31 results about "Variance Criterion" patented technology

An efficient and non-iterative 3D post processing method and system is proposed for mosquito noise reduction, block localization and correction in DCT block-based decoded images. The 3D post processing is based on a simple classification that segments a picture in multiple regions such as Edge, Near Edge, Flat, Near Flat and Texture regions. The proposed technique comprises also an efficient and shape adaptive local power estimation for equivalent additive noise and provides simple noise power weighting for each above cited region. Temporal filtering configurations using Minimum Noise Variance Criterion are proposed for reducing temporally varying coding artifacts. A Minimum Mean Square Error or Minimum Mean Square Error-like noise reduction with robust and effective shape adaptive windowing is utilized for smoothing mosquito and/or random noise for the whole image, particularly for Edge regions. The proposed technique comprises also signal domain histogram analysis based Block Localization and adaptive edge based Block artifact correction. Finally, is also proposed an optional adaptive detail enhancer which can enhances the luminance signal in eight directions differently.

Visual information regarding intensity and intensity-based gradient information is used in a multi-step process for accurately locating and decoding an encoded visual symbol matrix. A symbol is preprocessed with filtering and morphology. A set of coarse locations of candidate symbols is filtered according to coarse geometric criteria to determine which image locations are most viable. Depending upon the symbol type, and information about the edges of the symbols, additional symbol-specific filtering and refinements can be applied or skipped. For 2D matrices filtering according to intensity variance criteria is followed by an edge-locating refinement and by identifying finder patterns and timing patterns. Once finder patterns and timing patterns are precisely located, the data region is decoded. Where a data region does not decode validly, another pass is run, using the non-morphed image, resulting in an overall saving of time, as rejections may require operator intervention.

The present invention is a method and system of identifying supply chain business scenarios using data from one or more Serialized Global Trade Identification Number (sGTIN) tag reads or other explicit supply chain data. Scenarios are asserted by calculating changes in data and comparing those with user definitions of scenario event combinations. A processor acquires the scenario definitions from a user defined metadata of products, locations, and measure variance criteria and correlates the sGTIN event reads and other events with this metadata to identify defined events that are not observed in the sGTIN event reads or other explicit data, or not observable by the tag readers. The system has a method for communicating these implicit events back to users.

The invention relates to a signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method. The signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method includes the steps that sampling signals received by array elements are delayed, subjected to forward-backward smoothing and subjected to diagonal loading treatment, and an estimated sample covariance matrix is obtained; the estimated covariance matrix is subjected to characteristic decomposition, and signal subspace is constructed; in the desired signal subspace, according to the minimum-variance criterion, an adaptive beam-forming weight value is calculated; then a post-filtering coefficient is designed according to the signal coherence, a noise weighting coefficient is introduced according to the input signal noise ratio, and a signal-noise-ratio-filtering coefficient is calculated; the adaptive beam-forming weight value and the signal-noise-ratio-filtering coefficient are fused to obtain the novel weight vector; finally, multiple pieces of data subjected to forward-backward smoothing treatment are weighted and summed through the obtained minimum-variance weight value fusing the signal-noise-ratio post filtering and the characteristic space, and an adaptive beam signal is obtained. By means of the signal-noise-ratio-post-filtering-and-characteristic-space-fusion minimum-variance ultrasonic imaging method, the properties of ultrasonic images in the resolution ratio aspect, the contrast ratio aspect, the noise robustness aspect and the like can be improved, and therefore the quality of ultrasonic imaging is improved as a whole.

A method and apparatus for design for manufacturing includes importing real-time manufacturing data (106) for a first product (110) into a design-for-manufacturing software module (102), importing design data (108) for a second product (112) into the design-for-manufacturing software module (102). Design-for-manufacturing software module (102) compares the real-time manufacturing data to the design data and flags an overlapping element (114) of the real-time manufacturing data and the design data, wherein the overlapping element meets a variance criteria (116).

