152 results about "Complex wavelet transform" patented technology

The invention discloses a method for testing the assembly performance of a rotor of an aircraft engine, which comprises the following steps of: firstly exciting and vibrating a rotor of an aircraft engine with a vibration exciter; obtaining a multiple carrier-coupled impulse response signal of the rotor of the aircraft engine with a vibrating sensor and signal-acquiring system software; analyzing the obtained multiple carrier-coupled impulse response signal of the rotor of the aircraft engine by means of dual-tree complex wavelet transform to obtain eight signal carrier-coupled impulse response signals of the rotor of the aircraft engine; and distilling the average assembly performance index of the obtained eight signal carrier-coupled impulse response signals of the rotor of the aircraft engine, wherein the assembly performance of the rotor of the aircraft engine is judged to be qualified if the obtained average assembly performance index is larger than or equal to 10, and the assembly performance of the rotor of the aircraft engine is judged not to be qualified if the obtained average assembly performance index is less than 10, so that the rotor needs to be repaired.

The invention discloses a method for recognizing a road traffic sign for an unmanned vehicle, comprising the following steps of: (1) changing the RGB (Red, Green and Blue) pixel value of an image to strengthen a traffic sign feature color region, and cutting the image by using a threshold; (2) carrying out edge detection and connection on a gray level image to reconstruct an interested region; (3) extracting a labeled graph of the interested region as a shape feature of the interested region, classifying the shape of the region by using a nearest neighbor classification method, and removing a non-traffic sign region; and (4) graying and normalizing the image of the interested region of the traffic sign, carrying out dual-tree complex wavelet transform on the image to form a feature vector of the image, reducing the dimension of the feature vector by using a two-dimension independent component analysis method, and sending the feature vector into a support vector machine of a radial basis function to judge the type of the traffic sign of the interested region. By using the method, various types of traffic signs in a running environment of the unmanned vehicle can be stably and efficiently detected and recognized.

This invention relates to a method for de-noising digital radiographic images based upon a wavelet-domain Hidden Markov Tree (HMT) model. The method uses the Anscombe's transformation to adjust the original image to a Gaussian noise model. The image is then decomposed in different sub-bands of frequency and orientation responses using a dual-tree complex wavelet transform, and the HMT is used to model the marginal distribution of the wavelet coefficients. Two different methods were used to denoise the wavelet coefficients. Finally, the modified wavelet coefficients are transformed back into the original domain to get the de-noised image.

A method for adaptive filtering of EEG signals in the wavelet domain using a nearly shift-invariant complex wavelet transform. EEG signal data is segmented into a set of K “trials” or “light averages” of M-frames of data each. These trials are overlapped by a number of frames P, where P<M. A dual-tree complex wavelet transform is computed for each light average K of EEG signal data. Next, the phase variance of each resulting normalized wavelet coefficient is computed, and the magnitude of each wavelet coefficient is selectively scaled according to the phase variance of the coefficients. The resulting wavelet coefficients are then utilized to reconstruct the ABR signal extracted from the EEG data.

There is provided herein a New system arid method of local attribute match filtering which operates in the local attribute domain via the use of complex wavelet transform technology. This approach is adaptable to address various noise types in seismic data and, more particularly, is well suited to reduce the noise in geophone data as long as an associated hydrophone signal is relatively noise-free.

An infrared and visible light sequence image character level amalgamation method based object detecting belongs to image amalgamation technology field. It uses area growth method to divide up each frame fountain image and automatically get background area and object area according to effective detecting method and takes dual-tree complex wavelet transform method to the fountain image list. After the transform, apply different amalgamation rules for object and background area to realize the list image amalgamation with character level. After get the wavelet amalgamation coefficient of different area, use dual-tree complex wavelet inverse transform to get amalgamation list image. It uses different amalgamation rules to keep interesting goal information as more as possible. The amalgamation list image owns good inflexibility, stability of time and coherence, and high calculating efficiency for using the application with dual-tree complex wavelet transform.

