501 results about "Wave transformation" patented technology

The present invention provides a method and apparatus for logging an earth formation and acquiring subsurface information wherein a logging tool is conveyed in a borehole to obtain parameters of interest. The parameters of interest obtained may be density, acoustic, magnetic or electrical values as known in the art. As necessary, azimuths associated with the measurements are obtained and corrections applied. The corrected data may be filtered and / or smoothed. The parameters of interest associated with azimuthal sectors are depth matched, resolution matched and filtered, and the acquisition effects removed. The data are denoised using a multi-resolution wavelet transform. The data acquired with separate transducers are resolution matched to obtain a resolution matched data series. Subsequently the resolution matched data may be further denoised using a multi-resolution wavelet transform.

Methods and apparatus are provided for discriminating high rate polymorphic QRS complexes from high rate monomorphic QRS complexes to increase the specificity of detection of polymorphic VT and VF employing wavelet transform signal processing of the QRS complexes are disclosed. Wavelet transforms are applied to the sampled amplitude values of a sequence of QRS complexes to develop wavelet transform coefficient (WTC) data sets. At least selected ones of the WTC data sets are processed and comparisons are made to determine a wavelet match score. A determination is made as a function of the wavelet match scores of the series of successive QRS complexes that characterizes the most recent QRS complex as more or less likely to signify polymorphic VT or VF.

The method termed WaveCluster for mining spatial data. WaveCluster considers spatial data as a multidimensional signals and applies wavelet transforms, a signal-processing technique, to convert the spatial data into the frequency domain. The wavelet transforms produce a transformed space where natural clusters in the data become more distinguishable. The method quantizes a feature space to determine cells of the feature space, assigns objects to the cells, applies a wavelet transform on the quantized feature space to obtain a transformed feature space, finds connected clusters in sub bands at different levels of the transformed feature space, assigns labels to the cells, creates a look-up table, and maps the objects to the clusters. The method can manage spatial data in a two-dimensional feature space. The method also is applicable to a feature space that is made up of an image taken by a satellite.

Methods and apparatus are provided for discriminating high rate polymorphic QRS complexes from high rate monomorphic QRS complexes to increase the specificity of detection of polymorphic VT and VF employing wavelet transform signal processing of the QRS complexes are disclosed. Wavelet transforms are applied to the sampled amplitude values of a sequence of QRS complexes to develop wavelet transform coefficient (WTC) data sets. At least selected ones of the WTC data sets are processed and comparisons are made to determine a wavelet match score. A determination is made as a function of the wavelet match scores of the series of successive QRS complexes that characterizes the most recent QRS complex as more or less likely to signify polymorphic VT or VF.

The invention relates to an auto-stability method for camera image and the auxiliary device used in hand held state or motion state. It uses angular velocity sensor to check the angular velocity of the camera rotating round the three space axes in real-time and records synchronistical with image signal. The variance of the attitude angle between two pictures would be gained by integral in afterward process software, and the attitude angle variance related to slow-varying low frequency element in motion angular velocity is reserved, meanwhile the attitude angle variance related to dithering angle element would be eliminated. Then, the image dithering would be eliminated by translation and rotation the image. The device is made up of three same circuits. And every process circuit is connected by angular velocity checking circuit which is belonged to signal collection circuit, power lever shifting circuit which is belonged to signal process circuit, voltage / frequency transformation circuit, frequency division circuit, wave transformation circuit, signal compound and extent regulation circuit in series. The output signal is sent to the interface of camera.

The invention provides a bearing fault diagnosis method and system device based on improved empirical wavelet transform. The method comprises: step one, collecting different fault bearing signals as analysis signals and converting a time domain waveform into a frequency domain waveform; step two, drawing an upper envelope of a frequency spectrum and transforming a frequency peak with a tight support into a flat top; step three, screening flat tops in the frequency domain based on criteria, removing meaningless flat tops, and keeping a main frequency; step four, using a minimum value between adjacent flat tops as the boundary of spectrum segmentation; step five, establishing wavelet filters respectively for segmented frequency spectrums and decomposing the signals into N mode components; step six, calculating similarity values between mode components and the original signals by using a cross-correlation coefficient and selecting a component with the highest similarity value; and step seven, taking a fault sample, calculating an IMF component with the largest correlation coefficient of the sample, calculating a multi-scale entropy of the IMF component, and drawing the multi-scale entropy curve of the sample to realize fault classification.

