45 results about "Spectral transformation" patented technology

The invention relates to the technical field of control systems, in particular to a control system and a control method of an intelligent permanent magnet vacuum circuit breaker, and the system comprises a multi-source signal synchronous acquisition module, an operation mechanism box body of the permanent magnet vacuum circuit breaker and acceleration sensors arranged at preset positions around a permanent magnet operation mechanism, a voltage transformer and a current transformer are connected to a line interface, and a unified sampling frequency and a trigger condition are set. According to the method, vibration, voltage and current signals are synchronously collected at key parts of the permanent magnet vacuum circuit breaker, timestamps are applied, a data basis of multi-source information strong association is constructed, and the direct time sequence relation between the electrical transient state and the mechanical response can be revealed. Segmented windowing and frequency spectrum transformation are carried out on collected vibration signals, a time frequency energy distribution diagram is generated, and instantaneous frequency components and energy evolution details generated by non-stationary events such as impact and friction in the opening and closing process are captured.

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

The invention relates to the technical field of soil monitoring, and discloses a power transmission and transformation project soil pollutant monitoring method and system, and the method comprises the steps: collecting hyperspectral image data in a power transmission and transformation project region; processing the hyperspectral image data by adopting a multi-stage spectrum transformation mechanism to obtain a characteristic wave band representing the pollutant content; inputting the characteristic wave band into a preset spectrum inversion model to generate a concentration spatial distribution diagram of the soil pollutants; and constructing a soil and groundwater diffusion model according to the concentration spatial distribution map, and generating a migration path identification map of soil pollutants and an underground pollution risk area identification map based on the soil and groundwater diffusion model. The method has the beneficial effect of improving pollution identification precision and migration prediction accuracy.

The invention discloses a hyperspectral image classification method based on double-flow heterogeneous spatial feature collaboration. The method comprises the following steps: step 1, obtaining a hyperspectral image classification data set; step 2, designing an overall structure of a double-flow heterogeneous spatial feature collaborative modeling method (DHSFCM); step 3, setting experimental parameters; according to the hyperspectral image classification method based on double-flow heterogeneous spatial feature collaboration, noise reduction and feature enhancement are carried out through a spectrum transformation layer, then complementary features are extracted through a non-Euclidean feature extraction network (NEFEN) and an Euclidean feature extraction network (EFEN), and the hyperspectral image classification method based on double-flow heterogeneous spatial feature collaboration is obtained. And effective feature integration and interaction are realized by means of a cross attention fusion module (CAFM), and finally a classification result is output. According to the technology, accurate classification support can be provided for intelligent agriculture, resource exploration and ecological monitoring, and a new thought with engineering landing value is provided for intelligent analysis of hyperspectral images.

The invention discloses a pavement crack accurate extraction method and system based on multi-scale image segmentation, and belongs to the technical field of pavement detection, and the method comprises the steps: obtaining a to-be-detected pavement image, carrying out the brightness normalization and geometric correction of the pavement image, and constructing a multi-scale image set with different resolutions; generating a spatial texture channel and a spectral domain response channel for each scale image in the multi-scale image set, and fusing the spatial texture channel and the spectral domain response channel to obtain a spectral-space coupling input tensor, wherein the spectral domain response channel is obtained by extracting energy characteristics of a plurality of frequency bands after performing spectrum transformation on the scale image; a multi-scale image set is constructed for road surface images, and coupling expression of spatial texture information and spectral domain response information is introduced, so that feature consistency of fine cracks under different resolutions is enhanced, and scale drift and response distortion caused by shadows, light reflection and seam textures are inhibited at the same time; therefore, the separability and the stability of the crack in a complex scene are improved.

A system may generate embedded subsets by projecting each subset of the subsets into a vector space to generate a corresponding embedded subset. A system may encode the embedded subsets into an encoded image using a dataset encoder including a gated spectral state space model, the gated spectral state space model being a gated neural network that includes a spectral state space model, the spectral state space model being a state space model that represents features of the input dataset using at least a spectral transformation of each embedded subset of the embedded subsets. A system may predict a classification for the input dataset using the encoded image.

