346 results about "Wavelet reconstruction" patented technology

The invention relates to an optimized human face recognition preprocessing method. The method comprises the following steps: firstly, converting a color human face image from a camera into a gray level image, then performing a scale normalization processing on the gray level image to cause the human face images to have the same size and posture, dividing the human face images into low-frequency components and high-frequency components by wavelet transformation, performing a histogram equalization processing on the low-frequency components only, executing wavelet reconstruction on the processed low-frequency components and the high-frequency components, and finally processing the reconstructed images by optimized median filtering. The method has the advantages of regulating the gray level range of the human face images, enhancing the contrast, better improving the human face gray level images with higher brightness, and enhancing the human face identification efficiency in a complicated illumination environment with different postures.

The invention discloses a method for diagnosing bearing breakdown of a wind generating set. The method comprises the steps that vibration signals of a bearing are acquired; a wavelet packet analysis method is used for conducting three-layer decomposition on the vibration signals, soft threshold quantitative processing is conducted on high-frequency coefficients under all decomposition scales, and one-dimensional wavelet reconstruction is conducted according to the low-frequency coefficients and the high-frequency coefficients of a bottommost layer of wavelet decomposition; wavelet packet decomposition is conducted on the reconstructed vibration signals, the energy of all frequency bands on a third layer is extracted, the energy of all the frequency bands constitutes a breakdown diagnosis input vector with a breakdown feature input vector used as a BP neural network, and a three-layer BP neural network is established; a feature input vector sample of historical breakdown data is acquired and input to the three-layer BP neural network for training; a breakdown diagnosis feature vector of real-time operation data of the bearing is acquired and input to the trained BP neural network; intelligent diagnosis of bearing breakdown types is achieved. The method for diagnosing bearing breakdown of the wind generating set can be used for precisely diagnosing the bearing breakdown types and precisely positioning breakdown positions.

The invention relates to a digital image reinforcing method based on wavelet reconstruction and decomposition and a device thereof, which belongs to the image data processing field. The digital image reinforcing method comprises image pretreatment, decomposition process, transformation process and reconstruction process, and is characterized in that the image decomposition process comprises multi-group integrals and down-sampling process; the transformation process comprises the statistics, analysis and modification processes of mid data information; the reconstruction process comprises multi-group up-sampling, integrals and adding processes; in the transformation process, adaptive histogram equalization technology and noise restraining are adopted. The invention, based on multi-scale analysis, realizes to reinforce the contrast of digital image and prevents noise simultaneously through the wavelet decomposition, transformation and reconstruction and adopting the adaptive histogram equalization technology and noise restraining in transform domain and can be widely used in such fields related to the improvement of digital image quality as medical information systems, industrial nondestructive testing systems and other similar image systems.

The invention discloses an anti-interference method for high-frequency partial discharge signal detection. The anti-interference method includes the steps of 1, building partial discharge signal simulation testing circuit system to acquire signals which are generated from local discharge pulse signals carrying noises after passing through the partial discharge signal simulation testing circuit system, and analyzing characteristics of the signals within a wavelet domain to acquire optimal parameter that is denoised by a discrete wavelet transform based threshold process; 2, acquiring the field partial discharge signals carrying noises, and acquiring the coefficient of the partial discharge signals within the wavelet domain by denoising the partial discharge signals carrying noises by the discrete wavelet transform based threshold process according to the optimal parameter; 3, performing wavelet reconstruction according to the coefficient of the partial discharge pulse signals within the wavelet domain to acquire the denoised partial discharge signals. The signals are denoised by firstly performing threshold processing to decomposition coefficients of different dimensions by threshold selection and then performing wavelet transform.

The invention relates to an improved threshold function-based wavelet transformation image denoising method which comprises the following steps of: firstly selecting a base wavelet, determining the number of wavelet decomposition layers and carrying out multi-scale wavelet transformation on noise-containing images; respectively determining a threshold for each layer of detail coefficient; carrying out threshold treatment by using improved threshold functions; and finally carrying out wavelet reconstruction on low-frequency coefficients and threshold treated high-frequency coefficients to obtain the denoised images. According to the denoising method provided by the invention, the defect of discontinuity of the hard threshold functions is overcome and the constant deviation in the soft threshold functions is decreased at the same time. By using the denoising method, the signals and the noises can be distinguished effectively, the image information edges are protected when the noises are removed, the peak signal to noise ratio of the images is improved, and better image denoising effect is achieved.

