16 results about "Energy (signal processing)" patented technology

This invention discloses an improved method for generating driver fatigue EEG data using a latent diffusion model, belonging to the field of EEG signal processing. The method includes: processing the original multi-channel driver fatigue EEG signal through multi-scale wavelet denoising, adaptive thresholding, and an improved soft thresholding function; then performing inter-channel covariance alignment and whitening to eliminate redundancy to obtain a preprocessed signal; performing short-time Fourier transform and logarithmic energy normalization to obtain normalized time-frequency features; inputting the time-frequency features into an encoder to obtain the mean and variance of latent variables, and obtaining latent variables through reparameterized sampling; reconstructing the time-frequency features using a decoder combined with fatigue state labels; training a conditional variational autoencoder by minimizing reconstruction loss and KL divergence loss; adding noise through forward diffusion in the latent space; training a denoising network to remove noise based on fatigue state labels during reverse denoising; inputting the denoised latent variables into the decoder, combining them with a specified fatigue state to generate new time-frequency features, and reconstructing them into the target EEG signal. This invention can enhance the training dataset and improve the accuracy of driver fatigue monitoring.

This invention belongs to the field of radar signal processing and target detection technology, specifically relating to a statistical scattering method for clutter particles in radar detection. The method includes: acquiring single scattering parameters and simulation control parameters of the clutter particles; dynamically determining the simulation space length based on the single extinction coefficient and asymmetry factor; initializing a quasi-Monte Carlo low-discrepancy sequence; after initializing each photon packet, randomly generating a propagation step size and updating the cumulative propagation step size; when the photon packet does not exceed the boundary, determining whether scattering has occurred based on the single extinction coefficient and cumulative propagation step size; if scattering has occurred, updating the scattering direction based on the Henyey-Greenstein phase function and attenuating the energy weight; employing an adaptive dynamic threshold roulette wheel mechanism to dynamically adjust the survival probability based on scattering efficiency, photon position, and propagation direction, and maintaining statistical unbiasedness through energy weight compensation; after traversing all photon packets, calculating the total transmitted energy and total reflected energy, and calculating the equivalent extinction coefficient and radar cross section. This invention significantly improves simulation efficiency while ensuring statistical accuracy through physically guided importance sampling and a quasi-Monte Carlo low-discrepancy sequence, providing reliable support for radar anti-environmental clutter interference design.

A speed pulse recognition method based on zero-crossing closing interval and decoupling criterion belongs to the field of earthquake engineering and seismic signal processing, and comprises the following steps: step 1, speed time history acquisition and pretreatment; step 2, candidate interval of closing pulse is constructed; step 3, energy calculation and candidate screening; step 4, global energy concentration window construction and screening: cumulative energy curve is constructed, and consistent constraint screening is carried out based on the global energy concentration window; step 5, adjacent interval continuity discrimination and boundary marking: the continuity of adjacent intervals is discriminated, and the decoupling and to-be-merged boundary are marked; step 6, merging and reorganization iteration and pulse group generation; step 7, energy concentration review and determination output: the concentration is reviewed, and the pulse type and key parameters are output. And a speed pulse recognition device based on zero-crossing closing interval and decoupling criterion is provided. The present application improves the reliability and engineering applicability of the pulse recognition result under complex seismic motion conditions.

The application discloses a kind of based on multi-branch convolution network's electroencephalogram motor imagination decoding method, it is related to electroencephalogram signal processing technical field, decoding method includes: the parallel input of preprocessed electroencephalogram signal respectively corresponding network branch of four physiological frequency bands of delta, theta, mu, beta;Each branch is according to the center frequency of corresponding frequency band to calculate time convolution kernel scale to extract time feature;Energy feature is extracted to the core mu frequency band using fully connected space reinforcement module, and sparse expression is realized to secondary delta, theta, beta frequency band using depth separable convolution module;After each branch feature is dynamically calibrated weight by independent self-attention mechanism and residual concatenation, the decoding result is output by global fusion module.The application based on multi-branch convolution network's electroencephalogram motor imagination decoding method realizes differentiating accurate feature extraction, effectively suppresses redundant noise, and significantly improves the generalization robustness of cross-subject and cross-period.

The application provides a time-frequency cross signal representation and extraction method and device based on time-frequency analysis, and relates to the technical field of signal processing. The method comprises the following steps: obtaining a signal to be analyzed and performing modulation transformation based on discrete modulation parameters; performing block division on a time-frequency result, calculating the spectral flatness of each time-frequency block, selecting an optimal modulation parameter, and obtaining a spectral flatness matching modulation transformation result based on the optimal modulation parameter; extracting a time-frequency ridge based on the result and constructing an extraction operator; and performing time-frequency coefficient extraction by using the extraction operator to obtain a representation result. The application uses spectral flatness as an evaluation index, effectively avoids energy ambiguity caused by local optimization, constructs an extraction operator based on a ridge, solves the feature missing problem in a time-frequency cross region, realizes high-precision and high-energy-concentration signal representation and extraction, and is suitable for aero-engine rotor fault diagnosis.

