130 results about "Window function" patented technology

The invention belongs to the field of structural modal identification, and particularly relates to an automatic modal parameter identification method based on a multi-scale space, which comprises the following steps: collecting a vibration data sample from a target structure, the vibration data sample comprising an acceleration signal of the structure; selecting power spectrum calculation parameters including a window function and a fast Fourier transform (FFT) length; performing frequency spectrum estimation on the acceleration signal by using interpolation power spectrum estimation to obtain frequency spectrum data; analyzing the spectrum data by using a multi-scale space algorithm, and detecting a peak value in the spectrum data to obtain a peak value frequency; through a frequency domain decomposition method, according to the peak frequency, modal parameters of the structure are calculated, the modal parameters comprise the modal frequency and the damping ratio, interpolation power spectrum estimation and a multi-scale space algorithm are innovatively combined, automatic and accurate identification of the modal parameters is achieved, and the identification precision and efficiency are remarkably improved.

The invention relates to the field of GNSS-R SAR image cross-correlation registration, in particular to a GNSS-R SAR image cross-correlation registration method based on an improved window function. The scheme comprises the following steps: extracting a matching region from a main image, and extracting a reference region from a slave image; performing sliding traversal in the matching region and the reference region by using an improved elliptical weighted window function, and calculating coherence coefficients under translation positions one by one to obtain a coarse registration offset; performing multiple cubic interpolation on the time sequence image to obtain a primary image and secondary image interpolation result with sub-pixel precision, re-selecting a matching area and a reference area on the interpolated image, and repeating sliding traversal and coherence coefficient calculation to obtain a fine registration offset; and performing interpolation resampling on all the slave images based on the final registration offset to obtain a time sequence image set of which the registration precision is a sub-pixel level. The GNSS-R SAR image cross-correlation registration method is suitable for GNSS-R SAR image cross-correlation registration.

The invention relates to the technical field of audio sound calibration, and discloses a room sound calibration method based on intermediate frequency reverberation time and related equipment, which are used for improving the accuracy of a sound calibration result. The room sound calibration method based on the intermediate frequency reverberation time comprises the following steps: acquiring a room impulse response of a target room and calculating the intermediate frequency reverberation time; according to the intermediate frequency reverberation time, the recommended window length of the frequency correlation window is determined by querying a predefined mapping relation, and the mapping relation is established based on acoustic association between the room reverberation time and the analysis window length; based on the recommended window length, determining the actual window length of the actual application in combination with the user setting mode; performing time domain windowing and frequency-related transfer function estimation processing on the room impulse response according to the actual window length to obtain window function weighted transfer function estimation; filter calibration coefficients are generated based on the transfer function estimates and applied for audio calibration.

The invention requests to protect an ADC dynamic parameter test method based on spectrum correction, and the method comprises the following core steps: a primary spectrum correction stage: carrying out the spectrum error correction of an ADC output signal through employing a spectrum three-interpolation algorithm, and extracting a fundamental frequency; the sine wave amplitude, the phase and the direct current offset are solved through least square fitting, an initial ideal sine reference sequence is constructed, and noise separation is carried out for the first time. In the secondary spectrum optimization stage, a classic Hanning window function and a frequency domain two-point interpolation algorithm are adopted; and a sine wave error compensation sequence is constructed, and secondary noise extraction is carried out. In the coherent parameter reconstruction stage, the ADC output sequence is restored to a standard test state through amplitude and frequency conversion. According to the invention, the risk of test failure caused by the fact that the amplitude of the input signal exceeds the ADC range can be effectively avoided. On the basis, the secondary spectrum optimization significantly improves the recognition precision of the test algorithm on the harmonic distortion of the ADC output sequence, and effectively reduces the measurement error caused by nonlinear distortion.