The invention relates to an improved minimum variance ultrasonic imaging method, belonging to the ultrasonic imaging technical field. The method comprises: performing delay processing and anterior-posterior dimension smoothing processing on sampling signals of receiving array elements to obtain sample covariance matrix estimation; performing eigenvalue decomposition on the anterior-posterior dimension covariance matrix estimation, constructing signal subspaces, and meanwhile adding a pair of constraint conditions to a direction vector by utilizing an ellipsoid covering direction vector value domain and a limitation direction vector module value; in an expected signal subspace, calculating adaptive beamforming weights based on a minimum variance criterion; and employing the adaptive beamforming weights to perform weighted summation on multi-channel data after anterior-posterior dimension smoothing processing to obtain a channel of adaptive beam signals. The method can solve the problems of image resolution, contrast and noise robustness of a present minimum variance algorithm, and improve ultrasonic imaging quality on the whole.

A sensor data processing system and method is described. Contemplated systems and methods derive a first recognition trait of an object from a first data set that represents the object in a first environmental state. A second recognition trait of the object is then derived from a second data set that represents the object in a second environmental state. The sensor data processing systems and methods then identifies a mapping of elements of the first and second recognition traits in a new representation space. The mapping of elements satisfies a variance criterion for corresponding elements, which allows the mapping to be used for object recognition. The sensor data processing systems and methods described herein provide new object recognition techniques that are computationally efficient and can be performed in real-time by the mobile phone technology that is currently available.

The invention discloses a near-field linear constrained minimum variance adaptive weighted-frequency-invariant beam forming method. The method is based on linear constrained minimum variance. A balance matrix of an array spatial response bias function is defined based on the spatial response bias function, and the matrix is introduced into a near-field linear constrained minimum variance beam forming method to obtain the near-field linear constrained minimum variance weighted-frequency-invariant beam forming method. Then, the weighting coefficient in the near-field linear constrained minimum variance weighted-frequency-invariant beam forming method is defined as a function of the field spot distance and the signal frequency, and the function is dynamic and is updated adaptively. The method of the invention is widely applied to multi-channel speech enhancement, man-machine voice interaction systems, hearing aids, vehicular hands-free voice communication, remote video conferencing systems, robot hearing, and other fields.

The invention belongs to the technical field of teaching, training and recording, especially relates to an intelligent shooting system based on distribution cloud. The intelligent shooting system provided by the invention mainly comprises a basic equipment layer, a core equipment layer and a terminal receiving layer, a standing identification algorithm of a middle school student shooting system in the basic equipment layer is improved, and a background differencing method is used as a moving target detection algorithm, which mainly comprises the following aspects: background updating, image threshold segmentation and contour extraction. A sliding average algorithm is adopted in the background updating; the maximum between-cluster variance criterion is selected for the image threshold segmentation; then a contour is extracted on an image subjected to binaryzation by using a method of hollowing out contour points, and the contour extraction algorithm is further improved by using the contour defect repairing technique; and finally, the extracted contour is expressed by using a Freeman link code, and the algorithm can well extract the contour of a moving target so as to provide a good technical guarantee for the implementation of the intelligent shooting system.

The invention provides an adaptive compensation method and system for environmental magnetic field suppression. The method includes acquiring signal channel signals and reference channel signals output by a detection system to acquire the signal channel data and reference channel data; extracting the reference channel data from an initial time point through a window with the fixed length, increasing gradually according to the initial time point from 0 by adopting 1 as the gradient, and acquiring data of a plurality of compensation channels sequentially; establishing an error function containing a compensation coefficient, and acquiring the compensation coefficient by solving the error function according to the minimum variance criterion; performing environmental noise suppression on the signal channel data according to the compensation coefficient. Once a parameter of the length of the window is set, an initial value of a subtraction coefficient is omitted, the convergence process is avoided, the optimal subtraction coefficient can be acquired stably in an adaptive manner, and the gradient output with fine performance can be implemented.

The invention discloses a method for adaptively resisting spatial distribution interference based on a logarithmic frequency offset frequency diversity array, and mainly solves a problem that the periodic spectral peak of a conventional fixed frequency offset frequency diversity array in a distance dimension causes the decrease of the anti-interference performance. The implementation scheme of theinvention is that the method comprises the steps: constructing the logarithmic frequency offset frequency diversity array, and emitting a signal through the logarithmic frequency offset frequency diversity array; carrying out the separation and rearrangement of the signal and extracting a receiving end normalization guide vector; obtaining an adaptive wave beam forming weight vector according tothe normalization guide vector and a received data autocorrelation matrix through a linear minimum constraint variance criterion; finally carrying out the beam forming through the adaptive wave beam forming weight vector, wherein an obtained directional diagram guarantees the single mapping of the beam spectral peak and a target position while achieving the resisting of the spatial distribution interference. The method can guarantee the single mapping of the beam spectral peak and the target position while achieving the resisting of the spatial distribution interference, and can be used for target tracking.

