35results about How to "Overcome the disadvantage of large amount of calculation" patented technology

The invention discloses a block chain asset transaction transfer method based on a weighted threshold signature algorithm. The specific implementation steps include: 1, generating a public-key privatekey pair of a group master; 2, distributING a weighted private key component; 3, generatING a message to be signed; 4, a plurality of group memberS form a signature group; 5, that group mast judges whet the signature group satisfies the threshold condition; 6, calculatING a signature private key component; 7, calculatING a temporary public key; 8, sending a signature component; 9, synthesizing acomplete signature; 10, verifying that signature; 11. Exiting the system. The invention adopts threshold signature algorithm, reduces the risk of private key disclosure, and guarantees the security ofblock chain asset transaction transfer. A weighted secret sharing technique based on Chinese Residue Theorem (CRT) is used to improve the efficiency of signature generation algorithm.

An aerial photography transport video vehicle rapid detection method comprises the steps that: step 100, the space-based coding part adopts the global motion estimation to determine the global motion vector of a background; step 200, the residual difference value is calculated according to the global motion vector to divide background regions and motion regions; step 300, whether the regions are the background regions or not is judged, an image of which the regions are all the background regions is changed to the next frame, and the step 200 is executed; otherwise, the motion regions of the image execute the step 400; step 400, the ground part firstly determines a self-adaptive gradient threshold which is lower and can correctly divide various objects to carry out the primary marker extraction; then two parameters of an area and a water collection depth are introduced for further screening of the extracted marker, thus determining a final marker point; then the marker point is taken as the region minimum value for carrying out the VS watershed division; finally, the regions are merged according to the texture information of the regions; step 500, the shadow is detected in an HSV color space, thus filtering out phony targets and finally detecting vehicles. The method of the invention solves the problems that the calculation amount during the decompression of the aerial photography image and the detection of motion targets is greater, and the real-time property and the robustness requirement are difficult to be satisfied at the same time.

The invention belongs to the technical field of traffic surveillance and video processing and particularly discloses a video-based method for rapidly detecting transit vehicles. Based on a background difference method, a gradient frame difference method and a frame difference method, the method can simultaneously eliminate the daytime shadows, respiration effect, night headlamp ground-reflected light and other impacts, accurately detect the position of a moving object and judge the lane, where the vehicle is located. According to test results, the method of the invention can overcome the defect that the calculation load is higher in the conventional method and meet the requirement for the real-time performance in the video vehicle detection carried out by an electronic police; and the method is capable of accurately detecting the vehicles in the sight, and has better robustness against the interference, such as light changes, shadows and the like.

The invention discloses a texture image segmentation method combined with sparse neighbor propagation and rapid spectral clustering, and the method mainly solves the problems that in an existing texture image segmentation method, the segmentation accuracy is low and the computation complexity is high. The method comprises the steps that 1, an image to be segmented is input; 2, parameters are set; 3, a grayscale-probability square matrix is generated; 4, the number of data points is counted; 5, a sparse similarity matrix is built; 6, data representative points are selected; 7, clustering is performed on the data points; 8, the image to be segmented is marked; 9, a segmented image is output. Compared with some existing texture image segmentation technologies, the texture image segmentation method combined with sparse neighbor propagation and rapid spectral clustering can better maintain the region consistency of the texture image, the texture image with high segmentation accuracy and good segmentation stability can be obtained, and the computation complexity is low.

The invention provides an offshore alignment method of an autonomous underwater vehicle based on a second order interpolating filter, comprising the following steps of: (1) collecting data output by an optical fiber gyroscope and a quartz flexible accelerometer, and primarily ensuring transfer matrix Cb<c> from a carrier coordinate system to a computing navigation coordinate system; (2) building anonlinear state equation which takes speed error and three error misalignment angles as state variable and a linear observation equation which takes speed error as observed quantity when a fiber optic gyro strap-down inertial navigation system has a lager azimuth error angle; (3) discretizing a nonlinear continuous system, filtering the discretized nonlinear system with the second order interpolating filter, and estimating the azimuth error angle; and (4) error compensating attitude matrix Cb<c> with the estimated azimuth error angle to obtain the exact attitude matrix, obtaining carrier attitude according to the attitude matrix, completing initial alignment and entering into navigation. The method can meet requirement with higher applicable precision, has high alignment precision and short alignment time, and is easily realized.

