62 results about "Ordered statistics" patented technology

The present invention belongs to the technical field of BCH code decoding, and specifically relates to an error correction decoding method based on BCH codes. The steps include constructing a neural network model suitable for UDE detection. If the output result is approval, decoding ends; otherwise, it is determined to be an undetectable decoding error (UDE), triggering an ordered statistical decoder (OSD) for further decoding; constructing a DIA correction model to improve the bit reliability metric of the received sequence before OSD decoding; using the ALMLT algorithm to obtain the order of test error patterns as the initial value of the OSD decoding path, and then obtaining the frequency distribution characteristics of the actual error pattern or estimated error pattern corresponding to the decoded codeword, continuously fine-tuning the order of the test error patterns, obtaining an optimized decoding path, and performing OSD decoding on the BCH code. The present invention significantly improves the reliability and efficiency of BCH codes in NMS decoding, while reducing computational complexity and latency.

The invention relates to the technical field of data processing, and particularly discloses a data analysis method and device fusing a large number rule, equipment and a medium, and the method comprises the steps: determining a minimum convergence amount through hierarchical aggregation; calculating a cumulative sample mean trajectory and carrying out convergence diagnosis; performing noise attenuation weighting based on LLN convergence characteristics; performing distribution and component decomposition under steady moment constraint; performing consistency check and re-extraction robustness of LLN guidance; lLN-constrained model fitting and uncertainty calibration output are carried out; according to the method, based on noise attenuation weighting of LLN convergence characteristics, samples which are close to a steady state obtain greater influence in estimation, and unstable or sparse samples are naturally weakened, so that self-adaptive suppression of heterogeneity and small sample noise is realized, and the robustness of overall estimation is improved; steady moment constraint is introduced into distribution / component decomposition, it can be guaranteed that components obtained through decomposition are consistent with observation convergence characteristics in the aspect of high-order statistics, and component mismatching caused by extreme values or local fluctuation is reduced.

The invention discloses a radar anti-active slice forwarding interference method based on high-order statistics, and the method comprises the steps: S10, carrying out the preprocessing of a radar receiving signal, carrying out the high-order cumulant construction of the radar receiving signal, completing the signal preprocessing, the calculation of a fourth-order cumulant, and the construction of a feature tensor, the high-order cumulant feature tensor of each time window corresponds to the corresponding time domain signal according to the time dimension and serves as a source of subsequent training input; s20, generating a label mask required by supervision training, and constructing an input feature and a supervision label so as to form a data structure in which the input and the label are paired; s30, constructing a MoE deep neural network based on high-order cumulant characteristics; s40, constructing a loss function and an optimization target, and calculating results of all loss items; s50, constructing an end-to-end blind interference suppression framework; s60, performing model training and parameter optimization; and S70, completing interference suppression and target output generation.

The invention belongs to the technical field of BCH code decoding, and particularly relates to an error correction decoding method based on a BCH code, which comprises the following steps: constructing a neural network model suitable for UDE detection, if an output result is approved, ending decoding, otherwise, judging that decoding error UDE cannot be detected, and triggering an ordered statistical decoder OSD for further decoding; constructing a DIA correction model, and improving the bit reliability measurement of a received sequence before OSD decoding; an ALMLT algorithm is utilized to obtain a sequence of test error modes, the sequence is used as an initial value of a decoding path of the OSD, then frequency distribution characteristics of a real error mode or an estimated error mode corresponding to a decoding code word are obtained, the sequence of the test error modes is continuously finely adjusted, an optimized decoding path is obtained, and OSD decoding is carried out on a BCH code. According to the method, the reliability and the efficiency of the BCH code in NMS decoding are remarkably improved, and meanwhile, the calculation complexity and the time delay are reduced.