The invention relates to an electrocardio atrial fibrillation classification detection method, system and device capable of resisting individual difference and a storage medium. The system comprises a signal acquisition module used for acquiring ECG signals; the invention discloses an anti-individual-difference preprocessing circuit. The signal segment reading module is used for reading a signal segment according to an overlapping segmentation strategy, the Hanning window weighting module is used for performing window function weighting on the signal segment and only storing a first half coefficient through address mapping multiplexing by utilizing the symmetry of a window function, and the FFT frequency domain transformation module is used for converting a time domain signal into a frequency domain; the QRS frequency domain enhancement filtering module is used for enhancing QRS spectrum components and attenuating individual difference frequency components, the IFFT time domain reconstruction module is used for converting frequency domain signals back to a time domain, and the amplitude normalization output module is used for eliminating signal amplitude differences among individuals; and the deep learning classification module is used for performing atrial fibrillation detection on the standardized signal. Based on a CPU and FPGA heterogeneous architecture, the accuracy and robustness of atrial fibrillation detection are improved, and the method is suitable for the fields of wearable monitoring, telemedicine and the like.

This invention relates to a method for extracting guided wavenumbers in variable thickness structures, belonging to the field of nondestructive testing technology. It utilizes an excitation signal to generate guided wave signals on the surface of the object under test. Based on a set window function and a set window length, the two-dimensional wavefield signal obtained by sampling the guided wave signal is divided into multiple local wavefield signals. A short-space Fourier transform is then performed on each local wavefield signal to obtain the amplitude-frequency-wavenumber array corresponding to each local space. A frequency range is then selected by summing the amplitude-frequency-wavenumber arrays of each local space, using the center frequency of the excitation signal as the midpoint. The weighted average wavenumber of each frequency point within the selected frequency range is calculated using the amplitude corresponding to the wavenumber sequence at each frequency point as the weight. The mean of the sum of the weighted average wavenumbers at each frequency point is then used as the wavenumber corresponding to the center position of each local signal space, and these values ​​are sequentially spliced ​​to obtain the wavenumber curve of the guided wave signal. This method solves the problem in existing technologies where the obtained guided wavenumbers cannot accurately reflect the thickness changes of variable thickness structures.

The application discloses a space-time-frequency wave number domain synchronous extrusion superimposed dense channel reservoir characterization method, which comprises the following steps: S1, inputting original seismic data s(t, x) to be analyzed, wherein x is the number of channels, and t is time; S2, obtaining short-time Fourier transform V2(t, x, omega, k) of the signal by using a Gaussian window function g(t, x), wherein omega is frequency, and k is wave number; S3, calculating an instantaneous frequency and an instantaneous wave number estimation of the signal according to S2; S4, defining an extrusion operator TDSTO according to S3; S5, constructing a space-time-frequency wave number domain synchronous extrusion transform characterization method TDSST(t, omega, x, k) according to the extrusion principle; S6, fixing the wave number by maximizing the energy of TDSST, so as to obtain a TDSST time-space-frequency domain three-dimensional data body TDSST(t, x, omega, k(t, x, omega)); and S7, performing feature extraction of frequency f0 in S6, so as to obtain a single-frequency profile to characterize the morphological structure characteristics of a complex superimposed dense channel sandstone reservoir. The application can provide a space-time reservoir morphological characterization method considering the lateral characteristics of a complex superimposed dense channel sandstone.

The invention discloses an SAR (Synthetic Aperture Radar) water depth inversion method based on a self-adaptive elliptic window function, which comprises the following steps of: realizing self-adaptive adjustment in the direction of an elliptic window, the proportion of a long axis to a short axis and the type of a window function by analyzing the energy distribution characteristics of a main peak of a two-dimensional frequency spectrum of an SAR sub-graph; therefore, the directivity expression of the spectrum main peak and the accuracy of wavelength extraction are remarkably improved. According to the method, the definition of the main peak of the frequency spectrum and the water depth inversion precision can be remarkably improved, and a more stable and accurate technical path is provided for SAR water depth inversion in a complex near-shore environment.

The application discloses a method for sidelobe suppression of single-bit quantization Chirp signal, and the main technical content of the method comprises sidelobe suppression of single-bit quantization Chirp signal based on a convolution window function. The technical content performs sidelobe suppression through the following steps: step S1, generating a Chirp echo signal; step S2, single-bit quantization of the Chirp signal; step S3, single-bit quantization of a Chirp signal matching filter and adding a convolution window; and step S4, pulse compression of the single-bit quantization Chirp signal. The application can achieve a good compromise between quantization order and pulse compression effect, single-bit quantization can reduce quantization complexity to the maximum extent, and the sidelobe suppression method based on the convolution window function can effectively solve the problem of the decrease of the main sidelobe ratio caused by single-bit quantization. For Chirp signals and other radar signals with large time-bandwidth product, the application can improve the pulse compression effect, improve the range resolution of the radar system, and has certain reference significance for improved design in radar engineering technology.

