to the method provided by the invention, the fault separation and reconstruction problems of non-linear data of the fused magnesium furnace are solved, the test data when the fault occurs in the online monitoring can be monitored, only a fault model corresponding to a current fault can correctly remove fault information in the data, based on this, the fault type can be determined, and the purpose of fault separation is realized.

The invention discloses an FDD large-scale MIMO channel estimation pilot frequency optimization method based on compressed sensing, and the method comprises the steps: firstly enabling a channel to be modeled into a formula in a large-scale MIMO system: Y=HX+N, wherein H (shown in the description) is a channel matrix, X (shown in the description) is a pilot frequency matrix, Y (shown in the description) is a receiving signal matrix, and N (shown in the description) is channel noise, M is the number of transmitting antennas, and T is the number of pilot frequencies; secondly carrying out the conversion of the channel matrix, and solving the conjugate matrix (shown in the description) of Y, wherein the conjugate matrix (shown in the description) of the channel matrix represents the conversion form of the channel matrix, the conjugate matrix (shown in the description) of the pilot frequency matrix represents the conversion form of the pilot frequency matrix, and the conjugate matrix (shown in the description) of the receiving signal matrix represents the conversion form of the receiving signals of a receiving end; and finally solving an optimal pilot frequency matrix. Because the conjugate matrix (shown in the description) of the channel matrix is a sparse vector, a channel estimation problem can be modeled into a compressed sensing reconstruction problem shown in the description, wherein ||*||<1> represents 1-norm, ||*||<2> represents 2-norm, and epsilon is greater than zero and less than one. The method can guarantee that the FDD MIMO downlink channel estimation based on compressed sensing can remarkably reduce the mean square error of channel estimation, and improves the channel estimation performance.

The invention discloses a dynamic PET (positron emission tomography) image reconstruction method based on a Poisson TV. According to the method, PET images are reconstructed through conversion of a reconstruction problem mathematical model and an equivalence problem on the basis of a Low Rank method, and an alternate minimization algorithm is adopted during solution of the equivalence model problem when the PET images are reconstructed in combination with Poisson and Low Rank models. Therefore, the Low-Rank algorithm is utilized effectively, and problems of low resolution and noise interference of results produced during dynamic PET image reconstruction by a computer are solved; compared with existing reconstruction method through tests, the method can realize the better reconstruction effect.

The invention discloses a reconstruction method for video compressed sensing. Images of frames are reconstructed independently after a receiving end receives measurement values of the frames; motion estimation and motion compensation based on blocks are carried out among the reconstructed images to obtain predicted images of the frames; reconstruction problems are built for the images of the frames by means of the predicted images and the measurement values, and the reconstruction problems are divided into five sub-problems, namely a single-frame image reconstruction problem, a residual image reconstruction problem, an image gradient variable reconstruction problem, a residual gradient variable reconstruction problem and an image orthogonal basis coefficient variable reconstruction problem, for alternant iteration solving. Every time the five sub-problems are solved through alternant iteration solving, image reconstruction quality is further improved by the adoption of a bivariate shrinkage threshold method. If inner loop end conditions are met, the iteration solving process of each frame of image is finished; if the upper limit of outside loop iteration times is reached, the construction procedures are over. By means of the method, performance and the effect of image reconstruction can be effectively improved.

The invention belongs to the technical field of wireless communication, and especially relates to a method which reduces the antenna training cost of iteration wave beam shaping by utilizing the channel space sparseness in a wireless MIMO communication system. The rapid iteration wave beam shaping method based on channel space sparseness comprises the steps of initialization, LANCZOS iteration, cycled iteration control and the like. In the time division duplex MIMO system, Uplink and downlink channels can be interchanged, iterated antenna training is carried out by introducing the LANCZOS algorithm, the channel space sparseness is utilized, problems in estimating reception vectors in the iteration process is modeled into a sparse reconstruction problem, theory related to compressed sensing further reduce cost of iteration training, and antenna training is completed at an extremely high speed.

The invention discloses a bioluminescence tomography reconstruction method based on multi-light-source resolution. Optical property parameters and anatomical structure information are utilized as priori knowledge, and multispectral fluorescent data are combined, so that accuracy of BLT light source reconstruction and resolution of reconstructed images are improved; a self-adaptive feasible region iterative shrinkage strategy is adopted, so that morbidness of BLT reconstruction problem is reduced, and dependence, of reconstruction results, on reconstruction algorithms is lowered. A mixed clustering method combining AP clustering and K-means clustering is provided, so that limitation and defects caused when respective clustering algorithms are adopted solely are overcome. In the method, energy distribution information of grid nodes is used directly, and information such as energy, central coordinates and size of a tetrahedron to which reconstructed nodes belong is fully considered, so that stability of clustering algorithms is improved to greatest extent, and calculation time and complexity of the algorithms are reduced.

