75 results about "Maximum a posteriori estimator" patented technology

A timing error recovery system includes a phase locked loop that receives a continuous time input signal, samples the input signal at a sampling rate and generates a voltage control signal. A statistical estimator, such as a maximum a posteriori estimator, compares the voltage control signal with an expected error based upon a statistical model and produces an adjusted voltage control signal that drives a voltage controlled oscillator to adjust the sampling rate.

The invention discloses a maximal posteriori optimized image reconstruction method for leading in a general Gibbs experiment in the PET imaging. The method comprises the following procedures: (1) PET imaging equipment is utilized to collect detector data before imaging, and the corrective parameter value and the system matrix of various data in the imaging equipment are obtained simultaneously; (2) a mathematical statistic statistical model used for reconstructing an image is reconstructed according to a statistical feature that is met by the corrective data acquainted by the procedure (1) before imaging; (3) the general Gibbs experiment is led in aiming at the compute of a mathematical model in the procedure (2) , a maximal posteriori estimate method is adopted to perform the conversion of a reconstruction model, to obtain an optimized equation with a constrained objective function used for obtaining a PET reconstruction image; (4) a parabola is adopted to replace a coordinate descent algorithm, to perform the iterative computation treatment and to reconstruct the image based on the selection of a global parameter in the optimized equation through a result obtained by the procedure (3). The invention can greatly improve the quality of the PET reconstruction image.

The invention provides an online multi-target tracking method for coupling target detection and data association, which is used for real-time tracking of multiple interested targets in video, and belongs to the technical fields of computer vision and video monitoring. Starting from the intermediate results provided by the target detector, the present invention implements target tracking and optimizes target detection results by introducing sequential trajectory priors, which can effectively alleviate the problem of inaccurate detection results of the target detector; the present invention adopts the method of maximum a posteriori estimation The probabilistic framework calculates the association cost between the target and the detection result, improves the accuracy of data association, and can effectively deal with the mutual interference between targets; the present invention establishes a close connection between target detection and data association, making both Become two mutually promoting processes, which improves the performance of multi-object tracking.

The invention discloses a method for reducing the speckles of a synthetic aperture radar (SAR) image by combining dual-tree complex wavelet transform with a bivariate model, which mainly solves the problems that speckle noise cannot be well inhibited and part of edge information and detailed information are lost in the conventional method for reducing the speckles of the SAR image. The method comprises the following steps of: performing dual-tree complex wavelet decomposition on the original SAR image to obtain a real part and an imaginary part of a decomposition coefficient on each scale; solving the variance of a noise coefficient by using a non-logarithmic additive noise model; solving the edge variances of the real parts and the imaginary parts of the complex wavelet coefficient by using a local neighborhood window; solving a threshold contraction function by maximum posterior estimation and performing threshold contraction on the dual-tree complex wavelet decomposition coefficient; and performing dual-tree complex wavelet reconfiguration on the contracted coefficient to obtain an image of which the speckles are reduced. The method has the advantages of capability of effectively removing the speckle noise from the SAR image and high edge preserving performance, and can be used for reducing the speckles of the SAR images with abundant edge information and detailed information, particularly the airport, runway and road-containing SAR images.

The invention provides an adaptive variation remotely sensed image fusion method. In the method, multi-spectrum image and full-color image observation models are respectively established by analyzing the image degrading process, an inverse problem corresponding to the image fusion is described by utilizing a maximum posteriori estimation theoretical frame, and a variation image fusion model consisting of a multi-spectrum image data consistency constraint, a full-color image data consistency constraint and an image transcendental constraint is established; and in the resolving process, iterative solving is carried out by a gradient degressive algorithm, regularization parameters are adaptively solved by establishing a proper function, and an adjustable parameter is reserved simultaneously so as to meet the requirements of different users. The method can improve the original multi-spectrum image space resolution, effectively maintain the conventional spectrum information, and automatically select the proper regularization parameters adaptively according to different image data, so the method has the characteristics of high fidelity, high adaptation degree and the like.