The invention discloses a power quality disturbance recognition and classification method based on PSO (Particle Swarm Optimization) for an SVM (Support Vector Machine). Detection and positioning are performed on a disturbing signal by use of complex wavelet transform, and a feature vector of dynamic power quality disturbance is effectively extracted; after parameters of the SVM are optimized by virtue of a PSO algorithm, automatic recognition and classification are performed on the dynamic power quality disturbance according to an extracted feature signal; complex wavelet transform can be used for overcoming the defect that original wavelet change can be used for only analyzing signal amplitude frequency, meanwhile resolving the amplitude frequency and phase frequency characteristics of signals, providing multiple types of combination information and more accurately recognizing most common dynamic disturbing signals in a power system. Compared with a traditional method of recognizing an interference signal by use of a neural network and the like, the method disclosed by the invention is accurate and reliable in recognition and higher in accuracy rate.

The invention discloses a composite fault diagnosis method and system of a gear case. The composite fault diagnosis method comprises firstly measuring and storing vibration signals of the gear case; then adopting 1 / 4 sampling translation dual tree complex wavelet transform for resolving; and finally extracting a plurality of fault characteristics by adopting an energy operator demodulation method in a plurality of sub-band signals obtained by resolving, and further identifying a composite fault mode. The composite fault diagnosis method is fused with a complementary characteristic using dual tree complex wavelets and energy operator demodulation, and the obtained gear case is high in recognition capability of composite fault characteristic extraction. By means of speed capability of parallel realization of the dual tree complex wavelet transform and the energy operator demodulation method, partial polling and on-line monitoring of the gear case in a working condition can be completely applied, sudden accidents can be avoided, and the composite fault diagnosis method and system can be applicable to gear case portions in different models.

The invention discloses a texture image segmentation method based on an independent Gaussian hybrid model, which comprises the following segmentation steps: simultaneously performing three-layer wavelet transformation, dual-tree complex wavelet transformation and Contourlet transformation to training texture images; extracting the characteristics of the corresponding training texture images; selecting the characteristics by adopting an immunity clone algorithm on each layer; performing unsupervised learning of the Gaussian hybrid model to each layer of each training image, adaptively obtaining the corresponding component number, and thus obtaining the parameter of the Gaussian hybrid model; simultaneously performing wavelet transformation, dual-tree complex wavelet transformation and Contourlet transformation to test texture images; calculating the corresponding final likelihood value of each layer according to the transformation coefficient and the component number; obtaining the primary segmentation result through comparing the corresponding likelihood value of each texture; and obtaining the segmentation result through multi-scale fusion of the primary segmentation result. The invention has the characteristics of good consistence of segmentation area, complete information retaining, and accurate edge positioning, and can be used for the image texture recognition.

A union of DCT (discrete cosine transform) and wavelet transform can generate a much sparser representation of the digital image signal than either of them alone. After the block-based DCT, the coefficients are rearranged into a number of frequency groups such that the coefficients locating at the same coordinate in all transform blocks are in one group. Then, one or more such groups are further decomposed by wavelet transform. After quantization, each frequency group is divided into squares. The squares are identified and encoded as either all-zero or not-all-zero. Inside those not-all-zero squares, the coefficients are encoded bit-plane by bit-plane in a 2-dimensional quaternary reaching pattern. Compared to existing peer systems, the compression performance is improved up to 30%, especially in high quality cases. For lossless compression, the image data is decomposed by a union of a reversible DCT approximant and a reversible wavelet transform. Besides, the coefficients are quantized by a remnant-preserved, partial quantization scheme. The lossless compression performance is improved about 20% against JPEG2000.

The invention discloses a rotary machine fault detection method of dual-tree complex wavelet transformation with adjustable quality factors. The rotary machine fault detection method of dual-tree complex wavelet transformation with the adjustable quality factors comprises the steps of (1) building a reasonable sampling parameter set, building dual-tree complex wavelet base functions with different quality factors, (2) using each built dual-tree complex wavelet base function to carry out time-scale analysis on a vibration response signal of a rotary machine, calculating kurtosis information entropy of wavelet coefficients of each layer with participation of each dual-tree complex wavelet base function, selecting a dual-tree complex wavelet base function corresponding to the maximum feature kurtosis information entropy as the dual-tree complex wavelet base function which is in optimal matching with an impact component of the vibration signal, and (3) analyzing the vibration signal through the optimal dual-tree complex wavelet base function, and carrying out fault diagnosis. According to the rotary machine fault detection method of dual-tree complex wavelet transformation with the adjustable quality factors, the dual-tree complex wavelet base functions with any frequency-band focusing performance and time-domain oscillation performance can be built, the base function with the optimal matching performance can be selected in a self-adaptation mode, and accurate detection of periodicity impact type fault features and information of the impact period of a rotary machine device can be achieved.

