253 results about "Continuous wavelet transform" patented technology

This invention pertains to the extraction of the slowness dispersion characteristics of acoustic waves received by an array of two or more sensors by the application of a continuous wavelet transform on the received array waveforms (data). This produces a time-frequency map of the data for each sensor that facilitates the separation of the propagating components thereon. Two different methods are described to achieve the dispersion extraction by exploiting the time frequency localization of the propagating mode and the continuity of the dispersion curve as a function of frequency. The first method uses some features on the modulus map such as the peak to determine the time locus of the energy of each mode as a function of frequency. The second method uses a new modified Radon transform applied to the coefficients of the time frequency representation of the waveform traces received by the aforementioned sensors. Both methods are appropriate for automated extraction of the dispersion estimates from the data without the need for expert user input or supervision

A method and system for detecting effort events is disclosed. Effort may be determined through feature analysis of the signal as transformed by a continuous wavelet transform, which may be compared against a reference effort measure to trigger an effort event flag that signals the onset and / or severity of an effort event. For example, a respiratory effort measure may be determined based at least in part on a wavelet transform of a photoplethysmograph (PP G) signal and features of the transformed signal. A respiratory reference effort measure may be based at least in part on past values of the respiratory effort measure, and a threshold test may be used to trigger an effort event flag, which may indicate a marked change in respiratory effort exerted by a patient.

An inversion-based algorithm for computing the time frequency analysis of reflection seismograms using constrained least-squares spectral analysis is formulated and applied to modeled seismic waveforms and real seismic data. The Fourier series coefficients are computed as a function of time directly by inverting a basis of truncated sinusoidal kernels for a moving time window. Spectra may be provided that have reduced window smearing for a given window length relative to the discrete Fourier transform irrespective of window shape, and a time-frequency analysis with a combination of time and frequency resolution that is superior to the short time Fourier transform and the continuous wavelet transform. The reduction in spectral smoothing enables enhanced determination of spectral characteristics of interfering reflections within a short window. The degree of resolution improvement relative to the short time Fourier transform increases as window length decreases.

The invention discloses an automatic baseline correction method, which includes the following steps: converting raw data into spectrogram data; utilizing continuous wavelet transform to calculate the numerical derivative of a spectrogram; respectively utilizing the sliding window method and the iterative threshold method to identify narrow peak signals and broad peak signals in the spectrogram; carrying out contour fitting on the broad peak signals to identify the edges of the broad peak signals; calculating a baseline model; correcting the spectrogram data according to the baseline model; searching distorted signals in the corrected spectrogram data, and recalculating the baseline model with the most severely distorted points as baseline points until distorted signals do not exist, so that a final baseline model and final corrected spectrogram data can be obtained. Compared with the prior art, the automatic baseline correction method can eliminate the inaccuracy of noise calculation caused by baseline distortion, accurately identify the narrow peak signals and the broad peak signals in the spectrogram, correct the spectrogram with the severely distorted baseline and prevent spectral peak distortion introduced into the complex spectrogram by baseline correction.

The invention relates to the technical field of pipeline geographic information measurement, in particular to a positioning device and method for pipeline detection. The problems that in the prior art, the precision is low, and remote measurement cannot be achieved are solved. The positioning device comprises a power supply module, two supporting rollers, a first plastic sealing ring, an inertia measuring module, a data processing unit, a second plastic sealing ring, a data storage unit, a mileage instrument, a flux leakage sensor, a communication cable and a magnetic tracking module. The inertia measuring module is composed of a gyroscope and two accelerometers; on the basis of the mileage instrument, and three-dimensional attitude angle, speed and position coordinate measurement inside the pipeline can be achieved. According to the positioning method, a complex continuous wavelet transform method is adopted for analyzing operating data of the inertia measuring module inside the pipeline, a pipeline connector is detected, and the method is used for correcting azimuth angle errors of the positioning device for pipeline detection. The device and method are applicable to detection ofland and underwater oil and gas pipelines.

