1508 results about "Wavelet decomposition" patented technology

The present invention provides methods and systems for patient-specific seizure onset detection. In one embodiment, at least one EEG waveform of the patient is recorded, and at least one epoch (sample) of the waveform is extracted. The waveform sample is decomposed into one or more subband signals via a wavelet decomposition of the waveform sample, and one or more feature vectors are computed based on the subband signals. A seizure onset can then be identified based on classification of the feature vectors to a seizure or a non-seizure class by comparing the feature vectors with a decision measure previously computed for that patient. The decision measure can be derived based on reference seizure and non-seizure EEG waveforms of the patient. In another aspect, similar methodology is employed for automatic detection of alpha waves. In other aspects, the invention provides diagnostic and imaging systems that incorporate the above seizure-onset and alpha-wave detection methodology.

Image data is compressed by decomposing the image data into a plurality of resolutions or sized by wavelet decomposition, followed by compression of the resulting data sets. Adaptive Huffman code compression is optimized for compressing the data sets, predictive errors for a low frequency band and the wavelet transformed values for high frequency bands. The image data is thus compressed losslessly and in multiple resolutions such that desired resolution images can be reconstructed and transmitted in accordance with available-bandwidth and viewing capabilities.

A method and apparatus to monitor the neurologic state of a patient undergoing general anesthesia is provided. Previous automated systems to monitor the neurologic state of a patient undergoing general anesthesia involve a significant time delay between the patient's true hypnotic state and the computed indices. The present invention reduces this time delay by using a different analysis technique applied to spontaneous EEG. A wavelet decomposition and statistical analysis of the observed EEG is conducted and compared to reference data to provide a numerical indicator. In addition, this indicator is more consistent with the patient's loss of consciousness indicated by the loss of count event than previous systems.

The invention relates to an eccentric pump vibration accidence fusion diagnose method and relative vibration signal collector, wherein said invention is characterized in that: it uses the eccentric pump vibration signal collector to collect the normal state, the quantity imbalance, asymmetry rotate and loose base of eccentric pump; uses wavelet decomposition and reconstruction to extract the character of vibration signal; and inputs the character vectors into sub fuzzy neural networks I and II; to be treated and replace the relation factor matched with sensor signal function; the whole fuzzy neural network comprises data fuzzy layer, input layer, hidden layer and output layer; uses D-S theory to obtain the fused signal function distribution, realize the fusion diagnose on normal state, quantity imbalance, asymmetry rotate and loose base. The invention has simple structure and high effect.

A multimedia compression system for generating frame rate scaleable data in the case of universally scaleable data. Universally scaleable data is scaleable across all of the relevant characteristics of the data (e.g., frame rate, resolution, and quality for video). The scaleable data generated by the compression system includes multiple additive layers for each characteristic across which the data is scaleable. For video, the frame rate layers are additive temporal layers, the resolution layers are additive base and enhancement layers, and the quality layers are additive index planes of embedded codes. Various techniques can be used for generating these layers (e.g., Laplacian pyramid decomposition or wavelet decomposition for generating the resolution layers; tree structured vector quantization or tree structured scalar quantization for generating the quality layers). The system further provides for embedded inter-frame compression in the context of frame rate scalability, and non-redundant layered multicast network delivery of the scaleable data.

An identifying method based on pick-up and match of finger vein includes collecting finger vein image by image collection device, carrying out pretreatment on collected vein image, dividing vein image to be image block in specific size, applying wavelet decomposing algorithm to carry out wavelet decomposition and wavelet moment pick-up on sub-image, picking up characteristic by carrying out PCA and LDA transform and carrying out match and identification by utilizing fuzzy threshold means.

The invention relates to a vibration signal identification method for an optical fiber perimeter system, which comprises the following steps of: (1) signal acquisition; (2) windowing treatment; (3) bandpass filtering; (4) wavelet denoising; (5) vibration event detection; (6) characteristic parameter extraction; and (7) pattern matching and classification. The invention has the advantages that the method introduces more characteristic parameters such as short-term energy E, short-time average magnitude M, short-term average zero-crossing rate Z, each wavelet decomposition scale detail signal energy Ew and vibration signal power spectrum P in comparison with the prior art, accurately judges the classification of external vibration signals, and reduces the probability of false alarm.