The application discloses a kind of self-adapting downsampling time series encoding method and system.The method includes providing step prediction module based on learnable network, dynamically generating prediction window parameters based on the global feature of multidimensional time series signal, generating smoothing window function according to the prediction window parameters, weighting multidimensional time series signal and smoothing window function to obtain multiple groups of soft block signals, performing two-dimensional self-attention calculation on each group of signals and extracting feature vectors, and outputting sequence representation after reorganization through global self-attention calculation.The system corresponds to each corresponding functional module, including step learner based on learnable network, and introducing regularization term to optimize training loss.The application gets rid of artificial experience dependence, realizes the adaptive downsampling of time series signal, avoids boundary information loss and extreme downsampling strategy, improves encoding efficiency, accuracy and universality, and can be widely applied to astronomical gravitational wave monitoring, medical treatment, industrial and other cross-domain time series signal processing scenarios.

The invention discloses a sparse inversion time-frequency analysis method based on a semi-norm, and the method comprises the steps: designing a window function for original seismic data, so as to reduce the influence of a window truncation effect on time-frequency analysis; constructing a Fourier forward matrix and a windowed Fourier matrix according to the size of the window function and the size of the frequency spectrum; in-window seismic data extraction is carried out on sample points of single-channel seismic data, and a semi-norm constrained time-frequency spectrum sparse inversion objective function is constructed; performing time-frequency spectrum coefficient inversion on the time-frequency spectrum sparse inversion target function to obtain a sparse inversion time-frequency spectrum result of the seismic data in the window; and sliding the sample points of the seismic data to repeat the process, and performing time-frequency spectrum sparse inversion on all the seismic data to obtain a final time-frequency analysis result. According to the method, the time-frequency spectrum precision and the time-frequency focusing property of the seismic signal can be effectively improved, and a time-frequency analysis result with higher precision is provided for subsequent reservoir identification and oil and gas prediction work.

The application discloses a trigonometric function window generating method based on DSP, which comprises the following steps: converting the cosine term of the trigonometric function window into the n-th power form of the complex exponential fixed value based on the Euler formula; after the program initializes the calculation of the fixed value, generating the complex exponential term of each point sequence by recursive multiplication and storing the complex exponential term in the first-level storage space of the DSP; then, the SIMD parallel operation is realized by using the in-line function of the DSP, the first half of the window function values are calculated and stored; the first half of the window function values are inversely symmetrical copied to generate the second half of the window function values; finally, the hardware adaptive memory allocation is completed by combining the hierarchical storage architecture of the DSP, the stack is allocated to the first-level storage space, and the window function generation is completed by summarizing the data. The application greatly reduces the operation amount and the storage occupation, guarantees the calculation precision, deeply adapts to the hardware characteristics of the DSP, can efficiently generate the Hanning, Hamming and Blackman trigonometric function windows, and meets the real-time and high-precision requirements of the signal processing with large bandwidth and high sampling rate.

This invention provides a method for preprocessing and classifying satellite photometric curve data. The method includes: converting phase angle-magnitude curves with discontinuous observations and uneven sampling frequencies into fixed-dimensional window function feature vectors using a frame-segmentation and windowing preprocessing strategy; employing a random erasure data augmentation strategy on these window function feature vectors to generate different photometric curve feature vectors through multiple random erasures while simulating phase observation gaps; performing positional encoding of the phase angle features on the photometric curve feature vectors to obtain photometric features containing phase information; constructing query vectors, index vectors, and key value vectors using a transformer model to extract global features; and classifying the photometric curve feature vectors using a fully connected layer. This method effectively captures both local and global feature information of the photometric curves, exhibiting superior performance compared to one-dimensional convolutional neural networks.

