32results about How to "Avoid estimated impact" patented technology

The invention discloses a modified TCPW congestion control method in a wireless network. After a specified data receiving terminal receives a data packet each time, a timestamp is stamped in a reply packet corresponding to the data packet, after a data sending terminal receives the reply packet, the arrive time of the current reply packet is not recorded, but the timestamp on the reply packet is extracted, so as to obtain the timestamp difference, namely the sending time of the last data packet on the sending route, in the next arrived reply packet, the size of the arrived data packet and the sending time of the data packet on the sending route are respectively subjected to averaging at the sending terminal, and then a bandwidth sampling value is obtained according to the specific value of average values. Finally a bandwidth estimated value is smoothly obtained through an exponential weighting moving average filter according to the obtained bandwidth sampling value. The method is more accurate to estimate a current practical available bandwidth value and effectively improves network QoS.

The invention relates to a cyclic correlation channel estimation method and device. The method comprises the following steps: S110, providing a cyclic sequence set [CH1 CH2], wherein the content of the sequence CH1 is the same as the content of the sequence CH2; S120, performing channel estimation for the cyclic sequence set [CH1 CH2] through cyclic shift to obtain a time-domain channel h; S130, performing FFT for the time-domain channel to obtain a frequency-domain channel H; S140, adjusting the amplitude of the frequency-domain channel to ensure that the signal effective value RMS(h) of the frequency-domain channel is equal to RMS(H); and S150, performing interpolation calculation for the adjusted frequency-domain channel, and filtering to obtain a channel estimation value. According to the cyclic correlation channel estimation method and device disclosed by the invention, the problem that a single carrier communication system is difficult to quickly estimate the amplitude and phase information of each path in multi-path channels can be solved.

The invention provides a battery health state estimation method for an electric vehicle. The battery health state estimation method comprises the steps of: firstly, collecting battery health state parameters of the same model through utilizing a big data platform, acquiring a database, and establishing a regression model according to the battery health state parameter data in the database; secondly, dividing output data of the regression model into outliers and normal points, dividing the outliers into capacity regeneration points and abnormal points according to a preset threshold value, substituting the capacity regeneration points and a processed abnormal point mean value into a first prediction model to generate a capacity regeneration result, performing data distribution balance processing on the normal points, and substituting the normal points into a second prediction model to generate a normal result; and finally generating a combination result, and outputting the combination result as an estimation result of the battery health state of the electric vehicle. The whole method flow can be realized by establishing a mathematical model, the influence of a capacity regenerationphenomenon is comprehensively considered, and the influence of a chemical reaction on battery capacity estimation is avoided, so that higher prediction precision is achieved.

The invention provides a method and system for removing magnetic resonance diffusion tensor imaging noise. The method is based on a magnetic resonance diffusion weighted imaging model and the Gaussian distribution character of sampling noise, the sparsity of a diffusion weighted image is utilized, a maximum posterior probability estimating method is adopted, and a denoised diffusion tensor matrix corresponding to each space position is directly obtained from K space data. According to the method and system, the influence of image denoising errors on diffusion tensor estimation can be eliminated, noise in the diffusion tensor can be restrained more effectively, and the estimation precision of the diffusion tensor can be improved.

The invention provides a method and system for removing magnetic resonance diffusion tensor imaging noise. The method is based on a magnetic resonance diffusion weighted imaging model and the Gaussian distribution character of sampling noise, the sparsity of a diffusion weighted image is utilized, a maximum posterior probability estimating method is adopted, and a denoised diffusion tensor matrix corresponding to each space position is directly obtained from K space data. According to the method and system, the influence of image denoising errors on diffusion tensor estimation can be eliminated, noise in the diffusion tensor can be restrained more effectively, and the estimation precision of the diffusion tensor can be improved.

The invention provides a magnetic resonance diffusion tensor de-noising method and a magnetic resonance diffusion tensor de-noising system. The method comprises the following step: based on a magnetic resonance diffusion-weighted imaging model and Gaussian distribution properties of sampling noises, obtaining a de-noised diffusion tensor matrix corresponding to each spatial position from K spatial data through a maximum posterior probability estimation method by utilizing sparsity of a dispersion tensor characteristic value. According to the magnetic resonance diffusion tensor de-noising method disclosed by the invention, influence of image de-noising errors to diffusion tensor estimation can be avoided, noises in diffusion tensor can be effectively restrained, and estimation precision of the diffusion tensor can be improved.

