32 results about "Wavelet Tree" patented technology

The invention relates to a wavelet domain seismic data real-time compression and high-precision reconstruction method based on compressed sensing. The wavelet domain seismic data real-time compression and high-precision reconstruction method comprises the steps of: firstly, carrying out sparse representation on a microseismic signal in a wavelet domain; secondly, constructing a chaos Bernoulli measurement matrix (CBMM) by utilizing a Logistic chaotic sequence, and performing compressed observation on the sparsely-represented microseismic signal by using the chaos Bernoulli measurement matrix; and finally, adopting a Bayesian wavelet tree structure tree structure reconstruction method (BTSWCS), and recovering complete original data. Actual contrast experimental results show that the compression time can be shortened to 10<-5> s by using the wavelet domain seismic data real-time compression and high-precision reconstruction method for compressing data with total sampling points being 2<8>, that is, if a sampling rate of a seismometer is 1 KSPS, the CBMM measurement matrix can realize real-time compression on the acquired 0.25 s data basically. The wavelet domain seismic data real-time compression and high-precision reconstruction method increases a PSNR value by at least 5 dB, significantly improves the peak signal to noise ratio when compared with the greedy algorithm, and reduces reconstruction errors.

The invention relates to an embedded zero-tree coding mode and its circuit structure. It divides the embedded zero-tree coding into two processing stages, and simultaneously makes coding multiple bit planes in parallel. In the first-stage processing, firstly reading wavelet transform coefficients in a mode of scanning from low layer subband to high layer subband group by group, layer by layer and tree by tree; then executing multistage quantization for corresponding multiple threshold values; preprocessing the quantized data; after processing, independently coding the processed data on their respective bit planes; registering the symbols generated by data coding in an output buffer; after all the wavelet transform coefficients of a wavelet tree are allocated with symbols and stored into the output buffer, starting the second-stage processing; according to the scanning sequence from high layer subband to low layer subband, screening effective coded symbol output. It can raise coding rate, reduce coding time, has low requirements for IC performance and resources and is flexible to apply and easy to implement. It can be applied to the field of video data compression or image data compression.

The invention provides a sonar image information hiding method with a fractal and a wavelet combined. The method comprises the steps of (1) enabling a 3-bit binary system sequence to serve as a unit, converting information to decimal system sequences with values of 0 to 7; (2) enabling a sonar image to serve as a carrier, conducting wavelet transformation for the carrier image, dividing father-blocks and sub-blocks in different sub-band images which are arranged in the same direction of a wavelet tree; (3) using the decimal system sequences with the values of 0 to 7 to serve as classes of parameter appointed father-block affine transformation, calculating the optimal contrast ratio and luminance of the transformation blocks, replacing corresponding sub-blocks, writing down the position of the replaced sub-block; (4) sequentially processing each father-block, if the target sub-block is replaced, repeating the step of (3); (5) finishing embedding all the information, conducting wavelet inverse transformation for the carrier image, and obtaining a hiding image. According to the fact that the information is embedded through control of classes of affine transformation, an original image block with 1 bit information embedded can improvingly provided with 3 bit information in an embedded mode, and a volume for information hiding is improved.

The invention discloses a video coding method based on an important probability balanced tree. The method comprises the following steps: transforming 3d wavelet; weighting coefficients; constructing an important probability balanced 3d wavelet tree; initializing; searching important coefficients from the important probability balanced 3d wavelet tree and determining the sequence of the important coefficients; and detailing the amplitudes of the important coefficients. In the structure of the important probability balanced wavelet tree, through independently treating subsidiary nodes in the space direction and subsidiary nodes in the time direction, important coefficients with similar probabilities are placed in the same layer of a tree structure, so as to enable unimportant coefficients to be arranged at the tail of the tree to form zero subtree, and place important coefficients at the front end of a code stream. In the invention, relativities in dimensions and among dimensions of the coefficients are used at the same time, and synchronous information expenses are effectively reduced. Through the method provided by the invention, lower reconstruction mean square errors and better rate-distortion performance can be obtained.

