376 results about "Threshold function" patented technology

A system and method for unorchestrated determination of data sequences using “sticky byte” factoring to determine breakpoints in digital sequences such that common sequences can be identified. Sticky byte factoring provides an efficient method of dividing a data set into pieces that generally yields near optimal commonality. This is effectuated by employing a rolling hashsum and, in an exemplary embodiment disclosed herein, a threshold function to deterministically set divisions in a sequence of data. Both the rolling hash and the threshold function are designed to require minimal computation. This low overhead makes it possible to rapidly partition a data sequence for presentation to a factoring engine or other applications that prefer subsequent synchronization across the data set.

The invention provides an image denoising method based on a self-adaptive wavelet threshold and a two-sided filter and aims to improve the effect of a wavelet threshold denoising algorithm and better protect the edge and the detailed information of an image. The algorithm comprises the following steps of decomposing the image by adopting a discrete wavelet to obtain a plurality of sub-bands and the wavelet coefficients of the sub-bands; selecting a threshold which is self-adaptively changed along with the changes of wavelet-decomposing scales and the sub-bands, and carrying out quantitative threshold processing by adopting a soft threshold function; carrying out inverse wavelet transformation to obtain a reconstructed image; filtering the reconstructed image by adopting the two-sided filter so as to obtain the clear image. According to the image denoising method, wavelet threshold denoising is carried out by utilizing the threshold which is self-adapted to the wavelet-decomposing scales and the sub-bands, and filtering is carried out by combining the two-sided filter, so that through the designed denoising algorithm, not only can white gaussian noise be effectively removed, but also the edge and the detailed information of the image can be well reserved.

The invention relates to an improved threshold function-based wavelet transformation image denoising method which comprises the following steps of: firstly selecting a base wavelet, determining the number of wavelet decomposition layers and carrying out multi-scale wavelet transformation on noise-containing images; respectively determining a threshold for each layer of detail coefficient; carrying out threshold treatment by using improved threshold functions; and finally carrying out wavelet reconstruction on low-frequency coefficients and threshold treated high-frequency coefficients to obtain the denoised images. According to the denoising method provided by the invention, the defect of discontinuity of the hard threshold functions is overcome and the constant deviation in the soft threshold functions is decreased at the same time. By using the denoising method, the signals and the noises can be distinguished effectively, the image information edges are protected when the noises are removed, the peak signal to noise ratio of the images is improved, and better image denoising effect is achieved.

The invention relates to a partial discharge signal denoising method based on lifting wavelet transform, which includes the following steps: (1) a partial discharge signal to be denoised is inputted; (2) lifting wavelet decomposition is carried out on the partial discharge signal, so that high-frequency coefficient components of different decomposition scales and a low-frequency coefficient component of the highest scale are obtained; (3) wavelet entropy-based layered thresholds and a soft threshold function are adopted to quantify the high-frequency coefficient components in order to remove noise components, and the high-frequency coefficient components are stored as new high-frequency coefficient components; (4) the new high-frequency coefficient components and the low-frequency coefficient component of the highest scale obtained in step (3) are utilized to compose a coefficient component for signal reconstruction, signal reconstruction is carried out on the coefficient, and thereby a denoised partial discharge signal is obtained. Lifting wavelets are completely transformed in a time (space) domain, and high-pass and low-pass filters are turned into a series of relatively simple prediction and update steps. Therefore the denoising speed of lifting wavelet transform is high, the design is flexible and simple, and the partial discharge signal denoising method is easy to put into practice.

A method of concealing bit errors in a signal is provided. The method comprises encoding a signal parameter according to a set of constraints placed on a signal parameter quantizer. The encoded signal parameter is decoded and compared against the set of constraints. Finally, the method includes declaring the decoded signal parameter invalid when the set of constraints is violated. Training binned ranges of gain values provide a threshold for selecting data segments to examine for violation of constraints on gain differences. Further, an additional method comprises training a threshold function T(qlg(m−1), Δqlg(m−1) used in a codec bit error detecting technique. The threshold function is based upon a first training file having N signal segments. The method includes encoding the first training file and determining gain values qlg(m) of each of the N signal segments within the encoded first training file. The gain values form a range and the range is divided into bins. Next, the gain values are stored in a second training file. The method associates each of particular ones of the gain values and gain differences within the sequence qlg(m−1) and Δqlg(m−1) with a corresponding one of the bins.