The invention provides an electroencephalogram signal characteristic extracting method. Network average route lengths and clustering coefficients are calculated through wavelet reconstruction, windowing horizontal visibility map complex network conversion and complex network analysis. The average route lengths and clustering coefficients composed of electroencephalogram signals are calculated to achieve characteristic analysis of electroencephalogram signals and chaotic time sequence signals of the electroencephalogram signals of different rhythms. The electroencephalogram signal characteristic extracting method has the advantages that one-dimensional chaotic time sequences are converted into complex networks, according to analysis of network characteristic parameters, fractal characters of the electroencephalogram signals are revealed, the complex non-linearity signals of the electroencephalogram signals are depicted from a brand new angle. The electroencephalogram signal characteristics can be applied to automatic diagnosis of mental disease and a characteristic identifying module of a brain-machine port system. The electroencephalogram signal characteristic extracting method can effectively distinguish the electroencephalogram signals of an epilepsia attach stage and an epilepsia non-attach stage, the equation p<0.1 is met after Mann-Whitney detection, and the electroencephalogram signal characteristic extracting method can be applied to epilepsia electroencephalogram automatic identification.

The invention relates to a neighborhood normalized gradient and neighborhood standard deviation-based multi-focus image fusion method. The method comprises the following steps of: firstly, performing multi-scale decomposition on images by using wavelet transform to acquire low-frequency and high-frequency information of the images under different resolutions and different directions; secondly, processing the images by adopting different fusion rules according to the respective characteristics of the low-frequency and high-frequency information, wherein a neighborhood normalized gradient-based fusion method is adopted for the low-frequency sub images to overcome the defect that the traditional low-frequency component fusion method neglects edge information, and a neighborhood standard deviation-based fusion method is adopted for the high-frequency sub images so as to furthest keep detailed information of the images; and finally, performing wavelet reconstruction to acquire a fused image. The method overcomes of edge distortion of the traditional fusion algorithm, obviously improves the quality and the definition of the fused image, and can be applied to various military or civil multi-focus image fusion systems.

The invention discloses a short-term prediction method for occupancy of an effective parking space of a parking lot. The short-term prediction method comprises the steps as follows: 1) determining a time sequence of the occupancy of the effective parking space of the parking lot; 2) carrying out wavelet decomposition to the time sequence of the occupancy of the effective parking space through a wavelet function, thus obtaining a low-frequency coefficient vector and a high-frequency coefficient vector; implementing the wavelet reconstruction to the low-frequency coefficient vector and high-frequency coefficient vector, so as to obtain the time sequence of N+1 reconstructions; 3) establishing a wavelet neural network model to the time sequence of the N+1 reconstructions for predicting, thus obtaining N+1 prediction results; and 4) accumulating the N+1 prediction results, so as to obtain the prediction results corresponding to the time sequence of the occupancy of the effective parking space. According to the short-term prediction method disclosed by the invention, a wavelet analysis-wavelet neural network combinational prediction model is raised to perform short-term prediction to the occupancy of the effective parking space of the parking lot according to the short-term variation characteristic of the occupancy of the effective parking space of the parking lot, therefore, the prediction accuracy and the stability are improved.

The invention relates to a wavelet transformation and multi-channel PCNN-based hyperspectral image fusion method, which comprises the following steps: firstly, performing preprocessing of registering and grey level adjustment on hyperspectral images of N wave bands to be fused, and performing the wavelet transformation to obtain low-frequency sub-band images and high-frequency sub-band images; secondly, performing primary nonlinear fusion processing on the low-frequency sub-band images and the high-frequency sub-band images respectively by using a multi-channel PCNN model, obtaining corresponding ignition frequency map, performing linear mapping of corresponding coefficient range on the ignition frequency map for the low-frequency sub-band images, and taking a mapping result as a fusion result; thirdly, performing the region segmentation on the high-frequency sub-band images in each direction by using the ignition frequency map, and performing the fusion processing on different regions by using different fusion rules; and finally, processing wavelet reconstruction and obtaining a final result image. The method can realize the hyperspectral image fusion of a plurality of hyperspectral wave bands and can achieve a better fusion effect.