The invention discloses an emotional music recommendation method based on brain-computer interaction. Music corresponding to emotions is automatically searched and recommended to a user by acquiring electroencephalogram signals of the user. The process includes the steps: firstly, extracting the EEG (electroencephalogram) signals of the user by an electroencephalogram acquisition instrument, performing wavelet decomposition on the EEG signals into four wave bands alpha, beta, gamma and delta, taking frequency band energy of the four wave bands as a feature, recognizing the emotions by a trained electroencephalogram emotion recognition model EMSVM, and judging emotion categories corresponding to the EEG signals; averagely decomposing external music signals into eight frequency bands within the range of 20Hz-20kHz, taking energy values of the eight frequency bands as characteristic values, recognizing music emotions by a trained music emotion recognition model MMSVM and building a music emotion database MMD; recommending the music corresponding to index numbers to the user according to the emotion categories of the electroencephalogram signals, and implementing an emotion-based music recommendation system. By the emotional music recommendation method, a new approach can be brought for infant music cultivation, sleep treatment and music search.

The invention discloses a vacuum breaker mechanical state detecting method based on vibration signal analysis. The method comprises the following steps: 1), acquiring the vibration signal in the action process of a VS1 vacuum breaker; 2), performing wavelet decomposition to the vibration signal acquired in the step 1) in order to de-noise; 3), for the signal subjected to the wavelet decomposition, reconstructing the signal in each frequency band, drawing the reconstructed signal of each frequency band in a time-frequency-signal amplitude spectrum of the signal by mat lab, and calculating the information entropy of each frequency domain band and combining the information entropy of each frequency band of the vibration signal with each other to form a fault feature vector; and 4), inputting the fault feature vector calculated in the step 3) into a well trained neutral network and sort, so as to realize the state detection of the vacuum breaker.

The invention relates to a self-adaptive method for fault line selection of a cable-wire mixed line of an electric distribution network by using full-band wavelet energy relative entropy. The method comprises the following steps: when the zero-sequence voltage instantaneous value of a bus is out of limit, starting a fault line selection device immediately; performing wavelet decomposition on zero-sequence current in 1 / 4 cycle after the fault of each line, performing single reconstruction on wavelet decomposition coefficients under each scale, and calculating the wavelet energy under each band of the zero-sequence current of each line by using reconstruction coefficients; and calculating the wavelet energy relative entropy under each band of each line and solving the comprehensive wavelet energy relative entropy of each line by combining the super-strong recognition capability of the relative entropy to slight differences between signals, and self-adaptively selecting out the fault line by comparing the size of the full band comprehensive wavelet energy relative entropy of each line. Theoretical analyses and a large number of emulations show that the method effectively avoids the influence caused by a CT saturation discontinuous angle on the line selection, and is applicable to the cable-wire mixed lines, pure cable lines and pure aerial lines.

A video encoding / decoding system based on 3D wavelet decomposition and the human perceptual model is implemented. JND is applied in quantizer design to improve the subjective quality of compressed video. The 3D wavelet decomposition helps to remove spatial and temporal redundancy and provides scalability of video quality. In order to conceal the errors that may, occur under bad wireless channel conditions, a slicing method and a joint source channel coding scenario, that combines RCPC with CRC and utilizes the distortion information to allocate convolutional coding rates are proposed. A new subjective quality index based on JND is presented and used to evaluate the overall system performance at different signal to noise rations (SNR) and at different compression ratios. Due to the wide use of arithmetic coding (AC) in data compression, it is considered as a readily available unit in the video codec system for broadcasting. A new scheme for conditional access (CA) sub-system is designed based on the compression graphic property of arithmetic coding. Its performance is analyzed along with its application in a multi-resolution video compression system. This scheme simplifies the conditional access sub-system and provides satisfactory system reliability.

The invention relates to a video encoding method for the compression of a video sequence, comprising the steps of generating from the original video sequence, by means of a wavelet decomposition, a low resolution sequence, performing on said low resolution sequence a low resolution decomposition, by means of a motion compensated spatio-temporal analysis, generating from said low resolution decomposition a full resolution sequence, by means of an anchoring of the high frequency sparial subbands resulting from the wavelet decomposition to said low resolution decomposition and coding said full resolution sequence and the motion vectors generated during the motion compensated spatio-temporal analysis. The invention also relates to a corresponding decoding method, and to corresponding encoding and decoding devices.