Disclosed are a method and an apparatus for transmitting a sparse signal, and a method and an apparatus for recovering the sparse signal. The method for recovering a sparse signal by using a sparse signal recovering device that recovers a target signal from a received signal includes receiving a measurement signal with a noise signal from a sparse signal transmitting device which scans a target signal based on a measurement matrix, performing a mutual update procedure in which a likelihood probability is calculated by using a posterior probability of the target signal based on a relation between the target signal and the measurement signal, and the posterior probability is updated by using the likelihood probability, and recovering the target signal by performing maximum a posterior estimation for a final posterior probability output through the mutual update procedure.

The invention discloses a recommendation system and a recommendation method based on relationship type cooperative topic regression. The system at least comprises an RCTR (Relationship type Cooperative Topic Regression) model establishing module, a parameter studying module and a predicted value calculating module, wherein the RCTR model establishing module is used for integrating user-item rating information, item content information and a relation structure between items into a hierarchy bayesian model to establish an RCTR model; the parameter studying module is used for utilizing maximum posteriori estimation to study parameters in the RCTR model, and finally obtaining a parameter user implicit vector, an item implicit vector, an item relation vector and a full posteriori possibility of an item topic ratio; and the predicted value calculating module is used for utilizing the user implicit vector, the item topic ratio and point estimation of item implicit deviation to calculate a predicated value of evaluation by using a predicted value calculating formula. According to the recommendation system and the method disclosed by the invention, the user-item rating information, the item content information and the relation structure between the items is integrated to one hierarchy bayesian model seamlessly to integrate a social network between the items into a recommendation process, so that the recommendation accuracy is improved.

The invention provides a pulse wave signal denoising method based on DTCWT-Spline. The method includes the steps that firstly, original noise-containing pulse wave signals are subjected to double-tree complex wavelet decomposition, wavelet coefficients on all layers are denoised through a Bayes maximum posteriori estimation threshold value, then double-tree complex wavelet inverse transformation is carried out, and pulse wave signals obtained after high-frequency noise is filtered out are obtained; the obtained pulse wave signals obtained after the high-frequency noise is filtered out are detected through a sliding window method, wave trough points in the pulse wave signals obtained after the high-frequency noise is removed are recognized, a wave trough curve is fit through a cubic spline interpolation method to serve as the estimated baseline drifting distance, finally subtracting the estimated baseline drifting distance from the pulse wave signals obtained after the high-frequency noise is removed is carried out, and the pulse wave signals are denoised. The high-frequency noise and baseline drifting can be effectively removed, overall characteristic information of the original pulse wave signals is well kept, the method is simple, low in calculated quantity and low in occupied memory, and a technical foundation is provided for research and development of miniaturized and mobile noninvasive continuous blood pressure detection equipment based on pulse waves.

A method for recovering a sparse signal of a finite field may include: updating discrete probability information of a target signal element of the finite field and discrete probability information of a measurement signal element of the finite field by exchanging the discrete probability information of the target signal element with the discrete probability information of the measurement signal element a predetermined number of times, wherein the target signal element and the measurement signal element are related to each other; calculating a final posteriori probability based on a priori probability of the target signal element and the discrete probability information of the measurement signal element, acquired as the exchange result; and recovering the target signal by performing maximum posteriori estimation to maximize the final posteriori probability.

The invention discloses an abdominal component analyzer and an analysis method thereof; an abdominal component analysis model of a maximum posteriori estimation method is firstly used, then the model is trained by detecting sample human body abdominal bioelectrical impedance, and finally the human body abdominal bioelectrical impedance of a detected person is sent into the trained model so as to obtain the content of abdomen components of the detected person; meanwhile, in the process of measuring the bioelectrical impedance of the abdomen of the human body, the accuracy of impedance data measurement is improved in a multi-frequency detection and self-correction mode, and experimental results show that the error of a measured value of the abdomen of the human body can be controlled within 10%. In addition, compared with doctors and common large-scale human body composition analyzers and abdominal fat analyzers, the device has the advantages of higher pertinence, smaller size, noninvasive property, safety, simplicity, convenience and low cost, so that the product is a feasible solution for current household detection.