The invention relates to a method for de-noising a dual-tree complex wavelet image on the basis of partial differential equation. The method comprises the following steps: inputting a noised digital image; carrying out the dual-tree complex wavelet transform decomposition on the inputted noised digital image to obtain two low-frequency sub-band images and six high-frequency detailed sub-band images; carrying out the isotropic diffusion on the two decomposed low-frequency sub-band images; designing an improved adaptive model; calculating the dual-tree complex wavelet transform modulus and gradient modulus of the high-frequency detain sub-band images on each direction, and designing an adaptive diffusion coefficient function to improve the P-M (Perona-Malik) model (i.e., the isotropic diffusion model) by using the weighted average of the dual-tree complex wavelet transform modulus and gradient modulus; carrying out the diffusion processing on the improved adaptive model; carrying out the isotropic diffusion on the six high-frequency sub-band images; and carrying out the dual-tree complex wavelet transform, and outputting the de-noised digital image. The invention has the beneficial effect that more detailed information of the image can be preserved on the premise that the higher rate of image de-noising is maintained.

The invention provides a fiber F-P sensor cavity length wavelet phase extraction demodulation method. At first, the interference spectrum is calculated by means of a fast fourier transform algorithm to obtain a cavity length value to be a cavity length rough measuring value; the searching scope of a scale factor is determined by taking the 2-3 times of the precision of the fast fourier transform algorithm as the radius, and the phase information corresponding to each point in the interference spectrum is solved by continuous complex wavelet transform; and the cavity length value of the fiber F-P sensor is calculated by the linear gradient obtained by the linear fitting phase and wavenumber to be the final cavity length value. The absolute measurement of the F-P sensor cavity length can be achieved, so that the high-precision and high-resolution measurement of physical quantity can be achieved.

The invention discloses a method for cooperatively classifying perceived solid wood panel surface textures and defects by feature extraction and compressive sensing based on dual-tree complex wavlet, and relates to the field of solid wood panel surface defect detecting. The method is used for solving the problems of low classifying precision, low classifying efficiency, and the like of the existing solid wood panel surface texture and defect classifying method. The method comprises the following steps: performing feature dimension reduction after performing feature extraction by dual-tree complex wavelet transform on solid wood panel images; classifying optimized feature vectors based on a compressive sensing theory; using the optimized feature vectors as a sample row, and establishing a data dictionary matrix by a training sample matrix; linearly representing a measuring sample by using training samples, calculating a sparse representation vector on a data dictionary of a test sample, and determining the category with smallest residual error as the category of the test sample. Due to good directionality of the dual-tree complex wavlet, complex information of the panel surface can be expressed, and the classifying efficiency can be further improved based on feature selection of a particle swarm algorithm. Compared with the conventional classifier, the compressive sensing classifier is simple in structure and relatively high in classifying precision.

The invention discloses a neighborhood adaptive Bayes shrinkage image denoising method based on a dual-tree complex wavelet domain. The method comprises the following steps: 1) performing dual-tree complex wavelet transform on a noisy image, and performing three-level decomposition to obtain multiple sub-band coefficients; 2) estimating the noise variance by use of a robust median device; 3) processing each sub-band coefficient except the low-pass sub-band coefficient in the following steps: a) calculating the variance of the noisy image in corresponding neighborhood window for each DT-CWT (dual-tree complex wavelet transform) coefficient; b) averaging the variances of the noisy image corresponding to all the coefficients to estimate the neighborhood variance of the noisy image of the sub-band; and c) assuming that a statistical model of the DT-CWT coefficients of the image obeys a GGD (general Gaussian distribution) model, estimating the optimal threshold through a minimal Bayes risk function, and softening the wavelet coefficient in the sub-band; and 4) performing dual-tree complex wavelet inverse transform reconstruction on the wavelet coefficient to obtain the denoised image. The method disclosed by the invention has perfect denoising performance and good adaptivity.

