35 results about "Second order statistics" patented technology

A system for extracting glacier boundaries using multiparametric analysis, consisting of: a data acquisition module configured to acquire Synthetic Aperture Radar (SAR) data of a glacial region; a data processing unit configured to process the SAR data to generate coherence images and backscatter intensity maps, to extract terrain parameters including slope and curvature information from a digital elevation model (DEM), to apply thresholds to the coherence images, backscatter intensity maps, and terrain parameters, to generate a multi-band stack comprising the thresholded coherence images, the thresholded backscatter intensity maps, the thresholded slope information, and the curvature information, and a principal component analysis module configured to standardize the multi-band stack, calculate a covariance matrix from the standardized data, calculate eigenvalues and eigenvectors of the covariance matrix, and transform the original data into a principal component space with reduced dimensionality; a texture feature extraction module configured to calculate first- and second-order statistics from the output of the principal component analysis; generating a texture feature set comprising at least one of the following: sum average, entropy, difference entropy, sum entropy, variance, difference variance, inverse difference moment, contrast, correlation, information measures of correlation, and maximum correlation coefficient; a connected component segmentation module configured to: convert the set of textural features into a binary format that distinguishes glacier ice from the background; group spatially connected pixels of similar intensity into segments; extract a vector shape file corresponding to the glacier boundary; an output generation module configured to generate a glacier boundary delineation output; and a user interface having a display configured to display the output generated by the output generation module.

The invention provides a non-circular signal sensing method based on a weighted coherence matrix, which belongs to the technical field of wireless communication, and comprises the following steps: acquiring a complex value receiving signal, and calculating a real value augmented sample covariance matrix; based on the real value augmented sample covariance matrix, constructing a real value sample coherence matrix, and calculating a corresponding weight coefficient; constructing test statistics based on the weighted sum of squares of the real value sample coherence matrix and the weight coefficient; and calculating a sensing threshold based on the false alarm probability, comparing the sensing threshold with the test statistics, and determining a spectrum sensing result. According to the method, the second-order statistical property of the non-circular signal is described by using the real-value augmented sample covariance matrix, and weighted square fusion is performed on the real-value sample coherence matrix in combination with the weight coefficient driven by the sample data, so that better sensing performance can be obtained.

The invention discloses an IQ imbalance blind calibration method and an IQ modulation demodulator, and the method comprises the steps: determining a regularity judgment condition based on the second-order statistical characteristics of a complex signal under an ideal condition; building an IQ imbalance mathematical model to determine a compensation mode; obtaining a compensation coefficient formula based on the compensation mode, the regularity judgment condition and the step diagonal matrix; substituting the complex signal into a compensation coefficient formula to obtain a high-order LMS dual-channel algorithm model; a nonlinear step size error model is introduced into a high-order LMS double-channel algorithm model. The invention provides a frequency-related square difference operation variable step size IQ imbalance dual-channel high-order blind compensation filtering algorithm without any prior information. Algorithm parameters are adjusted according to signal mismatch caused by phase mismatch and amplitude imbalance of input signals, so that the signals keep an ideal orthogonal relation.

A method includes determining estimated features comprising second order statistics based on at least one received signal. The method also includes classifying, using a machine learning network, each channel of the at least one received signal into a channel profile based on the estimated features. The method also includes obtaining multiple minimum mean square error (MMSE) channel estimation weights from a database based on the estimated features, the database storing (i) representative MMSE estimation weights and (ii) channel cluster representatives indexed by the estimated features. The method also includes applying a respective MMSE channel estimation weight for each channel.