The invention discloses a tag recommending system and method based on synergistic topic regression with social regularization. The tag recommending system comprises a CTR (Common Technical Regulation) model establishing module, a CTR model establishing module with social regularization, a parameter studying module and a tag recommending module, wherein the CTR model establishing module is used for establishing CTR models to all tags; the CTR model establishing module with social regularization is used for integrating an article-tag matrix, content information of the articles and a social network of the articles into a level Bayesian model to establish a CTR-SR model; the parameter studying module is used for studying parameters in the model established by the CTR model establishing module with social regularization by utilizing maximum posterior estimation, and finally obtaining the whole posterior probability of all the parameters; the tag recommending module is used for carrying out tag recommendation according to the studied parameters. The tag recommending system and method disclosed by the invention has the advantages that the CTR model is applied in tag recommendation, and the level Bayesian model is provided by expanding the CTR, so that the article-tag matrix and the content information of the articles are effectively integrated, the network relationship among the articles are utilized, and further the accuracy of recommendation is improved.

The present invention provides a microorganism association network prediction method and apparatus. The method comprises obtaining abundances of various microorganisms in metagenome sequencing samples and an environmental factor measurement value corresponding to each metagenome sequencing sample; establishing a hierarchical Bayesian model according to a data generation process of each metagenome sequencing sample and the abundances of the various microorganisms and the corresponding environmental factor measurement values in each metagenome sequencing sample; learning the hierarchical Bayesian model by using a maximum posteriori estimation algorithm, and determining an objective function of the hierarchical Bayesian model; optimizing the objective function; and predicting association between the microorganisms and association between the microorganisms and the environmental factor measurement values by using the optimized hierarchical Bayesian model. Accuracy and practicability of prediction tasks of microorganism association and microorganism and environmental factor association are improved.

The invention provides an iterative massage passing (IMP) PN code capturing method based on a sum-product algorithm on the condition of a low signal to noise ratio. According to the method, an m sequence is modelized into a special linear block code; a sum-product decoding algorithm is executed on a Tanner graph to obtain maximum posteriori estimation of the m sequence; and an initial state is selected by introducing a posteriori log-likelihood ratio to generate a local m sequence; and at last, serial correlation verification is carried out with reception data and capturing is completed. The greatest advantage of the method provided in the invention is as follows: maximum posteriori estimation of an m sequence is directly decoded by utilization of a sum-product decoding algorithm and a posteriori log-likelihood ratio is introduced to select an initial state. Besides, the method has advantages of rapid capturing speed, low complexity and excellent performance under a low signal to noise ratio. According to the invention, a fundamental principle on IMP PN code capturing is mainly introduced and then an IMP PN code capturing method based on a sum-product algorithm is provided; and an analysis on a selection process of an initial state of an m sequence is emphasized and a detailed flow chart is provided; at last, a capturing performance and a complexity of the method are analyzed though examples.

The invention relates to the technical field of bridge deck elevation measurement in bridge engineering, and discloses a bridge deck elevation fitting method based on a Bayesian-Kriging model. The method comprises the steps that S1 a Bayesian-Kriging fitting model is established; S2 test optimization of an elevation test point sample is carried out; S3 a Bayesian-Kriging prediction model is established; and S4 elevation fitting evaluation is carried out on the whole bridge deck. The model has the advantages that Bayes and Kriging are combined: based on non-information super-priority, multi-layer priori constraints are imposed on the basis function coefficients and related parameters of a Kriging model; an EM algorithm is used for the solving of the basis function coefficients and the maximum posteriori estimation of the related parameters; the Kriging model is improved, and the Bayesian-Kriging model is established; the adaptability and robustness of the model are enhanced; and the test optimization design of an elevation measurement sample based on a number theory method is carried out.