The invention relates to the technical field of visual inspection, in particular to an intelligent detection and evaluation system for aero-engine blade repair performance, and the system comprises an aero-engine blade image collection module which is used for obtaining a to-be-detected aero-engine blade repair area image, carrying out the illumination normalization of the image, and generating a repair area subgraph sequence with coordinates. According to the method, the aero-engine blade repair area image is divided into the overlapped sub-areas with the space coordinate indexes, so that the full coverage of detection analysis and the accuracy of defect positioning are ensured, and the contrast ratio, the energy and other second-order statistics are obtained by calculating the gray-level co-occurrence matrix; and in combination with radial and angular energy distribution extracted by two-dimensional fast Fourier transform, a multi-dimensional texture feature set fusing a spatial domain local pixel relationship and a frequency domain periodic structure is constructed, so that the cladding texture state of a repair region can be described more comprehensively, and the identification capability of texture uniformity and directional anomaly is improved.

The invention discloses an improved two-dimensional ordered statistical constant false alarm rate detector implementation method based on a CPU. The implementation steps are as follows: inputting a two-dimensional data matrix; performing initialization setting; for each detection unit, judging whether the sample value of the detection unit is the maximum value in a nine-block grid taking the detection unit as the center or not before detection; if yes, judging whether the sample value of the detection unit is greater than k arbitrary values of all reference unit sample values multiplied by a threshold factor or not through parallel processing; if yes, the detection unit is judged as a target, and the adjacent undetected unit does not need to be detected subsequently. According to the method, the number of detection units is greatly reduced, the processing time is shortened, meanwhile, in the detection process, a CPU does not need to open up an extra memory space to store all reference units to be sorted, data sorting work which is complex in traditional two-dimensional ordered statistics constant false alarm rate and long in consumed time is ingeniously avoided, the time complexity and the space complexity of an algorithm are reduced, and the detection efficiency is improved. And the real-time performance of target detection is ensured.

This invention discloses a DOA estimation method based on the anti-diagonal line of the second-order statistical matrix of a non-circular near-field quasi-stationary signal, belonging to the field of array signal processing. Addressing the difficulty in discerning DOA estimation for non-circular near-field quasi-stationary signals when the source angle difference is small, this invention reconstructs a virtual equivalent signal by using the anti-diagonal line of the pseudo-covariance matrix and the parallel lines of the anti-diagonal line of the covariance matrix, thus achieving DOA estimation for non-circular near-field signals. The method implementation steps include: calculating the covariance matrix and pseudo-covariance matrix of the signal at each time frame of the quasi-stationary signal; extracting the parallel lines of the anti-diagonal line of the covariance matrix and the elements of the anti-diagonal line of the pseudo-covariance matrix, reconstructing them into an equivalent signal; and using the classical MUSIC algorithm to estimate the parameters of the equivalent signal. This scheme avoids the spectral peak overlap problem that occurs when the source angle difference is small, improving the accuracy of DOA estimation.

Distribution fitting Constant False Alarm Rate (CFAR) detection is described. Noise data in cells or bins around a target cell are fit to a noise distribution model, such as a Rayleigh distribution model. With a suitable noise distribution curve from the distribution model, a CFAR threshold for that cell along the curve can be determined. A quantile function of the noise distribution model for a bin or cell provides the CFAR threshold to use for that bin or cell. Distribution fitting CFAR enables a more-accurate CFAR threshold to be set for each bin or cell and may use far fewer computing resources than Ordered-Statistics CFAR. A radar detector can better prevent false alarm detections across multiple different driving scenarios by adapting to different environments and dynamically changing the noise distribution curve used depending on best-fit analysis by a noise distribution model of noise characteristics of the neighboring bins or cells.

The invention provides a semiconductor defect analysis method, device and equipment based on an intra-cluster square error, and belongs to the field of semiconductor manufacturing, and the method specifically comprises the steps: obtaining to-be-clustered data in a data set; the clustering numbers are traversed from 1 to N, the intra-cluster square error of the to-be-clustered data corresponding to each clustering number under the K-means algorithm is calculated, and N is a natural number not smaller than 5; dynamically determining the optimal clustering number for the to-be-clustered data based on the difference second-order statistics of the intra-cluster square error; and clustering the to-be-clustered data according to the optimal clustering number, analyzing the clustering center of each category, and determining the defect of each category. Through the processing scheme provided by the invention, the optimal clustering number is dynamically determined, and the defect analysis accuracy is improved.