An integrated power amplifier can include a carrier amplifier, where the carrier amplifier is connected to a first quarter wave transformer at the input of the carrier amplifier. In addition, the power amplifier can further include at least one peaking amplifier connected in parallel with the carrier amplifier; a first differential combining structure, where the first combining structure includes a first plurality of quarter wave transformers that are configured to combine respective first differential outputs of the carrier amplifier in phase to generate a first single-ended output signal, and a second differential combining structure, where the second combining structures includes a second plurality of quarter wave transformers that are configured to combine respective second differential outputs of the at least one peaking amplifier in phase to generate a second single-ended output signal, where the first single-ended output signal and the second single-ended output signal are combinable in-phase to provide an overall output.

The invention discloses a multi-focusing image fusion method utilizing core Fisher classification and redundant wavelet transformation. The method comprises the following steps: firstly, carrying out image block segmentation on source images and calculating definition characteristics of each image block; secondly, taking part of areas of the source images as a training sample and obtaining various parameters of a core Fisher classifier after training; thirdly, utilizing the known core Fisher classifier to obtain preliminary fusion images; and finally, utilizing redundant wavelet transformation and space correlation coefficients to carry out fusion processing on the image blocks positioned at the junction of the clear and fuzzy areas of the source images to obtain final fusion images. The invention has better image fusion performance, does not have obvious blocking artifacts and artifacts in fusion results, obtains better compromise between the effective enhancement of the image fusion quality and the reduction of the calculation quantity and can be used in the subsequent image processing and display. When wavelet decomposition layers with less number are adopted, the invention is more suitable for an occasion with higher real-time requirement.

The invention relates to a user behavior and action detection and recognition method. Data acquisition of three-dimensional accelerated speeds is achieved through a three-axis acceleration sensor, original data is filtered with a wavelet transformation filtering algorithm, and reliability is improved. On the basis, a data set is effectively optimized with the principle component analysis, four different pattern recognition classification algorithms and three kinds of classification model optimizing algorithm are compared at the same time, and an optimal parameter optimization algorithm and an optimal classifier are finally obtained, so that user behaviors and actions are detected and effectively recognized, and alarming is carried out on abnormal behaviors; besides, experiment results show that the method has the advantages of being high in accuracy, low in relative calculation complexity, high in real-time performance and the like.

The invention relates to an inverter fault diagnosis method based on wavelet analysis and SVM. The method comprises firstly establishing a diode NPC three-level inverter model and determining a principle of classification, respectively subtracting three phase voltages of an AC output side when an inverter breaks down from three phase voltages (Ua, Ub and Uc) of the AC output side when the inverter normally works to obtain [delta]Ua, [delta] Ub and [delta]Uc, carrying out d-q inversion on the [delta]Ua, the [delta] Ub and the [delta]Uc, changing three phases into two phases to obtain Ud and Uq, and analyzing the Ud and the Uq through wavelet transformation; secondly respectively extracting energy from each frequency band signal of the Ud and the Uq subjected through the wavelet transmission, determining a fault feature vector, and establishing a data sample based on the fault feature vector; and finally establishing a multi-valued classifier using the MATLAB and a LIBSVM tool box, so as to carry out fault diagnosis on a cross double-phase bridge of the diode NPC three-level inverter.

The invention discloses a partial discharge detection identification method based on ultrasound and ultraviolet information fusion. The method comprises the following steps: S1. uniformly setting sensing detection circuits in surrounding space of a detected object; S2. real-timely collecting an ultrasonic signal and an ultraviolet signal generated when partial discharge is generated in a detected area, after the signals are processed, sending to a digital signal processing circuit; S3. carrying out digital filtering processing; S4. extracting dual density wavelet transform wavelet coefficient Shannon entropy x from the ultrasonic signal generated in S3, extracting wavelet packet wavelet coefficient Shannon entropy y, and sending a characteristic vector x and a characteristic vector y to a detection system host via a communication module; S5. carrying out characteristic fusion by the detection system host so as to obtain the characteristic vector z after the fusion; S6. classifying the vector z obtained in S5 after the fusion by using support vector machine classification trees. By using the method of the invention, application of a detection identification technology based on the ultrasound and ultraviolet information fusion can be promoted; high accuracy, reliability and instantaneity of the partial discharge detection can be ensured.