The invention discloses a method and a system for wireless channel measurement based on wavelet decomposition threshold de-nosing. The method includes the following steps: (1) using a spread spectrum sliding correlation method to finish wireless channel measurement so as to obtain channel impulse response containing noise, (2) conducting multi-resolution wavelet decomposition for the channel impulse response and leading the channel impulse response to be converted from time domain to wavelet domain, (3) reasonably selecting a threshold function and a threshold and processing wavelet coefficients corresponding to the noise according to the threshold function; (4) conducting wavelet reconstruction and leading the de-noised channel impulse response to be converted from the wavelet domain back to the time domain, and (5) storing the de-noised channel impulse response for researching channel characteristics. Through steps of wavelet decomposition, threshold de-nosing, wavelet reconstruction and the like, the method and the system can reduce interference on the channel impulse response caused by the noise, improve accuracy and effectiveness of channel measurement results, and provide reliable data basis for follow-up researching of channel characteristics.

The invention discloses an EMD generalized energy-based wheeltrack vibration signal fault feature extraction method which comprises the following steps: collecting a vibration acceleration signal of a real-time running train, integrating the train speed to determine the starting and stopping moments corresponding to one revolution of the wheel, and intercepting the acceleration signal of corresponding time history by using the starting and stopping moments; carrying out wavelet decomposition, threshold processing of each layer of wavelet coefficient and wavelet reconstruction on the collected vibration acceleration signal to realize wavelet denoising; carrying out empirical mode decomposition on the obtained axle box vibration acceleration signal to obtain a series of intrinsic mode functions; finally determining the energy weight coefficient by combining the vibration acceleration signal under fault excitation simulated by a vehicle track coupling kinetic model, calculating the empirical mode decomposition generalized energy and determining the fault feature according to the value. The EMD generalized energy-based wheeltrack vibration signal fault feature extraction method has the advantages of being low in cost, and high in feature extraction resolution ratio and real-time performance.

The invention discloses an electrocardiographic signal de-noising method based on adaptive threshold wavelet transform. The method is characterized by comprising following steps: step 1: using the Mallat algorithm, the wavelet function sym6 and the number of decomposition layers J are selected, and the noisy ECG signal is decomposed by wavelet to obtain approximate coefficients and detail coefficients; step 2: setting the threshold for adaptive detail coefficients at each layer and selecting the threshold function; step 3: performing adaptive threshold processing on the detail coefficients ofeach layer, removing power frequency interference and myoelectric interference, and removing baseline drift by processing the approximation coefficients; step 4: performing wavelet reconstruction on the electrocardiographic signals after processing to obtain approximate optimal estimate value of signals. The method of the present invention makes full use of the multiresolution feature of the wavelet transform. An adaptive threshold selection method is provided. Different thresholds are used at each level to separate the noise and signal flexibly, improving the separability of signal characteristics; in the three aspects of visual, mean square error, and signal-to-noise ratio, the effect is better than the traditional method, and the detailed information of the image is retained better, which has higher practical value.

The invention relates to a super-high voltage direct-current power transmission line region internal and external fault identification method, and belongs to the field of high voltage direct-current power transmission system relay protection. The method comprises the steps of: firstly collecting fault voltage data; carrying out wavelet multi-scale decomposition on detected fault voltage signals to obtain a wavelet reconstruction high frequency coefficient of each layer, forming a characteristic vector matrix with singular-spectrum entropy of the high frequency coefficients of all layers, and dividing the data in the characteristic vector matrix into a training set and a testing set; then setting a training set label and a testing set label; carrying out training on the training set; then setting storage positions of prediction labels and prediction precision; inputting the testing set to an SVM classifier for testing, and obtaining a classification result and prediction precision; and then determining whether the classification result stored in a prediction label storage space is correct. By adopting the method provided by the invention, faults at three different positions can be identified at the same time; in addition, the method is simple and effective, the calculating time is short, and automation is realized in the whole classification process.

