455 results about "Data decomposition" patented technology

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 methodology for automatic a priori data pattern analysis is provided. Described methods allow consistent and objective determination of outliers; trend; seasonality; and level shifts; and the production of better models and more accurate forecasts. In addition, a two-step way to automatically determine seasonality and locate possible events in the data set is described. Decomposition of data into seasonal, trend and level components; detection of outliers and level-shift events in the time series based on statistical analysis of the time series; detection of seasonality based on statistical analysis of clusters of data, known as cluster-based seasonality analysis, or CBSA; evaluation of the goodness of fit of a model to data, using the existing goodness of fit indicator, R²; and seasonality analysis, using a sequence of cluster-based seasonality analysis (CBSA) and Fourier analysis are described.

A system and method of a diagnostic imaging system includes a high frequency electromagnetic energy source that emits a beam of high frequency electromagnetic energy toward an object to be imaged, a detector that receives high frequency electromagnetic energy emitted by the high frequency electromagnetic energy source and attenuated by the object, a data acquisition system (DAS) operably connected to the detector, and a computer operably connected to the DAS. The computer is programmed to obtain CT scan data with two or more incident energy spectra, decompose the obtained CT scan data into projection CT data of two or more basis materials, reconstruct linearly weighted projections of the two or more basis materials, determine an optimized energy for the two or more basis materials within a region-of-interest (ROI), and form a monochromatic image of the projection CT data at the optimized energy using the two or more basis material projections.

For multi-dimensional temporal-spatial data, EEMD is applied to time series of each spatial location to obtain IMF-like components of different time scales. All the ith IMF-like components of all the time series of all spatial locations are arranged to obtain ith temporal-spatial multi-dimensional IMF-like component. For two-dimensional spatial data or images, the two-dimensional spatial data or images are consider as a collection of one-dimensional series in first direction along locations in second direction. The same approach to the one used in temporal-spatial data decomposition is used to obtain the resulting two-dimensional IMF-like components. Each of the resulted IMF-like components are taken as the new two-dimensional data for further decomposition, but the data is considered as a collection of one-dimensional series in second-direction along locations in first-direction.

A method for encoding an image having color components of each image pixel represented by a value of a high dynamic range (HDR), the method comprising: decomposing the image into image blocks; determining a scaling factor for each image block, said scaling factor, when applied to a corresponding image block, for converting the values of the color components into a normalized range; and compressing the normalized image blocks and the scaling factors of each image block independently of each other, whereby the normalized image blocks are encoded according to a low dynamic range (LDR) compression method. In a decoding phase, the encoded image data are decomposed into encoded image blocks, which are decoded according to the LDR compression method. The values of the color components are scaled with a corresponding scaling factor included in the auxiliary data; and the scaled image blocks are composed into an image with the original dynamic range.

An imaging scanner includes a radiation source, a radiation detector, and a computer programmed to decompose CT data acquired by the radiation detector into a set of pixels, each pixel having at least a first basis material content and a second basis material content. The computer is further programmed to identify a first subset of the set of pixels as a possible embolism, based on the content of the first basis material and the content of the second basis material.

The invention relates to a device and a method for coding video data. The device comprising means for coding each picture in slices of pictures, each slice being coded independently of the other slices. According to the invention, the device comprises means for inserting into the data stream at least one message indicating the structure relating to the breakdown of the pictures into slices. The invention also relates to a system for decoding and a method for decoding video data, the said video data having been coded in slices, each of the slices being coded independently of the other slices. According to the invention, the coding system comprises means for analysing the video data in order to ascertain the breakdown of the data into slices, means for decoding the video data slice by slice, means for reconstructing the various video data after they have been decoded in order to reconstruct the decoded video data.

An efficient program analysis method is provided for lazily decomposing aggregates (such as records and arrays) into simpler components based on the access patterns specific to a given program. This process allows us both to identify implicit aggregate structure not evident from declarative information in the program, and to simplify the representation of declared aggregates when references are made only to a subset of their components. The method can be exploited to yield: (i) a fast type analysis method applicable to program maintenance applications (such as date usage inference for the Year 2000 problem); and (ii) an efficient method for atomization of aggregates. More specifically, aggregate atomization decomposes all of the data that can be manipulated by the program into a set of disjoint atoms such that each data reference can be modeled as one or more references to atoms without loss of semantic information. Aggregate atomization can be used to adapt program analyses and representations designed for scalar data to aggregate data. In particular, atomization can be used to build more precise versions of program representations such as SSA form or PDGs. Such representations can in turn yield more accurate results for problems such as program slicing. Our techniques are especially useful in weakly-typed languages such as Cobol (where a variable need not be declared as an aggregate to store an aggregate value) and in languages where references to statically-defined sub-ranges of data such as arrays or strings are allowed.