Method and device for estimating the pitch of a human voice of an audio signal The present invention relates to a method (50) for estimating the pitch of a human voice of an audio signal received as input, said method being implemented by an electronic device for estimating the pitch of a human voice and comprising the following steps: - decomposition (52) of said signal into frames of predetermined finite size; - for each frame, - calculation (54): - of an autocorrelation of said frame; and - of a spectral transformation of said frame; - supplying (62) the pair of results of said calculation as input to a neural network with attention mechanism, previously trained, and configured to supply as output the probability that the shift of the autocorrelation corresponds to the period of the pitch of said voice of the audio signal received as input; - from said probability, estimation (76) of said pitch. Figure for abstract: Figure 3

To provide a method and an audio processing unit for decoding an encoded audio bitstream.SOLUTION: A method for decoding an audio bitstream includes receiving an encoded audio bitstream, decoding audio data to generate a decoded low-band audio signal, extracting high frequency reconstruction metadata and filtering the decoded low-band audio signal with an analysis filterbank to generate a filtered low-band audio signal, and extracting a flag indicating whether spectral transformation or harmonic transposition is to be performed on the audio data and regenerating a high-band portion of the audio signal using the high frequency reconstruction metadata and the filtered low-band audio signal according to the flag.SELECTED DRAWING: Figure 1

The invention discloses an adversarial sample generation method and an evaluation method for robustness evaluation of an AIGI detector, and relates to the technical field of robustness evaluation. A pre-trained substitution model is selected and comprises a feature extractor and a classifier, K additional models are added behind the feature extractor in parallel, and a Bayesian model is constructed and used for simulating an attacked model; performing frequency domain attack on the Bayesian model by using the adversarial sample, during each attack, adding disturbance to the spatial domain of the original adversarial sample, converting the original adversarial sample from the spatial domain to the frequency domain, performing random spectrum transformation, and according to an attack optimization target, calculating a frequency domain gradient for updating the adversarial sample; furthermore, the frequency domain attack and the space domain attack are mixed, the space domain gradient is calculated during each attack, the frequency domain gradient and the space domain gradient are added and averaged to obtain a uniform gradient direction, the gradient direction is used to update the adversarial sample, and the final adversarial sample is obtained after the set iteration attack times. According to the method, an adversarial sample is generated by using a frequency-based post-training Bayesian attack (FPBA), so that high-quality attacks with certain generalization ability are performed on the AIGI detector, and the robustness of the AIGI detector is evaluated under white-box attacks and black-box attacks.

This invention discloses a method for classifying abnormal heart sounds, comprising: a data input module receiving collected raw heart sound data; a data preprocessing module preprocessing the raw heart sound data, including reading feature parameters of the raw data, pre-emphasizing the heart sound data, eliminating high-frequency signals, and controlling the degree of pre-emphasis by adjusting the pre-emphasis coefficient until high-frequency signals are eliminated; normalizing the data to a value range of 0 to 1; obtaining spectral data through spectral transformation and generating Mel-spectral cepstral coefficients; generating a training dataset and a validation dataset; a training module generating a gated recurrent unit network (GRN) model and training and optimizing the parameters of the GRN model using the training dataset; a validation module importing the parameters of the training model, inputting the validation dataset into the validation module, and validating the accuracy of the GRN model; and an output module outputting the classification results. This method enables automatic classification of abnormal heart sounds, reducing the workload of doctors and improving work efficiency; it also avoids the influence of human factors and enhances the reliability of diagnostic results.

The application relates to the technical field of soil monitoring, and discloses a method and system for monitoring soil pollutants in a power transmission and transformation project, wherein the method comprises the following steps: collecting hyperspectral image data in a power transmission and transformation project area; processing the hyperspectral image data by adopting a multistage spectral transformation mechanism to obtain characteristic wave bands representing pollutant contents; inputting the characteristic wave bands into a preset spectral inversion model to generate a concentration spatial distribution map of soil pollutants; constructing a soil and groundwater diffusion model according to the concentration spatial distribution map; and generating a migration path identification map of soil pollutants and an underground pollution risk area identification map based on the soil and groundwater diffusion model. The method has the beneficial effect of improving pollution identification precision and migration prediction accuracy.

