142 results about "Constraint matrix" patented technology

A method for generating coefficients for a set of convolution kernels for use in interpolating pixel values in an image sensor is described. The coefficients are computed by applying a constraint matrix specifying one or more constraints. The method includes generating ideal sensor data representative of a test image in a first color plane, generating sensor data of the test image, generating f data matrices including pixel data from multiple neighborhoods of pixels in the pixel array, and determining the coefficients for f convolution kernels using the ideal sensor data, the f data matrices and by applying one or more constraints. The use of a constraint matrix greatly simplifies the computation of the coefficients and can be applied in image processing to generate a high quality full color image.

A method is for establishing a nuclear reactor core loading pattern (LP) for fuel assemblies and burnable absorbers (BAs). The method establishes an optimum LP through the steps of: a) providing nuclear data representing fuel assemblies and BAs in a nuclear reactor core; b) depleting the nuclear data to form a reference core depletion; c) incorporating the nuclear data into a system of linear equations of a nuclear design quality flux solution method; d) defining the system of linear equations to include constraints which accurately represent the neutron physics of the reactor; employing the equations as a constraint matrix for a MIP solver to find an optimum core pattern solution; f) repeating steps b) through e) updating the constraints and objective functions to satisfy specified engineering requirements and establish an optimum core loading pattern. An algorithm for deriving the system of equations is also disclosed.

Methods and corresponding systems for allocating resources in a communications system includes determining feasible sets of subchannels for allocation to a user subject to an allocation constraint. In one approach, a constraint matrix representing constraints for allocating subchannels to users in allocations of selected subchannels is computed, subject to the allocation constraint. Then a vector containing metrics corresponding to allocations of selected subchannels to the communication links is estimated. A binary decision vector representing an allocation of the subchannels to the users is computed using binary integer processing. In another approach a greedy heuristic allocation is used. The allocation constraint can be a restriction limiting multiple subchannels allocated to a user to be adjacent to one another. The metrics can be weighted capacities of allocations of selected subchannels to the users.

The invention relates to a high-precision magnetic resonance and transient electromagnetic spatial constraint joint inversion method. The joint inversion method is based on a forward modeling method combining a continued fraction expansion numerical integration equivalent transformation method and a digital filtering method. The magnetic field calculation precision and speed are both taken into account, an original model and a simplest model are provided, a rotary coefficient matrix equation is designed, and exciting field distribution is simulated and detected when different slope dip angles appear and profile angles change. According to a forward modeling result obtained under the condition of changed dip angle parameters, the forward modeling result is combined with a hierarchical full collection data inversion method, a joint inversion objective function, an iterative equation and a three-dimensional ground model are constructed, a prior information constraint matrix, a roughness matrix, a layer thickness constraint matrix and other spatial constraint matrices are introduced to construct an inversion iterative equation set, based on a large scale matrix of a preconditioning conjugate gradient method, smooth and continuous constraint for the specific resistance, the layer thickness, the water content and the relaxation time is achieved, the problem of inversion non-uniqueness is solved, and the problem that optimization and calculation of a large-scale matrix for spatial constraint inversion involves mass data is solved.

The invention discloses an assembly time evaluating method based on an artificial neural network and a virtual assembly. The method comprises the step of establishing a virtual working platform, planning a DELMIA assembly sequence and a DELMIA path and generating an assembly path, the step of generating the assembly sequence through a constraint matrix method and establishing the constraint relation among components, the step of preassembling the product components through a DELMIA assembly module, establishing a priority dismounting matrix, extracting the assembly sequence meeting the condition from the priority dismounting matrix and planning the assembly sequence through the strong data collecting capacity of the matrix, the step of establishing a virtual assembly work environment and the virtual assembly action process, the step of measuring the assembly path and calculating the assembly time, the step of establishing an assembly time evaluating model, the step of establishing the model and standardizing a sample, and the step of evaluating the sample. The assembly time evaluating method solves the problems that the product assembly period is long, cost is high, and the effect is poor.