The invention discloses a design method for an adaptive beam forming optimal weight of a uniform power broadband signal, and relates to a design method for an adaptive digital beam forming optimal weight. The objective of the invention is to solve the problem of large angle error caused by the fact that wave beam pointing of wave beam forming and self-adaptive interference suppression pointing areinfluenced when the bandwidth is very large for broadband signals in the prior art. The method specifically comprises the following steps: step 1, constructing an expression of a uniform power broadband signal in any array; Step 2, applying the expression of the uniform power broadband signal in any array in the step 1 to a uniform linear phased array to obtain a covariance matrix of a useful signal, an interference signal and a noise signal; and step 3, substituting the covariance matrixes of the useful signal, the interference signal and the noise signal obtained in the step 2 into a linearconstraint minimum variance criterion to obtain an optimal adaptive weight. The method is applied to the field of broadband signal self-adaptive beam forming optimal weight design.

The invention discloses an FDA-MIMO radar distance fuzzy clutter suppression method based on auxiliary multi-waveform pulses. The method comprises the steps that 1, a waveform having the ultralow cross-correlation property with a main transmitted waveform is selected; 2, the auxiliary pulses are transmitted within coherent integration time according to system parameters and designed OFDM signals;3, a fuzzy clutter covariance matrix of a main observation interval is obtained by utilizing the auxiliary pulses; 4, distance fuzzy clutters in the main observation interval are suppressed based on the minimum variance criterion; and 5, remaining clutters are suppressed according to a traditional non-side-looking mode. The non-self-adaptivity defect of fixed weighting of a traditional FDA-MIMO method is overcome by designing a matched filter through the auxiliary pulses due to the consistency of the auxiliary pulses and the distance fuzzy clutters of the main observation interval, and the stability of array errors is improved.

The invention provides a Capon beamforming-based positioning method and device. According to the Capon beamforming-based positioning method and device, K receiving antennas are set to form a signal receiving array, and M signal transmitting points are set; a covariance matrix Rxx is calculated by using a signal matrix outputted by the signal receiving array; the constraint optimization problem of the linearly constraint minimum variance criterion is solved, so that a spectral function can be constructed; M maximum spectral peaks are searched; and a direction of arrival is calculated. According to the method, beacon points deployed in a monitoring area are adopted to be networked with a wireless gateway so as to communicate with the wireless gateway; data are transmitted to a cloud end through a wireless communication module; cooperative sensing is performed, and the information of signal transmitting points in a network coverage area is acquired and positioned; the monitoring data of the cloud end are matched with a map and the like, so that intelligent parking can be realized. With the method adopted, the number of vehicles does not need to be known in advance or estimated, and therefore sampling data length can be decreased; desired signals are enhanced, interference is suppressed, and therefore, the signal to interference plus noise ratio of the output of a system can be improved; and a robust Capon algorithm is modified to improve the deviation of a direction vector from an actual environment, and therefore, positioning performance can be improved.

A third mobile communication system network layer capability speedy simulation method, which uses for under the load control, the simulation of the activating number by the system and the capability curve of the system calling failing probability. Firstly, the method carry dynamic simulation with loud call intensity, then establish the user dynamic stochastic connect-in network stable forecast model under the load control, and search two parameters of the single user load average value and variance that the forecast model needs according to the smallest average variance criterion, forecast the network capability curve under the low call intensity according to the forecast model. The method can ensure the accuracy of the capability curve, simultaneity, easy to compress the dynamic simulation to 1/3 of the original one, the simulation time compress more as the more change of the calling intensity. In addition, when the need of the dynamic simulation for the memory and the CPU overstep the capability of the personal computer with the small calling intensity, the system however give the good simulation forecast result by using the personal computer.

