61 results about "Linear operators" patented technology

A system for detecting subtle differences in a signal in a set of linearly and / or non-linearly related signals that characterize a sensor-instrumented machine, process or living system. The system employs an improved similarity operator for signal differentiation. Signals or data representative of several linearly and / or non-linearly related parameters that describe a machine, process or living system are input to the inventive system, which compares the input to acceptable modeled states. If one or more of the input signals or data are different than expected, given the relationships between the parameters, the inventive system will indicate that difference. The system can provide expected parameter values, as well as the differences between expected and input signals; or the system can provide raw measures of similarity between the collection of input signals and the collection of acceptable modeled states. The system can be embodied in software or in a micro-controller.

Patterns (e.g., such as images, waveforms such as sounds, electromagnetic waves, or other signals, etc.) are synthesized, modulated and / or analyzed through the use of a computer programmed with a novel mathematical formula. The formula acts as a linear operator and can be used to create a variety of shapes, waveforms, and other representations. The formula greatly enhances ability in computer operations and provides a great savings in computer memory and a substantial increase in computing power.

Systems, methods, and computer program products implementing techniques for training classifiers. The techniques include receiving a training set that includes positive images and negative images, receiving a restricted set of linear operators, and using a boosting process to train a classifier to discriminate between the positive and negative images. The boosting process is an iterative process. The iterations include a first iteration where a classifier is trained by (1) testing some, but not all linear operators in the restricted set against a weighted version of the training set, (2) selecting for use by the classifier the linear operator with the lowest error rate, and (3) generating a re-weighted version of the training set. The iterations also include subsequent iterations during which another classifier is trained by repeating steps (1), (2), and (3), but using in step (1) the re-weighted version of the training set generated during a previous iteration.

The present invention is an apparatus and method for predicting the reactions of a subject, e.g., a human being to a proposition posed to the subject during a subject-object or a subject-subject interaction that takes place online or in real life. The quantum mechanical model adopted herein assigns a first subject qubit |iss1> to a primary internal state of the subject with eigenvalues corresponding to measurable indications a, b of the primary internal state. A response qubit |rsp> that can yield at least two mutually exclusive responses corresponding to two eigenvalues is also assigned to the subject. A proposition matrix PR in the form of a linear operator designed to act on response qubit |rsp> is assigned to enable a quantum mechanical derivation of response probabilities and expectation values for response to the same underlying proposition in various contexts, including incompatible contexts in the Heisenberg sense.

The invention discloses a model constructing method of sparse microwave imaging processing. The model constructing method comprises the following steps of: firstly, establishing a linear regression model of microwave imaging by using a linear operator characteristic of microwave imaging according to a radar system parameter and a geometrical relationship of a radar and a scene; secondly, establishing a linear regression model of sparse microwave imaging though construction of a sparse observation model and a sparse transformation matrix; and finally, establishing a reconstruction model of sparse microwave imaging by applying a sparse signal reconstruction theory in sparse signal processing. The model constructing method of the sparse microwave imaging processing provided by the invention provides a theoretical basis for the sparse microwave imaging.

Computing device, computer instructions and method for deghosting seismic data related to a subsurface of a body of water. The method includes receiving seismic data recorded by seismic receivers that are towed by a vessel, wherein the seismic data is recorded in a time-space domain and the seismic receivers are located at different depths (zr) in the body of water; modeling the seismic data in a shot or common midpoint domain as a function of linear operators that re-ghost the receiver to derive a model; using the derived model to remove the receiver ghost from the seismic data to obtain deghosted seismic data; and generating a final image of the subsurface based on the deghosted seismic data.

Computing device, computer instructions and method for processing energy at a free-surface reflection relating to an air-water interface. The method includes receiving input seismic data recorded with seismic sensors; receiving wave-height data that describes an actual shape of a top surface of a body of water; processing up-going energy at a receiver and down-going energy following a reflection at the sea-surface, using the input seismic data and a linear operator modified to take into account the wave-height data; and generating an image of the subsurface based on the up-going energy or the down-going energy or a combination of the input seismic data and one of the up-going or down-going energy.

In object registration and recognition based on a set of known templates, the tremendous set of possible transformations that may relate the template and an observed signature makes any detection and recognition problem ill-defined unless this variability is taken into account. The present invention estimates the deformation that transforms some pre-chosen representation of an object (template) into the current observation. The method employs a set of non-linear operators to replace a high dimensional problem by an equivalent linear problem, expressed in terms of the unknown parameters of the transformation model. The solution is applicable to any homeomorphic transformation regardless of its magnitude. In the special case where the transformation is affine the solution is shown to be exact.

