108 results about "Orthogonal wavelet" patented technology

The invention relates to an extraction method for early failure sensitive characteristics based on ensemble empirical mode decomposition (EEMD) and wavelet packet transform. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform includes the following steps: (1), collected original vibration signals of mechanical and electrical equipment are decomposed according to the EEMD, white noise is added, and intrinsic mode function (IMF) components are obtained through decomposition; (2), the sensitive IMF components closely related to failure are chosen, and other irrelative IMF components are ignored; (3), the sensitive IMF components chosen through step (2) are decomposed in an orthogonal wavelet packet mode, and a wavelet coefficient of each node is obtained; and (4), envelopes are extracted from the obtained wavelet packet coefficients by adoption of the Hilbert transform and the Fourier transform, power spectrums are calculated, the power spectrum corresponding to each wavelet packet coefficient is obtained and serves as the early failure sensitive characteristic , and the sensitive characteristics are automatically obtained. Self-adapting signals can be decomposed, the sensitive characteristics can be convenient to obtain automatically, diagnosis precision and speed are improved, and a mechanical and electrical system can be diagnosed quickly, accurately and stably. The extraction method for the early failure sensitive characteristics based on the EEMD and the wavelet packet transform can be applied to the field of mechanical and electrical equipment failure diagnosis.

The invention discloses a method and a device for detecting cloth flaws based on adaptive orthogonal wavelet transform. Manual eye detection and an original automatic flaw detecting method in which wavelet transform is carried out after a wavelet basis is manually selected are replaced. The defects of traditional manual eye detection, such as low detection speed, low efficiency, false detection and high detection leaking rate are overcome. The problem of the original flaw detecting method based on wavelet transform that the detection precision is low as the wavelet basis is not optimized is solved. The optimal wavelet basis matched with a cloth texture is selected by an improved quantum rotating gate quantum genetic algorithm, the quantum rotating angle is regulated by a dynamic strategy, fine adaptive search is realized, the variety is enriched through introducing mutation operation, and the optimization capability of the algorithnm is improved through combining chaotic search. The flaw detecting method has the advantages of high speed, high accuracy, simplicity in operation and high efficiency and has great application prospects.

The invention discloses a particle swarm optimization based orthogonal wavelet blind equalization method. The method comprises the following steps of: allowing a transmitted signal a(k) to pass through a pulse response channel h(k) to acquire a channel output signal x(k); acquiring an orthogonal wavelet transformation (WT) input signal y(k) through channel noise n(k) and x(k); performing WT on the input signal y(k) to acquire an output signal R(k); taking the input signal y(k) as input of a particle swarm optimization (PSO) algorithm and randomly initializing a group of weight vectors, wherein each particle corresponds to each group of weight vectors one to one; determining a fitness function of PSO through a cost function of an orthogonal wavelet transformation-constant module algorithm (WT-CMA) blind equalization method; when a fitness value is the maximum, finding out an optimal position vector in the group and taking the optimal position vector as an initialization weight vector W(k) of the WT-CMA; and acquiring an equalizer output signal z(k) from the output signal R(k) and initialization weight vector W(k). In the method, the optimal equalizer initialization weight vector is sought through PSO, and the autocorrelation of the signal is reduced by WT. Compared with WT-CMA, the method has higher convergence rate and lower steady-state error.

A leak positioning method combining self-adapting threshold value leak detection and multi-scale rapid time delay search belongs to the malfunction diagnosis technology field of oil (gap) pipeline, wherein pressure data at both ends of the pipeline and standard error of re-constructing signal difference value of the data based on orthogonal wavelet transformation de-noising process multiplying coefficient as threshold values at both ends. If average values before or after catastrophe point when the pressure at both ends reduces exceed the threshold value, judging that there is leak and positioning: calculating approximate wavelet transformation coefficient of pressure data at both ends, initializing searching interval, calculating a signal time delay value corresponding to approximate wavelet transformation coefficient in the searching interval from maximal scale N; converting the time delay value under scale N to that under N-1 by geometric proportion relation; updating the searching interval, repeating above processes until the scale is 0 and positioning based on the time delay value. The invention is capable of reducing rate of false report and rate of missing report effectively, reducing time delay estimated calculated amount, which is beneficial to accomplish arithmetic online.