This invention discloses a monostatic symmetric nested array MIMO system and a near-field positioning method, belonging to the field of monostatic MIMO near-field positioning technology. This invention can effectively improve the positioning performance during near-field estimation. The monostatic symmetric nested array MIMO system proposed in this invention has a symmetrically distributed transmit and receive arrays. Compared to other symmetric MIMO systems, its differential array is a positionally continuous array with more degrees of freedom. With the same physical array aperture, fewer physical array elements are actually used, reducing costs. The near-field positioning method proposed in this invention calculates the fourth-order statistics of the signal and quantizes them to obtain a virtual differential array of the differential array, achieving accurate near-field positioning of sparse MIMO structure arrays. Furthermore, it can be extended to more sparse MIMO symmetric systems.

The invention discloses a radar ranging method and device and a storage medium, and relates to the technical field of radar ranging, and the method comprises the steps: transmitting a detection signal to a target wall; in response to an echo signal of the detection signal, according to the detection signal and the echo signal, calculating a threshold value of each sampling point through a reference unit averaging mode or an ordered statistical mode; screening out candidate target points of which the signal intensity is greater than a threshold value; if the preset number of continuous peak values appear at the candidate target point, improving the threshold value of the back surface echo peak value of the candidate target point; taking a peak value with the earliest arrival time or the highest signal intensity in the processed candidate target points as a target detection point; and calculating a distance parameter between the unmanned aerial vehicle and the target wall according to the target detection point. According to the invention, the technical effect of accurate distance measurement of the target wall surface can be realized.

This invention proposes a multi-target detection method based on entangled photons. First, a pump light is generated using a continuous narrow-linewidth semiconductor laser, which, after polarization modulation and focusing, illuminates a PPKTP crystal. A spontaneous parametric down-conversion process generates polarization-entangled photon pairs, which are then separated into signal and reference light. These are received by a single-photon detector to obtain two time series. Second, coincidence measurements are performed on the time series in different scanning directions within the observation area to obtain a coincidence count matrix. Then, a two-dimensional constant false alarm rate (CFAR) detection algorithm is introduced, using ordered statistics to estimate background noise and adaptively adjust the detection threshold to screen candidate target points. Third, a density clustering algorithm based on correlation strength weighting is employed, combining the correlation strength and spatial distribution of entangled photon pairs to detect different targets and suppress isolated noise. Finally, the coincidence count peak points within each target cluster are selected as the final detection targets, thereby achieving multi-target detection with unknown numbers and randomly distributed locations.

The present application discloses a modular order management method and system for write-off operations. The method includes configuring merchant information according to merchant information and merchant business rules; verifying a business import file, and when the business import file meets the preset requirements of the merchant, entering the business import file; verifying the data content of the successfully entered business import file, and when the content verification is successful, conducting an audit to obtain audit-passed data information; determining the commodity type based on the audit-passed data information and generating a write-off form based on the commodity type; and conducting order write-off based on transaction information and the write-off form to generate order write-off data. This solves the technical problem that the settlement amount of external write-off operations is manually operated, which is time-consuming, laborious, and prone to errors, and reliable management of write-off operations cannot be achieved. The technical effect is achieved of constructing a systematic, process-based, accurate, and highly versatile intelligent order statistics management system, effectively improving the management level of write-off operation orders.