The invention brings forward a self-adaptive wavelet threshold image de-noising algorithm and device. The image de-noising algorithm comprises the following steps: a noised image is subjected to wavelet transformation operation, and wavelet coefficients of all layers can be obtained; with signal correlation considered, coefficients in an area adjacent to each coefficient are averaged in wavelet coefficients of each layer; threshold is determined based on a wavelet coefficient which is obtained via an absolute mean value estimation method, and a self-adaptive threshold method is adopted for determining thresholds suitable for all different scales; as for the wavelet coefficients and thresholds, self-adaptive threshold functions for all directions at all layers are constructed, wavelet inverse transformation and reconstruction are performed, and a de-noised image can be obtained. According to the image de-noising algorithm, the self-adaptive threshold method is adopted for determining the thresholds, an overall uniform threshold is replaced with thresholds for different scales, wavelet threshold de-noising operation is performed via use of the self-adaptive thresholds and the self-adaptive threshold functions, and detailed information of the image can be protected; the self-adaptive wavelet threshold image de-noising algorithm is better than a conventional wavelet threshold de-noising algorithm in terms of peak signal to noise ratio and visual perception.

The invention provides a field effect transistor that achieves the storage function and the preparation method, which belongs to the filed of semiconductor integrated circuit and manufacturing technology. The transistor comprises a source region, a drain region and a control grid, wherein the control grid utilizes a grid stepped construction which comprises a bottom layer of a tunneling oxidizing layer, an interface layer of a resistance-varying material layer and a top layer of a conductive electrode layer. The field effect transistor obtains an electrically programmable multi-threshold function, the source and drain currents of which are different when a same reading voltage is imposed on the grid, thereby achieving the information storage on two different states or other functions. Utilizing the invention, a plurality of devices and circuits with new functions, high performance and high reliability can be composed, thereby meeting the application of different circuit functions. Meanwhile the invention can adopt the compatibility with the CMOS technology of the conventional PN source or drain junction structure, and can also adopt the compatibility with the CMOS technology of the Schottky source or drain junction structure, with a greater flexibility in technology selection.

The invention relates to a method for conducting signal denoising based on a wavelet packet. The method is mainly applied to signal processing in engineering surveying. Low-frequency parts and high-frequency parts of signals are respectively processed through wavelet packet denoising, and the resolution ratios of high frequencies and low frequencies are improved. When a signal processing method used at present is used for conducting denoising processing on the signals, negative phenomena of low speed and low efficiency will be caused by limitation of data; meanwhile, as hard threshold functions and soft threshold functions are numerously adopted in data analysis so as to conduct quantization processing on decomposition coefficients, the denoising signals are lack of fidelity, and the relatively-ideal denoising signals cannot be obtained.

The invention belongs to the technical field of multisource information fusion, and discloses a flight path fusion method. The flight path fusion method comprises the following steps of: establishing a relative distance matrix between data by using observation information of a plurality of sensors; computing a support threshold function to obtain a support threshold matrix, establishing an equation set, and solving a weighting factor; multiplying the weighting factor with a corresponding observed value, obtaining corresponding filter values respectively through filtering, and adding all obtained filter values to obtain a filter fusion value with an observation coefficient; and updating an estimated value of target state step by step by using Kalman filtering, wherein the filter fusion value serves as a state updating input value. In the invention, as the filter fusion of the observation coefficient is carried out through the observation information of the plurality of sensors, influence on the fusion of flight paths due to the uncertainty of the observation information is reduced under the condition that data processing complexity is not increased; and correlation of the observation information is taken into consideration during the filter fusion of the observation coefficient, so that observation accuracy is increased, and the reliable tracking of a target is obtained.

The invention provides a multi-model fusion video hand division method based on Kinect, which comprises the following steps of (1) capturing video information, (2) dividing images in a video respectively to obtain division results, namely a depth model, a skin color model and a background model, (3) calculating an overlapping rate of every two division results as a characteristic of judging division effects of the results and inputting the three overlapping rates into a neural network, (4) allowing the neural network to output three coefficients (namely confidence coefficients) showing respective reliability of the three models, and weighting the three division results with the confidence coefficients, (5) conducting linear superposition on the weighted division results of the three models, (6) outputting a final binary image of a superposed result through a threshold function and finally dividing an obtained video hand region, and (7) updating the background model, wherein the division results are expressed as binary images. The method has the advantages of low cost, good flexibility and the like.