The invention provides a method for testing vibration characteristics of an inflatable wing. The method comprises the following steps: S1, spraying speckles on a wing surface of a to-be-tested inflatable wing; s2, fixing the inflatable wing to be tested on a vibration table, and adjusting the high-speed camera until the visual angle covers the wing surface; s3, starting the acceleration test device and the DIC test device, and configuring a first test parameter for the vibration table to perform device calibration; s4, configuring a second test parameter for the fine adjustment valve and the vibration table, and carrying out a vibration frequency sweeping test to obtain first test data; s5, closing the DIC test device, configuring third test parameters for the fine adjustment valve and the vibration table, carrying out random vibration test, and recording second test data; and S6, performing signal data analysis and atlas transformation on the first test data and the second test data to obtain inherent frequencies, vibration modes and damping ratios of the first several orders of the inflatable wing to be tested. The beneficial effects are that the method can comprehensively and accurately test the vibration characteristics of the inflatable wing, and improves the flight performance and stability of an aircraft.

In embodiments of the present disclosure, a device and method for determining the heart rate of a fetus and the heart rate variability of a pregnant woman and fetus is envisaged. The method comprises: generating a spectral transform from a waveform representative of an individual-specific hemodynamic parameter; determining a candidate residual signal included in a region independent of the excluded region of the spectral transformation; identifying candidate residual signals from among the candidate residual signals; comparing the candidate residual signal with a target signal representative of the oxygen level present in the blood flow of the individual, the target signal derived from a hemodynamic parameter; and upon identifying an inverse correlation between the candidate residual signal and a target signal of the individual, determining that the candidate residual signal is associated with a heart rate of a fetus located in the individual.

The invention discloses a binocular stereo matching depth estimation method and system based on hyperspectral reconstruction feature enhancement. The method comprises the following steps: S1, receiving left and right binocular RGB images; s2, performing high-dimensional spectral feature reconstruction on the left binocular RGB image and the right binocular RGB image by using a multi-stage spectral transformation module, and outputting a multi-channel hyperspectral feature map covering a visible light wave band by learning spectral reflectivity priori of a material; s3, adaptive channel selection and feature recombination are carried out on the multi-channel hyperspectral feature map through a learnable spectrum dimension reduction layer, and a left pseudo-color feature map and a right pseudo-color feature map with enhanced material physical attribute differences are generated; and S4, performing geometric feature extraction and parallax iterative optimization based on the left and right pseudo-color feature maps by using a stereo matching module, and outputting a final compact parallax map. According to the method, optical physical priori is learned through model training, so that low-cost RGB hardware can reproduce high-fidelity spectral features, and the problem of depth perception under complex materials and extreme shadows is effectively solved.

To disclose a method for decoding an encoded audio bitstream.SOLUTION: A method includes receiving an encoded audio bitstream and decoding audio data to generate a decoded lowband audio signal. The method further includes extracting high frequency reconstruction metadata and filtering the decoded lowband audio signal with an analytical filterbank to generate a filtered lowband audio signal. The method also includes extracting a flag indicating whether either spectral translation or harmonic transposition is to be performed on the audio data, and regenerating a highband portion of the audio signal using the filtered lowband audio signal and the high frequency reconstruction metadata in accordance with the flag. The high frequency regeneration is performed as a post-processing operation with a delay of 3010 samples per audio channel.SELECTED DRAWING: Figure 5

The application discloses a method for detecting thiocyanate in a complex serum sample and application. The method comprises the following steps: pretreating a serum sample, performing SERS detection, performing on-chip spectral quality control, and performing ratio type quantification. The application realizes rapid, trace, accurate and reliable quantitative detection of SCN ‑ In the complex serum sample by combining an interface self-assembly gold nano array base, 4-MBA ratio type internal standard correction and an on-chip quality control strategy based on ultrafiltration induced spectral transformation, and can be applied to the fields of smoking exposure evaluation and smoking cessation process monitoring.