The invention provides a wavelet transform and joint sparse representation-based infrared and invisible light image fusion method, and relates to the field of image fusion. The method comprises the following steps of: firstly, carrying out DWT transform on a source image, decomposing the source image into a low-frequency sub-band matrix coefficient and a high-frequency sub-band coefficient, decomposing the low-frequency sub-band coefficient into a matrix by using a sliding window strategy and learning a dictionary in allusion to the decomposed low-frequency sub-band matrix; secondly, respectively fusing the low-frequency sub-band coefficient and the high-frequency sub-band coefficient; and finally reconstructing a fused image through DWT inverse transform. According to the method, effective sparse representation can be carried out on outstanding detail features of source images, and multi-scale fusion can be carried out on detail information of the images, so that target information of infrared images and background information such as details, profiles and the like of invisible light images are well retained, the target identification ability is improved, and benefit is brought to subsequent processing systems to extract and use the information; and compared with the traditional wavelet transform-based fusion method and the existing joint sparse representation-based fusion method, method provided by the invention has advantages.

The invention discloses a denoising method of synthetic aperture radar image based on a non-lower sampling configuration wave, which is mainly to solve the problem that the image detail is difficult to keep effectively by the existing method, the new method comprises: (1) inputting a SAR image X and performing the L layer non-lower sampling configuration wave transformation; (2) calculating speckle noise variance delta C#-[B] of subband in each high-frequency direction of different dimensions; (3) distinguishing the high-frequency direction subband coefficients into the signal or the noise transformation coefficients by the local average value mean[C1, i(a, b)] high-frequency direction suband coefficient C1 and the i (a, b); (4) reserving the signal part in the judged high-frequency direction subband coefficient C1 and i (a, b) to obtain the denoised high-frequency direction subband coefficient C1 and i (a, b); (5) performing the non-lower sampling configuration wave inverse transformation for the low-frequency subband amd the denoised high-frequency direction subband coefficient C1 and I (a, b) to obtain the denoised SAR image X . The invention can effectively eliminate the coherent speckle noise, meanwhile can effectively keep the image detail, the denoised image has no shake and distortion and can be used for the preprocessing stage of the synthetic aperture radar image.

The invention relates to a modal parameter identification method based on response signal time-frequency joint distribution characteristics. According to the modal parameter identification method based on the response signal time-frequency joint distribution characteristics, signal analysis and structural modal parameter identification are carried out directly through a structural vibration response. The modal parameter identification method based on the response signal time-frequency joint distribution characteristics comprises the steps of firstly carrying out complex wavelet continuous transformation on a structural response signal, obtaining energy distribution characteristics of various wavelet transformation domains (a real domain, a virtual domain, a modal domain and a phase domain), obtaining a time average wavelet energy spectrum through a wavelet transformation coefficient, therefore carrying out quantification on selection of model orders and the scale corresponding to each order modality, on the basis, obtaining the optimum scale required by parameter identification, achieving pre-identification of modal frequency through the corresponding relation of the scale and the frequency, finally extracting a wavelet transformation coefficient slice at the specific scale, carrying out linear fitting through an amplitude value and a phase component, and achieving structural identification of inherent frequency and a damping ratio. As simulation and experiment results show, even if an external incentive function is not included, accurate identification of structural modal parameters can be achieved through the modal parameter identification method based on the response signal time-frequency joint distribution characteristics.

The invention discloses a turbine blade CT detection device and a detection method thereof. An opening-closing fixture structure is arranged on a fan-beam industrial CT device base; blades are fixed on a fixture so as to be scanned, and the simultaneous scanning for a plurality of blades can be realized; and cracks perpendicular to a scanning sector can be no longer perpendicular to the scanning sector by adjusting the inclination angle of the blades on the fixture. The device can inhibit the influence of dotted noise in CT images on measurement results, is accurate in extraction result to the cracks perpendicular to an X-ray surface, misses no crack detection, is high in measurement precision, adopts a proposal of extracting a crack region by a multi-scale curved-wave transformation method, utilizes the characteristics of curved wave, can adapt to the shape changes of the cracks, and can separate the crack region according to the shapes of the cracks, so as to accurately measure the cracks.