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 application provides a two-stage denoising neural network-based direction of arrival estimation method, device and electronic equipment. The method comprises the following steps: constructing an array receiving signal matrix and extracting an amplitude matrix and a phase matrix according to the receiving signal of a linear uniform array; extracting the median of all elements in the amplitude matrix and performing range compression to obtain an amplitude statistical feature; inputting the amplitude statistical feature into an adaptive threshold subnetwork to generate a compression coefficient; performing amplitude limiting processing on the amplitude matrix by using the compression coefficient, combining the new amplitude matrix and the phase matrix into a limited amplitude signal matrix; constructing a bounded covariance matrix with a second-order statistical form based on the limited amplitude signal matrix; inputting the bounded covariance matrix into a complex residual convolution subnetwork to generate a noise residual matrix; obtaining an equivalent covariance matrix based on the bounded covariance matrix and the noise residual matrix; and performing spatial spectrum search on the equivalent covariance matrix by using a MUSIC method to obtain a direction of arrival estimation result of a signal source.

This invention provides a non-circular signal sensing method based on a weighted coherence matrix, belonging to the field of wireless communication technology. The method includes: acquiring a complex-valued received signal and calculating a real-valued augmented sample covariance matrix; constructing a real-valued sample coherence matrix based on the real-valued augmented sample covariance matrix and calculating corresponding weighting coefficients; constructing a test statistic based on the weighted sum of squares of the real-valued sample coherence matrix and the weighting coefficients; calculating a sensing threshold based on the false alarm probability and comparing the sensing threshold with the test statistic to determine the result of the spectrum sensing. This invention utilizes the real-valued augmented sample covariance matrix to describe the second-order statistical properties of non-circular signals and combines sample data-driven weighting coefficients to perform weighted square fusion of the real-valued sample coherence matrix, achieving superior sensing performance.

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.

The invention particularly relates to random response analysis of a wake flow type hybrid energy collector under fluctuating wind excitation, which comprises the following steps: on the basis of a simplified model that a static upstream bluff body is arranged in front of a main bluff body, and periodic vortex shedding wake flow force is utilized to excite vibration, establishing an equivalent electromechanical coupling dynamic model; a random phase is introduced into wake-induced vibration external excitation to represent random fluctuation of wake force under the fluctuating wind; after dimensionless and parameter scale re-calibration are carried out on the model, a multi-scale method is adopted to derive an amplitude-phase slow modulation equation set and first-order asymptotic analysis response expressions of displacement, voltage and current; further linearizing the slow-varying equation under a steady-state condition, solving a second-order statistic of amplitude and phase by combining a moment method and an Ischi differential rule, and determining steady-state moments of the output voltage and the output current and average output power according to the second-order statistic; and the output performance rapid evaluation and parameter design support of the wake flow type hybrid energy collector under random excitation are realized.

The invention relates to the technical field of uplink transmission, and discloses an adaptive adjustment method for a data transmission link of a micro-nano unmanned aerial vehicle in a complex environment, which comprises the following steps: calculating a second-order statistical variance of a downlink to represent environmental fluctuation, and determining the segmentation granularity of an uplink transmission block; on the premise of keeping the total power constant, the data amplitude is reduced and the pilot frequency amplitude is improved along with the increase of the segmentation granularity, and asymmetric power distribution is established; dividing the transmission block into a plurality of independent verification micro-frames for sending; according to the method, a physical layer structure and energy distribution reconstruction are directly driven through the environmental volatility, the problems of link self-adaption failure and channel estimation bottleneck caused by feedback lag in a fast fading environment are solved, and through micro-framing spatial isolation and power zero-sum gaming, the self-adaption performance of a channel is improved. And the uplink stability and the effective throughput in a complex multipath environment are improved.

The application discloses an indoor-oriented three-dimensional Gaussian diffusion model point cloud repairing method, expresses a missing point cloud as a three-dimensional point set, introduces a point cloud centroid as a spatial reference center, constructs a progressive-axis distance component of each point relative to the centroid, and calculates a second-order statistic in each axis direction as a directional scale signal; according to the deviation of the directional scale signal from a reference scale, the noise intensity is re-calibrated in each axis direction to form a diagonal covariance form of a directional adaptive three-dimensional Gaussian forward diffusion model; a denoising network is used to predict noise components in each diffusion step and update according to the progressive-axis diffusion intensity, while an observation consistency constraint is introduced; finally, a diffusion standard loss function is used to train the error between the real noise injected in the forward diffusion model and the predicted noise, and an optimized diffusion model is obtained. The method can be applied to indoor three-dimensional reconstruction, digital twinning, robot perception and indoor point cloud dataset enhancement scenes.

