7 results about "Spectral transformation" patented technology

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.

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 application discloses a soil heavy metal hyperspectral remote sensing inversion method and device, system and storage medium, and comprises the following steps: acquiring indoor hyperspectral data, heavy metal copper content and hyperspectral remote sensing image spectral data; performing SG spectral smoothing on the indoor hyperspectral data after removing noise bands; correcting the hyperspectral image spectral data according to the indoor hyperspectral data after SG spectral smoothing processing; performing fractional order differential spectral transformation on the corrected hyperspectral image spectral data; inputting the heavy metal copper content as a dependent variable and the fractional order differential spectral transformed hyperspectral image spectral data as an independent variable into a Transformer feature selection framework; and using XGBoost to establish a soil copper content inversion model to verify the effectiveness of the Transformer feature selection. According to the technical scheme, the feature wave of the heavy metal can be quickly and effectively identified and extracted.

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 present application provides a kind of vegetation classification method, device, equipment and medium based on space-spectrum neural network, method includes: high spectral dataset is divided into training set, verification set and test set, and quantitative evaluation index is set;Based on the information separation of space-spectrum mixed search space, the differentiable architecture search strategy based on gradient optimization is used to search the neural network architecture, and the target network architecture is obtained, the information separation spectrum transformation operator for extracting spectral features is included in the space-spectrum mixed search space, and the information separation space attention depth convolution operator for extracting spatial features;Based on training set and verification set, the target network architecture is trained to obtain a classification model;Test set is input into the classification model to obtain vegetation classification result, and the classification result is evaluated according to quantitative evaluation index, solves the problem that classification accuracy and efficiency still have room for improvement in complex vegetation fine classification task, cannot meet the demand of large-scale, high-precision remote sensing monitoring.

This application provides a music retrieval method, a music retrieval device, an electronic device, and a storage medium, belonging to the field of artificial intelligence technology. The method includes: acquiring target descriptive text and candidate music, wherein the target descriptive text includes the target object's description of the music; performing word recognition on the target descriptive text to obtain genre description words; performing spectral transformation on the candidate music to obtain candidate music spectrum sequences; based on the candidate music spectrum sequences, obtaining candidate music genre representation vectors corresponding to the candidate music; performing genre identification on the candidate music based on the candidate music genre representation vectors to obtain genre tag data for the candidate music; filtering the candidate music based on the genre description words and genre tag data to obtain target music; and feeding the target music back to the target object. This application embodiment can improve the accuracy of music retrieval.

Method for generating a multi-tone radio frequency signal and associated radar transceiver device. This method for generating a radio frequency signal comprising N distinct elementary frequency components includes at least one transmission-reception, in calibration mode, of a calibration radio frequency signal and obtaining a spectral transformation of said calibration radio frequency signal into a line spectrum, performed by a processing module of the radar transceiver device, and a calibration of attenuation signals, comprising: - a determination (38), by measurement in the line spectrum or by calculation, of at least one intermodulation frequency of an intermodulation line to be attenuated, - for each intermodulation frequency, a determination (40) of amplitude and phase parameters of a corresponding attenuation signal, as a function of the amplitude levels of the lines in the line spectrum.the attenuation signal having a frequency equal to said intermodulation frequency. The generated multi-tone radio frequency signal (50) comprises respectively said N components at elementary frequencies and said attenuation signals. Figure for the abbreviation: Figure 3,