The invention discloses a method and device for extracting a weak characteristic from a fault signal of an antifriction bearing. The method comprises that a vibration signal of the antifriction bearing is obtained, and continuous wavelet transform is carried out on the collected vibration signal to obtain a time-frequency graph; auto-correlation operation is carried out on a wavelet coefficient corresponding to each frequency in the time-frequency graph, noise interference is removed by filtering, and a periodic fault component is extracted; and Hilbert transform is used to carry out envelop demodulation, Fourier transform is carried out to obtain an envelope power spectrum, and a fault characteristic frequency is obtained. The method can be used to extract the weak periodic impact component from an early-stage fault of the antifriction bearing, and provides key support for state monitoring and fault diagnosis of machines.

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.

Slowness dispersion characteristics of multiple possibly interfering signals in broadband acoustic waves as received by an array of two or more sensors are extracted without using a physical model. The problem of dispersion extraction is mapped to the problem of reconstructing signals having a sparse representation in an appropriately chosen over-complete dictionary of basis elements. A sparsity penalized signal reconstruction algorithm is described where the sparsity constraints are implemented by imposing a l1 norm type penalty. The candidate modes that are extracted are consolidated by means of a clustering algorithm to extract phase and group slowness estimates at a number of frequencies which are then used to reconstruct the desired dispersion curves. These estimates can be further refined by building time domain propagators when signals are known to be time compact, such as by using the continuous wavelet transform.

This invention pertains to the extraction of the slowness dispersion characteristics of acoustic waves received by an array of two or more sensors by the application of a continuous wavelet transform on the received array waveforms (data). This produces a time-frequency map of the data for each sensor that facilitates the separation of the propagating components thereon. Two different methods are described to achieve the dispersion extraction by exploiting the time frequency localization of the propagating mode and the continuity of the dispersion curve as a function of frequency. The first method uses some features on the modulus map such as the peak to determine the time locus of the energy of each mode as a function of frequency. The second method uses a new modified Radon transform applied to the coefficients of the time frequency representation of the waveform traces received by the aforementioned sensors. Both methods are appropriate for automated extraction of the dispersion estimates from the data without the need for expert user input or supervision.

The invention discloses a metal plate defect location method based on laser Lamb wave frequency-wave number analysis and belongs to the field of laser ultrasonic non-destructive testing. The method comprises the following steps: firstly, excitation is performed by a laser excitation probe in a fixed position on a metal plate, a laser receiving probe scans defect lines to receive N groups of Lamb wave data, and a time-space wave field on a scanning path is obtained; secondly, signals under specific center frequency are extracted from the acquired N groups of Lamb wave data with a continuous wavelet transform method, and analyzability of a defect location result is guaranteed; finally, the extracted wave field signals are subjected to space-frequency-wave number imaging with short-space two-dimensional Fourier transform algorithm, and defect location is realized according to characteristics of a wave number spectrum on the scanning path. One completely non-contact type detection method is provided, and action rules between Lamb waves and defects in the wave number field are disclosed.

The invention discloses a log domain analog wavelet transform circuit for detecting an electrocardiogram signal QRS wave, comprising a forming circuit of differential input current signals and a current mirror circuit, four parallel log domain differential voltage filter circuits, four log differential compression circuits and four exponential differential expanded circuits. Each input of the log domain differential voltage filter circuits is connected with the forming circuit of differential input current signals and the current mirror circuit through the log differential compression circuits. Each output of the log domain differential voltage filter circuits is connected with an input of the exponential differential expanded circuits. The log domain analog wavelet transform circuit has the advantages of being integrated in a piece of analog integrated circuit, real-time realizing continuous wavelet transform under the characteristic dimension of the electrocardiogram signals with low power consumption, satisfying application requirements of an implanted artificial cardiac pacemaker for low power consumption and small size.