The invention relates to the field of sonar image processing, in particular to an image registration method of acoustic and visual three-dimensional imaging with an underwater vehicle, which overcomes target sonar image rotation due to swing of the underwater vehicle, and eliminates adverse effects of three-dimensional reconstruction distortion. The method comprises the following steps of (1), detecting a rotation angle of a target image, (2), correcting rotation of the target image, and (3), reconstructing a corrected image sequence. The method can correct sonar image sequences in a self-adapting manner without any prior knowledge to allow the sonar image sequences to be aligned axially, overcomes the defect that a reference image is required in the traditional registration method, is a quick automatic registration method, and is small in computation amount.

The invention provides a method for realizing digital audio encoding by using a new psychoacoustic model and quick bit allocation. The audio encoding method comprises the following steps of: by using a new psychoacoustic model, determining window types according to the variance of input audio signals; allocating quantized noise according to the determined window types and the energy of the input audio signals, namely calculating threshold values; performing quick bit allocation, giving up the conventional method of performing rate-distortion control by two layers of nested loops, taking a value of a global scale factor of the last frame as an initial value for global scale factors according to the short-time stable properties of the audio signals, and acquiring accurate an initial valve for each section of scale factor through model deduction; and finally acquiring values of a final global scale factor and each section of scale factor through fine adjustment. The method has the characteristics of low computational complexity and unconspicuous reduction of perceived tone quality.

The invention discloses a three-dimensional face recognition method based on Bayesian multivariate distribution characteristic extraction. The method comprises three-dimensional data preprocessing, characteristic extraction and identification classification. The method has the advantages that the defect of large computation amount in the prior art is overcome; a three-dimensional face depth map is used for identifying, so that the computation amount can be reduced, and the identification efficiency is increased; the problem of insufficient training samples in single sample identification can be solved, and the training samples are added with a partitioning method; a characteristic extraction method based on Bayesian analysis is provided on the basis, so that the obtained characteristics have a minimum intra-class distance and a maximum inter-class distance, namely, optimal separability is realized; moreover, a classification method based on Mahalanobis distance is adopted, so that optimal identification classification is realized. As proved by experiment data, the method disclosed by the invention has a good three-dimensional face recognition result.

The invention relates to a low noise GPS (Global Positioning System) carrier wave tracking method based on an RLS (Recursive least square filter). The low noise GPS carrier wave tracking method comprises the following steps of: measuring an instantaneous phase difference of a local carrier wave and a tracking satellite by a carrier wave tracking loop by utilizing a table look-up type second quadrant arc tangent phase discriminator; introducing the RLS to filter measurement noises; meanwhile estimating a frequency difference according to phase difference variation; utilizing the frequency difference and the phase difference as frequency discrimination input and phase discrimination input of a loop filter with a second order lock frequency loop assistant in a three-order phase locking loop; filtering by the loop filter to output a carrier wave control adjustment variable with high precision and low noise, and correspondingly adjusting carrier wave NCO (Numerical Control Oscillation) to control the local carrier wave so that a receiver keeps phases and frequencies of the local carrier and a tracking satellite carrier wave consistent. In the invention, the low noise GPS carrier wave tracking method based on the RSL ensures that the GPS carrier wave tracking loop can work stably under the condition of low signal to noise ratio and certain dynamic state, keeps the phase not to overturn or skip cycles and ensures the carrier wave tracking precision.

The invention discloses an image spectral clustering method based on quick selection of landmark points and mainly aims to solve the problems that existing image spectral clustering methods are low inclustering precision and high in calculation complexity. The method comprises the steps that (1) all images to be subjected to spectral clustering are read; (2) neighbor images of the images to be subjected to spectral clustering are calculated; (3) the landmark points are selected; (4) a feature sparse representation matrix of the images to be subjected to spectral clustering is calculated; (5)a relevant matrix of the images to be subjected to spectral clustering is calculated; (6) a right singular feature matrix of the sparse representation matrix is calculated; and (7) recognition clustering is performed. Compared with some existing image spectral clustering technologies, through the method, a sparse representation error of the images can be lowered, the accuracy of a spectral clustering result is improved, and the calculation complexity is low.