The invention relates to the field of machine learning and pattern recognition, and particularly discloses a variance-covariance subspace distance-based multi-kernel subspace clustering method, which comprises the following steps of: S1, acquiring a high-dimensional data matrix, calculating a variance-covariance matrix and second-order statistical information thereof, defining a preliminary VCSD kernel value and obtaining a VCSD kernel matrix through exponential transformation and normalization, constructing a multi-core pool of r base cores; s2, constructing an optimized objective function based on non-negative matrix factorization, and solving to obtain a local affinity feature map of a block diagonal structure under constraint conditions; s3, capturing high-order feature association through a matrix method, and converting tensor optimization into matrix operation; s4, solving the target function by adopting an alternating optimization strategy, reducing the calculation complexity, and generating a target affinity graph; and S5, clustering the target affinity spectrum, and outputting a result. According to the technical scheme provided by the invention, the problems of missing statistical characteristics, complex calculation and nuclear noise interference of the existing method are solved, and the clustering precision and efficiency are effectively improved.

The present invention provides a method and system for calculating the passive rotational speed of special equipment. Among them, the method takes into account the differences in the rotational frequencies of rotating equipment and interference components at different positions of the mechanical body. The multi-channel vibration data with richer information can be jointly used as input parameters, thereby excluding the incoherent frequency components in a single channel. An improved method of second-order statistical blind source separation combined with Vold-Kalman filtering is adopted, which can automatically determine the number of sources to be separated and optimize the multi-time delay input, and accurately identify the instantaneous rotational speed of special equipment. The solution proposed by the present invention can accurately estimate the instantaneous rotational speed in complex environments, such as adjacent frequency interference and high noise levels.

The application is an ultrasonic signal disturbance suppression method based on covariance whitening method. The application relates to the technical field of medical ultrasonic sensor signal disturbance suppression, and establishes a second-order statistical model of array elements in the channel by using original RF data collected in multiple probe states, and constructs a whitening transformation or a reference domain covariance alignment transformation in the channel dimension, so that statistical changes related to probe disturbance are normalized and suppressed. The application normalizes and aligns the second-order statistical differences caused by the probe disturbance in the original RF channel space, so as to reduce the sensitivity of subsequent signal analysis, feature extraction, classification model or regression model to non-target acquisition state factors. The corrected data still maintains the original two-dimensional RF matrix form, and when used for traditional beam forming or image reconstruction, diagonal whitening, local strip approximation, scale recovery or double branch processing mode can be adopted to give consideration to statistical stability and array element physical consistency.

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.

Provided is an improved beam control method of an array antenna based on frequency diversity. The method includes: signals from a transmitter are received by a receiving array which includes a first sub-array and a second sub-array, wherein the first sub-array includes M uniformly arranged array elements, the second sub-array includes N uniformly arranged array elements, M and N are coprime integers, and N≥0; a signal received by each array element in the receiving array is input into a constructed model, and a covariance matrix is output after processing the signal via the model; a virtual array is constructed at a receiver, wherein a virtual array element in the virtual array is a second-order statistic calculated according to the covariance matrix; and a frequency is randomly selected for each virtual array element from a frequency set of the virtual array, so as to form a beam pattern.

The invention discloses a cross-domain hand action recognition method based on arm surface electromyogram signals, and belongs to the field of body area network intelligent information systems. By introducing a domain adaptation algorithm based on space and geometry alignment, the problem of arm electromyographic signal distribution drift in a cross-gender, cross-environment and cross-individual scene is effectively solved. The method comprises the following steps: generating subspace substrates of a source domain and a target domain through principal component analysis in subspace alignment, and minimizing substrate difference by using a Frobenius norm; the second-order statistical distribution alignment minimizes the difference between the covariance of the source domain and the covariance of the target domain through a transformation matrix. According to the invention, by combining geometric alignment and statistical distribution alignment, the cross-domain data difference is effectively reduced, and the accuracy and robustness of hand action recognition are improved.