A method for optimizing flip angles in a magnetic resonance variable flip angle pulse sequence, a CEST imaging method, a medium, and a device are presented. A signal-to-noise ratio (SNR) enhancement problem is modeled as a flip angle optimization problem, and a total objective function composed of an SNR maximization objective term and a resolution penalty term is constructed, wherein the total objective function may be solved for an optimal solution by finding derivatives with respect to flip angles, so as to obtain an optimal flip angle capable of maximizing SNR. The objective function includes the resolution penalty term, so that resolution may also be considered in variable flip angle CEST when the SNR is optimized. Compared with a conventional filtering method, the present disclosure avoids noise amplification caused by filtering, and does not require manual presetting of a window function.

The invention discloses a simple and convenient method for improving time-frequency estimation precision, which comprises the following steps of: carrying out segmented cross-correlation operation on a local copy of a main synchronization sequence and a received signal; carrying out secondary filtering on a segmented cross-correlation operation result by utilizing a window function; the window function can be a predefined static window, such as a Gaussian window, a triangular window, a rectangular window and the like, and can also be calculated by using a delay power spectrum of a channel; the maximum value is found in the result of the secondary filtering, and the position of the maximum value is the timing position; and obtaining phase differences of correlation results of different segments at a timing position, and calculating a mean value of the phase differences to obtain frequency offset estimation. According to the method, timing estimation performance and frequency offset estimation performance can be improved, and the synchronization signal detection probability is also improved.

The invention discloses a self-adaptive overlapped framing and variable window phase measurement method, device and system based on signal state perception, and belongs to the technical field of digital signal processing and phase measurement. Aiming at the technical problems that a fixed overlapping ratio and a single window function are adopted in an existing phase measurement method, resolution ratios of a time domain and a frequency domain cannot be considered under a time-varying signal, and frequency spectrum leakage and phase jump are easily introduced, the invention provides a joint self-adaptive preprocessing method. The method comprises the following steps: firstly, framing collected signals, and judging whether the signal state is a steady state or an unsteady state according to smoothness detection of absolute value sums of adjacent sampling points; according to the determined signal state, adaptively configuring an overlapping rate between adjacent data frames and window function type parameters, and then, before fast Fourier transform is carried out, introducing a window function coherent gain compensation mechanism to eliminate an amplitude reference error caused by window type switching; and finally calculating the phase difference based on the corrected spectrum result. According to the method, the overlapping framing and window function parameters are adaptively adjusted according to the signal state, the phase measurement continuity is ensured, the phase measurement precision and stability under the multi-frequency signal are improved, and the method has good real-time performance and engineering application value.

The invention belongs to the technical field of intelligent transportation, and provides a truck stop point prediction method based on directed graph spatial-temporal feature fusion, comprising the following steps: S1, preprocessing truck trajectory data through a multi-modal trajectory preprocessing subsystem; s2, constructing a spatial-temporal characteristic matrix with the stay point as a core; s3, encoding the data by using a space-time directed graph encoder, and extracting related features of a stay point; s4, constructing a graph neural network architecture to predict a track and a truck stop point; the step S1 comprises dynamic interruption detection, semantic stay identification and intention analysis, adaptive interpolation and sparsification processing, and stay point determination by using a space-time window function. According to the method, refined staying point classification prediction facing business requirements can be provided. And effective early warning of future key staying behaviors of the truck can be realized.

The embodiment of the present application discloses a spectrum analysis method, device and signal processing device, and the embodiment of the present application introduces a convolutional neural network to process discrete real part signals and discrete imaginary part signals in a frequency domain on the basis of an existing cycle graph spectrum analysis method, in the convolutional neural network, a convolution operation is performed by using frequency domain correlation characteristics of a window function used in a time domain, so that the problem of frequency domain distortion such as spectrum leakage, fence loss and the like caused by time domain processing of the window function in the frequency domain can be eliminated to a certain extent, and meanwhile, the accuracy of spectrum analysis can be effectively improved by combining the trained convolutional neural network.