The invention relates to a method for solving camera intrinsic parameter by using the image of the center of a sphere and orthogonality. The method comprises: firstly, taking three pictures of a calibrated sphere from different directions and reading the images, extracting coordinates of three quadratic curves from the three images; according to imaging process of the sphere and the center of the sphere under the camera, solving to obtain image coordinates of three centers of spheres, and according to projections of the centers of spheres, calculating end points on orthogonal directions, and finally by using the end points on orthogonal directions, solving images of absolute conics, and according to the images of quadratic curves, and solving a constraint matrix of camera intrinsic parameters, so as to obtain the camera intrinsic parameter. By using the spheres which are targets, full-automatic calibration can be realized, and errors caused by measurement in a calibration process are reduced. Since the quadratic curve is a simpler and more global element, and projection contours of the spheres can be completely extracted from the images, calibration precision is improved in a camera calibration process.

Techniques are provided for modeling indoor scenes including receiving a request for a 3D model of an indoor scene based on multiple flat images of the indoor scene, where the images obey no more than a limited overlap requirement, are absent of depth information, and are taken from one or more viewpoints. The techniques proceed by determining vanishing points in the images, receiving floor contour information that was determined based on the vanishing points; reconstructing the 3D vertex positions of two or more floor plan parts using a geometric constraint matrix that encodes coordinate equalities among said vertices, based on the floor contour information; and assembling a combined floor plan based at least in part on the floor plan parts. The techniques proceed by receiving a floor plan outline indicating walls and generating the 3D model of the indoor scene based on the floor plan outline.

The invention provides a device for stabilizing a video image sequence capable of doing multi-degree of freedom movement in real time. The device comprises a programmable logic device, a programmable logic device connecting analog-digital converter, a digital analog converter, two digital signal processors, an image sequence buffer, a parameter configuration static memory and an image sequence buffer. The invention further provides a method for stabilizing the video image sequence capable of doing the multi-degree freedom of movement in real time, an image sequence interframe epipole converting model is adopted, the accurate positions, where a feature point is located, on image frames are forecasted, the image sequence interframe multi-degree of freedom movement is drawn by establishing a video image forecast feature point set virtual curve family, the accurate position, where the forecast feature point is located, on a corresponding stabilizing image is obtained by utilizing the dimensionality reduction image data processing method, a constraint matrix is established, and a stable output video is obtained by reestablishing an output image. According to the device and method, an electronic stabilized image can be fast achieved in a high-precision mode, and the processing efficiency of the system is greatly improved.

The invention discloses a traffic cell division method based on multi-angle of view fusion. The method comprises the steps that (1) probe vehicle data are read and preprocessed, the probe vehicle data are handled, and probe vehicle longitude and latitude data are extracted from the prove vehicle data and stored as X1; (2) bayonet data are read and preprocessed, and bayonet longitude and latitude data are extracted and stored as X2; (3) on the basis of traffic cell division principles, natural constraint information is collected to be stored as X3; (4) a constraint matrix is calculated; (5) spectral clustering analysis is carried out based on flexible constraint; (6) according to a clustering result, a finial traffic cell division scheme is obtained. According to the traffic cell division method based on multi-angle of view fusion, multi-source data fusion is achieved, clustering analysis is carried out under the constraint of quantified influence factors, and transportation conditions are comprehensively reflected.

A method clusters samples using a mean shift procedure. A kernel matrix is determined from the samples in a first dimension. A constraint matrix and a scaling matrix are determined from a constraint set. The kernel matrix is projected to a feature space having a second dimension using the constraint matrix, wherein the second dimension is higher than the first dimension. Then, the samples are clustered according to the kernel matrix.

The architecture is able to switch to Non-blocking check-node-update (CNU) scheduling architecture which has better performance than blocking CNU scheduling architecture. The architecture uses an Offset Min-Sum with Beta=1 with a clock domain operating at 440 MHz. The constraint macro-matrix is a spare matrix where each “1’ corresponds to a sub-array of a cyclically shifted identity matrix which is a shifted version of an identity matrix. Four core processors are used in the layered architecture where the constraint matrix uses a sub-array of 42 (check nodes)×42 (variable nodes) in the macro-array of 168×672 bits. Pipeline processing is used where the delay for each layer only requires 4 clock cycles.