A method is provided for improved diagnostics of an internal combustion engine that includes cylinders, a crankshaft, crankshaft rotation angle sensor (CRAS), a vibro-sensor, a logic-mark sensor, each sensor produces respective signals. The method contemplates receiving the signals dividing them into a plurality of idling cycles, determining the crankshaft's position based on stochastic filtration, quasicontinuous representation of the signal, the Kalman filter, a nonlinear stochastic filter, based on CRAS characteristics and quasicontinuous representation, determining the Riesz generator basis function based on technical characteristics of the vibro-sensor, secondary discreeting of the signals with a replacement of argument, discreeting of a reciprocal function, obtaining a discrete wavelet transform, obtaining a continuous wavelet transform, obtaining a discrete wavelet transform of a reciprocal function, and producing output data. Two-dimensional arrays are divided relatively to predetermined indexes in the discrete transforms, an empirical probability distribution function is computed. Further optional diagnostic processing is provided.

For a signal waveform to be processed, the continuous wavelet transform is performed with various scale factors, and a wavelet coefficient at each point in time is calculated. On an image showing the strength of the wavelet coefficient with respect to the scale factor and time, ridge lines are detected, and based on these ridge lines, positive and negative peak candidates are extracted, after which an error in the position and width of the peak due to the influence of a neighboring peak is corrected. Subsequently, the degree of non-symmetry of the peak shape or other features are examined to remove false negative peaks due to negative peak artifacts. Subsequently, a true peak cluster, a false peak cluster resulting from the removal of high-frequency components of a high-frequency noise or other causes, and other kinds of peaks are identified, and the obtained result is used to remove false peaks.

The invention relates to a method for ranging the single terminal fault of a single outlet power transmission line based on opposite terminal bus reflected wave identification, and belongs to the technical field of fault ranging of power systems. When a power transmission line has a fault, the method includes the following steps: acquiring three-phase currents of the line from a head terminal of the line and separately resolving a zero modulus component and an aerial modulus component of the three-phase currents; separately performing continuous wavelet transform on the zero modulus componentsand the aerial modulus components to resolve the modulus maxima thereof, and separately determining the time t<0>, t<1> when the zero modulus current travelling wave and the aerial modulus current travelling wave arrive at the measurement point at the head terminal; determining the wave head of the zero modulus current travelling wave of the opposite terminal bus reflected wave, determining the time t<2> when the zero modulus current reflected wave which is reflected by the opposite terminal bus arrives at the measurement point at the head terminal; based on the determined wave head of the zero modulus current travelling wave of the opposite terminal bus reflected wave, determining the wave head of the opposite terminal bus reflected wave that corresponds to the aerial modulus current travelling wave, and determining the time t3 when the measurement point arrives at the head terminal; and resolving the fault distance x by using the wave head of the aerial modulus current travelling wave and a novel fault ranging formula of the wave head of the opposite terminal bus reflected wave.

The invention relates to a method for detecting the EEG (electroencephalogram) alertness based on continuous wavelet transform in the technical field of signal processing, which comprises the following steps of: obtaining characteristic values of wavelet coefficients of EEG (electroencephalogram) sequences as a characteristic set by using a wavelet function; and sorting and simplifying the characteristic set through a random forest method, training a support vector machine by using a sample, and detecting the EEG (electroencephalogram) alertness by using the support vector machine obtained by training. The method for detecting the EEG (electroencephalogram) alertness based on the continuous wavelet transform realizes the processing and the analysis of EEG (electroencephalogram) to distinguish different alertness levels of people through the continuous wavelet transform processing of the EEG (electroencephalogram), calculation and characteristic extraction based on wavelet coefficients, characteristic sorting and selection based on a random forest, and the training and the classification of the support vector machine.

A defect positioning method for a condensed laser sensor array based Lamb corrugated plate structure belongs to the field of laser ultrasonic non-destructive testing. Multiple sensor array elements are distributed at a center position of a metal plat structure to form an n* n condensed array for detecting defects of a whole plate structure. Single sensor array elements excite the rest sensor array elements to receive, thus collecting (see the description) groups of data. Continuous wavelet transform is performed on collected data to extract signals nearby specific frequencies. Then the extracted signals are subjected to dispersion compensating to remove the dispersion phenomenon of the signals, which is favorable for accurately recognizing the defects. The signals are subjected to total focusing imaging and symbol coherent factor imaging, and imaging results of the two are subjected to image fusing, thus accurately positioning the defects.