There is provided with a transmission apparatus including: a first modulator configured to perform modulation processing on data to be transmitted to a first reception apparatus and thereby generate amplitude-phase data made up of data of amplitude and phase; a second modulator configured to perform modulation processing so as to decompose data to be transmitted to a second reception apparatus into a plurality of frequency components and thereby generate frequency data which is data on a frequency domain; and a transmitter configured to allocate the amplitude-phase data to first subcarriers out of a plurality of subcarriers and allocate the frequency data to second subcarriers which are different from the first subcarriers out of the plurality of subcarriers, thereby generate subcarrier data and transmit generated subcarrier data to the first reception apparatus and the second reception apparatus.

A method and apparatus for processing image data are described. In one embodiment, a method of processing image data comprises decomposing the image data into multiple decomposition levels by applying a wavelet transform to the image data, and modifying coefficients in at least two of the decomposition levels by scaling coefficients in theses decomposition levels using different scale dependent parameters for each of the decomposition levels.

The invention belongs to the technical field of remote sensing image processing, and specifically relates to a low-rank expression and learning dictionary-based hyperspectral image abnormity detection algorithm. According to the algorithm, a method for introducing low-rank expression in the abnormity detection problems is used for decomposing the two-dimensional hyperspectral image data into the sum of a low-rank matrix expressing background and a sparse matrix expressing abnormity, and then enabling a basic abnormity detection algorithm to act on the sparse matrix to obtain the abnormity detection result; and furthermore, the concept of a learning dictionary is imported in the low-rank expression algorithm, and the learning dictionary is obtained through an algorithm of random selection and gradient descent and is capable of expressing the background spectrums in hyperspectral images. Through the importing of the learning dictionary, the abnormity information can be better separated from the hyperspectral image data, so that better detection result can be obtained; and meanwhile, the robustness of the algorithm for the initial parameters can be improved, so that the computing cost is reduced and important value is provided for the actual abnormity detection application.

The invention relates to a short-term load predicting method of a power grid. The method comprises the steps: step 1, acquiring historical data and pre-treating the data; step2, decomposing the historical load sample data into a plurality of different-frequency sub-sequences by using wavelet decomposition; step 3, performing single-branch reconstruction to each sub-sequence; step 4, dynamically choosing training samples and establishing a neural network predicting model optimized by a vertical and horizontal intersection algorithm; step 5, predicting each sub-sequence 24 hours in advance by using the optimal neural network predicting model; and step 6, superposing the predicted value of each sub-sequence to obtain a whole prediction result. The inherent defects of the neutral network can be overcome by optimizing BP neutral network parameters by a brand-new swarm intelligence algorithm, that is, the vertical and horizontal intersection algorithm instead of the traditional algorithm; the burr problem caused by the impact load processing is solved by the wavelet decomposition, the precision declining resulting from the removal of the effective load in the burr pre-treatment is solved and the predicted value of the hybrid algorithm is more approximate to the actual measured load value.

A method and system for migrating source data from a source database to a destination database based on energy efficiency and conservation. A migration server evaluates the source data for usage and requirements and defines data usage and requirement tags for the source data. The source data is disaggregated into one or more source data sets based on the data usage and requirement tags. The migration server then identifies candidate destinations for the source data, wherein each candidate destination has stored data identified with usage and requirement tags. The data migration server compares the first usage and requirement tags of the source data with the second usage and requirement tags of the stored data and identifies an optimal destination database based on the comparing step. The data migration server migrates the source data to the optimal destination database.

A method and apparatus for processing raw image data to create processed images. Raw image data is acquired. The raw image data is decomposed by a data decomposer into N subsets of raw image data. The number N is based on a number of available image generation processors. The N subsets of raw image data are processed by at least one image generation processor to create processed image data. If more than one image generation processor is available, the image generation processors perform image processing on the raw image data in parallel with respect to each other.

A method is disclosed for deghosting and water surface multiple reflection attenuation in marine seismic data. The method includes decomposing data acquired at two water depths with sensors that measure the same parameter into upgoing and downgoing wavefield components. The decomposing includes, in one embodiment, transforming the data into the spatial Fourier domain and separating the upgoing and downgoing wavefield components in the transformed data. A substantially multiple-free wavefield is then determined from the decomposed wavefield components.