The invention relates to the technical field of artificial intelligence and machine learning, and discloses a method and a system for realizing broadband noise point identification of an automobile audio based on time-frequency analysis, and the method comprises the steps: adding a variable-length window function of an audio analysis fragment, obtaining a windowed audio analysis fragment, analyzing an initial time-frequency spectrum of the windowed audio analysis fragment, and obtaining an initial time-frequency spectrum of the windowed audio analysis fragment; generating an instantaneous frequency estimation matrix of the windowed audio analysis segment; combining the instantaneous frequency estimation matrix with the initial time-frequency spectrum to obtain enhanced time-frequency representation of the windowed audio analysis fragment, analyzing noise time-frequency units of the enhanced time-frequency representation, and communicating the noise time-frequency units to obtain a broadband noise point candidate region of the windowed audio analysis fragment; and analyzing the real noise point probability of the broadband noise point candidate region, and determining broadband real noise points of the broadband noise point candidate region. According to the invention, the efficiency and accuracy of automobile audio broadband noise point identification can be improved.

This invention discloses an AI interaction system and method with multi-window task splitting and real-time task response. By designing multiple independent AI dialogue input windows, each pre-bound with specific functional instructions (such as analysis or memorization), the system can directly trigger the corresponding function after the user inputs task content, achieving synchronous execution of input and instructions, eliminating the redundant steps of the traditional "input-select instruction-execution" process. The multiple AI dialogue input windows are functionally independent and support parallel operation, avoiding task conflicts and significantly improving multi-task processing efficiency. Clear window function binding reduces the risk of errors in semantic ambiguity parsing, ensuring the accuracy of task execution. This invention overcomes the limitations of existing single-window multi-instruction interaction modes, achieving significant improvements in operational efficiency, interactive experience, task accuracy, and flexibility.

This application discloses a method and system for compressing and reconstructing borehole elastic wave signals based on segmented windowing and sparse coding, relating to the field of geophysical signal processing. The method includes segmenting the original borehole elastic wave time-series signal into multiple signal segments; applying a combined Hann window function to each signal segment for windowing and selecting effective signal segments; obtaining the sparse coefficient vector and atom index corresponding to each effective signal segment based on a pre-built dictionary; calculating the average sparse coefficient vector of all effective signal segments to obtain the average sparsity; reconstructing any effective signal segment according to the average sparsity to obtain a reconstructed effective signal segment; and performing inverse weighting processing using the Hann window function corresponding to the reconstructed effective signal segment to obtain the final reconstructed signal. This application achieves efficient compression and high-fidelity reconstruction of borehole elastic wave time-series signals.

The application discloses a segmented adaptive Fourier transform analysis method of power quality disturbance. The method firstly collects power quality disturbance signals in a power grid and performs Fourier transform, acquires peak envelope points in a frequency spectrum through a maximum envelope method to dynamically select characteristic frequency points and calculate frequency spectrum segmentation points; subsequently, a composite objective function is used to optimize Gaussian window parameters for each segment; finally, the signal is analyzed by using the optimized window function to obtain time-frequency results with adaptive resolution. The application allocates higher time resolution to strong time-varying components and higher frequency resolution to near steady-state components through segmented adaptive resolution and a composite objective function optimization strategy, effectively avoids the spectrum overlap and energy diffusion problems that are prone to occur in traditional methods, improves the accuracy and robustness of disturbance feature extraction, and can be widely applied to power grid fault diagnosis, disturbance identification and early warning.

This invention discloses a method for extracting transformer vibration features based on multi-point collaborative sampling and time-frequency matrix reconstruction, comprising the following steps: S1. Establishing a finite element simulation model based on the transformer structure and acquiring original vibration signals from multiple locations on the surface of the tank; S2. Performing normalization preprocessing on the original vibration signals and dividing them into multiple data samples through sliding window sampling; S3. Multiplying each data sample by a window function, calculating the Fourier transform of each window, and constructing the short-time Fourier transform time-frequency matrix of the vibration signal by spectral splicing; S4. Based on the short-time Fourier transform time-frequency matrix, calculating the instantaneous frequency after signal compression, and mapping pseudo-frequency components within a set width to the instantaneous frequency according to the mapping relationship, thereby obtaining a high-resolution time-frequency matrix reconstructed by synchronous Fourier compression transform. This invention has the advantages of high feature extraction accuracy and high reliability.

This signal processing device comprises: a window function unit (21) that multiplies an input signal by a new window function obtained by multiplying a window function by a function that is a linear sum of trigonometric functions, and extracts a specific signal component present in the input signal; and a frequency converting unit (22) that frequency-converts the specific signal component present in the input signal extracted by the window function unit (21).