The invention discloses an adaptive target area conversion method combining image segmentation and a deformation registration technology. According to the method, a threatening organ and a thoracic and abdominal tumor area are segmented automatically; multi-modal registration is performed on CBCT (Cone Beam Computed Tomography) images and planed CT (Computed Tomography) images by adopting a deformation registration algorithm; information on the planned CT is automatically transferred to the CBCT according to the calculated deformation area. After the rigid registration of the planed CT and the CBCT images, in order to compensate the deformation range which cannot recover, the transfer of a threatening organ, a tumor profile and a radiation treatment plan from the planned CT to CBCT is realized by adopting an orthogonal wavelet basis function-based deformation registration algorithm through the calculated deformation domain, so that a target position is accurately positioned.

The invention discloses a super-exponential interative combined blind equalization method based on orthogonal wavelet transformation (WT-CSEI). The WT-CSEI algorithm is implemented by incorporating the orthogonal wavelet transformation in the super-exponential interative (SEI) algorithm, correcting by use of the weight vector iterative formula of the orthogonal wavelet transformation relative to the SEI algorithm, simultaneously combining the decision directed (DD) algorithm with the soft-switching method, and introducing a phase-locked loop (PLL) technique with regard to the characteristics of time-variable Doppler frequency shift of under acoustic channel. The WT-CSEI algorithm corrects the phase rotation by means of the PLL technique, accelerates the convergence rate by means of the orthogonal wavelet transformation, reduces the steady-state error by means of the combination of the soft switching method and the DD algorithm, can effectively track the time varying characteristics of the channel, and is applied to the blind equalization of the time-variable multipath fading underwater acoustic channel.

The invention relates to a classification method of heart beat based on BiLSTM-Attention deep neural network. The classification method includes the following steps: 1) data pre-processing, using bi-orthogonal wavelet transformation to remove high-frequency noise and baseline drift; 2) feature extracting, detecting R-wave peak value by dyadic spline wavelet transformation, and further calculatingRR interval and extracting QRS wave complex data; and 3) model training, carrying out deep learning and classification on the detected waveform in step 2 through BiLSTM-Attention neural network. The classification method has advantages of accurate and effective classification of electrocardiogram (ECG) signals and deep learning and classification of ECG signals

The invention discloses a modeling method of a symmetrical type plate spring virtual model enhancing haptic feedback, which is characterized by comprising the following steps of: only feeding back and outputting 1 / f noise signals based on orthogonal wavelet bases before a virtual agent collides with a virtual flexible body; feeding back and outputting signals obtained by overlying the 1 / f noise signals fitting to the response law of human bodies on irritation and the haptic information of the real-time deformation simulation of the flexible body, which is calculated by adopting the symmetrical type plate spring virtual model and responded under the action of pulling force, together in an interactive process, wherein the overlying of the deformation amount sum of each layer of symmetrical type single-plate spring of the symmetrical type plate spring virtual model enhancing the haptic feedback is externally equivalent to the deformation of the surface of the flexible body, and the sum of pulling force consumed when each layer of the symmetrical type single-plate spring is stretched is equivalent to given virtual contact pulling force. The modeling method can realize the real-time deformation simulation of the flexible body, has real haptic feeling and vivid deformation effect and meets the requirements of a virtual reality system on virtual surgery simulation.

The invention discloses a feeder line single phase fault and multiphase fault distance measuring method based on wavelet decomposition frequency band feature, wherein the single phase fault distance measuring method consists of using non-fault phase transient current composition as the fault distance measurement information, and carrying out data acquisition, earth connection error protection detecting, obtaining fault recording wave data, extracting frequency band characteristic measurement vector by using orthogonal wavelet packet decomposition, angular correction treatment to the frequency band characteristic measurement vector and neural net computation. The multiphase fault distance measuring method utilizes the failure current information as the fault distance measurement basis.