The invention relates to the technical field of image style migration, and provides a diffusion style migration method and system based on latent distribution re-anchoring dynamic injection, and the method comprises the steps: carrying out the second-order statistical alignment of a content hidden variable and a style hidden variable through a potential covariance re-coloring method; automatically positioning an injection starting point of the style according to the edge intensity of the content image, and injecting hidden distribution reanchoring noise into the initialized mixed noise hidden variable to realize hidden distribution reanchoring; the content self-attention features and the style self-attention features are fused through a soft + dynamic style injection module, in the reverse sampling process of the calibrated hidden variables, the fused attention features are gradually injected into an attention layer in the diffusion process at the injection starting point, diffusion denoising is carried out, and a style migration image is generated. According to the method, the image with rich structure details is protected, and the excellent content structure maintaining capability and the accurate style reappearance are realized at the same time.

Methods, apparatus, systems, and articles of manufacture to estimate cardinality through ordered statistics are disclosed. In an example, an apparatus includes processor circuitry to selects a sample dataset from a first reference dataset of media assets and partitions the sample dataset into m mutually exclusive subsets of approximately equal size. The processor circuitry then estimates a ratio of a sample weighted average and empirical cumulative distribution of an approximately largest order statistic from at least one of the m subsets and generates an estimate of a total cardinality of the first reference dataset by multiplying the ratio by approximately m.

The invention belongs to the technical field of engineering construction, and particularly relates to an automatic muck truck work order generation method based on space-time covariance matching, and the method comprises the following steps: S1, constructing an electronic fence path topological matrix, and constraining a legal path transfer rule between electronic fence nodes; s2, defining a Kalman filtering state vector fusing the position, the speed, the load and the path deviation; and S3, performing work order cutting based on load sudden change and path sequence dual verification. According to the method, the problem of work order mistakes and omissions caused by many-to-many path identification, key node misjudgment and work order conflicts in work order statistics can be solved, and the method has a good market application prospect.

The present invention discloses a distributed multi-pulse detection method based on local order statistics, comprising the following steps: initializing a distributed radar target detection system; performing square law and DMOS-CFAR processing on each radar station to obtain local statistics; maximizing the data of the Doppler channel at each radar station and calculating its probability density function; calculating a test statistic based on a likelihood ratio test using the probability density function of the maximum value of the Doppler channel data at each radar station, and transmitting the result to a fusion center via a communication link; obtaining a global optimal statistic from the local test statistic, calculating a global decision threshold based on the global optimal statistic and a false alarm rate, and finally obtaining a target detection result in a spatial resolution unit based on the threshold. The present invention significantly improves the anti-interference capability of the distributed pulse Doppler radar system and enhances the detection capability and false alarm control capability of the radar target detection algorithm under non-uniform backgrounds such as multi-false target interference and clutter edges.

The application discloses a kind of based on dynamic weighting joint area CFAR's unmanned vehicle target detection and speed-distance dictionary construction method, it is related to target detection technical field, including: two-dimensional FFT processing is carried out to radar echo signal, constructs distance-Doppler joint spectrum matrix;In distance dimension, detection is carried out using ordered statistics CFAR, in Doppler dimension, detection is carried out using minimum selection CFAR, and respectively generates continuous type confidence score;Through introducing clutter intensity index and target density index, the weight of distance dimension and Doppler dimension is dynamically calculated, and joint confidence index is obtained by weighted fusion;Adopt adaptive dynamic threshold to carry out target decision, finally the detected target unit is mapped as speed-distance pair, constructs distance aggregation dictionary indexed by speed;The present application effectively suppresses false alarm propagation under dense target environment, improves detection robustness, and provides structured speed-distance dictionary data support for target tracking.

The present invention discloses an underdetermined parameter joint estimation method based on high-order statistics and non-uniform arrays, including: obtaining single-sample frequency-domain channel data; calculating the fourth-order cumulant diagonal slice matrix by using the forward-backward smoothing technique, constructing a time-delay estimation spatial spectrum by using the orthogonal propagation operator method, and searching for the spectral peak to obtain the time-delay estimation value; calculating the time-delay domain filtering vector and separating the frequency-domain channel data corresponding to each time-delay estimation value; calculating the high-order cumulant matrix by using the frequency-domain channel data corresponding to each time-delay estimation value, constructing an angle-of-arrival estimation spatial spectrum, and searching for the spectral peak to obtain the angle-of-arrival estimation value; using the already estimated and automatically paired time-delay estimation value and angle-of-arrival estimation value to vectorize the channel data, and estimating the complex gain value of the multipath signal based on the least squares principle. The present invention only requires a single sample, can perform high-precision and high-accuracy multi-parameter joint estimation in an underdetermined scenario, and has a large estimation degree of freedom, high estimation accuracy, small computational complexity, and high robustness to noise.