The invention relates to the power distribution network fault positioning technology field and particularly relates to a power distribution network fault positioning method based on wavelet transformation and the CNN. Wavelet transform multi-scale analysis is utilized to decompose fault current data, in the parallel coordinate system, modulus maxima are calibrated in order, points are connected insequence to form a line graph, and lastly, the line graph is processed to be a grayscale image used as the input of the CNN, the powerful feature extraction capability of the CNN is utilized to extract hidden topological structure features in the data, a machine is enabled to automatically identify the traveling wave head, and the B-type traveling wave ranging method is utilized to realize faultpositioning. The method is advantaged in that shortcomings of not-enough wavelet transform high-scale time resolution, large low-scale noise interference and easy false determination of the travelingwave head are overcome, the parallel coordinate system is utilized to fully combine the characteristics of wavelet transform and the convolutional neural network to achieve high-scale to low-scale automatic search for the traveling wave head, and strong anti-interference ability and high accuracy are achieved.

The present invention discloses an counterfeit-proof system of the identification certificate with the two-dimension barcode and the automatic identifying method thereof which obtains the information carried in the two-dimension barcode input by a scanning equipment through the image identifying technique. This system and method includes the manufacture of the counterfeit-proof identification certificate, the process of the photograph of the certificate holder, the formation of the two-dimension barcode and the identification of the real / false of the identification certificate. The photograph of the identification certificate is processed using the compression technique which comprises the small wave transformation, the main element analysis, the self-organic characteristic mapping nerve-cell web, the self-adaptive variable gathering sub-block and Huffman encode. In the identification of the two-dimension barcode, the input of the identification certificate with the barcode, the access of the area where the barcode stays and the process of the barcode area are performed by the scanning equipment to obtain the PDF417 encode according to the national standard, and then the carried information is obtained according to the compression of encode and the encryption of information. The real / false of the certificate is determined by comparing the literal information and the numerical information of the certificate obtained by decoding with the information stored in the data base and the information of the determined real certificate can be stored automatically in the data base according to the userí»s requirement.

A method of image compression with wavelet transforms applied locally rather than globally by image component partitioning into independently transformed macroblocks plus overlapping data for filter length compensation.

The invention discloses a wavelet transform and time search strategy based double-end traveling wave fault location method for hybrid power transmission line. The method comprises the following stepsof 1) determining an electrical topological structure of the hybrid power transmission line MN; 2) acquiring the motion time of traveling waves on a section line according to the length and motion wave speed of each section line; 3) according to fault traveling wave information acquired by traveling wave distance measuring devices at the two ends of the line, adopting wavelet transform to detect and identify the moment when the fault traveling wave moves to each bus measuring end for the first time so as to obtain a time difference when the traveling wave arrives at the two ends of the bus, and calculating t1 an t2 according to formulas by combining movement time of the fault traveling wave on the whole power transmission line; and 4) starts comparing from the end M of the bus, judging that the fault position is behind the xj1 section when t1>deltat1, and judging that the fault position is on the xd1 section when deltat1<t1<deltat1+deltat2, thus calculating the fault position. The method can be used to identify the initial traveling wave of a fault more accurately, and effectively solve the problem in fault distance measurement of the cable and overhead line hybrid power transmission line of the power system.

The invention relates to a method for inverting vegetation parameters by remote sensing based on reflection spectrum wavelet transform. The method comprises the following steps of: 1) acquiring the vegetation parameters and the original spectrum thereof under different conditions, and performing spectrum transform on the original spectrum; 2) performing continuous wavelet transform on the original spectrum by using different wavelet functions, and generating wavelet coefficients with different frequencies; 3) performing stepwise regression by taking different scales of wavelet coefficients as independent variables and taking the vegetation parameters as dependent variables, selecting spectrum wave bands needed by the inversion of the vegetation parameters, constructing a model of quantitative inversion of the vegetation parameters, and calculating R2 of the model; and 4) comparing modeling R2 of the constructed model according to different wavelet decomposition scales, and determining the model with the maximum modeling R2 as the optimal model. By the method, the hyperspectral remote sensing inversion precision of the vegetation parameters can be obviously improved, and the remote sensing inversion precision of biochemical parameters can be improved preferably. The method has wide parameter applicability, is applicable to leaf or canopy reflection spectrum, and is applicable to satellite remote sensing hyperspectral data.