The invention discloses a self-adaptive multi-scale texture image segmentation method based on wavelet and mean shift, and relates to the image processing technical field. The invention aims to solve the contradiction between the region consistency of a texture image and the edge accuracy of adjacent regions in the regular method to achieve the effect that the image is effectively segmented under the condition of failing to obtain the priori knowledge. The realization process of the method is that: based on the orthogonal wavelet transformation, mean shift clustering without supervision is used to realize the segmentation of the feature of a wavelet transform coefficient on different scales; through the information transmission of the features among different scales, self-adaptive different regions as the image select appropriate segmentation scales, namely the interior of a texture region uses a coarse scale feature, the boundaries of different texture regions use a finer scale feature, thus the edge of the image is located more accurately while the region consistency is assured until the final segmentation result is obtained. The invention can be used to solve the texture image segmentation issue without any priori knowledge.

The invention discloses a chaos-optimized orthogonal wavelet multi-mode blind equalization method (CO-WT-MMA). It includes the following steps: pass the transmitted signal a(k) through the impulse response channel h(k) to obtain the channel output vector x(k); use the channel noise n(k) and the channel output vector x(k) to obtain the orthogonal wavelet transformer ( WT) input signal y(k)=n(k)+x(k); After the real part and imaginary part of y(k) are subjected to orthogonal wavelet transform and chaos initialization respectively, and then through the corresponding real part and imaginary part The partial equalizer is output to the complex adder to get the output z(k). On the basis of the multi-mode blind equalization method (MMA), the multi-mode blind equalization method (WT-MMA) based on the orthogonal wavelet transform obtained after the normalized orthogonal wavelet transform accelerates the convergence speed, and at the same time utilizes The ergodicity of the chaotic variable disturbs the current point of the weight vector, and the time-varying parameters are used to gradually reduce the disturbance amplitude during the search process, so that the weight vector reaches the global optimal value. The underwater acoustic channel simulation results show that, compared with MMA and WT-MMA, the CO-WT-MMA of the present invention has faster convergence speed and smaller steady-state mean square error.

The invention discloses an orthogonal wavelet transformation super-exponential iteration (SEI) blind equalization method based on ant colony optimization. The method utilizes ant colony optimization to search the optimal weight vector as an initial weight vector value inputted by an equalizer so as to avoid local convergence of the algorithm. The method utilizes a positive feedback mechanism to increase convergence rate, utilizes the whitening action of the super-exponential iteration (SEI) method on data, utilizes orthogonal wavelet transformation to perform decorrelation on signals and fully utilizes the global convergence of ant colony optimization. Underwater acoustic channel simulation results show that compared with the orthogonal wavelet transformation super-exponential iteration constant modulus algorithm (WT-SEI-CMA), the method has higher convergence rate and smaller state error, and an equalized eye pattern is clearer and compacter. Thus, the method has certain practical valve.

The invention discloses an orthogonal wavelet transform constant modulus blind equalization algorithm based on optimization of the DAN genetic algorithm (DNA-GA-WTCMA). According to the algorithm, the DNA genetic algorithm is combined with the WTCMA and the advantages of the WT-CMA and the advantages of the DNA genetic algorithm are thoroughly utilized. According to the orthogonal wavelet transform constant modulus blind equalization algorithm, a weight vector of the blind equalization algorithm is shown according to a coding method based on a DNA nucleotide chain and interlace operation and mutation operation are conducted on the coded DNA chain to find an optimal individual in a DAN group, the decoded optimal individual serves as an optimal initial weight vector of a blind equalization device, and the shortages that the WTCMA is low in convergence rate, large in mean square error and prone to getting into local minimum are overcome. Compared with the WTCMA and the GA-WTCMA, the DNA-GA-WTCMA is the highest in convergence rate, the smallest in mean square error, globally optimal in performance and high in practical value in the communication technical field.

The invention discloses a blind equalization method with the integration of orthogonal wavelet transformation and time diversity technique. Integrated with time diversity technique, wavelet transformation technique, phase-lock ring technique and decision-directed (DD) algorithm, the blind equalization method can reduce the influences of multipath effect by using time diversity, can overcome the disadvantages of asymmetry of constant modulus error function by using a hyperbolic tangent error function, can lower the self-correlativity of input signals of a blind equalizer by using orthogonal wavelet transformation to improve the convergence speed, can reduce stable error by using DD algorithm, and can correct phase rotation by using digital phase-lock ring technique. The method has the advantages of high convergence speed and less mean square error, and can effectively suppress phase rotation.