The invention discloses a method for plastic sample element identification based on laser-induced breakdown spectroscopy. The method includes a step (1) of establishing an elemental characteristic line database based on an NIST database; a step (2) performing an experiment on a plastic sample through am LIBS device to obtain a plastic sample 2D data matrix; a step (3) of performing baseline correction on LIBS spectra based on a discrete wavelet transform method; a step (4) of performing spectral peak identification on the corrected spectra according to a continuous wavelet transform method; and a step (5) of comparing data in LIBS with data in the established characteristic line database to determine an element attribution of a spectral peak. According to the method, elements in the plastic sample can be automatically identified, the influence of garbage on the recognition process is reduced, and the goals of improving the element identification accuracy rate, reducing the classification time, and reducing the calculation cost are achieved.

The invention discloses a heart rate detection method, system and equipment based on PPG and a storage medium. The method comprises the steps that skin coarse detection is conducted on an input facialimage to obtain an interested skin area image; spatial pixel averaging is conducted on the green channel of the skin area image, and interpolation calculation is conducted on the facial image with apreset frame number to obtain a time sequence signal containing cardiac impulse information; filtering treatment is conducted on the time sequence signal using continuous wavelet transform to obtain impulse waves; a heart rate value is obtained by using Fourier transform to achieve the non-contacting type body heart rate measurement, the accuracy of the measuring result is high, and the method canbe applied to the fields of medicine, polygraph test and the like.

The invention relates to a high-dimensional wavelet transform-based method for discontinuity detection of seismic data. The method comprises the following steps: (1), collecting seismic data and carrying out pretreatment to obtain a three-dimensional seismic data volume; (2), carrying out Hilbert transform on each channel of the three-dimensional seismic data volume so as to obtain a three-dimensional phase cosine data volume; (3), carrying out partitioning on the three-dimensional phase cosine data volume and selecting searching scopes of scale, inclination and azimuth according to discontinuous scales and geographical objects; (4), carrying out three-dimensional continuous wavelet transform on each subblock of the three-dimensional phase cosine data volume; (5), giving discontinuity tolerances of the seismic data according to a three-dimensional continuous wavelet transform coefficient modulus of each the subblock of the three-dimensional phase cosine data volume so as to obtain a partitioning discontinuous data volume; (6), carrying out splicing on the generated partitioning discontinuous data volume to obtain a discontinuous data volume of the whole seismic data. According to the method provided in the invention, computation quantity is small and multiscale detection can be carried out on discontinuity of seismic data; therefore, the method can widely applied to the detection process of the seismic data.

The invention discloses a motion image-electroencephalograph (MI-EEG) recognition method based on brain-derived domain space. The recognition method comprises the steps as follows: performing common average reference and band-pass filtering and other preprocessing on acquired MI-EEG; performing inverse transformation on the EEG by standardized low-resolution brain electromagnetic CT algorithm to obtain a brain-derived domain dipole moment amplitude time sequence; primarily selecting dipoles on the basis of dipole moment amplitude by a data driven method, performing time-frequency analysis on the dipoles by continuous wavelet transform to select dipoles and determine optimal time period; and extracting dipole wavelet coefficient powder sequence characteristics by a one-to-one common spatialpattern algorithm, and inputting the characteristics in a support vector machine for classification. The time domain, frequency domain and spatial domain information are fully utilized in optimization of dipoles, determination of optimal time period and characteristic extraction while the spatial resolution is improved, and the method has great significance in improving calculation efficiency andclassification accuracy.