The present invention discloses a high-order spectrum-driven homomorphic filtering high-resolution seismic data processing method. First, a seismic record is input, and a spectral simulation method is used to estimate the amplitude spectrum of the seismic wavelet from the seismic record. The third-order cumulants of the seismic record are calculated and Fourier transformed to obtain its phase spectrum. The phase spectrum of the mixed-phase wavelet at the separation point is calculated. A particle swarm algorithm is used to determine the optimal separation point between the seismic wavelet and the reflection coefficient sequence from the reciprocal spectrum sequence of the seismic record. The mixed-phase seismic wavelet is separated from the seismic record, and the seismic record is subjected to mixed-phase wavelet deconvolution to obtain an output record of the mixed-phase wavelet deconvolution. The method of the present invention reduces the complex high-dimensional functional inversion problem to a single-parameter inversion problem through the combined application of homomorphic filtering and high-order spectral transformation, thereby improving the stability and calculation accuracy of the mixed-phase wavelet estimation and the resolution of the seismic record, and enhancing the ability of the mixed-phase wavelet deconvolution method to reveal thin underground layer structures.

This invention relates to the field of crack detection technology, and discloses a method and apparatus for detecting warehouse wall cracks based on three-dimensional laser point cloud data. The method includes: acquiring cross-sectional data of the target warehouse wall; performing spectral transformation on the cross-sectional data to obtain the cross-sectional data spectrum; acquiring three-dimensional laser point cloud data of the target warehouse wall; separating the warehouse wall control contour from the three-dimensional laser point cloud data based on the cross-sectional data spectrum; determining the texture distribution features of each cross-section of the target warehouse wall based on the control contour elevation; calculating a segmentation threshold for the cross-section based on the texture distribution features; determining suspected crack points for each cross-section based on the segmentation threshold and the control contour elevation; combining each suspected crack point to obtain a binary image representing the crack; performing region recognition on the binary image representing the crack to obtain crack regions; and detecting cracks in the target warehouse wall based on the crack regions. This invention can improve the accuracy of warehouse wall crack detection.

An adaptive near-infrared spectral transformation method based on correlation and Gaussian curve fitting is proposed, belonging to the field of near-infrared spectral transformation technology. This invention addresses the significant errors in the quantitative analysis of analytes caused by peak overlap in near-infrared spectra. The method determines the number of Gaussian functions involved in Gaussian curve fitting using the number of discrete points in the near-infrared spectrum of the analyte, and determines the center position of the Gaussian function using the wavelength positions of the discrete points. Correlation analysis is used to determine the optimal bandwidth of the Gaussian function for extracting overlap information. Based on this, a set of equations for curve fitting is constructed. The height of the Gaussian function is determined by solving the equations, and the area integral of the Gaussian function is performed to obtain the transformed spectrum of the analyte, thereby constructing a content prediction model for the analyte. The proposed method has been applied to the prediction of COD content in wastewater and moisture content in corn, respectively. The prediction mean square error is reduced by at least 25% compared to before the transformation, indicating that the Gaussian function involved in curve fitting does not need to correspond to the true peak information to effectively decompose and recombine the overlap information in the original spectrum, thus reducing quantitative analysis errors.

This invention discloses a method for monitoring salinized arable land based on spectral analysis, comprising the following steps: Step 1: Data preparation: Collect and process data covering the target area; Step 2: Spectral index calculation and fusion: Fuse the salinity index after ground hyperspectral transformation with the salinity index calculated by Sentinel-2 multispectral analysis at the same time through univariate linear regression to generate a hyperspectral-multispectral fusion index dataset and construct a feature variable library; Step 3: Feature variable selection: Select key features from the feature variable library; Step 4: Support vector machine model construction and training: Train the support vector machine regression model; Step 5: Salinity inversion and accuracy verification: Output the predicted soil salinity value, generate a spatial distribution map of soil salinity in the Hetao Irrigation Area based on the predicted soil salinity value, and classify the salinization level of arable land based on the inversion results; Step 6: Dynamic monitoring: Repeat steps up to step 5 to generate a dynamic change map of salinization.