The invention belongs to the technical field of computer-aided diagnosis, and specifically relates to a brain glioma molecular marker nondestructive prediction method and a prediction system based on radiomics. The method comprises the following steps: adopting a three-dimensional magnetic resonance image automatic segmentation method based on a convolution neural network; registering a tumor obtained from segmentation to a standard brain atlas, and acquiring 116 position features of tumor distribution; getting 21 gray features, 15 shape features and 39 texture features through calculation; carrying out three-dimensional wavelet decomposition on the gray features and the texture features to get 480 wavelet features of eight sub-bands; acquiring 671 high-throughput features from the three-dimensional T2-Flair magnetic resonance image of each case; using a feature screening strategy combining p-value screening and a genetic algorithm to get 110 features highly associated with IDH1; and using a support vector machine and an AdaBoost classifier to get a classification of which the IDH1 prediction accuracy is 80%. As a novel method of radiomics, the method provides a nondestructive prediction scheme of important molecular markers for clinical diagnosis of gliomas.

The invention relates to image quality evaluation. To show the fidelity and third dimension of a generated three-dimensional image, the degree of damages of a compression algorithm to the three-dimensional image, the degree of interference of noises introduced by a transmission process on the quality of the three-dimensional image, the display naturalness of the three-dimensional image, and the like, the technical scheme adopted by the invention is that: a human-visual-system (HVS)-based structural similarity (SSIM) and characteristic matching three-dimensional image quality evaluation methodcomprises the following steps of: (1) comparing the luminance, contrast and structural similarity of left and right views of an original image with those of the left and right views of a test image by using a structure distortion method; (2) extracting luminance and contrast indexes; (3) simulating a human eye band-pass property principle according to wavelet decomposition to obtain a human visual signal to noise ratio evaluation index; (4) reflecting the third dimension of the three-dimensional image by using the ratio of number of left and right view matching points of the test image to thenumber of the left and right view matching points of the original image; and (5) rationally weighting all the indexes to obtain an overall evaluation index. The method is mainly applied to the image quality evaluation.

The invention discloses an ultrasonic phased array inspection oil-gas pipe circular weld defect automatic recognition method, in particular to an ultrasonic phased array inspection oil-gas pipe circular weld defect automatic recognition method for a support vector machine, which belongs to a pipe circular weld defect type recognition technology. The recognition method includes two parts of the construction of a support vector machine model and the automatic recognition of unknown defect types in circular weld. The construction steps of the support vector machine model include artificial defect signals extraction, wavelet decomposition promotion, feature extraction, feature optimization and the training of the support vector machine model. The automatic recognition steps of unknown defect types in circular weld include defect signals extraction, optimized feature calculation and automatic recognition based on the support vector machine. The invention combines a lifting wavelet transform with a fractal technology and has the advantages of high recognition accuracy and fast recognition speed. The automatic recognition of the defect types can be realized.

The invention discloses a power transmission line fault season identification method based on high and low frequency wavelet feature association, which comprises the steps of extracting fault phase current samples of N failure types, and building a sample database; performing high frequency and low frequency data sampling on a fault type T from the sample database, and respectively performing wavelet decomposition; performing feature extraction on a high frequency data wavelet coefficient and a low frequency data wavelet coefficient; S4, building an association relationship model of the fault type T; determining the association relationship model of the N fault types; S6, performing wavelet decomposition on sample data whose fault reason is to be tested, and extracting a feature vector; substituting the feature vector of the sample whose fault reason is to be tested to the association relationship models of the N fault types, and judging the fault type of the sample data whose fault reason is to be tested. According to the invention, fault phase current is analyzed and processed through combining a wavelet transform theory and an information entropy, thereby not only being capable of effectively analyzing suddenly changed signals, but also being capable of achieving a purpose of information fusion, and improving the identification accuracy.

A method and apparatus for improving the signal to noise ratio of an information carrying signal wherein a wavelet transform up to a predetermined level is computed, a frequency thresholded signal which is indicative of noise is derived from the wavelet transform, and the frequency thresholded signal is subtracted from the information carrying signal.

The invention belongs to the computer image processing technical field and relates to a gait feature extraction method and a gait recognition method adopting the feature extraction method. The invention uses an infrared thermal imager to collect a gait image sequence and split a human body target, makes a specificationized superposition processing to the gait image sequence and the human body target to obtain a gait feature image, then takes advantage of wavelet decomposition and invariant moment combined with a skeleton theory to extract gait feature parameters which are taken as gait feature recognition parameters to be input into a support vector machine for classification recognition. The gait feature extraction method and the gait recognition method provided by the invention can effectively detect the human body target, eliminate the influence caused by change factors such as backgrounds, lighting and so on, and improve the correct recognition rate of the gait.