The invention relates to a compressed sensing low-field magnetic resonance imaging algorithm. The algorithm comprises the steps that collected low-field magnetic resonance original data is subjected to undersampling, and random variable density K spacial data is obtained; undersampled K spacial data and a compressed sensing theory are combined for modeling, the modeling problem becomes a linear combination minimization problem containing a data fidelity term, a sparse prior term and a total variation term, and dual tree wavelet transformation and wavelet tree sparse joint serve as sparse transformation in the compressed sensing theory; by means of the dual tree wavelet transformation and wavelet tree sparse joint compressed sensing magnetic resonance imaging algorithm, solution is conducted, and a low-field magnetic resonance image rebuilt by the undersampled variable density K spacial data is obtained. Dual tree wavelet transformation is introduced to serve as sparse transformation ina CS-MRI model, and the defect that due to the fact that traditional wavelet transformation has sensibility and shift variant, lacks direction and has a rebuilding image, aliasing artifacts are caused is overcome. The computing accuracy is high, robustness is good, and the signal-to-noise ratio of the rebuilding image is increased while the imaging speed is increased.

The invention relates to a seismic data compression and reconstruction method. The seismic data compression and reconstruction method comprises the following steps: firstly, carrying out wavelet transformation on seismic data to increase the compressibility of the seismic data; secondly, constructing a measurement matrix capable of being realized by hardware according to a chaos sequence; carryingout compression observation on the seismic data subjected to the wavelet transformation by utilizing a chaotic measurement matrix; finally, improving a Bayesian wavelet tree structure compressed sensing reconstruction algorithm; recovering complete seismic data by utilizing an improved BTSWCS-vb algorithm. According to the seismic data compression and reconstruction method, the result of an actual data test shows that compared with a common random measurement matrix, the chaotic measurement matrix is convenient to be realized by the hardware; a plurality of chaotic measurement matrixes are adopted so that real-time compression with the seismic data compression ratio of 0.2 to 0.55 can be realized and the influence, caused by the chaos measurement matrixes, especially a Logistic sequence matrix, on a reconstruction effect, is also very good; the improved reconstruction algorithm, namely the BTSWCS-vb algorithm, is adopted so that the reconstruction precision is improved and the reconstruction time is remarkably shortened.

The invention provides an emotion recognition method based on EEG and a food picture data set. The method comprises the steps of collecting food pictures, and screening out the food picture data set serving as a stimulating material; adopting single-channel EEG measuring equipment to collect EEG signals of a tested person; adopting a denoising method of stationary wavelet transform based on an artifact removal algorithm to preprocess the collected EEG signals; adopting wavelet packet transform to perform wavelet tree decomposition on the preprocessed EEG signals, and reconstructing a rhythm wave in each frequency band of the EEG signals corresponding to the last layer of nodes by reordering the last layer of nodes; extracting amplitude values and power spectrum characteristics of the EEG signal rhythm waves, and drawing an EEG signal spectrogram and a spectrum energy distribution diagram; and identifying the emotional state of the tested person based on statistical analysis of the amplitude values and the power spectrum characteristics of the EEG signals. According to the scheme, the problems of single selection of emotional stimulating materials and subjectivity of emotional measurement in the prior art are solved, and the accuracy of emotion recognition is improved.

The application provides a post insulator fault identification method and device. The post insulator fault identification method comprises the steps: acquiring a vibration signal of a target post insulator; carrying out N-layer wavelet packet decomposition on the vibration signal to obtain N-layer wavelet trees; performing feature extraction on the N layers of wavelet trees to obtain an energy ratio of the energy value of each frequency band in the Nth layer of wavelet to the total energy value of the Nth layer of wavelet; determining whether the target post insulator fails or not according tothe energy ratio corresponding to each frequency band; if so, carrying out time-frequency analysis on the vibration information to obtain a wavelet time-frequency diagram; and determining the fault type of the target post insulator according to the wavelet time-frequency diagram. According to the provided method, the singular point of the vibration signal of the post insulator and the frequency band included by the singular point are analyzed by combining the wavelet packet energy with time-frequency analysis and thus spectral analysis is carried out on the abnormal condition of the signal jointly from the aspects of time domain and frequency domain, so that whether the post insulator has a fault or not and the fault type are identified quickly and accurately.