This invention discloses a hyperspectral image classification method, medium, device, and product, relating to the field of image processing. The method includes: performing standardized preprocessing and superpixel segmentation on a hyperspectral image, and constructing a superpixel graph structure; constructing a hyperspectral image classification model including three parallel branches; each branch includes a spectral transform sub-network, a superpixel-level graph sub-network, and a pixel-level convolutional sub-network, wherein the spectral transform sub-network is a convolutional network with different kernel sizes, used to extract features from the hyperspectral image; based on the hyperspectral image features and graph structure, the parallel superpixel-level graph and pixel-level convolutional sub-networks extract superpixel-level and pixel-level features and perform feature concatenation and fusion, and obtain a classification probability distribution based on the fused features; the three parallel branches of the classification model are trained independently, and the trained model is used to classify hyperspectral images; the classification results of the three branches are determined by a majority voting method to determine the final classification result.

The invention discloses a soil heavy metal hyperspectral remote sensing inversion method, device and system and a storage medium. The method comprises the following steps: acquiring indoor hyperspectral data, heavy metal copper content and hyperspectral remote sensing image spectral data; removing noise wave bands from the indoor hyperspectral data, and then carrying out SG spectrum smoothing; correcting spectral data of the hyperspectral image according to the indoor hyperspectral data subjected to SG spectrum smoothing processing; performing fractional order differential spectral transformation on the corrected spectral data of the hyperspectral image; taking the content of heavy metal copper as a dependent variable, taking spectral data of the hyperspectral image after fractional differential spectral transformation as an independent variable, inputting the dependent variable into a Transform feature selection framework, and establishing a soil copper content inversion model by using XGBoost to verify the effectiveness of Transform feature selection. By adopting the technical scheme of the invention, the characteristic waves of the heavy metals can be quickly and effectively identified and extracted.

A method for decoding an encoded audio bitstream is disclosed. The method includes receiving an encoded audio bitstream and decoding the audio data to generate a decoded low-band audio signal. The method further includes extracting high-frequency reconstruction metadata and filtering the decoded low-band audio signal with an analysis filterbank to generate a filtered low-band audio signal. The method also includes extracting a flag indicating whether a spectral transformation or harmonic transposition should be performed on the audio data, and regenerating a high-band portion of the audio signal using the filtered low-band audio signal and the high-frequency reconstruction metadata according to the flag. This high-frequency regeneration is performed as a post-processing operation with a delay of 3010 samples per audio channel.

The present application relates to a high-fidelity key cache compression method and system based on spectral quantization, comprising: step S1: spectral transformation and feature analysis process; receiving original key cache data from the model, mapping the original key cache data from the time domain to the frequency domain, and deeply analyzing the signal structure by using the energy concentration characteristics unique to the discrete cosine transform (DCT); step S2: frequency domain hybrid quantization encoding process; by using the main frequency extraction mechanism, the high frequency pre-emphasis technology and the hybrid bit width allocation strategy, the frequency domain coefficient is compressed into a sparse representation with extremely low bit; step S3: fusion decoding and sparse accumulation process; by using the hardware-aware fusion operator and the delay inverse transform technology, the attention score is calculated directly in the compression domain. The present application realizes the limit compression and efficient inference under the lossless precision.

The invention relates to the technical field of mineral resource exploration, in particular to a bauxite lithium-rich ore rapid optimization method and system based on an imaging hyperspectrum. The method comprises the following steps: collecting standard hyperspectral data according to a bauxite sample; using the standard hyperspectral data to construct a multi-dimensional fusion spectral data set; screening optimal characteristic wave band spectral data from the multi-dimensional fusion spectral data set; and constructing a lithium ore rapid optimization model based on the optimal characteristic wave band spectral data, and generating a lithium content spatial distribution thermodynamic diagram through the lithium ore rapid optimization model. According to the method, efficient and accurate recognition of the lithium-rich ore in the bauxite is achieved through multi-dimensional spectrum transformation, characteristic wave band screening and integrated estimation model construction, and the problems that a traditional method is high in cost, low in efficiency and insufficient in optimization precision are solved.

