32 results about "Wavelet Tree" patented technology

An improved self-indexer comprising a find function that caches a last found position and occurrence count of a symbol on each node level of a word-based wavelet tree for a particular symbol lookup and only uses a select function to call on data to the right of the position.

The invention discloses a method for detecting a brain function communicated area based on signal sparse approximation. The method comprises the following steps: 1, performing wavelet packet decomposition on each time point data in a functional magnetic resonance signal to obtain a wavelet tree on original time point data; 2, carrying out sparsity measurement on nodes in the wavelet tree, and selecting the node with strongest sparsity in the wavelet tree to form an effective sparse approximation data set on original functional magnetic resonance data; and 3, carrying out hybrid matrix optimization on the formed sparse approximation data set by adopting independent content analysis, and reconstructing a source signal in a functional area in combination with an original functional magnetic resonance mixed signal to finish accurate positioning and detection of the functional area. According to the method, sparse approximation of original mixed data is obtained by utilizing the more general supposed sparsity of the functional magnetic resonance signal, and the source signal is separated through the independent content analysis and the signal reconstruction, so that the purpose of accurately positioning the brain function communicated area is achieved.

The invention discloses an astronomic tide and storm tide separating method. An ocean hydrometric station only measures one group of real-time tide water level processes, the real-time process can be regarded as the superposition of a strong determination process (astronomic tides) and a weak random process (atmosphere activity interference including storm tides), and at the moment, the separation can be realized by utilizing mathematical measures such as frequency windows (frequency domains) and filters (time domains). The invention adopts the separation method based on wavelet packet analysis, the tide level is used as signals, firstly, the wavelet function and the wavelet packet decomposition layer number are determined, 170 partial tides are distributed to a unique leaf node according to frequencies, an optimal wavelet tree is obtained, then, the wavelet decomposition coefficient containing the astronomic partial tides is reconstructed, astronomic partial tide signals in each frequency band range are extracted, optimally, a wavelet filter is utilized for removing the astronomic partial tides from the tide level signals, and a storm tide surge curve is obtained.

The invention provides an adaptive down-sampling and lapped transform-based image compression method, and mainly solves the problems of low performance and high complexity existing in the prior down-sampling based compression method. The method comprises the following steps of: 1, adaptively down-sampling the prior image; 2, firstly performing lapped transform on the image after adaptive down-sampling between DCT blocks which are not subjected to the down-sampling, and then performing the DCT transformation on the whole image; 3, interweaving factors after transformation into a wavelet tree structure to obtain a low frequency subband DC and a high frequency subband AC; 4, performing the shape-adaptive DCT transformation on the low frequency subband DC, and interweaving the factors again; 5, by an object-oriented SPECK coding method, coding the factors with the wavelet tree structure to obtain a compressed bit stream; and 6, decompressing the bit stream transmitted to a decoding end toobtain a final re-constructed image. The method can obtain the performance higher than that of the prior image compression method under the condition of low bit rate, is low in complexity, and can beused for the low-bit-rate image coding which has strict requirements on complexity and real-time.

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 provides a full-text search method supporting a search request containing missing symbols. The full-text search method comprises the following steps: converting content symbols of a text into symbol strings, and splicing the symbol strings into a long symbol string; respectively carrying out BWT (Burrows-Wheeler transformation), (l,m)-skipping-BWT, w-ahead-BWT and SET-skipping, and w-ahead-BWT to the long symbol string; constructing a wavelet tree on the basis of the four conversion results, establishing a C table and a FT (functional test) array, and establishing a symbol rotation index; and matching the given search request with the full text, and outputting the search results, wherein the search request can contain one section or multiple sections of continuous missing symbols. The full-text search method provided by the invention supports different symbolization methods, and also supports the search request containing missing symbols, thereby meeting different kinds of search requirements.

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 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.