The invention discloses a partial discharge signal denoising method based on a wavelet adaptive threshold. The partial discharge signal denoising method based on the wavelet adaptive threshold comprises the following steps of (1) inputting a partial discharge signal to be denoised, (2) carrying out wavelet multi-scale decomposition on the partial discharge signal to obtain high-frequency coefficients of decomposition scales and a low-frequency coefficient of a maximum decomposition scale, (3) using a non-negative garrote threshold function and a adaptive threshold selection method based on particle swarm optimization to carry out quantitative processing on high-frequency coefficient components obtained in the step (2) so as to remove noise components, storing the result to serve as new high-frequency coefficient components, (4) carrying out signal reconstruction through the new high-frequency coefficient components and a low-frequency coefficient component, obtained in the step (2), of the maximum decomposition scale to obtain a partial discharge signal without noise, and (5) outputting the partial discharge signal without the noise. The partial discharge signal denoising method based on the wavelet adaptive threshold achieves wavelet coefficient threshold self-adaptation selection on the premise that any priori knowledge does not exist, and is applicable to various actual partial discharge conditions and good in effect of removing white noise, and the denoised partial discharge signal with higher quality can be obtained.

The invention discloses a method and a system for wireless channel measurement based on wavelet decomposition threshold de-nosing. The method includes the following steps: (1) using a spread spectrum sliding correlation method to finish wireless channel measurement so as to obtain channel impulse response containing noise, (2) conducting multi-resolution wavelet decomposition for the channel impulse response and leading the channel impulse response to be converted from time domain to wavelet domain, (3) reasonably selecting a threshold function and a threshold and processing wavelet coefficients corresponding to the noise according to the threshold function; (4) conducting wavelet reconstruction and leading the de-noised channel impulse response to be converted from the wavelet domain back to the time domain, and (5) storing the de-noised channel impulse response for researching channel characteristics. Through steps of wavelet decomposition, threshold de-nosing, wavelet reconstruction and the like, the method and the system can reduce interference on the channel impulse response caused by the noise, improve accuracy and effectiveness of channel measurement results, and provide reliable data basis for follow-up researching of channel characteristics.

The invention belongs to the technical field of social network modeling and analysis and particularly relates to a microblog rumor spreading analysis method. On the basis of a microblog rumor spreading model, an inducement mechanism of microblog rumor spreading is analyzed, the UASR microblog rumor spreading model conducts state division on the basis of field influences of users, characteristics of rumor microblogs and the action of external social factors on rumoring mind of the users in the rumor spreading process, a proper threshold function is built, and a node state conversion rule is defined so as to depict actions of different factors on actual rumor spreading routes and effects in the rumor spreading process as accurately as possible. The method can simulate the microblog rumor spreading process in real world completely and truly.

The invention discloses an electrocardiographic signal de-noising method based on adaptive threshold wavelet transform. The method is characterized by comprising following steps: step 1: using the Mallat algorithm, the wavelet function sym6 and the number of decomposition layers J are selected, and the noisy ECG signal is decomposed by wavelet to obtain approximate coefficients and detail coefficients; step 2: setting the threshold for adaptive detail coefficients at each layer and selecting the threshold function; step 3: performing adaptive threshold processing on the detail coefficients ofeach layer, removing power frequency interference and myoelectric interference, and removing baseline drift by processing the approximation coefficients; step 4: performing wavelet reconstruction on the electrocardiographic signals after processing to obtain approximate optimal estimate value of signals. The method of the present invention makes full use of the multiresolution feature of the wavelet transform. An adaptive threshold selection method is provided. Different thresholds are used at each level to separate the noise and signal flexibly, improving the separability of signal characteristics; in the three aspects of visual, mean square error, and signal-to-noise ratio, the effect is better than the traditional method, and the detailed information of the image is retained better, which has higher practical value.

A monitor on-board an aircraft which uses radio altitude measurements as the basic observable altitude during runway approach. The basic concept utilizes the aircraft's navigation system, which includes means to store and retrieve radio altitude thresholds as a function of the distance along the desired path from the runway thresholds. These threshold functions are determined in advance based on a radio altitude reference which is defined as the expected radio altimeter measurement that would be made if the airplane were exactly on the desired reference path. Vertical containment monitoring is achieved by comparing the radio altitude measurement to computed thresholds for both too high and too low. During the approach, an annunciation message can be generated if the radio altitude measurement is above or below the threshold limits. Using this monitor ensures that the total system error for the aircraft is contained within a bound called the Vertical Containment Level of the desired reference path in space with a probability that is specified.