The invention discloses a segment hidden crack identification method based on matching pursuit and wavelet transform, and a technology of combining orthogonal matching pursuit and wavelet transformation is adopted to process a shield tunnel lining hidden water-containing micro-crack geological radar detection signal, the influence of a strong impedance interface can be effectively weakened, and micro-weak reflection signals of a target detection object are enhanced, so that the purpose of accurately detecting and identifying the shield tunnel lining hidden quality defects is achieved. Firstly,according to the sparse representation theory, a surface strong reflection and abnormal strong reflection stripping method for layer and wavelet constraint matching pursuit is provided, and by combining a matching pursuit algorithm and a strong reflection forming mechanism, a sparse dictionary matched with the characteristics of the strong reflection signals is selected, and two-time matching decomposition is carried out on each signal, so that the micro-weak target reflected signals submerged in the strong reflection can be well displayed. Secondly, a wavelet basis function matched with thesignal and a proper wavelet transformation scale are selected, and the image profile is processed and enhanced again by adopting a continuous wavelet transform method, so that the hidden water-containing micro-crack signals are effectively highlighted.

The invention discloses an earthquake stratum thickness variation analysis method in which a high-resolution instantaneous frequency decomposed on the basis of an empirical mode is used to analyze stratum thickness variation. Firstly, an earthquake signal is subjected to an empirical mode decomposing process to obtain a multilevel intrinsic mode function; then, the instantaneous frequency of the intrinsic mode function is calculated; and according to the variation of the instantaneous frequency, the stratum thickness variation is analyzed. According to the earthquake stratum thickness variation analysis method, a triple B spline is adopted as an interpolating function in the empirical mode decomposing process; the instantaneous frequency is calculated by using an instantaneous frequency calculation method with a damping factor on the basis of continuous wavelet transform; the obtained intrinsic mode function and instantaneous frequency profile have good lateral continuity and few false appearances, accord with the reality and also have good noise immunity. The earthquake stratum thickness variation analysis method is favorable for improvement of the reliability and the effectiveness of the earthquake stratum thickness variation analysis, is suitable for processing and explaining practical earthquake data, provides a powerful basis for predicting a reservoir stratum, and improves the precision of oil-gas field exploration and development.

The invention finds a method capable of simultaneously finishing LC / MS or LC signal base line correction, peak position distinguish and peak position alignment. The invention mainly comprises the following steps: step 1, correcting a metabolic spectrum base line and distinguishing peak position, wherein the main process comprises: 1, within the range of 1-12 feet, taking the Mexican hat function as a mother wavelet, and carrying out continuous wavelet transform (CWT) of which the step length is 1 to each mass spectrum signal; 2, at each time point, detecting the maximum value of the wavelet transform of each mass spectrum signal on each scale to obtain a new signal of which the horizontal coordinate is time and the vertical coordinate is the maximum value of the wavelet transform; and step 2, aligning the peak position. A threshold value (the maximum deviation of the peak position of the same component in different samples) is given, the peak of the deviation in the wavelet transform signal in each sample within the threshold value is incorporated into the same peak, and the corresponding peak position in each sample is modified.

The invention discloses an automatic balancing machine vibration signal processing method. Aiming to solve common problems of frequency multiplication interference, non-integral harmonic interference and strong noise interference to vibration signals to be processed, the method includes fitting general frequency values of different frequency components of the signals by the least square method to obtain wavelet transform scale factors, performing band-pass filtering on the signals through the Morlet complex continuous wavelet transform to accurately extract unbalanced vibration signals with revolving speed the same with that of a rotor, and finally calculating amplitude and frequency of the vibration signals with both measurement precision and speed increased. Since phase of the vibration signals is calculated by the full-phase FFT (fast Fourier transform) spectral analysis, initial phase of the vibration signals can be acquired accurately by the method.

A method for extracting data from an overall signal, generated by a plurality of impulses, by use of a computer using wavelet transform analysis of timing and strength of each of said plurality of impulses is disclosed. The method includes using a Discrete Wavelet Transform analysis or a Continuous Wavelet Transform analysis.