A digital-signal processing apparatus in which the efficiency of processing data can be prevented uniformly. The apparatus includes: an extracting unit that extracts a part of real data input; a selecting unit that selects a data size from similar data sizes falling in a range, in accordance with the size of the extracted data, which is a reference value, and with data-decomposing conditions described in an algorithm that realizes fast Fourier transform; and an operation unit that decomposes the data extracted and having the selected size into data items, in accordance with the data-decomposing conditions, and that performs Fourier transform on each data item, thereby effecting convolution on the data item.

A circuit for a flat panel display includes an image data storage and processing block, a display and timing controller block, an image pixel matrix containing a multitude of row and column arranged pixel elements, one or more controlled row and column driver blocks, and a tagged multi line addressing (TMLA) pixel element display operation. That TMLA operation comprises a decomposition of image data by searching all lines of an image for groups of identical lines by tagging each of these lines with a unique code and thus decomposes image data into multi line and single line domain data in such a way, that lines with matching tags, indicating their common and identical contents, are outputted as image data into related groups of the multi line domain with no left over residual image data and thus the related groups in the single line domain data are all zeroes.

The invention relates to a traffic flow prediction method based on a multivariable grey model time sequence. The method comprises the following steps: inputting collected observation station traffic flow, related external variable data and observation station information data; performing data preprocessing on the input data; inputting the data subjected to data preprocessing into a multivariable time sequence fusion prediction model based on data decomposition and a multivariable time sequence fusion prediction model based on result weighting for prediction; and comparing the predicted value with the actual value, and outputting a final result. The traffic flow of the expressway is predicted through fusion of multiple multivariable time sequence prediction models, the prediction precisionis improved, and through application to the expressway in the traffic field, the traffic management department can be helped to improve the intelligent management level, and the operation cost is reduced; through the display of the application demonstration system, data support can be visually provided for managers so as to make corresponding decisions in time and implement the decisions.

The invention provides a space-spectrum joint sparse prior based satellitic hyperspectral compressed sensing reconstruction method. The method includes hyperspectral data-block diagonal random measurement of an onboard encoding end and compressed sensing construction of a ground decoding end. The onboard encoding-end adopts block-diagonal hyperspectral-data random measurement matrixes to perform independent random sampling on each spectral band so as to acquire measurement data and then sends the measurement data to the ground decoding end through a data link, the ground decoding end decomposes the data into low-rank components and sparse components, low-rand prior between the hyperspectral-data spectral bands and sparse prior in the spectral bands are jointed to establish a convex optimization reconstruction model, iteration solution is performed to obtain low-rank components and sparse components of reconstructed hyperspectral data, and the low-rank components and the sparse components are merged to obtain the reconstructed hyperspectral data. By the method, precision and efficiency of satellitic hyperspectral-image compressed sensing reconstruction are improved.

A system and method for collecting, inventorying, stocking and performing metered delivery of standard parts to an assembly operation. Standard parts are collected from multiple suppliers in an optimized pick-up route, and delivered to a standard parts metering warehouse. The standard parts are broken down into production shift quantities based upon production data of the assembly operation. The production shift quantities of standard parts are labeled according to part type or identity and a drop zone within the assembly operation. The standard parts are then stocked in the metering warehouse according to an optimized route which corresponds to a drop zone delivery sequence in the assembly operation. Limited production shift quantities of standard parts are then metered from the metering warehouse in accordance with a production schedule of the assembly operation, and delivered directly to pedestals at workstations at the corresponding drop zones in the assembly operation.

The disclosure concerns processing of electronic images, such as hyperspectral or multispectral images. In particular, but is not limited to methods, software and computer systems for determining underlying spectra of an image of a scene. The image data comprises for each pixel location a sampled image spectrum that is a mixture of plural reflectance spectra. Processor 310 determines or accesses plural hyperplanes that each have plural linearly independent basis vectors. Each hyperplane represents an estimate of one of the plural reflectance spectra. The processor 310 then determines for each pixel location, a contribution of the plural basis vectors of each hyperplane to the image spectrum of that pixel location. The processor 310 determines or accesses plural hyperplanes and not plural endmembers directly. Hyperplanes are two-dimensional while endmembers are only one-dimensional. As a result, hyperplanes carry more information, such as the illumination spectrum and are therefore of greater use. The decomposition of the image data into hyperplanes is possible without knowing the illumination spectrum. This decomposition allows for a range of new applications such as endmember extraction and compact representation.