32 results about "Multiresolution analysis" patented technology

A new multiresolution analysis (wavelet) assisted reinforcement learning (RL) based control strategy that can effectively deal with both multiscale disturbances in processes and the lack of process models. The application of wavelet aided RL based controller represents a paradigm shift in the control of large scale stochastic dynamic systems of which the control problem is a subset. The control strategy is termed a WRL-RbR controller. The WRL-RbR controller is tested on a multiple-input-multiple-output (MIMO) Chemical Mechanical Planarization (CMP) process of wafer fabrication for which process model is available. Results show that the RL controller outperforms EWMA based controllers for low autocorrelation. The new controller also performs quite well for strongly autocorrelated processes for which the EWMA controllers are known to fail. Convergence analysis of the new breed of WRL-RbR controller is presented. Further enhancement of the controller to deal with model free processes and for inputs coming from spatially distributed environments are also addressed.

Function spaces defined by scaling functions are used to generate bandlimited noise octaves and other attribute data sets. Scaling functions are basis functions that admit multiresolution analysis and include piecewise constant scaling functions, piecewise polynomial scaling functions, bandlimited scaling functions, Daubeschies scaling functions, as well as other multiresolution analysis scaling basis functions known to those of skill in the art. Scaling basis functions can be locally supported or have infinite support. The properties of the scaling basis functions used to construct bandlimited noise octaves may ensure that any bandlimited noise octave at resolution level N is orthogonal to bandlimited noise octaves and their associated scaling basis functions at all resolution levels less than N. Bandlimited noise octaves can be scaled to any resolution level and guaranteed to have no effect on images at any lower resolution level.

A neural network system for predicting time series data. The system receives analyzed data obtained by multiresolution analysis of the time series data. The input processing layer of the system includes a series of neurons corresponding to the different levels of analysis, each neuron receiving the analyzed data for it own level. The output of each of these neurons is supplied as an additional input to the neuron for the next lower level of analysis. A predicted value is derived from the output of the neuron at the lowest level. The passing of results from one level to another improves prediction accuracy and simplifies the structure of any further processing layers.

The invention discloses a synthetic aperture sonar speckle noise suppression method based on multiresolution analysis. The method comprises the steps that 1, speckle noise in an original synthetic aperture sonar image is converted into additive noise through logarithm processing, and a logarithm processing image is obtained; 2, wavelet threshold de-noising processing is conducted on the logarithm processing image, and a reconstituted image is obtained; 3, index processing is conducted on the reconstituted image. The synthetic aperture sonar speckle noise suppression method based on multiresolution analysis solves the problem that synthetic aperture sonar speckle noise in a synthetic aperture sonar image cannot be processed in the prior art. The synthetic aperture sonar speckle noise suppression method based on multiresolution analysis improves the gray scale contrast ratio of the target and background of the synthetic aperture sonar image with the target, a subsequent image processing process is facilitated, the edges of different substrate areas of the landform synthetic aperture sonar image are made to be more obvious, and therefore the subsequent image processing such as image segmentation and substrate characteristic analysis is facilitated.

An electrophotographic photoreceptor including a conductive substrate, a photosensitive layer, and a surface layer, which satisfies the following inequations:0.005<WRa(LMH)<0.03  (i)0.010<WRa(LHH)<0.03  (ii)0.005<WRa(LML)<0.20  (iii)WRa(LLH)>WRa(LMH)  (iv)WRa(LLH)>WRa(LHH)  (v)wherein WRa (μm) represents a center-line average roughness of frequency components LHH, LHL, LMH, LML, LLH, and LLL that are obtained by subjecting a one-dimensional data array of a surface profile of the electrophotographic photoreceptor to wavelet transformation multiresolution analysis so as to be separated into 6 frequency components; thinning a one-dimensional data array of the lowest frequency component so that the number of data array is reduced to 1 / 40; and subjecting the thinned one-dimensional data array to wavelet transformation multiresolution analysis so as to be separated into the 6 frequency components LHH, LHL, LMH, LML, LLH, and LLL.

The invention provides an electric energy quality regulation method, voltage temporary change detection method and dynamic voltage restorer. The voltage temporary change detection method comprises thefollowing steps: acquiring an electric grid voltage signal, selecting a decomposition scale, performing initial wavelet transformation, and acquiring wavelet coefficients of a first set frequency band and a second frequency band; performing single reconstruction for the wavelet coefficient of the first frequency band, and acquiring a fundamental wave signal of the electric grid voltage; performing secondary wavelet transformation, acquiring a first frequency band component and a second frequency band component; performing single reconstruction for the second frequency band component, and determining start and stop time of occurrence of voltage temporary change; and performing single reconstruction for the first frequency band component, and determining fundamental wave electric grid voltage temporary amplitude information. Double wavelet transformations are used for detection of the voltage signal containing temporary change and harmonic waves, based on wavelet transformation multiresolution analysis characteristics and singularity detection property, decoupling of harmonic waves and temporary change electric energy quality problems is solved, noise influence is restrained, accurate positioning of a temporary change signal is completed, time delay can be reduced effectively, and influence on electric grid real-time compensation is reduced.