The invention relates to an electroencephalogram signal denoising method based on self-adaption threshold processing. The method comprises the following steps: first improving a threshold function on the basis of a soft threshold; second, conducting multi-layer decomposition on an acquired electroencephalogram signal, and obtaining a corresponding wavelet detail coefficient; then, improving the threshold according to the statistical correlation of the wavelet coefficient after wavelet decomposition, conducting self-adaption threshold processing on the wavelet coefficient; finally, reconstructing the wavelet coefficient after zooming to obtain a denoised EEG signal. Compared with a hard threshold method, a soft threshold method and a Garrote threshold method, the electroencephalogram signal denoising method has the advantages that smoothness of the soft threshold method is maintained, the Gibbs phenomenon is reduced, gaussian noise is effectively suppressed, most usable detail information in an EEG is reserved, and a good foundation is laid for EEG characteristic extraction and mode identification in the next step.

The invention relates to an EEMD (Ensemble Empirical Mode Decomposition) and wavelet threshold based motor imagery electroencephalogram signal denoising method. The method comprises the steps of firstly, performing EEMD on an original signal to obtain a series of IMF (Intrinsic Mode Function) components; secondly, improving a conventional wavelet threshold method with a new threshold function and a threshold selection method; thirdly, processing a high-frequency IMF component with the improved wavelet threshold method; and finally, reconstructing the processed IMF component and other IMF components to obtain a denoised motor imagery EEG (electroencephalogram) signal. The method has the advantages that effective information in the high-frequency component is reserved, the suppression of the wavelet threshold method to a weak-energy effective signal is reduced, most of useful detailed information is reserved while a large amount of noises are eliminated, and a good foundation is laid for motor imagery EEG signal feature extraction and mode identification in the next step.

The invention discloses an aggregated white blood cell segmentation counting system. The system comprises an image acquisition module for dyeing white blood cells in a blood sample, dissolving red blood cells in the blood sample by using red blood cell lysate and acquiring a white blood cell image, an image preprocessing module used for performing image background removal on the white blood cellimage and obtaining an optimal segmentation threshold by using a maximum inter-class variance method and roughly segmenting a white blood cell region, an aggregated cell determination module used forobtaining a coarse segmentation image according to the rough segmentation of the white blood cell region, setting a discriminant function of a cell area and obtaining a multi-cell aggregation region,and an aggregated cell segmentation counting module used for extracting a cytoskeleton in each aggregation region and a gray curve at the cytoskeleton by using a morphological refinement method. According to the invention, by analyzing the gray scale characteristics of various white blood cell areas under a low power microscope, an adaptive threshold function is constructed, while a white blood cell count is obtained, the number of oxyphil cells is obtained, the cells in the aggregation region are quickly and accurately divided and counted, the method is quick and simple and is easy to implement.

The invention provides a gesture recognition method based on 77GHz millimeter wave radar signals. The method comprises the following steps: acquiring intermediate frequency signals of different gesture actions through radar; innovatively utilizing an improved wavelet threshold function to preprocess a low-frequency coefficient of the wavelet threshold function so as to solve the problem that a close-range gesture cannot be recognized due to an antenna coupling phenomenon; secondly, extracting a time-distance spectrogram, a time-speed spectrogram and a time-angle spectrogram from the preprocessed intermediate frequency signal; innovatively splicing the three characteristic spectrograms to obtain the diversified characteristic diagram, and inputting the diversified characteristic diagram into the convolutional neural network for training. The problem of incomplete information expression of a traditional identification algorithm is optimized, the network structure is simplified, and a relatively good identification effect is finally obtained.

The aim of the invention is to devise an improved anti-trap protection unit for an electric window lifter (5) of a vehicle or generally to efficiently limit excess forces of an adjusting element (4) that can be driven by means of a drive unit (A). For this purpose, a compensation trajectory of the drive unit (A) is determined independent of the temperature, and a variable threshold function (33, 34) which is dependent on the temperature is determined as a function of the drive trajectory or the drive time, the threshold function (33, 34) approaching a defined threshold value asymptotically, and the drive force especially in the form of a measured speed of a drive motor (15) being continuously determined and being monitored in accordance with the determined threshold function (33, 34).