The present invention provides embedded coding of a monaural audio signal and audio signals of multiple channels in which the algorithm delay in stereo coding / decoding is not greater than the algorithm delay in monaural coding / decoding. A coding device (100) codes the audio signals of multiple channels in units of frames. A stereo coding unit (110) obtains and outputs a stereo code representing a feature parameter which indicates a feature regarding inter-channel difference between the audio signals of multiple channels. A down-mixing unit (150) obtains, as a down-mixed signal, a signal in which the audio signals of multiple channels are mixed. A monaural coding unit (120) obtains a monaural code by coding the down-mixed signal using a coding scheme which includes a process of multiplying overlapped parts between frames by a window function, and outputs the monaural code. An additional coding unit (130) obtains an additional code by coding a signal in a overlapped section between a current frame and the immediately subsequent frame of the down-mixed signal, and outputs the additional code.

The application provides a kind of based on the shear wave transformation of Meyer window function's sonar image noise reduction method and system.The method comprises: the transformation of reverberation noise model to Gaussian additive noise model is combined with shear wave transformation;In the process of shear wave transformation, considering the continuity, smoothness and compact support performance shown by wavelet function and scale function in time domain and frequency domain, Meyer window function based on the performance shown in time domain and frequency domain is proposed to construct shear wave filter;In order to avoid the influence of gray overflow in sonar image, and realize better reverberation suppression effect, noise variance estimation based on weak texture block is proposed, and the root mean square coefficient obtained by the shear wave transformation of simulated noise is combined to obtain adaptive filtering threshold in each scale and direction.Experiments show that the method proposed in the application can more effectively suppress reverberation noise, and has better edge preservation effect.

The invention relates to the technical field of signal capturing, in particular to a satellite signal capturing method for low-orbit high-speed movement of a satellite. The method comprises the following steps: acquiring a satellite signal and Doppler frequency shift of each channel at each moment in a preset time period; acquiring a first fluctuation parameter of the satellite signal according to the deviation condition, the fluctuation condition and the energy attenuation condition of the satellite signal under each channel; acquiring a second fluctuation parameter of the satellite signal according to the main lobe energy leakage condition of the satellite signal, the fluctuation condition of Doppler frequency shift and the first fluctuation parameter; and adjusting a tuning parameter of a window function in the PMF-FFT algorithm based on the second fluctuation parameter to obtain an adjusted tuning parameter, and capturing the satellite signal. According to the method, the tuning parameters are adaptively obtained and adjusted, so that the accuracy and reliability of satellite signal capture are effectively improved.

The invention discloses a subsynchronous harmonic compensation method and device of a power system. The method comprises the following steps: acquiring a real-time load current signal; based on a preset first window function, performing windowing processing on the real-time load current signal to obtain a first windowing current signal; carrying out peak detection on the first windowed current signal in a preset frequency range to obtain respective frequencies, amplitudes and phases of a plurality of subsynchronous harmonics; judging whether to maintain a first window function based on the frequencies and amplitudes of the plurality of subsynchronous harmonics; based on the respective frequencies, amplitudes and phases of the plurality of subsynchronous harmonics, sine wave signals corresponding to the plurality of subsynchronous harmonics are generated; and compensating the real-time load current signal through a unified power quality regulator based on the sine wave signals corresponding to the plurality of subsynchronous harmonics. According to the invention, the technical problems of low subsynchronous harmonic detection precision and weak separation capability of the existing unified power quality conditioner are solved.

A method for calculating time domain signal convolution in Nport modeling process, comprising the following steps: interpolating discrete impulse response spectrum by interpolation method to obtain spectrum on uniform frequency points; modulating the spectrum on the uniform frequency points by a window function to obtain windowed spectrum; after frequency continuation of the windowed spectrum, obtaining time domain impulse response function by inverse Fourier transform; correcting the value of the time domain impulse response function at zero time to obtain a corrected impulse response function; and performing convolution calculation on the input signal and the corrected impulse response function to obtain an output signal. The method of the present application corrects the value of the time domain impulse response at zero time after frequency domain windowing of the frequency domain impulse response and inverse Fourier transform, and obtains a more accurate time domain impulse response function, thereby improving the accuracy of the calculated output signal.