The application discloses a time-frequency difference parameter estimation algorithm based on RANSAC-WLS in a strong interference environment and belongs to the technical field of signal processing and target positioning. Firstly, a narrowband cross ambiguity function and a two-dimensional Fourier transform are used to extract candidate energy points; a RANSAC algorithm is used to construct a straight line model, iteratively screen an inlier point set, and effectively peel off interference outliers; based on the inlier points, a weighted least square method is used to optimize and solve ridge line parameters, and the bias influence of interference on estimation is eliminated; finally, combined with ridge line prior information, one-dimensional fine search is carried out through a wideband cross ambiguity function, and final time difference and scale difference are obtained. The application realizes effective discrimination of target features in a high interference environment, improves the algorithm theory breaking point to about 50%, and significantly enhances the robustness and accuracy of parameter estimation.

The application discloses a structural modal parameter automatic identification method and device and a storage medium, and belongs to the technical field of structural dynamics and signal processing. The method comprises the following steps: obtaining an original pole set obtained through system identification; clustering the original pole set based on an OPTICS algorithm, extracting a candidate modal cluster, and determining a representative pole of each candidate modal cluster; based on the representative pole, sequentially performing cluster merging processing based on frequency and mode shape similarity, pole elimination processing based on physical reasonableness of a damping ratio, and significance screening processing based on an energy contribution amount, to obtain a refined final representative pole set; and outputting the final representative pole set as identified physical modal parameters, which are used for analysis of structural dynamic characteristics, structural health monitoring or damage early warning. The application can effectively eliminate a large number of noise points in a stability diagram, and accurately extracts physical modes reflecting structural dynamic characteristics without manual intervention.

The application provides a signal detection method and electronic equipment, which can be applied to the technical field of signal processing. The method comprises the following steps: determining a quadratic time-frequency distribution of an intermediate frequency signal, wherein the quadratic time-frequency distribution satisfies a time-frequency edge condition, and in the case that the intermediate frequency signal comprises at least two separable signal components, a cross-term interference is generated, and the separable signal component is a signal component having a distinguishable energy concentration area in a time-frequency plane; performing a convolution smoothing process on the quadratic time-frequency distribution for suppressing the cross-term interference to obtain a target quadratic time-frequency distribution; determining a time-domain Rayleigh entropy serving as a time-domain information quantity measure and a frequency-domain Rayleigh entropy serving as a frequency-domain information quantity measure according to the target quadratic time-frequency distribution to obtain a time-frequency information quantity measure, wherein the time-frequency information quantity measure is used for evaluating a time-frequency concentration characteristic of the intermediate frequency signal; and performing signal detection according to the time-frequency information quantity measure to obtain a signal detection result.

The application provides an ultrasonic probe pipeline echo signal processing method based on mutual verification of multiple groups of echo signals, steps are as follows: obtaining multiple groups of echo data; sequentially performing double exponential filtering, generalized Hilbert-Huang transform and adaptive variable threshold binary processing on each group of echo data, and performing pulse width identification and adaptive width filtering to realize effective envelope wave band screening; screening and calculating defect distance and weighted energy value of a target wave band, cross verifying defect distances of all groups to obtain multiple similar distance groups; calculating amplitude energy, width and consistency three-dimensional credibility of each similar distance group, and obtaining a weighted comprehensive score through weighted addition, determining effective echo through the weighted comprehensive score, when the echo is effective, selecting a similar distance group corresponding to the maximum weighted comprehensive score, and obtaining a final defect positioning distance through weighted fusion calculation based on the weighted energy value. High-precision positioning of defect detection is realized, and the anti-interference performance and generalization ability are improved.

The application discloses a kind of prosthesis control methods based on motion intention recognition, belong to electroencephalogram signal processing technical field.The application is by main channel acquisition motion intention electroencephalogram signal, extracts μ frequency band and β frequency band energy signal and calculates intention intensity, obtains μ intention intensity vector and β intention intensity vector;Through auxiliary channel Fz acquisition electroencephalogram signal, extracts θ frequency band energy signal and calculates state consistency factor;According to μ and β intention intensity vector, calculate intention consistency factor;Double-branch neural network is constructed, based on the fusion characteristics of state consistency factor and intention consistency factor, the fusion characteristics of μ and β intention intensity vector are weighted, and multi-scale feature extraction and enhancement processing are carried out, to obtain motion intention recognition result;Finally, according to the recognition result control prosthesis.The application is by multi-band feature fusion and consistency factor weighting, significantly improve the accuracy of motion intention recognition and prosthesis control precision.