The invention discloses an information source number estimation method for a polarization sensitive array. The method includes the following steps that spatial electromagnetic wave signals are received through a polarization sensitive array formed by N polarization sensitive array elements, and receiving data are obtained; a covariance matrix is calculated according to the data received by the polarization sensitive array; feature value decomposition is conducted on the covariance matrix, 2 N feature values and feature vectors corresponding to the 2 N feature values are solved, and the 2 N feature values are sorted in a descending mode from big to small; feature removal treatment is conducted on the covariance matrix through the sorted front N feature values, corresponding N new covariance matrixes are obtained, and a projection matrix is built through sub spaces formed by the feature vectors corresponding to the sorted rear N feature values; projection on the projection matrix of the N new covariance matrixes is solved respectively; the information source number is estimated according to a projection structure criterion function. According to the information source number estimation method for the polarization sensitive array, the information source number is estimated through a noise sub spatial dimension feature of the polarization sensitive array, the calculated amount is reduced, and the cost is saved.

The invention discloses an MWHTS channel weight function calculation method based on a neural network, and the method comprises the steps: inputting an atmospheric parameter and an MWHTS observation altitude angle into a radiation transmission model RTTOV, and calculating the atmospheric transmittance from each layer in atmospheric stratification to a satellite load MWHTS; calculating an MWHTS channel weight function profile according to the atmospheric transmittance, and establishing a matching data set of the atmospheric parameters, the MWHTS observation elevation angle and the MWHTS channelweight function profile; training a BP neural network based on the matching data set, and establishing an optimal model for MWHTS channel weight function profile calculation based on the BP neural network for each channel of the MWHTS; establishing an MWHTS channel weight function maximum value sample as the output of the deep neural network; training the deep neural network by taking the atmospheric parameters and the MWHTS observation altitude angle as the input of the deep neural network, and establishing an optimal model of atmospheric stratification calculation in which the MWHTS channelweight function maximum value based on the deep neural network is located for each channel of the MWHTS. The method is high in calculation speed, small in calculation amount and simple and easy to operate.

The invention discloses an SSTMV minimum variance beam forming method for wideband radar. Array data is obtained by an antenna of the wideband radar, iterative segmentation and FFT conversion are conducted on the array data to obtain array frequency domain data, and conventional beam forming treatment is conducted on the array frequency domain data through a weighting vector for conventional beamforming; sub-array division and combination are conducted on the array frequency domain data obtained after conventional beam forming treatment to obtain sub-array data; calculation and accumulation of an STCM driving covariance matrix are conducted on the sub-array data, and the beam forming of a minimum variance wideband direction-finding algorithm is conducted through the obtained STCM matrix to obtain the minimum variance beam. By means of the method, the radar space resolution capacity can be effectively improved, good performance can be presented under a small number of periods without accumulating or modifying too large number of periods due to the coherent accumulation characteristic of SSTMB, and the method greatly reduces the calculation amount of the algorithm and is suitable for the wideband radar.

The invention provides a large power grid quiescent voltage stability situation evaluation method and system based on linear deduction. The method comprises the following steps: obtaining a stabilitymargin index and a voltage amplitude under an estimated tide section by utilizing linear deduction calculation under a second-level time scale; and constructing a voltage stability situation evaluation index reflecting the power grid operation development trend based on the stability margin difference between the adjacent sections, and judging the system operation development trend according to the size of the voltage stability evaluation index. According to the method, the stability margin index of the estimated section can be accurately and quickly estimated by adopting a linear deduction method; the method is advantaged in that a defect of large calculation amount caused by Jacobian matrix inversion operation of a traditional method is overcome, real-time demands of subsequent prevention and control optimization decisions are adapted, the voltage stability situation evaluation index is constructed, and the operation development trend of the electric power system can be directly determined through the index.

The invention discloses a networking radar resource allocation method based on mutual information maximization, and relates to the technical field of radars. The method comprises the steps of determining a networking radar model of a networking radar system, and carrying out the initialization of the networking radar model; determining an extended state vector and an extended state transition equation of each target at the current fusion moment; determining an extended state prediction value of each target at the first fusion moment; in the current fusion time interval, observing each target by a preset number of radars, and obtaining an asynchronous measurement value of each target in the current fusion time interval; and sending the asynchronous measurement value to a fusion center so as to enable the fusion center to determine an optimal solution of a resource allocation vector of each target at the first fusion moment after calculating a target tracking information measurement function of each target at the first fusion moment. According to the method, the defect that the objective function is relatively large in computation burden is overcome, and the real-time requirement is met while the resource allocation problem of the heterogeneous networking radar system is solved.