The invention discloses a planetary gear fault diagnosis method based on dual-tree complex wavelet transform-entropy feature fusion. The method comprises the following steps of collecting integration simulation experiment table data and acquiring a planetary gear shell original vibration signal; using dual-tree complex wavelet transform to decompose an original vibration signal and extracting a signal component of each frequency band; constructing an entropy feature extraction model from multiple angles and acquiring a high-dimension original feature; using a nucleus Fisher discriminant analysis method to carry out dimension reduction processing on an original feature set formed by a plurality of entropy features, determining a group of optimum discriminant vectors, extracting a projection of the original feature in the optimum discriminant vectors and taking as a sensitive fault feature so as to determine a fault type; verifying a necessity of describing feature information from the multiple angles and multiple spaces and validity of carrying out feature dimension reduction by using a KFDA method based on that. The method is suitable for the non-linear and non-stable planetary gear vibration signal with a high coupling feature. By using the method, the sensitive fault feature can be effectively extracted and accurate diagnosis of the planetary gear is realized.

The invention discloses a method for reducing the speckles of a synthetic aperture radar (SAR) image by combining dual-tree complex wavelet transform with a bivariate model, which mainly solves the problems that speckle noise cannot be well inhibited and part of edge information and detailed information are lost in the conventional method for reducing the speckles of the SAR image. The method comprises the following steps of: performing dual-tree complex wavelet decomposition on the original SAR image to obtain a real part and an imaginary part of a decomposition coefficient on each scale; solving the variance of a noise coefficient by using a non-logarithmic additive noise model; solving the edge variances of the real parts and the imaginary parts of the complex wavelet coefficient by using a local neighborhood window; solving a threshold contraction function by maximum posterior estimation and performing threshold contraction on the dual-tree complex wavelet decomposition coefficient; and performing dual-tree complex wavelet reconfiguration on the contracted coefficient to obtain an image of which the speckles are reduced. The method has the advantages of capability of effectively removing the speckle noise from the SAR image and high edge preserving performance, and can be used for reducing the speckles of the SAR images with abundant edge information and detailed information, particularly the airport, runway and road-containing SAR images.

The invention provides a front car identification method based on monocular vision. The method includes the steps that (1), an original image is collected from a vehicle-mounted camera, the edge of the image is extracted according to a Canny edge extraction method, influence of noise points is eliminated through morphological filter, projection is carried out in the horizontal direction, and an area of interest of a front car is obtained according to projection characteristics; (2), a shadow area at the car bottom is extracted and judged according to the geometrical shape of the shadow at the car bottom, edge characteristics are overlaid, and a car area is judged; (3), graying, normalization and binary tree complex wavelet transformation are carried out on small color images of candidate car areas of different shapes, and characteristic vectors are obtained; (4), the number of dimensions of the characteristic vectors is decreased through a two-dimension independent component analysis algorithm, the characteristic vectors are fed into a support vector machine based on a radial basis function kernel to be classified, and it is judged that whether the candidate car areas are the car area. Cars on the road ahead are detected accurately, and real-time and reliable road condition information can be supplied for unmanned cars.

The invention discloses a face recognition method combining dual-tree complex wavelet transform and discrete wavelet transform. The method comprises the following steps of: carrying out feature extraction on an input face image by using a method combining dual-tree complex wavelet transform and discrete wavelet transform; carrying out dimension reduction on an extracted feature vector X by using a supervised locally linear embedding method; carrying out cosine similarity calculation on the feature vector of the tested face image and the feature vectors of training set images; and setting the input image and a training image with highest similarity into one group so as to obtain a face recognition result. By utilizing the method combining dual-tree complex wavelet transform and discrete wavelet transform, multidirectional and abundant face feature extraction can be realized and dimension reduction can be realized quickly, thereby realizing accurate and efficient face recognition.

The invention relates to a coronary heart disease surveillance and diagnosis device. The coronary heart disease surveillance and diagnosis device comprises a heart-lung signal collection device and aheart-lung signal analyzing device, wherein the heart-lung signal collection device collects heart-lung sound overlapped signals and sends the heart-lung sound overlapped signals to the heart-lung signal analyzing device, and the heart-lung signal analyzing device separates the heart-lung sound overlapped signals according to the short-time Fourier transform method so as to obtain an original heart sound signal; the original heart sound signal is reduced according to the double-threshold method, the peak picking method and the endpoint algorithm so as to obtain a phonocardiogram; the phonocardiogram is subjected to complex wavelet transformation so as to extract a signal envelop of the complex wavelet transformation; the signal envelop is trained according to a trained BP neural network soas to obtain an analysis result, and the analysis result is output. Based on the characteristic that in the early period of the coronary heart disease, high-frequency pathological murmur can occur inthe heart sound signal, the heart-lung sound overlapped signals are analyzed by using the short-time Fourier transform method, it is noninvasively and early diagnosed whether an examinee suffers fromthe coronary heart disease or not, and the diagnosis efficiency is high.