The invention discloses a wavelet packet extraction method for grid-connected inverter network-side harmonic current information. The method is based on a wavelet tree optimized decomposition and reconstruction algorithm for db4 wavelet packets. A maximum harmonic current component real-time extraction technology based on wavelet packet transform is put forward. According to the method of the invention, the wavelet frequency band, the maintaining frequency band and the zero-setting frequency band are judged by the threshold criterion after each layer of decomposition, and only the wavelet frequency band is decomposed during next layer of decomposition. The method has the advantages of small amount of decomposition operation and high wavelet decomposition precision. Compared with the prior harmonic current information extraction method, the method of the invention takes into consideration the computation limit of a digital processor, the amount of computation of decomposition and reconstruction in the process of harmonic information extraction is reduced without sacrificing the amount of maximum harmonic current information, and the method is more practical in engineering and has the advantage of high harmonic information extraction precision.

The invention relates to an embedded zerotree coding method on the basis of inverse Loop subdivision, which comprises the following steps of: (1) constructing an offset wavelet tree, i.e. forming an offset d by obtaining difference of an actual vertex O and a forecast vertex O' and simultaneously, establishing a corresponding quad-tree relation of edges in each mesh Mj-1 and four edges in a thinner layer mesh Mj, wherein the offset d is generated on a corresponding edge of a delete vertex reconstruction mesh; (2) carrying out offset wavelet processing and selecting a threshold value; and (3) encoding and carrying out code-stream ordering, wherein important information is preferentially encoded by adopting an embedded encoding mode on the basis of a three-dimensional graph triangular mesh and a result obtained after compression is placed at an initial part of a code stream. The invention provides the embedded zerotree coding method on the basis of inverse Loop subdivision, through which compression efficiency is greatly improved.

The embodiment of the invention discloses a method and device for obtaining the inherent frequency band of a post insulator. The method comprises: according to a preset sampling frequency, acquiring avibration signal of a normal post insulator; carrying out wavelet packet decomposition on the vibration signal; and acquiring a multi-layer wavelet tree, acquiring each node frequency band of the last layer wavelet tree according to the sampling frequency, acquiring an energy ratio of each node frequency band, further acquiring an average energy ratio value of each node frequency band of a plurality of normal post insulators, and acquiring an inherent frequency band according to the average energy ratio value of each node frequency band. Since the inherent frequency band is the frequency bandrange acquired for a plurality of normal post insulators, whether the post insulators have defects or not can be judged through the inherent frequency band acquired by the invention, and the accuracyof a judgment result can be improved.

The invention discloses an image compression method combining wavelet packet transformation and singular value decomposition, which comprises the following steps of: performing wavelet packet decomposition on an original image to obtain a complete binary tree; taking logarithmic energy entropy as a cost function, sequentially and upwards comparing logarithmic energy entropy values of child nodes and father nodes from the bottom layer of the complete binary tree, retaining nodes with smaller logarithmic energy entropy values, deleting nodes with larger logarithmic energy entropy values, and further obtaining an optimal wavelet tree; reconstructing the optimal wavelet tree by adopting a wavelet packet reconstruction algorithm to obtain a reconstructed image; performing singular value decomposition on the two-dimensional time-frequency information of the reconstructed image to decompose the two-dimensional time-frequency information into texture vectors, geometric vectors and singular values; processing the data in an energy spectrum mode to obtain a characteristic value set; processing the feature value set by adopting a self-adaptive singular value decomposition method to obtain a singular value threshold number K, and selecting the first K feature values in the feature value set to form an approximate matrix; according to the invention, the image compression quality can be improved, and a high compression ratio can be obtained.