This invention discloses a method and system for pontine infarct segmentation and END prediction based on multimodal joint learning. The method includes acquiring and preprocessing multimodal data; constructing a wavelet transform-based feature encoding network to perform multi-scale decomposition and detail preservation of features; constructing a dual-task guided fusion module to align the deep semantics of clinical variables and imaging features and generate task-specific representations; constructing a Mamba-based global feature aggregation module to model sequence dependencies using a state-space model; constructing a multimodal second-order fusion classifier to enhance the clinical-image interaction modeling using second-order statistics; and employing a two-stage joint training strategy for training and prediction, and outputting the prediction results. This invention utilizes the DWT / IWT mechanism to significantly improve the accuracy of capturing small pontine infarct lesions; it achieves explicit interaction between segmentation evidence and prediction signals, significantly improving the segmentation accuracy of small lesions and the reliability of stroke risk assessment.

The invention discloses a bistatic multi-channel synthetic aperture radar (SAR) synchronous signal extraction method and device, and belongs to the technical field of synthetic aperture radar signal processing. According to the method, a bistatic SAR system with a plurality of receiving channels is constructed, and a synchronization signal and a scene echo signal are modeled into a linear mixed signal by using time delay and amplitude differences caused by physical position differences among the channels; a blind source separation algorithm based on high-order statistics is adopted to carry out centralization, whitening and joint diagonalization processing on multichannel received signals, and effective separation of synchronous signals and target echoes is realized. The method does not need to interrupt the imaging process, does not depend on priori knowledge or complex waveform design, remarkably improves the phase synchronization precision and the system flexibility, is suitable for various bistatic SAR systems, and has good practicability and universality.

The invention discloses a network resource cutover management method and system based on a resource capability center, belongs to the technical field of transmission network networking, and aims to solve the technical problem of how to improve the efficiency, success rate and safety of network resource cutover. According to the technical scheme, the method comprises the following steps: counting and displaying cutover work orders: automatically synchronizing type information of each type of cutover work order to complete cutover work order counting, displaying a cutover type proportion in detail, and obtaining the number of each type of work orders through the proportion number based on a lightweight statistical model parameter automatic optimization method; calculating the proportion of each cutover type, displaying the cutover types in a pie chart from high to low, and predicting the number of work orders and personnel arrangement in a future set time; creating a cutover announcement; changing work order distribution statistics; statistics of cutover time: performing classified statistics by day according to the starting date of the cutover work, and displaying through a histogram; performing cutover time deviation analysis; and analyzing a cutover result.

The application discloses a kind of based on magnetocardiogram's lesion model construction method and system, comprising: acquisition multi-channel magnetocardiogram and preprocessing are obtained preprocessing observation matrix;Based on second-order statistics blind source separation of second-order statistics blind source separation based on multi-delay covariance weighting joint diagonalization, generate interference suppression weight and reconstruction are obtained purification observation matrix;Coupling separation matrix and interference suppression weight generate inversion prior parameter and establish weighted forward model;Under the constraint of weighted forward model, using the improved FOCUSS of connected domain focus to the sparse reconstruction of purification observation matrix is obtained convergent source distribution;Based on convergent source distribution extraction lesion voxel boundary and reconstruction surface grid, combined with energy projection consistency check output three-dimensional lesion model.The application improves lesion positioning accuracy and three-dimensional model construction stability under strong noise and individual difference conditions.