Reconstructing the shape of the surface of an object in greater than two dimensions is performed using a noise-tolerant reconstruction process and / or a multi-resolution reconstruction process. The noise-tolerant reconstruction process can be a Bayesian reconstruction process that adds noise information representing the noise distribution in optical image(s) of the object to surface gradient information estimated from the images to determine surface height information that defines the shape of the surface of the object in greater than two dimensions. In the multi-resolution reconstruction process, for each resolution of the image, the surface gradient information is estimated and the surface height information is calculated using the estimated surface gradient information. To obtain the final surface height map, the surface height information from each resolution is combined to reconstruct the shape of the surface of the object in greater than two dimensions. The multi-resolution reconstruction process can be used with the Bayesian reconstruction process or with another decomposition process, such as a wavelet decomposition process.

The invention relates to a high capacity information hiding method using JPEG2000 compressing images as the carrier. The operation steps are: applying wavelet decomposition, wavelet coefficients quantification and bit-plane coding to images, and determining the truncation position of the code flow of all coding blocks and the lowest bit-plane of the embeddable information by rate-distortion optimization; primarily choosing a wavelet coefficient which is more than the threshold value as the embedding point, and making a redundancy estimation and adjusting the embedding point and the embedding strength; embedding the synchronous information and the hidden information into the quantified wavelet coefficient in an order of the bit plane from the lowest to the highest starting from the lowest embeddable bit-plane; making a second bit-plane coding and organizing the code flow after embedding the information. The high capacity information hiding method is based on a second coding strategy to determine the embedding position and improves the information hiding volume by self-adaptively choosing the embedding point and adjusting the embedding strength, The high capacity information hiding method has the advantages of being transparent completely for the standard JPEG2000 decoders and being able to be conveniently integrated in the JPEG2000 coders. Meanwhile, the code flow still can decode normally after being embedded with hiding information.

A curved wavelet transform and a related image / video compression system are disclosed. The curved wavelet transform (CWT) is carried out by applying one-dimensional (1-D) wavelet filters along curves, rather than along only horizontal and vertical directions. The image / video compression system includes a curve determination unit, a curved wavelet transform unit, a wavelet coefficient quantization unit, a wavelet coefficient coding unit, and a curve coding unit. The quantization and coding of the wavelet coefficients are related to the curves. In one embodiment, recursive wavelet filters are used for inverse wavelet decomposition. The system provides higher compression capability than conventional wavelet-based image compression systems.

A medical ultrasound image denoising method based on wavelet transform and quick bilateral filtering comprises the following steps of the first step of establishing a medical ultrasound image model, the second step of carrying out wavelet transform on an image obtained in the first step after logarithm transformation to obtain four frequency domains (LL1, LH1, HL1and HH1), continuously carrying out wavelet transform on the low frequency domain LL1 to obtain four frequency domains (LL2, LH2, HL2 and HH2) again and then repeating the step until resolving the maximum layer number, the third step of carrying out quick bilateral filtering on the low frequency portion (LLJ) at the last layer, the fourth step of carrying out threshold value method shrinkage on wavelet coefficients of the high frequency portions (LHj, HLj and HHj) of all layers and the fifth step of carrying out wavelet inverse transformation to obtain the denoised medical ultrasound image. In addition, the J is 1, 2, ..., J. If denoised ultrasound envelope signals are wanted, exponential transformation is carried out on the ultrasound image obtained in the fifth step.

The invention provides a method and system of the prediction of time series data. The method comprises the steps that wavelet decomposition is conducted on the sequence formed by t-1 moment data, n subsequences are obtained; stationary detection is conducted on n subsequences respectively; for non-stationary time series, an advance learning LSTM model is built using the t-1 moment data, and the values of t moment are predicted respectively, and forecasts of the non-stationary part are obtained by summing; similarly, for stationary sequences, ARMA models are respectively built and the values of t moment are predicted, and the forecasts on stationary part are obtained by summing; finally the prediction values of the non-stationary part and the stationary part at t moment are summed, to obtain the final forecast value. By the method and system of the prediction of time series data, by wavelet decomposition, the advantages of LSTM and ARMA are combined, in comparison with traditional methods, better effects are provided in dealing with non-stationary time series. In addition, by benefiting from the unique LSTM structure in the model, the forecasting and the generalization ability of the method is better, and the method is suitable for time series prediction in various fields.