The invention discloses a fast and precise MRI (Magnetic Resonance Imaging) reconstruction method. The method comprises steps: scanning is carried out in a target visual field to obtain K space full sampling data; the K space full sampling data are sampled by using radial sampling to obtain under sampling data; the under sampling data are subjected to multi-loop data recovery to obtain an MRI image; and double-density dual-tree complex wavelet transform is used as a sparse basis, and according to the priori information of the MRI image under the double-density dual-tree complex wavelet transform, an FISTA algorithm is combined for image reconstruction. The reconstruction method can better depict image details and information, and the quality of the recovered image is improved.

The present invention provides a water quality monitoring data online processing method and device. The method comprises the steps of: obtaining a spectrum curve of water quality to be detected, setting a standard water quality spectrum curve as a reference, employing an autocorrelation function to calculate a related peak distance between the spectrum curve of water quality to be detected and thestandard water quality spectrum curve, and according to the related peak distance and sampling intervals, performing dynamic calibration of the spectrum curve of water quality to be detected; and performing noise removing processing of the spectrum curve after the dynamic calibration by employing a dual tree complex wavelet transform method, a threshold de-noising method and a dual tree complex wavelet inverse transform method, filtering the interference of noise signals, and finally, measuring a water quality reference value through a spectrometer according to the spectrum signals after noise removing process. Therefore, the spectrum signals with good repeatability can be obtained, the interference of water quality detection from outside environmental noise is avoided, and the accuracy of water quality detection is improved.

The invention discloses a rubbing acoustic emission denoise method based on empirical wavelet transform. The method comprises the following steps that (1) an acoustic emission signal is acquired through a rubbing acoustic emission experimental device; (2) adaptive partition is performed on the acoustic emission signal according to the Fourier spectrum features; (3) a wavelet window is added after partition, and an empirical scale function and an empirical wavelet function are defined; (4) empirical wavelet transform is defined; and (5) wavelet denoising is performed on each empirical mode component fi and then reconstruction is performed based on EWT. The beneficial effects of the rubbing acoustic emission denoise method based on empirical wavelet transform are that adaptive partition is performed according to the Fourier spectrum of the acoustic emission signal, and a wavelet filter bank is constructed to extract different intrinsic mode components included in the acoustic emission signal so that less modes are decomposed and the phenomena of mode aliasing and endpoint effect can be effectively filtered; and wavelet denoising is performed on each empirical mode component, reconstruction is performed based on EWT and denoising is performed on the signal so that the denoised signal has high signal-to-noise ratio and the denoise effect is obvious.

The invention discloses a power frequency communication synchronous detection method and device for an industrial power grid, belonging to the technical field of power system network structure and communication. The power frequency communication synchronous detection device for the industrial power grid is formed by connecting a master station device and a telecommunication terminal of a distribution transformer end to a high voltage transmission line of a substation; down driving equipment in the master station device firstly sends synchronous information based on M sequence coding before sending down data information; when the communication terminal receives a signal, a composite signal is formed according to modulation coding, and the synchronous detection is realized on the composite signal through short-window Morlet complex wavelet transformation; after the synchronous detection is successful, the communication terminal can carry out data demodulation; because the synchronous information can be enhanced and does not need a detection threshold, the invention adapts to the severe channel environment of the industrial power grid and can utilize a house transformer of the substation as a signal modulation transformer, thereby greatly facilitating the application of power frequency communication in the industrial power grid.