The present invention relates a waveform transformation method and apparatus. It uses multilevel transformation module in series, and the output voltages of power modules at all levels are superposed to get the total output voltage, whereas each power transformation module realizes AC-to-AC direct conversion. The deviation between the output′ voltage and setting reference voltage at any time point is made as small as possible by selecting different transformation modules as current working circuit and selecting output voltage waveform of the each different transformation modules. The invention includes outputting n groups of electrical insulating AC and n transfonnation modules connected with AC. The wave transformation method and device of present invention eliminates the intermediate DC stage, so that the circuit is greatly simplified, the cost is reduced obviously and improve working efficiency. It makes voltage and current harmonics to be reduced and obtains higher power factor.

The invention discloses a fundamental frequency detection method based on improved experience wavelet transformation. The method comprises steps of step 1, preprocessing a speech signal: calculating short-term energy and zero-crossing rate of the speech signal, using a dual-threshold method to carry out rhythm segmentation and passing the segmented signal through a 50-1500Hz band-pass filter for filtering so as to obtain pre-processed speech signal; step 2, using the improved experience wavelet transformation method to decompose the preprocessed speech signal so as to obtain each mode function of the speech signal; step 3, according to the mode function, selecting the main mode of the speech signal; step 4, using Hilbert transformation to solve a transient fundamental frequency value of the main mode; and step 5, using a rectangular window function to carry smoothing processing on the transient fundamental frequency value obtained in the step 4 to finish the fundamental frequency detection. The method is characterized by high accuracy, quite good robustness and high time resolution.

The invention discloses an SAR image noise suppression method based on a direction wave domain mixture Gaussian model, belonging to the field of image processing, mainly solving the problems of fuzzydistortion of an SAR image caused by the current denoising method and improvement of excessive zero values added by coset decomposition of direction wave, and comprising the following steps: (1) carrying out cartoon texture model decomposition on the SAR image; (2) carrying out improved direction wave transformation on the texture part; (3) carrying out fitting of the mixture Gaussian model on thehigh frequency subband coefficient of the coset; (4) estimating noise variance; (5) estimating noise-free coefficient by Bayesian Minimum Mean-Square Error (MMSE); (6) carrying out improved directionwave inverse transformation on the noise-free coefficient to obtain the denoised texture part; and (7) adding the cartoon part and the denoised texture part to obtain a denoised SAR image. The SAR image noise suppression method has the advantages of improved definition, better maintenance of detail information and smoother uniform region of the denoised SAR image and can be used in filtering speckle noise in the SAR image.

The invention provides a method for detecting internal and external faults of ultra high voltage direct current transmission lines based on pole wave mathematical pattern spectrum. The method is characterized in that when a direct current (DC) line fault occurs, a starting component is started, according to positive pole line DC voltage U+(k) and DC I+(k) which are collected by a protection installation location, pole wave voltage of a positive pole line is obtained, and the positive pole line pole wave voltage is subjected to wavelet transform, pole wave voltage high frequency characteristic quantity PG+(k) of a high frequency portion of the positive pole line pole wave voltage is extracted. The pole wave voltage high frequency characteristic quantity is calculated by means of mathematical morphology, a mathematical morphology spectrum value of the pole wave voltage high frequency characteristic quantity is obtained, structure element scale needed is selected, and the mathematical morphology spectrum value is subjected to integration operation along abscissa axis structure element scale. Internal and external faults are distinguished according to the integration operation value. The method has the advantages of being sensitive in identifying the internal faults, reliable in identifying the external faults and applicable to the popularization and the usage in direct current transmission line systems.