The invention relates to a second-generation wavelet electromyographic signal noise eliminating method based on ensemble empirical mode decomposition. The method provided by the invention comprises the following steps of: obtaining electromyographic signal sample data of the human body; performing empirical modal decomposition after adding white noise in original eletromyoraphic signals; then performing secondary-generation wavelet decomposition and threshold processing on high-frequency intrinsic mode function components; performing wavelet reconstruction to high-frequency components; finally overlying processed high-frequency components and low-frequency components; and obtaining reconstituted signals as noise eliminating signals. According to the invention, as the signals are adaptively decomposed to different scales, the second-generation wavelet electromyographic signal noise eliminating method is suitable for processing nonlinear and nonstationary signals, not only has all advantages of wavelet analysis and but also has a more clear and accurate spectrum structure; in addition, the method can improve extreme point distribution of the signals, has anti-mixing decomposition capacity, can keep useful signals as far as possible, can effectively eliminate noise and can greatly improve the signal-to-noise ratio of the signals.

The invention relates to a detection method of transient harmonic signals of a power system based on the combination of Tsallis wavelet singular entropy and FFT computation, which can solve the problem that the prior art can not extract the frequency and the power information of the transient harmonic signals of the power system. The detection method comprises the following steps: carrying out theFFT computation on current signals, determining the sampling frequency and the decomposition scale of the mallat algorithm computation according to the result, carrying out the mallat algorithm computation on the current signals, carrying out the Tsallis wavelet singular entropy computation on wavelet coefficients with abnormal singularity degree or single-branch wavelet reconstruction signals, obtaining the occurrence time and the continuous time of the transient harmonic signals, further carrying out the time segmentation on all the wavelet coefficients of the wavelet scales with the frequency of higher than 650Hz or the single-branch wavelet reconstruction signals, carrying out the FFT computation analysis on the wavelet coefficients or the single-branch wavelet reconstruction signalsin all the time periods, and finally obtaining the frequency and the power information of the transient harmonic signals in all the time periods. The detection method overcomes the deficiencies in theprior art and can be used for detecting the transient harmonic signals of the power system.

The invention discloses a diagnostic method of an aero-engine rotor fault. According to the method, an eddy current acceleration sensor is firstly utilized to collect a vibration acceleration signal of an aero-engine rotor, then noise reduction processing is performed on the vibration acceleration signal of the aero-engine rotor, and the signal characteristic quantity of the aero-engine rotor after the noise reduction is extracted, and then clustering analysis is performed on the signal characteristic quantity based on automatic density peak clustering of Mahalanobis distance. According to thefault diagnosis method, wavelet decomposition is utilized, threshold processing is performed, and then noise interference is removed from a signal by lifting wavelet reconstruction; the signal characteristic quantity after the noise reduction is extracted, and the clustering analysis is performed on the signal characteristic quantity based on the automatic density peak clustering of Mahalanobis distance, so that the noise interference can be eliminated to a minimum value, the diagnosis of fault information of the aero-engine rotor can be realized, and fault results of various aero-engine rotors can be quickly obtained.

A method of image processing of magnetic resonance (MR) images for creating de-noised MR images, comprises the steps of providing image data sets including multiple complex MR images (S7), subjecting the MR images to a wavelet decomposition (S12) for creating coefficient data sets of wavelet coefficients (S_n,m) representing the MR images in a wavelet frequency domain, calculating normalized coefficient data sets of wavelet coefficients Formula (I) (S17), wherein the coefficient data sets are normalized with a quantitative amount of variation, in particular standard deviation Formula (II), of noise contributions included in the coefficient data sets (S_n,m), averaging the wavelet coefficients of each coefficient data set (S18) for providing averaged wavelet coefficients Formula (III) of the coefficient data sets, calculating phase difference maps (Δφ_n,m) for all coefficient data sets (S20), wherein the phase difference maps provide phase differences between the phase of each wavelet coefficient and the phase of the averaged wavelet coefficients Formula (III), calculating scaled averaged coefficient data sets of wavelet coefficients by scaling the averaged wavelet coefficients Formula (III) with scaling factors (C_n,m), which are obtained by comparing parts of the normalized wavelet coefficients of the normalized coefficient data sets Formula (I) that are in phase with the averaged wavelet coefficients Formula (III) (S22), calculating rescaled coefficient data sets of wavelet coefficients Formula (IV) (S24) by applying a transfer function Formula (V) on the coefficient data sets (S_n,m) and on the scaled averaged coefficient data sets, wherein the transfer function includes combined amplitude and phase filters, each depending on the normalized coefficient data sets Formula (I) and me phase difference maps (Δφ_n,m), resp., and subjecting the rescaled coefficient data sets to a wavelet reconstruction Formula (IV) (S25) for providing the denoised MR images.