The invention relates to a magnetic flux leakage testing defect reconstruction method based on an improved artificial bee colony algorithm. According to the method, a radial basis function neural network is used as a forward model, and an error square sum of a magnetic flux leakage signal predicted by the forward model and an actually measured magnetic flux leakage signal is used as a target function to improve the artificial bee colony algorithm; a current individual optimal solution and a global optimal solution are introduced to accelerate algorithm convergence speed; the improved artificial bee colony algorithm is used as an iterative algorithm to solve a reconstruction problem, and the finally obtained global optimal solution is a reconstructed defect outline. The magnetic flux leakage testing defect reconstruction method based on the improved artificial bee colony algorithm improves speed and precision of magnetic flux leakage testing defect reconstruction.

The invention discloses a total-variation regularization and variable splitting-based lens-less imaging rapid reconstruction method. Aiming at a thought that image reconstruction problems of lens-lessimaging systems adopt total-variation regularization and variable splitting, a to-be-solved target function is split to two sub-problems, and the sub-problems are alternately solved to obtain a finalresult. The method comprises the following steps of: firstly importing a total-variation regularization image reconstruction model according to a linear imaging mechanism in lens-less imaging; importing an auxiliary variable and splitting the to-be-solved target function into the two sub-problems by using a variable splitting method; solving the two sub-problems by using Tikhonov regularization and anisotropic total-variation (TV) regularization; and finally alternately solving the two sub-problems so as to obtain an optimum solution. According to the method, lens-less image reconstruction can be stably carried out under unsatisfactory factors, noises can be effectively removed, and detailed information such as edges and the like of reconstructed images can be kept at the same time.

The invention discloses a sparse representation depth image reconstruction algorithm based on a structure dictionary, belonging to the image processing technology field. Firstly, the sparse representation depth image reconstruction algorithm of the invention considers a corresponding depth image and a corresponding color image as a whole, and, during the solution process, the sparse representation depth image reconstruction algorithm improves the reconstruction effect of the depth image and the color image through constructing a structure dictionary having a logic corresponding relation. During the dictionary construction process, the sparse representation depth image reconstruction algorithm based on the structure dictionary utilizes the logical corresponding relation between the depth image and the color image and the similarity relation of the depth image to improve the efficiency and quality of dictionary training. The depth image reconstruction algorithm combines the related theories like the sparse coding on the basis of collecting and arranging a lot of home and abroad related data, analyzes the correlation between data according to the close correlation between the depth image and the color image, mainly solves the reconstruction problem of the sparse representation depth image based on the structure dictionary, reduces the operation complexity, and improves the reconstruction quality of the depth image and the corresponding color image.

The invention discloses a design method of a time-varying separable non-downsampled filter bank based on iterative calculation. The method comprises the steps of: firstly, based on the properties of atwo-dimensional separable filter, designing an analysis filter bank with a frequency response; and finally, converting the reconstruction problem of the output signal of an integrated filter bank toa global least squares problem, converting the global least squares problem into a local least squares problem, and performing solution through an iteration mode. The iteration calculation method is low in iteration times, the designed time-varying separable non-downsampled filter bank has the complete reconstruction feature and the better denoising performance, and the analysis filter has frequency response.

The invention discloses a PET (Positron Emission Tomography) image reconstruction method based a TV ( Total Variation) norm. The PET image reconstruction method is capable of reconstructing a PET image based on TV norm method by establishing a mathematical model of reconstruction problems and transforming an equivalence problem model, wherein in the process of reconstructing the PET image by virtue of a TV norm model, a TVAL3 algorithm is employed in the solving process of equivalence model problems. As a result, the TVAL3 algorithm is utilized effectively, and the problem of high requirement for data collection quantity in the reconstruction process of traditional PET image reconstruction is improved; experiments of comparing with the existing reconstruction method indicate that good reconstruction result can be maintained while the data collection quantity is reduced.