The utility model relates to a method for surveying oil and gas based on a Morlet spectrum, which comprises the steps as follows: firstly, Fourier analysis is implemented on seismic signals in a target reservoir of a seismic recording channel to obtain the frequency spectrum scope of the seismic signals; secondly, the seismic signals are analyzed by using continuous wavelet transform to obtain wavelet transform coefficient Wx (b, s); thirdly, the instantaneous amplitudes under different scale factors s are defined as follows: the instantaneous amplitude values [Ai(b0,1 / ai)i is equal to 0, 1,..., N] corresponding with different scale factors ai is obtained for each time point b0; Lagrange splines are used for implementing interpolation; the amplitude values of any frequence at a b point can be obtained through the substituting of the corresponding a value; in the way, a connecting line of the discrete points forms the frequency spectrum of the b point, namely the Morlet spectrum; and fourthly, the absorption coefficients of each point are obtained according to the instantaneous amplitudes at any time point obtained in step 3); if the absorption coefficient Xi I of the point is lower than the absorption coefficient of a wall rock by more than ten percent, then the position is represented to comprise an air layer.

The invention discloses a harmonic noise suppression method based on waveform morphology sparse modeling. The harmonic noise suppression method includes the steps of 1) constructing Chirplet transform according to waveform morphological characteristics of harmonic noise in seismic records acquired via vibroseis slip sweep, and constituting an over-complete dictionary with continuous wavelet transform; 2) quickly implementing Chirplet positive and inverse transforms; 3) determining Chirplet transform parameters based on time-frequency distribution characteristics of correlated data; and 4) according to the starting frequency of a reference scanning signal, determining the filter cutoff frequency of the harmonic noise to achieve fidelity separation of an effective signal from the harmonic noise. The invention solves the problem of harmonic noise interference in the seismic data acquired via vibroseis slip sweep, and achieves the purpose of improving the signal-to-noise ratio of the seismic data. The method of the invention determines the Chirplet transform according to the time-frequency distribution characteristics of the harmonic noise, and ensures the sparseness; the fast implementation of the Chirplet transform guarantees the efficiency of transform, and the correction coefficient guarantees the accuracy of inverse transform reconstruction; and the Chirplet transform parameters are automatically determined according to the data drive, a strong adaptability is gained and single-channel calculation facilitates parallel processing.

An improved method for detecting the presence of humans or animals concealed within in a vehicle uses a combination of the continuous wavelet transform and a ratio-based energy calculation to determine whether the motion detected using seismic sensors placed on the vehicle is due to the presence of a heartbeat within the vehicle or is the result of motion caused by external factors such as the wind. The method performs well in the presence of light to moderate ambient wind levels, producing far fewer false alarm indications. The new method significantly improves the range of ambient environmental conditions under which human presence detection systems can reliably operate.

Methods and systems are provided for analyzing a physiological signal by applying a continuous wavelet transform on the signal and comparing the wavelet transformation to a library of wavelet signatures corresponding to one or more physiological conditions and / or patient conditions. A pulse oximeter system may relate the wavelet transformation with one or more of the wavelet signatures based on filters and / or thresholds, and may determine that the wavelet transformation indicates that the patient of the physiological signal has a physiological condition indicated by the related wavelet signature. In some embodiments, the pulse oximeter system may use previous analyses in a neural network to update the library. Further, non-physiological components of the wavelet transformation may also be identified and removed.

A multi-target tracking method based on variable processing windows and variable coordinate systems includes: step one, performing an initialization process; step two, constructing a space-time processing block for the k frame image; step three, solving a position energy density of the space-time processing block constructed in the step two; step four, extracting position parameters of all targets in the k frame image; step five, matching the target with the position parameter in each processing block to obtain a position observed value Z (k+1) of each target; step six, calculating target speed parameters with the Kalman filter algorithm with weighted gain to reduce calculated amount, amending the position observed value of each object, and strengthening accuracy and robustness of the tracking; and step seven, calculating the motion scale of each target in the k frame image. According to the multi-target tracking method, the variable window, variable projection method and Kalman motion estimation are introduced on the basis of continuous wavelet transform and expectation maximization (EM) fitting algorithm, motion parameters are accurately extracted; and calculated amount is reduced; and accurate tracking of a plurality of motion targets is achieved.