This invention provides a method for picking the first arrival time of P-waves in microseismic signals based on deep learning and adaptive time windows, comprising: (1) collecting and preprocessing microseismic signals, creating a dataset, and dividing it into training and testing sets; (2) building an MSC-NET deep learning neural network, using the dataset as input for training, and adjusting the model parameters to achieve the optimal model effect; (3) applying the trained deep learning model to perform a microseismic signal classification task, dividing a large number of microseismic signals into two categories: effective and usable, and ineffective and unusable; (4) finally picking the first arrival time of P-waves in effective microseismic signals based on an energy ratio algorithm using an adaptive time window, obtaining a relatively reliable picking result. This method combines deep learning technology with improved traditional signal processing technology, significantly improving the reliability and accuracy of the automatic picking result of the first arrival time of P-waves in microseismic signals, while also having advantages such as robustness and strong applicability.

This invention discloses a robust detection method and system for high-speed, highly maneuverable targets based on Radon-LVD transform, belonging to the field of radar signal processing. The method estimates the target's velocity and acceleration from the pulse-compressed echo signal using Radon-LVD transform; constructs a slow-time-range frequency domain quadratic phase compensation function using the velocity and acceleration estimates; performs migration compensation on the slow-time-range frequency domain of the echo signal; and then performs moving target detection in the range-Doppler domain. The two searches during this process improve the accuracy of target parameter estimation. This invention combines Radon transform and LVD transform, which can compensate for range migration and Doppler migration respectively, achieving coherent accumulation of energy in the echo signal of a uniformly accelerated target.

The application provides a time-frequency cross signal representation and extraction method and device based on time-frequency analysis, and relates to the technical field of signal processing. The method comprises the following steps: obtaining a signal to be analyzed and performing modulation transformation based on discrete modulation parameters; performing block division on a time-frequency result, calculating the spectral flatness of each time-frequency block, selecting an optimal modulation parameter, and obtaining a spectral flatness matching modulation transformation result based on the optimal modulation parameter; extracting a time-frequency ridge based on the result and constructing an extraction operator; and performing time-frequency coefficient extraction by using the extraction operator to obtain a representation result. The application uses spectral flatness as an evaluation index, effectively avoids energy ambiguity caused by local optimization, constructs an extraction operator based on a ridge, solves the feature missing problem in a time-frequency cross region, realizes high-precision and high-energy-concentration signal representation and extraction, and is suitable for aero-engine rotor fault diagnosis.

This invention relates to the field of signal processing and analysis technology, specifically disclosing a method and system for identifying precursors and predicting the failure of fractured rocks. The method includes acquiring acoustic emission energy parameters of the rock sample and constructing an energy time series; obtaining the time-varying spectral width through MF-DFA processing, defining the turning point where it first abruptly enters a continuous high-value region as the initial precursor point; obtaining the time-varying acoustic emission energy variance through CSD processing, defining the characteristic point where the variance explodes near macroscopic failure as a failure precursor point. The initial precursor point is used to determine the predicted value of fracture closure time and the preliminary predicted value of failure time; the failure precursor point is used to determine the secondary predicted value of failure time, ultimately obtaining the predicted failure time of the fractured rock. This achieves precise, step-by-step monitoring and prediction of the entire process of rock failure, from localized damage and compaction to macroscopic crack penetration and fracture.

The application discloses a kind of fusion multi-scale morphological and adaptive energy threshold electrocardiosignal processing method and system, comprising: obtaining electrocardiosignal, utilize multi-scale morphological filter, to electrocardiosignal Parallel signal path calculation and baseline path calculation, the morphological reconstruction signal of weighted subtraction calculation result is obtained;By non-linear energy operator to morphological reconstruction signal is amplified, and energy sequence is obtained;Energy value in energy sequence is detected gradually, if the energy value is greater than main threshold, then directly confirm as R wave, and update main threshold;If the energy value is less than main threshold, judge distance last R wave time whether greater than 1.5 times average RR interval, if not greater than, then read next energy value;Otherwise, by backtracking search to obtain backtracking maximum value, judge backtracking maximum value whether greater than auxiliary threshold, if greater than, then mark as supplementary R wave;Otherwise, read next energy value;Based on confirmed R wave, output denoised electrocardiosignal waveform and heart rate parameter.

The application discloses a radar signal pulse sequence extraction method based on cross-correlation and estimated signal-to-noise ratio, and belongs to the field of radar signal processing. The method first frames the sampling signal, calculates the product sequence of the short-time energy and the zero-crossing rate of each frame, and constructs a rectangular wave according to the single pulse length and the frame shift. Then, the signal sequence is segmented according to the cross-correlation result of the rectangular wave and the product sequence, and each segment contains an effective pulse and the noise before and after the pulse. Subsequently, the effective pulse in each segment is accurately extracted: a segment of noise is taken in front of the segment, the estimated signal-to-noise ratio of the pulse is calculated, the whole sequence is subjected to noise normalization processing, and finally the starting point and the ending point of the pulse are detected according to the value of the estimated signal-to-noise ratio. The method does not depend on threshold setting, avoids the extraction error caused by improper threshold setting, has small operation amount, high practicability, and can quickly and accurately extract the radar pulse in multiple scenes.