The invention discloses a distributed multi-agent real-time fault detection method based on local cooperation, and relates to the technical field of multi-agent control. The method comprises the following steps: establishing a multi-agent system topology model, a node dynamic model and a local cooperation network model for a multi-agent system; building a residual generator framework based on local cooperation on the basis of the multi-agent system topology model, the node dynamic model and the local cooperation network model; further introducing a hybrid H2/H-infinity optimization index to the residual generator framework, and obtaining the optimal parameters of the residual generator framework by solving a hybrid optimization problem; performing the distributed construction of a residualgenerator under the optimal parameters to obtain a globally optimal residual signal; setting a threshold function for the globally optimal residual signal; determining that a target node has a faultif the amplitude of the globally optimal residual signal exceeds an amplitude limit value of a threshold function, or else determining that the target node does not have the fault.

The invention relates to a method for processing FBG signals based on a translation invariant wavelet of an improved threshold value. The method includes providing an improved threshold function and combining the threshold function with the translation invariant wavelet to denoise the FBG noisy signals; and fitting denoised FBG spectral signals through a Gaussian fitting formula so as to position the peak wavelength. The wavelet denoising portion mainly includes performing cycle spinning on the FBG noisy signals, performing discrete wavelet decomposition on the spun signals to extract the wavelet coefficient in each layer, performing threshold value quantization on the wavelet coefficients by using improved threshold function, reconstructing the wavelet coefficients after the threshold value quantization, performing reverse cycle spinning on the reconstructed signals, and averaging the reconstructed signals at different spinning positions to obtain the final denoised signals. A Gaussian fitting peak searching algorithm mainly includes fitting the denooised signals through the Gaussian formula and obtaining the wavelength corresponding to the peak to finish demodulation of the FBG spectral signals.

The invention discloses a rapid polar-finding de-noising processing method based on fibre optic gyro. The method comprises: firstly, according to the static data collected in four orthogonal positions of the fibre optic gyro, performing wavelet transform to obtain a corresponding wavelet coefficient; then configuring a generalized cross-validation function from the wavelet coefficient; using an optimization algorithm to calculate a threshold that minimizes the generalized cross-validation function; then using a threshold function to process the fibre optic gyro wavelet coefficient after wavelet transform according to the threshold; then performing the wavelet inverse transform to the processed wavelet coefficient to obtain the de-noised fibre optic gyro output data; then performing the polar-finding azimuth angle calculation from the de-noised data. The invention introduces the wavelet threshold de-noising method based on the generalized cross-validation principle to the four-position static polar finding of the fibre optic gyro. The de-noising method has strong self-adaptability and good stability, which greatly improves the orienting precision of the polar-finding measurement.

The invention discloses noise inhibition which is an important processing step in a micro-seismic signal processing process. Complete ensemble empirical mode decomposition CEEMD and wavelet transformation WT are widely applied to seismic noise elimination; however, the CEEMD is lack of theoretical foundations and the self-adaptability of the WT is relatively weak. Therefore, the noise eliminationeffect is poor. According to the invention, experience wavelet transformation (EWT) is combined with various threshold functions to carry out micro-seismic noise elimination for the first time. The EWT is used for establishing a self-adaptive wavelet filter group through spectrum segmentation to extract different frequency blocks of a detected signal. In the EWT, four types of spectrum segmentation methods are adopted; an experiment finds out that an adaptive algorithm can be used for separating an effective signal and noises of micro-seismic data very well; after the EWT is carried out, the signal can be divided into two components through analyzing a spectrum and energy of each module. A hard threshold function is applied to the component containing more effective signals and an improvedthreshold function is applied to the component containing less effective signals. An extraction method is compared with the CEEMD and the WT in an analogue signal and an actual signal to prove the effectiveness of a provided method.

The invention relates to an image denoising method based on a Shearlet contraction and an improved TV model, wherein a TV-denoising model is improved and a novel mixed denoising method by combining the Shearlet contraction is proposed. The method organically combines the sparse representation capability of Shearlet for a high dimension function with the protection capability of the TV-denoising model for an edge, wherein the method obtains a first-denoising image through a hard threshold function contraction, and then improves fidelity terms of a total variation model, and then makes a second denoising of the false Gibbs effect of the first-denoising image by combing the improved total variation model. On the prerequisite of protecting important information such as edges, etc., the method effectively inhibits the false Gibbs oscillation caused by the Shearlet contraction, and realizes a better visual effect and a lower computation complexity.