The invention provides electronic image stabilization method and device of the vehicle-mounted camera and readable storage medium. The method comprises determining a moving object recognition area ofa first reference image collected at the last moment and a plurality of feature points in an area outside the moving object recognition area of the first reference image; obtaining reference points corresponding to the feature points from a second reference image collected at the current moment; determining a first affine transformation matrix according to each feature point and each correspondingreference point; determining a compensation parameter according to a difference value between an observed quantity and a state quantity in the first affine transformation matrix; determining a secondaffine transformation matrix according to the compensation parameter, transforming the second reference image through the second affine transformation matrix to obtain the stable image at the current moment, and therefore the feature points in the moving object recognition area where the moving object in the image is located are removed, the influence of the moving object on moving parameter estimation is avoided, and the image stabilization accuracy is improved.

The present invention provides a magnetic resonance diffusion tensor denoising method and system. The method is based on the magnetic resonance diffusion weighted imaging model and the Gaussian distribution property of the sampling noise, utilizes the sparseness of the relative anisotropy of the diffusion, and adopts the maximum a posteriori probability estimation The method of K-space directly obtains the denoised diffusion tensor matrix corresponding to each spatial position. The invention can avoid the influence of the image denoising error on the estimation of the diffusion tensor, suppress the noise in the diffusion tensor more effectively, and improve the estimation precision of the diffusion tensor.

The invention relates to a cyclic correlation channel estimation method and device. The method comprises the following steps: S110, providing a cyclic sequence set [CH1 CH2], wherein the content of the sequence CH1 is the same as the content of the sequence CH2; S120, performing channel estimation for the cyclic sequence set [CH1 CH2] through cyclic shift to obtain a time-domain channel h; S130, performing FFT for the time-domain channel to obtain a frequency-domain channel H; S140, adjusting the amplitude of the frequency-domain channel to ensure that the signal effective value RMS(h) of the frequency-domain channel is equal to RMS(H); and S150, performing interpolation calculation for the adjusted frequency-domain channel, and filtering to obtain a channel estimation value. According to the cyclic correlation channel estimation method and device disclosed by the invention, the problem that a single carrier communication system is difficult to quickly estimate the amplitude and phase information of each path in multi-path channels can be solved.

The invention discloses a rigid target three-dimensional motion estimation method in a multi-view environment, and is specifically implemented according to the steps of: 1. building a multi-view target image acquisition environment, and obtaining a plurality of target images; 2. rebuilding a target three-dimensional shape S(t) according to the plurality of target images that are obtained in the Step 1, and initializing motion parameters of the target three-dimensional shape S(t); 3. building an energy function E which reflects distance between a multi-view two-dimensional projection of the target three-dimensional shape and the target images, judging whether the energy function E converges, if not, turning to Step 4, and if yes, outputting the motion parameters of the target three-dimensional shape S(t), i.e., the motion parameters of a rigid target three-dimensional shape S(t); and 4. updating the motion parameters of the target three-dimensional shape S(t), and turning to the Step 3. Estimation of target three-dimensional motion by the method is not influenced by illumination variation and chromatic aberration of a camera.

The invention relates to a global motion estimation method and device of an image sequence, and belongs to the technical field of computer vision. The global motion estimation method comprises the following steps: collecting two continuous frames of images to serve as target images, and carrying out row direction and line direction gray projection on the two frames of sequence images; carrying out even partitioning on the row direction and line direction gray projection, calculating the matching error of each block under a certain deviation position in the line or row direction, abandoning the block with poor matching according to a proportion of local motion in the image, and taking the sum of the matching errors of residual blocks as a matching metric under the position; and calculating a deviation position with the minimum matching metric, wherein the deviation position is an estimation result of global motion. According to the possible proportion of the local motion in the image, the line or row with poor gray projection matching is abandoned, the robustness of the method on local motion is guaranteed, and influence on global motion estimation by the local motion can be effectively avoided.

The invention provides a denoising method and a denoising system for magnetic resonance diffusion tensor imaging. The method is based on a magnetic resonance diffusion-weighted imaging model and the Gaussian distribution properties of sampled noise, and comprises obtaining a denoised diffusion tensor matrix corresponding to each spatial position from K spatial data directly by virtue of the sparsity of the mean diffusivity and by use of a maximum posterior probability estimation method. The denoising method is capable of avoiding the influence of the image denoising errors on diffusion tensor estimation, and further capable of more effectively inhibiting the noise in the diffusion tensor and improving the estimation accuracy of the diffusion tensor.