Disclosed are a method and a system for coal and rock boundary dividing based on coal and rock image feature extraction and classification and recognition. The method includes firstly, photographing coal and rock images on a coal mining working face, extracting texture feature information of the coal and rock images, and constructing a feature vector, wherein the texture feature information comprises the original images and the angular second moment, the contrast, the correlation, the mean value and the variance of every low-frequency coefficient image with Daubechies wavelet decomposition scale, and the texture feature information also comprises the total mean value and the total variance for calculating high-frequency coefficient images in horizontal, vertical and diagonal directions; and lastly, creating a best classifier to recognize the coal and rock boundary. The system comprises an image acquisition module, a feature module, a classification and recognition module, a result display interactive module, a memory module and a central control module. The method and the system for the coal and rock boundary dividing based on the coal and rock image feature extraction and the classification and recognition have simple calculation, less human intervention and low cost, can improve classification accuracy and efficiency of the coal and rock images in complex environments effectively, and provides accurate and reliable coal and rock boundary information.

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 discloses a linear prediction speech coding method and a speech synthesis method. The linear prediction speech coding method includes the following steps: speech is preprocessed; second-order backward linear prediction is carried out on the preprocessed speech, so that a residual signal is obtained; wavelet decomposition and compression are carried out on the residual signal, so that a wavelet coefficient is obtained, vector quantization is carried out on the wavelet coefficient, and meanwhile, the pitch period and gain parameters of the residual signal and the unvoicing and voicing characteristic of each sub-band are calculated and respectively and scalarly quantized. The speech synthesis method is based on the linear prediction speech coding method. After being adopted, the technical scheme of the invention can reduce the affection of noise on the quality of decoded speech, inhibit the deterioration of speech quality when unvoicing and voicing judgment is mistaken and improve the performance of coding unvoiced speech or background noise.

The invention discloses a method and a device for fusing infrared and visible light images based on spectral wavelet transformation. The method comprises the steps of performing spectral wavelet decomposition on an infrared image and a visible light image according to four decomposition scales to obtain a low-frequency sub-band coefficient and high-frequency sub-band coefficients, fusing the obtained low-frequency sub-band coefficient and the high-frequency sub-band coefficients at different decomposition scales according to a fusion rule, and performing spectral wavelet transformation on the fused low-frequency sub-band coefficient and high-frequency sub-band coefficients to obtain a fused image. The invention also discloses a device for fusing infrared and visible light images based on spectral wavelet transformation. According to the method and the device for fusing infrared and visible light images based on spectral wavelet transformation, the images of irregular images can be expressed better; a weight of image fusion is obtained by virtue of bilateral filtering so that the definition, the contrast ratio, the amount of information and the like of the fused image can be effectively improved, and consequently, a better-fused image can be obtained.

The invention relates to a method for identifying extra-high voltage direct current transmission line area inside and outside fault wavelet energy. The method comprises the following steps of: starting a starting element when a direct current fails and selecting bipolar direct current voltage data detected at a protection installation part after 5 ms of the fault, wherein the direct current voltage data are a discrete signal with sampling frequency of 100 kHz and sample sequence length of 500; finding out a direct current system line-mode voltage for four layers of wavelet decomposition by utilizing the bipolar direct current voltage so as to obtain low frequency coefficients and high frequency coefficients of the wavelet decomposition at different scales; and finding out wavelet high frequency energy from first scale to fourth scale and wavelet low frequency energy at the fourth scale according to the definition of wavelet energy of the signal at a certain scale, finding out the ratio of the wavelet high frequency energy to the wavelet low frequency energy, and comparing the size of the ratio of the wavelet high frequency energy to the wavelet low frequency energy so as to distinguish the inside faults and the outside faults. A large amount of simulation results show that the invention has good effect.

The invention provides an electrocardio signal QRS complex wave detection method based on morphology and wavelet transform. The method comprises a first step of inputting an electrocardiograph (ECG) signal which restrains base line drift and after high-frequency noise is removed and carrying out segmentation, a second step of using a db6 wavelet to carry out four-layer wavelet decomposition to the segmented ECG signal obtained in the first step, a third step of using a multiresolution morphology decomposition method to decompose the segmented ECG signal obtained in the first step and searching a common model maximum value point on a third layer detail component and a fourth layer detail component and regarding the common model maximum value point as an R peak location, a fourth step of carrying out the OR operation to the R peak locations obtained in the second step and the third step and preserving a result as a new R peak location if the result is 1, a fifth step of recalling an R peak, a sixth step of confirming a starting point and a terminal point of an QRS complex wave, and a seventh step of confirming whether total ECG signal detection is finished or not, finishing the operation if the detection is finished, and updating a threshold value and repeating the second step to the seventh step until an algorithm is finished, if the detection is not finished. The method can reach detection accuracy rate of more than 99%.