The invention relates to a method for removing peak noise in a nuclear magnetism signal, and the method is used for removing peak noise in a ground nuclear magnetic resonance underground water detection signal based on synchronization compression wavelet transformation and self-optimization non-linear threshold selection. The method comprises the steps: dividing the nuclear magnetism signal into a plurality of compression wavelet lengths; solving compressed wavelet coefficients of different compression wavelet domains; searching the position of the peak noise in the nuclear magnetism signal through employing a self-optimization algorithm, and removing the peak noise from the nuclear magnetism signal; carrying out the compensation for the nuclear magnetism signal where the peak noise is located through employing a non-linear threshold selection algorithm; and finally obtaining a final nuclear magnetism signal with the peak noise interference being removed through the compression wavelet reconstruction. The method will carry out the compensation of the nuclear magnetism signal where the peak noise is located while the peak noise is removed, and is effective for the removing of the peak noise mixed in a detection process of the nuclear magnetism signal.

A fake fingerprint detection method based on SVM-RFE (support vector machine-recursive feature elimination) includes the following steps: 1), segmenting an image; 2), extracting features; 2.1), performing discrete wavelet transform, 2.2), denoising by a hyperbolic shrinkage method; 2.3), performing wavelet reconstruction to acquire a denoised image; 2.4), differencing the original image with the denoised image to acquire a noise image; 2.5), respectively performing block-based LBP (local binary pattern) feature extraction on the denoised image and the noise image; 2.6), normalizing block features, and connecting the block features in series to acquire final fingerprint features; 3), selecting the features; 4), training to acquire a classifier. Compared with conventional methods of detecting fake fingerprints only by image noises, the fake fingerprint detection method has the advantages that the denoised image is utilized, an LBP method substitutes for a standard deviation method to extract the features, and an SVM-RFE feature selection method is introduced, ineffective and redundant features are eliminated effectively, and accordingly, reliability in fake fingerprint detection is improved.

The invention discloses a wood defect detecting and sorting device and method based on a depth camera and deep learning, the wood defect detecting and sorting device comprises an industrial personal computer, a detecting mechanism and a sorting mechanism arranged on the rear side of the detecting mechanism, and the detecting mechanism comprises a detecting conveying belt and a depth image collecting mechanism. According to the method, RGB images and depth information of the wood surface are collected through a depth camera, and RGBD color depth information is reconstructed through combinationof a GAN network and wavelet transform. In wavelet transformation, wood cracks in training data are manually marked, and a self-adaptive crack wavelet basis function is formed. Wavelet reconstructionis carried out on the basis to improve the efficiency of a subsequent algorithm, and the defect type is obtained through analysis in combination with a deep learning algorithm. The algorithm can sortwoods with different defect types, so that the defect discrimination efficiency is improved, and the sorting efficiency is greatly improved.

The invention relates to a method for extracting lightning strike signals and transient harmonic signals in a power system, which overcomes the problem of inaccurate extraction of the method for extracting the lightning strike signals and the transient harmonic signals in the power system in the prior art. The method comprises the steps of firstly converting the collected current signals to digital signals, further carrying out fast Fourier transform on the digital signals, determining the sampling frequency and the decomposition scale of wavelet decomposition according to the transform result, then carrying out wavelet decomposition on the digital signals, sequentially carrying out modulus maxima extraction, singularity detection and generalized wavelet entropy calculation on wavelet coefficients or single-branch wavelet reconstruction signals after the decomposition, and finally extracting the lightning strike signals and the transient harmonic signals according to the calculation result. The method can overcome the deficiencies of the prior art and be applicable to representing information features of the lightning signals and the transient harmonic signals in the time-frequencydomain.