The invention discloses a sub-band correlation registration method of a high-speed moving target, aiming to solve the problem that the prior art does not consider establishing a correlation registration model between sub-band signals of the target in the case of high-speed movement. Sub-band correlation registration method under high-speed target; this application uses radar transmission signals of different bands and target motion information to establish a broadband radar echo model, estimates the speed of the target according to the minimum entropy criterion, and performs speed compensation on the echo signal , and estimate the non-correlated phase factor, and compensate the estimated non-correlated factor to the non-reference subband; this application considers that the radar target is moving at high speed, which is more in line with the actual military needs, and the speed compensation for the moving target reduces the Due to the influence of one-dimensional range image distortion caused by the high-speed motion of the target, the estimation of subsequent correlation factors is more accurate, and high-precision parameter estimation is achieved at the same time; the present application is applicable to the technical field of sub-band synthesis forming ultra-wideband.

The invention relates to a direction-of-arrival estimation method, in particular to a two-dimensional sparse linear array direction-of-arrival estimation method based on matrix matching. The two-dimensional sparse linear array direction-of-arrival estimation method comprises the following steps of establishing a basic model for two-dimensional sparse linear array two-dimensional direction-of-arrival estimation, defining an incident angle of an lth signal as (theta l,), assuming that L far-field narrow-band signals are incident into the array in total, acquiring receiving data of two sub-arrays in the array at the k moment, acquiring a receiving data covariance matrix of the two sub-arrays according to the received data, acquiring virtual receiving data of a continuous ULA part in a virtual array formed by the two sub-arrays on the x axis and the y axis, constructing a Toeplitz matrix by using the virtual receiving data, then constructing spatial spectrum function to respectively obtain estimated values of two-dimensional angles and assuming that another corresponding angle estimated value in estimated values is sequentially subjected to least square algorithm and matrix characteristic decomposition to obtain angle estimated values.

The invention relates to a global motion estimation method and device of an image sequence, and belongs to the technical field of computer vision. The global motion estimation method comprises the following steps: collecting two continuous frames of images to serve as target images, and carrying out row direction and line direction gray projection on the two frames of sequence images; carrying out even partitioning on the row direction and line direction gray projection, calculating the matching error of each block under a certain deviation position in the line or row direction, abandoning the block with poor matching according to a proportion of local motion in the image, and taking the sum of the matching errors of residual blocks as a matching metric under the position; and calculating a deviation position with the minimum matching metric, wherein the deviation position is an estimation result of global motion. According to the possible proportion of the local motion in the image, the line or row with poor gray projection matching is abandoned, the robustness of the method on local motion is guaranteed, and influence on global motion estimation by the local motion can be effectively avoided.

The invention discloses an SAR interference baseline precise estimating method based on a flat-earth phase. The method comprises the steps that according to the geometrical relationship of an SAR interference baseline and the flat-earth phase, a rigorous SAR interference baseline estimating adjustment model is built, the geometric structure of baseline estimation is transferred to a regular reference ellipsoid face from the fluctuant and changing earth surface, and an iterative unknown parameter solution algorithm combining ridge estimation and truncated singular value decomposition is provided. According to the SAR interference baseline precise estimating method, the survey adjustment theory and an InSAR theory are organically combined, and the method comprises the following steps of (1) data preprocessing, (2) systemic signal removal, (3) model building and (4) model resolving. The SAR interference baseline precise estimating method is visual in principle, facilitates programming realization and application, is stable and reliable and can be used for improving the precision of measurement of the InSAR technology.

The invention discloses a channel estimation method and a channel estimation device. The channel estimation method includes the following steps: a pilot frequency sub-carrier distance and an insert position deviation value of pilot frequency symbols in adjacent frequency domains are determined; according to the pilot frequency sub-carrier distance and the insert position deviation value of the pilot frequency symbols in adjacent frequency domains, the pilot frequency symbols in the frequency domains are inserted in effective sub-carriers corresponding to the frequency domain pilot frequency symbols; according to a frequency domain symbol sequence obtained after inserting processing, time domain pilot frequency symbols are determined; and the time domain pilot frequency symbols are transmitted, so that an opposite end carries out channel estimation according to the time domain pilot frequency symbols after receiving the time domain pilot frequency symbols. By means of the channel estimation method and the channel estimation device, influences on transmission from frequency deviation and channel time-varying characteristics can be avoided, and the accuracy of the channel estimation is improved.