The application discloses a graph data backdoor defense method and device based on spectral domain transformation and edge weight learning, equipment, medium and program product, relates to the technical field of network security, and includes: obtaining and constructing a feature vector matrix and an eigenvalue matrix of an original graph data set; mapping each feature vector matrix from a spatial domain to a spectral space through spectral transformation to obtain a plurality of spectral domain node features, so as to construct a normal data distribution of the original graph data set, and determine whether each to-be-tested graph data is abnormal graph data; if it is determined that the graph data is abnormal, learning abnormal node features and abnormal adjacency relationships of the to-be-tested graph data through a multilayer perceptron to obtain abnormal edge weights; based on the abnormal edge weights, performing adaptive clipping on abnormal spectral components in the abnormal graph data through a Gaussian mixture model to obtain modified spectral domain node features; and performing inverse spectral transformation on each modified spectral domain node feature to reconstruct a target graph data set. The application can effectively resist multiple types of graph backdoor attacks.

The invention relates to the technical field of ecological environment monitoring, and discloses a mikania micrantha and eupatorium odoratum classification and identification method based on unmanned aerial vehicle hyperspectrum, and the method comprises the steps: obtaining unmanned aerial vehicle hyperspectral original image data of a target region, carrying out the preprocessing operation, carrying out the spectrum transformation and dimension reduction processing of an obtained standardized hyperspectral data set, and obtaining a target hyperspectral image; the method comprises the following steps: acquiring a plurality of groups of optimized feature data sets formed by combining different spectral transformations and dimension reduction processing, constructing a multi-type classification model based on the plurality of groups of optimized feature data sets, and screening out an optimal classification model through model training and performance evaluation so as to realize classification and identification of mikania micrantha and fragrant eupatorium herb in a target area and output a classification result. Therefore, the invention proposes and verifies a set of complete and optimized technical process from data acquisition, multiple pre-processing, PCA dimension reduction to construction of multiple classifiers, screens and determines an optimal technical combination for accurately identifying mikania micrantha and eupatorium odoratum, can effectively suppress noise, retains key distinguishing features, and realizes high-precision classification.

The invention discloses a deep soil nitrogen content monitoring method, a server and a storage medium, and belongs to the field of soil component monitoring, and the method comprises the following steps: obtaining remote sensing image data of surface soil of a soil sampling point, and collecting a soil sample of the corresponding soil sampling point; performing preprocessing, spectral transformation and spectral feature extraction on the remote sensing image data, and constructing a multi-dimensional feature space; analyzing the corresponding soil sample to obtain a change rule of surface nitrogen and deep nitrogen, and obtaining a surface-deep nitrogen relation model; screening a characteristic wave band combination of which the correlation with the surface nitrogen content exceeds a threshold value from the multi-dimensional characteristic space, and establishing a surface nitrogen content inversion model; in the actual use process, after remote sensing image data are collected, the nitrogen content of deep soil is monitored through the surface layer nitrogen content inversion model and the surface layer-deep layer nitrogen element relation model. The method avoids the problems of high cost and low timeliness of traditional sampling analysis.

The present invention relates to a method (50) for estimating the pitch of a human voice in an audio signal received as input, the method being implemented by an electronic device for estimating the pitch of a human voice and comprising the following steps: decomposing (52) the signal into frames of predetermined finite size; for each frame, calculating (54) an autocorrelation of the frame and a spectral transformation of the frame; providing (62) the pair of results of the calculation as input of a neural network with an attention mechanism, trained beforehand, and configured to output the probability that the offset of the autocorrelation corresponds to the period of the pitch of the voice in the audio signal received as input; and estimating (76) the pitch on the basis of such probability.