The invention provides a polarization difference and light intensity image multi-scale fusion method based on edge information enhancement. The method comprises: obtaining a polarization difference image and a light intensity image through a minimum mutual information polarization difference imaging method and polarization information analysis; secondly, denoising the light intensity image by adopting a three-dimensional block matching filtering algorithm, and enhancing the light intensity image by adopting a guide filtering algorithm; affine transformation and three-dimensional block matchingfiltering algorithm denoising are carried out on the polarization difference image; decomposing the light intensity image and the polarization difference image into a high-frequency coefficient and alow-frequency coefficient by adopting double-tree complex wavelet transform; the high-frequency coefficient images in different directions on different decomposition layers in the high-frequency coefficients adopt a fusion rule based on edge detection, and the low-frequency coefficient images in different directions in the low-frequency coefficients adopt a fusion rule based on regional varianceand variance matching degree; And obtaining a fused image through even complex wavelet inverse transformation.

The invention provides a shaft system fault identification method based on wavelet threshold de-noising and AdaBoost. According to the method, a dual-tree complex wavelet transform method capable of eliminating frequency aliasing is adopted to extract the features of signals; in the process of decomposition and reconstruction of the signals, an improved wavelet threshold de-noising method is provided to perform de-noising processing on the signals; the energy of the de-noised signals is extracted and adopted as a feature vector; the AdaBoost multi-classification method having a good unbalanceddata classification effect is used in combination; a plurality of simple single-level decision trees are used as AdaBoost weak classifiers; and a strong classifier can be constructed finally to distinguish various shaft system faults. The method of the invention can be realized through programming and has the advantages of low cost, high efficiency and easiness in implementation.

The invention discloses a motor train unit inverter IGBT (insulated gate bipolar translator) single-tube open-circuit fault diagnosis method. The method includes: constructing orthogonal compact-support complex wavelet according to three-phase output current under single-tube fault mode; acquiring amplitude and phase distribution characteristics of the three-phase output current according to real and imaginary parts of conversion of the constructed complex wavelet; calculating characteristic quantity of mean values of phase difference of all layers of decomposition coefficient of the complex wavelet according to the amplitude and phase distribution characteristics of the three-phase output current; calculating correlation among the mean values of the phase difference by the aid of maximum reciprocal function; performing inverter single-tube open-circuit fault diagnosis according to comparison validation of characteristic quantity and correlation among the mean values of the phase difference of the layers. Quantitative solving is performed through the correlation among the mean values of the phase difference, and accuracy in fault diagnosis is higher as compared with the characteristic quantity of the mean values of the phase difference.

The invention discloses a de-hazing method based on dual-tree complex wavelet and belongs to the technical field of image processing. The method comprises the following steps: to begin with, obtaining approximate value of atmospheric environment light through dual-tree complex wavelet transform; then, deducing a nonlinear normalized atmospheric transmissivity graph by utilizing an improved dark channel, and then, estimating a haze image; and finally, removing the haze to form a haze-free image according to the original image. The method has a better de-hazing effect for natural images of uniform haze and the like.

The invention discloses an emotional recognition research method based on an electroencephalogram signal of dual-tree complex wavelet. Decomposition and reconstitution of dual-tree complex wavelet transform are adopted to calculate phase information and sample entropy value of different wavebands of the electroencephalogram signal, and the two features serve as feature vectors of different emotions to be input into an SVM classifier, so that three emotions of calmness, pleasure and sadness are recognized effectively. The results show that the defect that discrete wavelet transform is poor in aliasing resistance and high in translation sensitivity is overcome through dual-tree complex wavelet transform, the extracted feature vectors has a good classifying recognition result, and new thoughts are provided for later researching of emotional recognition of the electroencephalogram signal.

The present invention discloses a low illumination image enhancement method based on dual-tree complex wavelet transform, belonging to the field of image processing. The method comprises a step of converting the color space of an image to be processed to obtain a first image, carrying out brightness compensation processing on the first image, and obtaining a processed brightness compensation image, a step of carrying out dual-tree complex wavelet transform on the brightness compensation image to obtain a processed image, and obtaining an output image according to the ratio of the brightness component in the processed image and the brightness component in the image to be processed. According to the method, through using the dual-tree complex wavelet transform to carry out contrast enhancement and image noise reduction in a wavelet domain in the processing process, due to shift invariance, good selectivity and remodeling characteristic, a guarantee is provided for low illumination image noise reduction, due to the classification of wavelet coefficients and the processing of the classified coefficients by using a nonlinear enhancement function, while the image contrast is enhanced, the noise is reduced, and the readability of a low illumination image is improved.