The invention discloses a short-wave infrared focal plane non-uniformity correction algorithm based on a wavelet transformation histogram. The short-wave infrared focal plane non-uniformity correction algorithm includes the steps that (1) a response statistic model of a short-wave infrared focal plane array is built; (2) a response density function is represented through the time domain histogram of the short-wave infrared focal plane array; (3) the response density function of a single detecting unit is specified to an intermediate value of the response density function of a neighborhood detecting unit with the single-scale intermediate-value histogram specification non-uniformity correction algorithm, and non-uniformity correction is accordingly achieved; (4) multi-scale correction based on orthogonal wavelet transformation resolving is carried out on an original image of the short-wave infrared focal plane array.

The present invention belongs to the field of biological characteristic distinction, and is especially the method of extracting geometric characteristic of iris based on characteristic edge distribution. The method includes first defining geometric characteristic of iris and describing its reliability and stability; subsequent extracting geometric characteristic edge of iris in antisymmetric double orthogonal wavelet process and constituting overall edge distribution characteristic in unequal interval grading according to the inhomogeneous distribution of geometric characteristic of iris; and finally introducing characteristic distance expression and proposing legal user judging method. The method has high distinction precision and less outer factor influence.

The invention discloses a wavelet weighted multi-modulus blind equalization algorithm based on fractional lower order statistics (WT-FLOSWMMA), which comprises the following steps of: obtaining a channel output vector x(n) from a transmitted signal a(n) through a pulse response channel c(n); obtaining an input signal y(n) of an orthogonal wavelet transformer (WT) by using [alpha] stable distribution channel noise w(n) and the channel output vector x(n); and processing y(n) by the orthogonal WT to obtain the input R(n) of an equalizer f(n), wherein the output of the equalizer f(n) is z(n), and meantime the WT-FLOSWMMA error and the iterative formula of a weight vector are as shown in the specification. In the invention, the [alpha] stable noise is suppressed by use of the fractional lower order statistics, the prior information of the signal source is sufficiently used, and the modulus is corrected adaptively in the iteration process; and moreover, orthogonal wavelet transformation is performed on the input signal of the equalizer, self correlation of the input signal is recued, and the equalizing performance is improved.

The invention discloses a wavelet packet noise reduction method for a meteor trail communication system, used for performing noise reduction on meteor trail communication signals. The method comprisesthe following steps of: selecting a proper wavelet basis function, performing wavelet decomposition on the signals by using an orthogonal wavelet decomposition algorithm, performing threshold quantization on the wavelet decomposition coefficients by using soft threshold functions, performing signal reconstruction by using a reconstructed function with a specific structure, and realizing the feature extraction of weak signals in the background of strong noise. The method disclosed by the invention is capable of obviously improving demodulation signal-to-noise ratio of the meteor trail communication system, enhancing the detection and utilization abilities for the weak signals, shortening the communication waiting time and improving the information transmission capacity, and is especially suitable for application scenarios in which the meteor trail communication signals are drown by strong background noise.

The invention discloses an orthogonal wavelet constant modulus blind equalization method based on an IWPA (Improved Wolf Pack optimization Algorithm). According to the invention, A CM (Complex Method) with high local searching capacity is embedded into a WPA with high global optimizing capacity; an update mechanism of a wolf pack is improved to obtain an excellent IWPA; the new method promotes optimizing capacity of the WPA; a reciprocal of a cost function in the constant modulus blind equalization method CMA is used as a fitness function of the IWPA; an input signal of the CMA is used as an input of the IWPA; a leader wolf position captured by utilizing the IWPA is used as an initial weight vector of the CMA; then signal correlation is reduced by a wavelet; signals are output in an equalizing mode by the CMA; and an excellent equalizing effect can be obtained. Compared with the prior art, the orthogonal wavelet constant modulus blind equalization method has the advantages that correlations between the signals and between the signals and noise can be reduced; an algorithm convergence speed is improved; a steady state error is reduced; balancing quality is improved; and the orthogonal wavelet constant modulus blind equalization method has a certain practical value.