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 present application belongs to the technical field of civil engineering structure identification, and particularly relates to a mode shape identification method based on statistical moment theory, comprising the following steps: step one, uniformly arranging measuring points on the surface of a structure along the same direction; step two, obtaining the acceleration response of each measuring point; step three, calculating the second-order statistical moment value of the measuring point by using the acceleration response; step four, calculating the second-order statistical moment ratio of two adjacent measuring points; and step five, constructing the first-order mode shape of the structure by the relationship between the second-order statistical moment and the first-order mode shape of the structure. The mode shape extracted by the method has great advantages in precision, efficiency, convenience and the like, and is not affected by human factors, so that the mode shape of the civil engineering structure can be quickly identified.

This invention discloses a method for tracing the source of generated images based on spatial-frequency cross-domain second-order statistics. This method extracts spatial domain residual anomalous features and frequency domain anomalous features from the generated image, and constructs a cross-domain second-order statistical coupling relationship between them, forming a cross-domain statistical fingerprint for source tracing. The cross-domain statistical fingerprint matrix is ​​obtained by calculating the cross-domain cross-correlation matrix, and then normalized and compressed to obtain a cross-domain second-order statistical feature representation. Based on the similarity matching or distance measurement results between this feature representation and the statistical fingerprint templates of each source category, the source category or confidence score of the generated image is output. This invention can explicitly characterize the coupling relationship between spatial anomalies and frequency anomalies, and still has strong source discrimination ability and good interpretability and generalization performance even when the generation models are highly similar.

The present invention relates to, for example, power delay profile (PDP) estimation in a 5G system, which is necessary for channel estimation and demodulation. Based on channel statistical knowledge, the power delay profile is estimated directly in the frequency domain using the LMMSE property. The present invention adopts an approximation by estimating the second-order statistics of the channel autocorrelation function in the time domain, which is represented by the variance of the derivative of the original channel estimate in the frequency domain.

The invention requests to protect a coupling signal separation method based on symmetric tensor joint decomposition, and provides a new BSS method, namely TenSOFO, for solving instantaneous blind source separation from the perspective of tensor decomposition. According to the method, a novel efficient alternating direction multiplier method framework is provided, and two symmetrical third-order tensors are allowed to be decomposed at the same time in an individual difference scale INDSCAL format. In addition, the basic relation between the BSS based on the second-order statistic-fourth-order statistic SO-FO and the combined INDSCAL decomposition is established, and the provided INDSCAL method can be effectively applied to the instantaneous BSS task. The TenSOFO has the advantages that memory is saved, effective decomposition is achieved, Gaussian noise items disappear when fourth-order FO is used, and source signals can be processed without prior information of other levels. Therefore, the TenSOFO is particularly beneficial to processing long-time sequence signals such as voice and audio sampled at a high rate. Experimental results show that the TenSOFO method provided by the invention is minimum in error measurement and optimal in performance.

The present invention belongs to the technical field of rapid detection of civil engineering structures, and particularly relates to a model-free detection method based on statistical moment ratios, including the following steps: Step 1, obtain the time history response of the measured point displacement; Step 2, calculate the statistical moment ratios; Step 3, plot the statistical moment ratio curve; Step 4, identify the position of sudden change in segment stiffness. Without quantifying the specific value of the segment stiffness of the structure, the present invention does not need to measure the dynamic response data of the structure in the initial reference state. Only by using the idea of relative change before and after the sudden change of the stiffness of each segment of the structure, directly extract the ratio of the second-order statistical moment of the relative displacement response of the equally spaced measured points of the structure, and utilize the mapping relationship between the statistical moment of the measured point response and the corresponding segment stiffness, combined with the preliminary geometric characteristics of the structure, can quickly identify the position of sudden change in the segment stiffness of the structure, and then determine the operating state of the structure.