The application relates to a millimeter wave radar multi-target behavior sensing multi-path elimination method, relates to the technical field of radar multi-path elimination, and aims to solve the problem that, in the prior art, in the case of multi-target human body detection, too many targets exist and interfere with each other, thereby affecting the detection accuracy. The application utilizes the characteristics of ordered statistics, effectively suppresses and eliminates multi-path interference and clutter, thereby solving the problem that, in the case of multi-target human body detection, too many targets exist and interfere with each other, thereby affecting the detection accuracy. Meanwhile, the application further verifies multi-path elimination through a coherence coefficient, thereby improving the robustness of the method. The method can effectively improve the target detection and tracking performance of a radar system, and has high practical value for behavior sensing and other applications.

The invention provides a two-dimensional *-shaped sliding window detection method and system, and belongs to the technical field of vehicle-mounted millimeter wave radar signal processing. The method comprises the following steps: firstly, carrying out boundary expansion on an input original two-dimensional data matrix to obtain an expanded data matrix; secondly, constructing a two-dimensional *-shaped sliding window taking the current detection unit as the center on the extended data matrix, and executing hybrid two-dimensional constant false alarm processing on a reference unit set in the sliding window to obtain a background clutter estimation value; then, generating a detection threshold according to the background clutter estimation value, comparing the detection threshold with the amplitude of the current detection unit, and judging a target trace point; and finally, updating the position of the current detection unit according to a preset path and repeating the steps until traversal is completed, and outputting all target trace point information. The spatial distribution of the reference unit is optimized through the design of the sliding window shaped like the Chinese character'mi ', and collaborative optimization of the detection performance and the processing efficiency is realized by combining a mixed processing mechanism of ordered statistics and unit averaging.

The invention discloses a coherent mode extraction method based on principal skewness analysis, and relates to the technical field of magnetic confinement nuclear fusion plasma diagnosis, and the method specifically comprises the following steps: S1, calculating Doppler translation according to a Doppler reflectometer system signal; s2, performing short-time Fourier transform on the obtained Doppler frequency shift to obtain a power spectrum matrix of the Doppler frequency shift; s3, constructing a co-skewness tensor according to a row vector of the power spectrum matrix, and solving a feature vector of the co-skewness tensor; and S4, performing noise suppression on the power spectrum matrix by using the feature vector of the co-skewness tensor. According to the method, the non-Gaussian features in Doppler frequency shift are extracted through high-order statistics, the limitation of a traditional method in noise suppression and transient mode detection is solved, the signal-to-noise ratio of coherent mode extraction is improved, and therefore the coherent mode can be displayed from background noise more effectively.

The invention provides a self-supervised (unsupervised) and fine-tuning-free visual few-sample classification method based on a neural network. According to the invention, two structural designs are integrated in a common coding and decoding network model: 1, a lateral connection design of a coding and decoding device containing second-order statistics; and 2, vector quantization design of the codec feature pattern. In addition, on the basis of the structural design, the invention provides a visual few-sample discrimination basis design which does not need fine adjustment of a model in a test stage. According to the scheme, a self-supervised and fine-tuning-free visual few-sample classification task can be realized.

The invention discloses a TIADC sampling time mismatch error estimation and calibration method, and belongs to the technical field of high-speed data acquisition and real-time transmission. According to the method, high-precision time error estimation is realized in a mode of combining improved first-order statistics and LMS adaptive filtering, dynamic correction is carried out by adopting a parallel Farrow fractional delay filter based on Lagrange interpolation, and a sub-ADC is used for sampling a difference value between a signal and an ideal signal and derivative information in an error estimation stage. A time mismatch initial estimation value is calculated through improved first-order statistics, the estimation value is iteratively optimized through an LMS algorithm, dynamic error tracking is achieved, optimized error parameters are input into an improved parallel Farrow filter, and K sub-filter banks are generated through a multi-phase decomposition structure. According to the invention, the spurious-free dynamic range and significant digits of the TIADC system are significantly improved, and the TIADC system is suitable for high-frequency signal acquisition scenes such as 5G communication and radar.