The present invention is an online methodology for end point detection for use in a chemical mechanical planarization process which is both robust and inexpensive while overcoming some of the drawbacks of the existing end point detection approaches currently known in the art. The present invention provides a system and method for identifying a significant event in a chemical mechanical planarization process including the steps of decomposing coefficient of friction data acquired from a chemical mechanical planarization process using wavelet-based multiresolution analysis, and applying a sequential probability ratio test for variance on the decomposed data to identify a significant event in the chemical mechanical planarization process.

The invention relates to a heart head and neck blood vessel combined imaging method, comprising the steps of: through a module coupling mode, determining noncollinear coupling characteristic points on a heart head and neck blood vessel image to be tested and a standard image; separately establishing the three-dimensional coordinate system of the standard image and the three-dimensional coordinate system of the heart head and neck blood vessel image to be tested based on the characteristic points; separately performing multiresolution analysis on the standard image and the heart head and neck blood vessel image to be tested in the three-dimensional coordinate systems to obtain multiresolution sub-images; starting registering from a sub-image of lowest resolution, and performing registering by degrees until obtaining registration parameters of the image of highest resolution and the heart head and neck blood vessel image to be tested after registering; and performing Force double source CT coarse pitch scanning on the heart head and neck blood vessel image to be tested, and fusing with the heart head and neck blood vessel image to be tested after registering to obtain the combined imaging of the heart head and neck blood vessel image to be tested. The heart head and neck blood vessel combined imaging method obtains the combined imaging of the heart head and neck blood vessel image to be tested through different modes.

An electrophotographic photoconductor having a photosensitive layer and a crosslinked resin surface layer over a support, wherein shapes of concaves and convexes in a surface of the electrophotographic photoconductor are measured by a surface roughness / profile measuring device to obtain one-dimensional data arrays, the arrays are subjected to multiresolution analysis (MRA-1) through wavelet transformation to be separated into six frequency components including HHH, HHL, HMH, HML, HLH and HLL to obtain one-dimensional data arrays, the arrays of the HHL are thinned out to be reduced 1 / 10 to 1 / 100, thereby producing one-dimensional data arrays, which are then subjected to multiresolution analysis (MRA-2) through wavelet transformation to be separated into six frequency components including LHH, LHL, LMH, LML, LLH and LLL to thereby obtain 12 frequency components in total; and a center-line average roughness (WRa) of the 12 frequency components satisfies relationship (i) below.1−597×WRa(HML)+238×WRa(HLH)−95×WRa(LHL)+84×WRa(LMH)−79×WRa(LML)+55×WRa(LLH)−17×WRa(LLL)>0  (i)

The image-processing unit (16) of an embodiment is provided with an expansion unit (161), a decomposition unit (162), a data-processing unit (163), a reconstruction unit (164), and a contraction unit (165). The expansion unit (161) expands the data to be processed with a magnification that is based on the optimal sample number, which is the ideal number of samples per unit length for image-processing. The decomposition unit (162) decomposes the expanded data using multiresolution analysis to generate data sets with the optimal number of samples. The data-processing unit (163) performs image processing on the data sets with the optimal number of samples to generate processed data sets. The reconstruction unit (164) reconstructs the processed data sets using multiresolution analysis to generate reconstructed data. The contraction unit (165) contracts the reconstructed data so that the number of samples per unit length equals the number of samples per unit length of the data that was processed.

The invention discloses a vein recognition method based on texture features. The method includes the steps of firstly, creating a hand vein image library, images in each category being randomly divided into a training set and a testing set, and the number of images in the training set being the same as the number of images in the testing set; secondly, acquiring and pre-processing hand vein images; and thirdly, recognizing the vein images based on the texture features. The method of the invention is capable of targeted extraction based on the characteristics of vein directivity, has strong robustness and rotation invariance, and can achieve multi-resolution and multi-directional analysis. Meanwhile, the problem of feature dimension is solved and excellent effects are gained in an actual test.

Function spaces defined by scaling functions are used to generate bandlimited noise octaves and other attribute data sets. Scaling functions are basis functions that admit multiresolution analysis and include piecewise constant scaling functions, piecewise polynomial scaling functions, bandlimited scaling functions, Daubeschies scaling functions, as well as other multiresolution analysis scaling basis functions known to those of skill in the art. Scaling basis functions can be locally supported or have infinite support. The properties of the scaling basis functions used to construct bandlimited noise octaves may ensure that any bandlimited noise octave at resolution level N is orthogonal to bandlimited noise octaves and their associated scaling basis functions at all resolution levels less than N. Bandlimited noise octaves can be scaled to any resolution level and guaranteed to have no effect on images at any lower resolution level.

The invention relates to an industrial device rapid locating method based on machine vision, which is a device locating method adopted by machine vision software running in a hardware environment built by an industrial lens, an industrial camera, an image gathering card and an industrial personal computer. The method provided by the invention is a method with combined multiresolution analysis andwavelet transformation characteristics, and the method comprises the following steps of: firstly locating approximately according to the projection transformation vectors of horizontal and vertical direction coefficients of wavelet transformation, then implementing template matching in a low-resolution image, and finally accurately locating in the approximately located field of the original image. The application effect of the method in semiconductor and electronic manufacturing industry is excellent, the locating speed is particularly and obviously improved by means of multiresolution analysis and location preparation, and the locating process of a wafer can be completed in about 10 milliseconds.