The invention discloses an orthogonal wavelet blind equalization method based on immune clone particle swarm optimization. The method comprises the steps of: by regarding a particle swarm as an immune system, randomly initializing particles in a search space, namely, initializing the speed and the positions of antibodies, wherein the position vectors of the particles are taken as the weight vectors of an equalizer and the number of the weight vectors is taken as the scale of the particles; randomly generating an initial population, wherein the position vector with a maximal fitness value (that is optimal solution) is used as an antigen, the particles are used as the antibodies, the affinity degree is used for presenting the approaching degree of the antibodies and the antigen, and a fitness degree function of the particle swarm optimization is selected as the affinity degree of the antibodies; carrying out immune clone operation on the antibodies with higher fitness degree through iteration to continuously generate a new-generation antibody population; carrying out the immune clone operation on the selected optimal solution in each iteration; selecting the optimal position vector (that is the generated maximal value of the corresponding fitness degree function) of the particles after the iteration ends; and iterating by taking the position vector as an initialization weight vector of the equalizer.

The invention relates to an orthogonal transformation used in image and video compression. Wherein, it first divides the image into 8*8 blocks as M, then uses 8*8 orthogonal transformation matrix D to process two-dimension transformation on each image block M, to obtain the transformation factor matrix N, then quantizes and codes each factor of N, to complete the image and video compression. The invention is based on orthogonal wavelet theory at octonary, to design one new 8*8 orthogonal transformation matrix, to compress image and video, to obtain better compression effect than the discrete cosine transformation, and improve the economic property and efficiency on the softwater and integrated circuit.

The invention provides an adaptive orthogonal wavelet image denoising method based on accurate local variance priori modeling. The method is characterized by carrying out accurate modeling on orthogonal wavelet transform detail coefficient local variance prior distribution through maximum likelihood estimation, thereby realizing a better denoising performance, improving peak signal-to-noise ratio of a de-noised image, and improving visual effect of the de-noised image. The beneficial effects are that the method overcomes the defect that an existing method is not high in local variance estimation, can better express statistical property of the image orthogonal wavelet decomposition detail coefficient, can give a denoising result adaptively, can remove additive white Gaussian noise in a natural image more effectively, and meanwhile, can better protect regions having rich edge and texture detail information and the like in an original image, and improves visual effect and peak signal-to-noise ratio of the de-noised image. The method is low in computation complexity, and is suitable for immense image denoising application in the age of big data.

The invention discloses a simulation annealing and fruit fly hybrid optimization wavelet generalized discrete multi-module blind equalization method. The method comprises initializing location vectors of fruit flies in a swarm of fruit flies to serve as the decision variable of the simulation annealing method and the fruit fly hybrid optimization method, taking an input signal of an orthogonal wavelet transformer as the input of the hybrid optimization method, determining a smell concentration function of the fruit flies by a cost function of the generalized discrete multi-modulus blind equalization method, performing simulation annealing operation on the optimal location vector of the swarm of the fruit flies obtained through the fruit fly optimization method, obtaining the global optimal location vector, which does not fall into a local minimum, of the swarm of the fruit flies, and taking the location vector as the initialization weight vector of the wavelet generalized discrete multi-modulus blind equalization method. The method, while processing high-order orthogonal amplitude modulation signals, is rapid in convergence, small in steady state error, overcomes a defect of falling into the local optimum, and has strong practicality.

The invention discloses a wavelet constant modulus blind equalization method in a MIMO (Multiple Input Multiple Output) system from the aspect of reducing?inter channel?and inter signal?correlation, so as to overcome defects that the traditional constant modulus blind equalization method in the MIMO system is weak in inter channel interference inhibition ability, slow in the convergence rate and large in steady-state errors. According to the method, singular value decomposition is used for reducing the correlation of channel output signals; an orthogonal wavelet base function is used for carrying out orthogonal wavelet transformation on MIMO channel output signals and autocorrelation of input signals of a blind equalizer is reduced; on the basis of fully considering the inter channel correlation, a cost function of MIMO system constant modulus blind equalization algorithm is redefined, and cross-correlation of output signals of the blind equalizer is reduced; and a variable step size function is used for controlling a blind equalization?weight vector?updating process, the convergence rate quickened, and the steady-state errors are reduced.