The invention discloses a semi-supervised dimensionality reduction method for high dimensional data clustering. The method comprises the following steps: (1) constructing a sample characteristic matrix; (2) constructing a constraint matrix; (3) constructing an iterative equation set and an iterative output transition matrix; and (4) obtaining a sample characteristic matrix after an operation of dimensionality reduction is performed. In the invention, through adding part of known class information as a constraint in the process of decomposing the sample characteristic matrix, and using an idea of concept decomposition, a coefficient matrix obtained by decomposition is used as the low-dimensionality representation of a high-dimensionality sample characteristic matrix, and when the low-dimensionality matrix is applied to clustering analysis, the clustering analysis becomes simple and effective; meanwhile, in the invention, data subjected to dimensionality reduction has a good interpretability; and compared with a dimensionality reduction method in the prior art, by using the dimensionality reduction method disclosed by the invention, the discrimination capacity of the clustering analysis can be further improved.

The invention discloses a method for planning the target and cooperation disassembly of a complex product supporting green design, which comprises the following steps: for the characteristics of detachable design in the green design, extracting assembly semantic constraints and disassembly priority constriction information between product components to construct a multi-constraint disassembly information model; performing matrix quantization on the information model, decomposing the information model into a plurality of single-constraint matrixes and inducing detachability constraint conditions; taking a disassembled component as a driving point to obtain a target disassembly sequence through two-way driving; and for the requirement that the large-scale complex product needs parallel disassembly, putting forwards a cooperation display planning problem and an adaptive resolution method. Through the method, the target and cooperation disassembly sequence of the complex product can be quickly obtained and the problem that 'combination explosion' is easy to occur in the conventional method is solved. The cooperation disassembly planning problem and the adaptive resolution method put forwards in the invention overcome the defect that the prior method is only applied to single-operator disassembly and realize the adaptive adjustment in a disassembly planning process.

The invention discloses a gray level image colorizing method, and belongs to the technical field of digital image processing. Directing at the problem that in an existing image colorizing method based on YUV joint correlation, colors of an edge region are mixed and distorted, the existing image colorizing method is improved. The gray level image colorizing method comprises the steps that firstly, the similarity degree of spatial position information and brightness information is considered comprehensively and used, and a plurality of extremely similar pixels are searched out from a larger window region with a pixel to be colorized as the center; secondarily, the post-normalization local brightness weighting coefficients of the extremely similar pixels are calculated; then a chromaticity-coefficient constraint matrix equation formed by chromaticity of all the pixels and the local brightness coefficients of all the pixels is solved to obtain the chromaticity of the pixel to be colorized; at last, an image is converted from a YUV space to a RGB space to obtained a colorized image. The invention further discloses a gray level image colorizing device using the method. The gray level image colorizing method and device can reduce color mixing and distortion of the edge region of the image, and improve the subjective and objective quality of the colorized image.

The invention provides a day-ahead power system economical dispatching method which includes the following steps: step S1, modeling a linear programming mathematical model of the day-ahead power system economical dispatching method; step S2, building constraint conditions which enables the power system to satisfy a load balancing constraint, a machine group operating constraint and a contract power constraint, and forming a constraint matrix A by ordering the constraint conditions according to an attribute of variable relating to each constraint condition; step S3, using a predictor-corrector interior point method to get a result which can satisfy the constraint matrix A of an objective function, wherein the objective function of the mathematical model is that T is session number in the period of system dispatching, M is set number of system generators, pi (t) is active power of generator set i in a t period, and Ci [pi (t)] is an operating cost of the generator set i in the t period. The day-ahead power system economical dispatching method uses the predictor-corrector interior point method to solve and the predictor-corrector interior point method is low in an initial point requirement, good in astringency and rapid in astringency speed.

The invention discloses a main lobe interference suppression method and device. The method includes calculating an optimal weight vector of main lobe interference according to a constraint matrix, a load electrical level, an array receive data matrix and a preprocessing block matrix; and using the optimal weight vector of main lobe interference to perform main lobe interference suppression. By means of the main lobe interference suppression method and device, main beams are enabled to point to an expected target direction, and simultaneously, main lobe interference is inhibited from forming deep null steering in other interference directions.