The invention discloses a measurement delay filtering method for a small field star sensor, and belongs to the technical field of aircraft attitude estimation. In view of the conditions that multiplicative noise exists in a non-linear attitude estimation system state model and unknown interference exists in a measurement model, an aircraft attitude estimation model with the existence of the indetermination of the two models is established, meanwhile, the upper boundary ranges of prediction variance and estimation variance are found by using a robust algorithm design based on an expansion Kalman filtering structure, and then optimal filtering gain is designed by using a minimum variance theoretical design. The aircraft attitude is determined by using expansion Kalman filtering, the truth isminimum variance criterion estimation which is different from that of general frequency domain filters, the method can be used for processing attitude estimation under undetermined conditions, the method has the advantages of high instantaneity, and low dependence degree on prior information, the problem that delay exists in the star sensor is solved, and relatively high attitude estimation accuracy can also be guaranteed even if in information uncertainty conditions.

The invention provides an image retrieval method based on a decomposable word pack model. The method comprises the following four portions: first, extracting a plurality of features of an image, and separately establishing an index for each feature; second, in view of the indexes in the features, organizing each feature data via the word pack model, clustering the feature data, and using a clustering center as an entry in the word pack model; third, establishing a linear judgment function for the retrieval of the plurality of features, learning significant weights of the features by the least-mean-square-error criterion in a pre-retrieval process, and using the significant weights as coefficients in the linear judgment function; and fourth, independently providing a candidate set of each feature in a retrieval process. The final retrieval results of the plurality of features are clustered by the linear judgment function by using the significant weights of the features. By adoption of the method, high retrieval accuracy can be obtained, and the method is suitable for actual large-scale image retrieval applications.

The invention discloses a signal source DOA estimation method based on a space-time structure for removing a pre-delay module, and belongs to the technical field of self-adaptive array signal processing. According to the method, based on the space-time processing structure for removing the pre-delay module, a weight within an angle search range is solved by utilizing a linear constraint minimum variance criterion, then a spectral function that P(Theta) is equal to a absolute value of WHRW is drawn out, spectral peak search is conducted on the spectral function, and the DOA estimation of signalsources is obtained according to the peak position of the spectral function. According to the method, the DOA estimation of the signal sources is achieved by using the space-time structure for pre-delay removal processing without decomposing a wideband signal into narrowband signals or estimating the number of the signal sources. Thus, compared with a signal source DOA estimation algorithm basedon a space-frequency structure, the DOA estimation method has the advantages that the calculation process is relatively simple, the calculation amount is greatly reduced, and under the condition of low signal data amount and low signal-to-noise ratio, the resolution is increased.

The invention discloses a planar array self-adaptive beam forming coupling self-correction method based on auxiliary array elements, and belongs to the technical field of array beam forming. The method comprises the following steps of firstly, constructing a physical model of a required auxiliary array element according to an actual array element; secondly, obtaining a receiving signal vector of the middle actual array element according to the physical model after the auxiliary array element is arranged; thirdly, correcting a guiding vector of a middle sub-array to obtain a corrected guiding vector; and finally, solving weight formed by self-adaptive beam forming according to a linearly constraint minimum variance criterion in combination with a lagrangian multiplier method, and realizingdecoupling self-correction of a planar array antenna. The method is suitable for decoupling of the planar array, and coupling self-correction can be carried out under a condition of an unknown mutualcoupling matrix; the planar array with the coupling phenomenon exists, the antenna array can generate accurate main lobe and null steering directions through coupling self-correction, the null steering depth is enough to meet the suppression of interference signals, and a minor lobe is low.

The invention discloses a statistics-based space-time null widening method for satellite navigation receivers. The method comprises the following steps that: 1) array parameters are determined, a space-time signal vector received by an array is obtained; 2) disturbance conforming to normal distribution is applied to the incident angle of interference signals, the covariance matrix of signals received by the array at the current moment is calculated on the basis of the space-time signal vector received by the array; and 3) an optimal time-adaptive weight is calculated based on the covariance matrix of the signals received by the array and according to a linear constrained minimum variance criterion. According to the statistics-based space-time null widening method for the satellite navigation receivers provided by the invention, wide null can be formed in an interference direction, so that the degree of freedom of space-time processing can be significantly increased, and therefore, interference which changes within a certain range can be effectively cancelled, and the robustness of the adaptive algorithm of a small-array satellite navigation receiver can be greatly improved.