The invention discloses a frequency-domain self-adaptation wavelet multi-mode blind equalization method for immune artificial shoal optimization. The frequency-domain self-adaptation wavelet multi-mode blind equalization method for the immune artificial shoal optimization comprises the following steps that an immune artificial shoal is a mixed group of an artificial shoal and an immune system antibody shoal, position vectors of the artificial shoal and antibody vectors of an immune system of a set of immune artificial shoal are initialized in a random mode, the position vectors and the antibody vectors serve as decision variables of an immune artificial shoal method, input signals of an orthogonal wavelet converter serve as input signals of the immune artificial shoal method, a fitness function of the immune artificial shoal is determined by a cost function of the frequency-domain self-adaptation wavelet multi-mode blind equalization method, and initialization weight vectors of the frequency-domain self-adaptation wavelet multi-mode blind equalization method are determined by an immune artificial shoal optimization method. The frequency-domain self-adaptation wavelet multi-mode blind equalization method for the immune artificial shoal optimization is high in rate of convergence, low in steady-state error, low in complexity and good in practicability when the frequency-domain self-adaptation wavelet multi-mode blind equalization method for the immune artificial shoal optimization is used for processing high-order QAM signals.

The invention discloses an ultrahigh pressure water jet velocity optimal control method extracted based on voice characteristics. A plurality of background noise signals are collected before a workpiece is cut, time domain characteristics and frequency domain characteristics are obtained through analysis, orthogonal wavelet decomposition of five scales is carried out on respective discrete series of selected N group water jet cutting field voice signals, approximate reflection voice signals are obtained, and amplitude envelope is determined; corresponding water jet cutting starting time and water jet cutting stopping time are obtained to obtain cutting time and calculate signal energy; the cutting time and the signal energy serve as input quantity, and optimum nozzle sidesway velocity is obtained though manual experience to serve as output quantity to build a least square support vector machine model; when the workpiece is cut, cutting field voice signals are collected on line, the cutting time and the signal energy are extracted to be input in the least square support vector machine model, the optimum nozzle sidesway velocity is obtained, and work efficiency of water jet cutting and quality of cut products are improved.

The invention discloses a two-stage data compression and decompression method on the basis of orthogonal wavelet packet transformation and a rotating door algorithm. Data compression comprises the following steps of: (1) carrying out orthogonal wavelet packet transformation on original data to obtain a wavelet packet coefficient; (2) carrying out threshold processing on the wavelet packet coefficient obtained in the step (1); and (3) carrying out secondary compression on the wavelet packet coefficient subjected to threshold processing by adopting the rotating door algorithm. Compressed data is stored into a historical database or a disk. Decompression on the compressed data comprises the following steps of: (4) carrying out linear interpolation on the compressed data and recovering to obtain primary compressed data; and (5) carrying out wavelet packet reconstitution on the primary compressed data to obtain the original data. The invention solves the problem of difficulty in compressing a nonstationary analog signal in a large-scale real-time database and provides the data compression and decompression method which is simple to implement, has a high data compression ratio and has an obvious compressing effect on the nonstationary analog signal.

The present invention discloses a match video image enhancement method based on wavelet analysis and pseudo-color processing. The method comprises the steps of a, utilizing the orthogonal wavelet transformation of images to carry out the transformation processing on the football match video images; b, carrying out the football match video image enhancement processing based on the orthogonal wavelet transformation; c, carrying out the pseudo-color football match video enhancement processing on the images. The match video image enhancement method based on wavelet analysis and pseudo-color processing of the present invention can overcome the defects in the prior art of complicated operation process, long time, low reliability, etc., and realizes the advantages of simple operation process, short time and high reliability.