The invention provides a seismic data reconstruction method and apparatus wherein the method comprises the following steps: obtaining the seismic data body of a to-be-reconstructed time-space domain; performing fast Fourier transform to the seismic data body of the to-be- reconstructed time-space domain along the time direction for the data body in the time-frequency spatial domain; performing non-uniform Fourier transform to the data body in the time-frequency spatial domain according to the actual spatial position in the spatial direction to obtain the data body of the time-frequency spatial wave number domain; calculating for the Gamma matrix corresponding to the data body of the time-frequency spatial wave number domain; calculating the constraint matrix according to the amplitude spectrum of the data body of the time-frequency spatial wave number domain; according to the constraint matrix, calculating and storing the wave number spectrum component of each time frequency slice in an output matrix; and inversely transforming the output matrix into the time-space domain to obtain the seismic data after reconstruction. With the above schemes, the spatial sampling properties of observed data are effectively improved and the imaging quality of seismic data is increased.

The invention discloses an electric impedance tomography method of a brain based on layered reconstruction, which comprises the steps as follows: layering a head model; layering the head model as a scalp layer omega scalp, a skull layer omega skull and a brain layer omega brain according to space distribution prior information; introducing a constraint matrix D to further layer and reconstruct so as to reduce artificial images of the skull layer; and layering and reconstructing the reconstructed head model in a second step to further reduce the interference of the scalp layer and highlight the target in the brain. By means of concept of layered image reconstruction, the method has the benefits of reducing the skull artificial images, lowering the interference of the scalp layer and improving the imaging quality.

A semi-blind MVDR (minimum variance distortionless response) beams forming method belongs to the array signal processing field of a beam former receiving wireless signals. A group of direction vectors with corresponding maximum values sent out from a traditional beam former is adopted to form a minimum variance constraint matrix and define a semi-blind MVDR beam former response vector satisfying the linear constraint and minimum variance constraint; a signal DOA is estimated according to the DOA corresponding to the maximum value of a projection on the semi-blind MVDR beam former response vector by an array receiving vector; a residual vector is defined in virtue of the signal DOA and the array receiving vector and the residual vector is adopted to displace the array receiving vector to repeat the above steps and the repetition time equals the number of signals or the repetition is stopped when the residual signal is less than a certain value. The method realizes the high accurate and high resolution estimation to the DOA when the number of the array receiving vectors is less, thus realizing the high accurate beam forming.

The invention discloses an unsupervised hyperspectral image implicit low-rank projection learning feature extraction method, and aims to provide an unsupervised hyperspectral feature extraction methodcapable of realizing rapidness and high robustness. The method is realized through the following technical scheme: firstly, dividing input hyperspectral image data into a training set and a test setin proportion; designing a robustness weight function, calculating the spectral similarity between every two training set samples, and constructing a spectral constraint matrix and a graph regularization constraint according to the training set; approximately decomposing row representation coefficients of the hidden low-rank representation model; constructing an implicit low-rank projection learning model by combining the spectral constraint matrix and the image regularization constraint; and optimizing and solving the hidden low-rank projection learning model by adopting an alternating iterative multiplier method, obtaining a low-dimensional projection matrix, outputting the categories of all test set samples, taking the low-dimensional features of the training set as the training samplesof the support vector machine, classifying the low-dimensional features of the test set, and evaluating the feature extraction performance according to the quality of a classification result.

The invention discloses an MV (minimum variance) wave beam formation and MV-based CF (correlation factor) fusion method. The method comprises the following steps of: constructing an MV constraint matrix by utilizing a direction vector corresponding to a group of maximal value output by a traditional wave beam forming device; acquiring the output of an MV wave beam formation by an MV wave beam forming algorithm, and replacing the correlation part of the CF by the output formed by the MV wave beam so as to form a CF with high resolution; and weighting the correction coefficient with high resolution and the output formed by the MV wave beam, thereby obtaining an output model formed by the wave beam finally. According to the invention, by utilizing the high resolution of MV, the correlation part of CF is replaced by the output formed by the MV wave beam so as to form the CF with high resolution, and the CF with high resolution and the output formed by the MV wave beam are subjected to weighting and fusion; and the simulation result verification of a point scattering target and a sound absorption target shows that compared with the prior art, the method provided by the invention can improve the resolution, contrast and robustness of images well.