The invention relates to an acceleration improvement algorithm based on cube edge sharing an equivalent point which comprises the following steps of: (1) storing related information of each edge of a cube by using a global array variable; (2) calculating equivalent point coordinates: before the equivalent point is calculated, firstly judging whether the edge is provided with the equivalent point or not , if so, extracting data stored in an array, wherein the data are used for calculating a triangular patch; if not, calculating coordinates and a normal vector of the equivalent point through linear interpolation; and recording the equivalent point of the edge of a current voxel, and giving an assignment for the equivalent point of the edge of other voxels sharing the edge; and (3) calculating the triangular patch by using a Marching Cubes algorithm, and processing the generated triangular patch through GPU (Ground Power Unit) acceleration image processing software to obtain a three-dimensional graph. By utilizing the method of the cube edges sharing the equivalent point, the algorithm efficiency can be improved and the three-dimensional reconstruction problem of medical images is effectively solved.

The invention discloses a parallel magnetic resonance imaging high quality reconstruction method based on self-consistency and containing combined total variation; the method is based on a SPIRiT frame, and provides the high quality reconstruction method aiming at parallel imaging reconstruction problems containing JTV and JL1 composite regular terms; firstly a constraint reconstruction problem is converted into an unconstrained reconstruction problem; then a data fidelity term and a self-correction term are simplified; then the operator split technology converts the simplified reconstruction problem into a gradient calculating problem and a denoising problem containing the JTV and JL1 composite regular terms, and the composite regular terms denoising problem is solved through a whole novel design algorithm based on Split Bregman technology; finally FISTA can accelerate the speed. The reconstruction performances of the novel algorithm and other normal algorithms are compared; test simulation shows that the convergence speed of the novel algorithm is close to a POCS algorithm, yet reconstruction image SNR can be largely improved.

A space target reconstruction method based on an accelerating Gaussian mixture model comprises the steps that point cloud data are read in; according to the number K of components of the given Gaussian mixture model, K points are screened out from point clouds to serve as initial clustering centers by using a K-means++ algorithm; the point clouds are clustered into K clusters by using a K-means algorithm; according to the situations of points included in each cluster, the weight, the mean value and a covariance matrix are initialized to serve as initial values of parameters of the Gaussian mixture model; and the parameters are iteratively updated by using the Gaussian mixture model to obtain final estimation values. According to the space target reconstruction method, the target three-dimensional structure reconstruction problem in close-distance perception of a spacecraft is researched, the target point clouds include a large number of points, and the space target reconstruction method is realized by combining algorithm procedure design and CPU-GPU heterogeneity; and in the reconstruction process, the calculating speed is high, and robustness is high.

The invention relates to a power distribution network reconstruction method based on an isolation ecological niche genetic algorithm. The power distribution network reconstruction method is characterized in: researching to use a target normalization method to obtain the efficient solution; taking minimum network loss as the main target; taking load balancing as the constrained condition to process and establish a compromise model; and bringing forward a chromosome integer coding mode to ensure one-to-one correspondence between the chromosome and the feasible solution. The power distribution network reconstruction method based on an isolation ecological niche genetic algorithm can effectively solve the premature convergence problem of the genetic algorithm and can quickly converge to the optimal solution by introducing the isolation ecological niche genetic algorithm, and at the same time, aiming at that binary coding cannot effectively reflect the structural features of the power distribution network reconstruction problem, the power distribution network reconstruction method provides the chromosome integer coding mode, thus greatly reducing the chromosome length and improving the operation speed; and by means of introduction of the isolation ecological niche technique, the power distribution network reconstruction method effectively solves the premature convergence problem of a traditional genetic algorithm on solving the power distribution network reconstruction problem.

The invention belongs to the technical field of molecular imaging, and discloses a method for reconstructing fluorescence molecular tomography based on semi-threshold tracking algorithm. The multi-point excitation and finite angle measurement are used to construct a sparse canonical model of a non-convex problem, and the linear relationship between the surface measurement data and fluorescence target distribution is established. The linear relationship is transformed into the 1/2 norm minimization problem to solve and obtain the three-dimensional distribution and concentration of the fluorescent targets within a reconstructed target. The model is solved by threshold iteration and matching tracing algorithm. The method reduces the morbidity of the problem. Optical characteristic parameters and the anatomical structure information are used as a priori knowledge to improve the accuracy of the reconstruction result and the quality of a reconstructed image. The reconstruction problem is transformed into a 1/2-norm minimization problem with constraint conditions and is solved by using the semi-threshold tracing algorithm, which makes the solution satisfy the minimum of 1/2- norm and guarantees the robustness of the reconstruction problem to the parameters and the acceleration reconstruction time.