A three-dimensional high-precision RL deconvolution beam forming method and device based on a second-order single-vector hydrophone belong to the field of underwater acoustic signal processing and direction of arrival estimation. The method and device are used for solving the problems of the existing single-vector hydrophone beam forming method, such as main lobe diffusion, outstanding sidelobe interference, incomplete three-dimensional modeling, limited multi-target resolution capability, high complexity of spherical convolution operation and poor real-time performance. The method and device comprise the following steps: synchronously collecting sound pressure signals and three-dimensional orthogonal velocity signals by using the second-order single-vector hydrophone; constructing three-dimensional combined second-order statistics and generating three-dimensional space beam output; establishing three-dimensional beam response and spherical convolution model only related to the spherical angle; solving by using the RL deconvolution iterative algorithm after non-negative preprocessing of the beam output; converting the convolution operation into spherical harmonic domain coefficient multiplication by using spherical harmonic expansion and spherical convolution theorem, and realizing fast iterative calculation; and finally extracting the target azimuth and elevation by peak value searching and neighborhood suppression.

The invention relates to the technical field of industrial automation control, and discloses an adaptive collaborative optimization control method for energy consumption and production quality in a weaving process, which comprises the following steps: constructing a weft insertion angle of arrival observation window and calculating first-order and second-order statistical moments in real time; an asymmetric double-layer control loop is operated, and logic response fluctuation is suppressed through variance so as to maintain stability; the method comprises the following steps: establishing a negative correlation function relationship between a second moment and a target first moment by using mean approximation logic, dynamically setting a limit target first moment approaching a physical failure boundary according to a real-time fluctuation degree, and driving an execution mechanism to converge to the target. On the premise of maintaining process convergence, a theoretical minimum energy input point is automatically locked, and dynamic optimal balance of energy consumption and quality is achieved.

The invention discloses a Mongolian multi-modal sentiment analysis method based on cross-modal information enhancement and fusion. In order to solve the problems that emotion expression is dispersed and implicit clues in multi-modal emotion information are difficult to capture due to Mongolian adhesive language characteristics, a three-level innovation scheme is provided: (1) a cross-modal explicit enhancement layer: converting the implicit emotion clues such as rhythm, expression and the like in audios and videos into emotion description texts by using a multi-modal large language model; the modal information is enriched through a path of implicit, explicit and semantic fusion; (2) a semantic alignment layer based on a Gram matrix: extracting a second-order statistical structure of a text modal as a semantic reference, mapping heterogeneous audio and video features to a unified semantic space, and solving cross-modal alignment difficulty caused by Mongolian morphological change; and (3) a gating displacement fusion layer: a self-adaptive displacement mechanism is adopted, accurate injection of non-text information is realized through fine tuning of semantic positions of text features, and the problem of modal submerging in a Mongolian long morpheme sequence is avoided.

The invention provides an uncertainty-guided analytic high-resolution range profile class incremental learning method, and mainly solves the problems that catastrophic forgetting is likely to occur in radar high-resolution range profile target recognition, and old class recognition performance is remarkably degraded. According to the method, multi-resolution structured feature representation is constructed, a statistical prototype containing a mean value and a second-order statistical magnitude is taken as a unique carrier of old category knowledge, variance or covariance information is updated through recursion, and old category distribution characteristics are depicted under the condition of no original sample; on the basis of a recursive least square closed-form solution of recursive tracking of a covariance matrix, progressive analytic updating is carried out on the classifier to replace back propagation training; and a self-adaptive prototype distillation and uncertainty guiding mechanism based on a statistical prototype is introduced, and old knowledge is kept in the incremental training process. Experimental results show that under the condition that old class samples are not used at all, high and stable overall recognition accuracy can be kept in a target class continuous expansion scene.

A wireless communication method includes receiving, by a first wireless device during a training phase, reference tones using a first number of resource elements from a transmitter of a second wireless device, wherein the first wireless device comprises multiple receiving antennas, estimating, by the first wireless device, from the receiving the reference tones, a second order statistics of wireless channels between the multiple receiving antennas and the transmitter of the second wireless device, and performing channel estimation, during an operational phase subsequent to the training phase, using the second order statistics and reference tones received on a second number of resource elements, wherein the second number is less than the first number.