The use of machine learning for pattern recognition in magnetocardiography (MCG) that measures magnetic fields emitted by the electrophysiological activity of the heart is disclosed herein. Direct kernel methods are used to separate abnormal MCG heart patterns from normal ones. For unsupervised learning, Direct Kernel based Self-Organizing Maps are introduced. For supervised learning Direct Kernel Partial Least Squares and (Direct) Kernel Ridge Regression are used. These results are then compared with classical Support Vector Machines and Kernel Partial Least Squares. The hyper-parameters for these methods are tuned on a validation subset of the training data before testing. Also investigated is the most effective pre-processing, using local, vertical, horizontal and two-dimensional (global) Mahanalobis scaling, wavelet transforms, and variable selection by filtering. The results, similar for all three methods, were encouraging, exceeding the quality of classification achieved by the trained experts. Thus, a device and associated method for classifying cardiography data is disclosed, comprising applying a kernel transform to sensed data acquired from sensors sensing electromagnetic heart activity, resulting in transformed data, prior to classifying the transformed data using machine learning.

The Present invention relates to a method of recognizing the human iris corresponding to a field of a biometric technology, and more particularly to a method of recognizing human iris using Daubechies wavelet transform, wherein the dimensions of characteristic vectors are reduced by extracting iris features from inputted iris image signals through the Daubechies wavelet transform, binary characteristic vectors are generated by applying quantiztion functions to the extracted characteristic values so that utility of human iris recognition can be improved since storage capacity arid processing time thereof can be improved since storage capacity characteristic vectors, and a measurement process suitable for the low capacity characteristic vectors is employed when measuring vectors and previously registered characteristic vectors.

The invention discloses a rolling bearing fault diagnosis method for improving a model migration strategy, and belongs to the technical field of rolling bearing fault diagnosis. The method is providedfor solving the problem of large distribution difference of data in the same state in a source domain and a target domain, and comprises: obtaining time-frequency spectrums of vibration signals of different types of bearings through wavelet transform, and constructing an image data set; selecting data of a certain model as a source domain, and selecting data of other models as a target domain; training a ResNet-34 deep convolutional network by using the source domain data to obtain a source domain data classification model; adaptively determining a migration knowledge level and knowledge content by using implicit gradient meta-learning to realize improvement of a model migration strategy and avoid a phenomenon that a gradient in a heterogeneous system structure is not easy to converge; introducing the migrated knowledge into a target domain ResNet-152 convolutional neural network data training process, and realizing model migration through parameter transmission; and optimizing network parameters by adopting a stochastic gradient descent algorithm when the source domain network and the target domain network are trained, and establishing fault diagnosis models of different types ofrolling bearings.

The invention discloses a radar based respiration and heartbeat signal detection method and system. The method comprises the steps of A, judging whether a target exists based on a transmitting signaland a receiving signal; and B, performing the following processing on a mixed vital sign signal of the target: B1, performing high-pass filtering and FFT on the mixed vital sign signal to obtain firstfrequency spectrum data; B2, if the frequency corresponding to a maximum amplitude value point is within a respiratory frequency range and the maximum amplitude value point exists at a Q frequency doubling position and / or near the Q frequency doubling position of the frequency, taking the frequency as a respiratory dominant frequency; B3, decomposing the mixed vital sign signal of the target based on empirical wavelet transform; and B4, if the maximum value frequency of a third component or a fourth component is within a preset heartbeat frequency range, taking the maximum value frequency asa heartbeat dominant frequency. Based on the empirical wavelet transform, respiration and heartbeat signals are adaptively separated and the frequencies of the respiration and heartbeat signals are accurately extracted, and the respiration and heartbeat frequencies of multiple persons can be simultaneously extracted and separated for a broadband / ultra-wideband radar.

A system and method for improving intelligibility of speech is provided. The system and method may include obtaining an input audio signal frame, classifying the input audio signal frame into a first category or a second category, wherein the first category corresponds to the noise being stronger than the speech signal, and the second category corresponds to the speech signal being stronger than the noise, decomposing the input audio signal frame into a plurality of sub-band components; de-noising each sub-band component of the input audio signal frame in parallel by applying a first wavelet de-noising method including a first wavelet transform and a predetermined threshold for the sub-band component, and a second wavelet de-noising method including a second wavelet transform and the predetermined threshold for the sub-band component, wherein the predetermined threshold for each sub-band component is based on at least one previous noise-dominant signal frame received by the receiving arrangement.