The invention discloses a non-uniform array design and direction of arrival (DOA) estimation method and mainly aims at solving the problems that arrangement is inflexible and computation complexity isrelatively high in the prior art. A realization process of the method comprises the following steps: according to maximum freedom degree of a nested array, determining head and tail position coefficients of a non-uniform array; calculating position coefficients of all the virtual array element positions meeting the non-uniform array and contained in a differential synthetic matrix; according to the position coefficients, finally obtaining positions of array elements of the non-uniform array; according to received data, calculating a data covariance matrix and vectorizing, so that received data r of a virtual differential synthetic array is obtained; performing redundancy elimination on the r and sorting to obtain received data of a virtual array, and then obtaining a non-singular matrix vof the virtual array; constructing a linear operator, and obtaining a signal subspace by estimating the linear operator; and constructing a selection matrix to obtain a rotation matrix, and finally estimating a direction of arrival by virtue of the rotation matrix. The method disclosed by the invention has the advantages that array configuration is flexible, characteristic decomposition does notneed to be performed on the data covariance matrix and spectral peak search does not need to be performed on the whole airspace angle under the same conditions.

The invention relates to a multi-channel satellite cloud picture fusion method based on Shearlet conversion and belongs to the field of weather prognoses. Firstly, two registered satellite cloud pictures are subjected to Shearlet conversion to acquire a low-frequency coefficient and a high-frequency coefficient; secondly, a low-frequency Shearlet domain part is divided again through a Laplacian pyramid, the mean value of the top layer of the Laplacian pyramid is worked out, and then reconstruction of other layers with large gray-level absolute values of the Laplacian pyramid is carried out; in the high-frequency Shearlet domain part, the information entropy, average gradient and standard deviation of each high-frequency sub-picture are worked out and are then subjected to normalization processing, the product of every group of three processed values is worked out, and the sub-picture with the large product serves as a fused sub-picture; the fused sub-picture is subjected to detail enhancement treatment through a non-linear operator; finally, a final fused picture is obtained through Shearlet inverse transformation. The method can be popularized to fusion of three or more satellite cloud picture to achieve multi-channel satellite cloud picture fusion and acquire high-precision typhoon center positioning results.

The invention provides a video recognition method based on a space-time pyramid network. The method comprises steps of extracting characteristics of each video clip sample in a video sample set through the convolution neural network, carrying out time-space linear operator processing so as to acquire a first vector and through a second convolution neural network, acquiring image information of image samples and acquiring a second vector; carrying out time-space linear operator processing on the vector obtained by splicing the first vector and the second vector; carrying out weighting pooling on an output result and the second vector so as to acquire a third vector; through average pooling, acquiring a fourth vector and a fifth vector and then carrying time-space linear operator processingso as to acquire a sixth vector; and according to a loss value, recognizing a to-be-detected video. According to the invention, through the dimension reduction operation and inverse transformation operation, problems of curse of dimensionality of bilinearity fusion and high operation complexity are solved; and by improving the bilinearity fusion operators, under the condition that two videos havethe similar background or the similar short films, better recognition effects are acquired.

The invention discloses a video behavior recognition method and system based on a space enhancement module, and the method comprises the following steps: decoding a to-be-detected video into a frame sequence, and storing the decoded frame sequence in an image form; dividing a video into a plurality of video clips by adopting a sparse sampling strategy, extracting a frame from each video clip, and combining to form a stacked frame sequence; calculating a mean value of three channels of all training video frames in the behavior recognition data set, and subtracting the calculated mean value from the sampled frame image; using a residual neural network 3D-ResNet-18 as a backbone network, and enabling a spatial enhancement module to construct a behavior recognition classification network; setting training parameters, inputting the training set into a behavior recognition classification network for training, and storing trained network parameters; in the model deployment stage, fusing linear operators of the space enhancement module; and inputting a to-be-detected video into the behavior recognition classification network, and outputting a final classification result. According to the invention, the behavior recognition effect is improved, and both effectiveness and universality are achieved.

The invention relates to a non-fluorescent eye fundus image based automatic segmentation method for retinal blood vessels. The method comprises following steps: step one, an image is preprocessed, blood vessel characteristics are enhanced, and background noise is weakened; step two, morphological characteristics of the retinal image are extracted with a multi-scale linear operator, and a refined blood vessel framework is segmented with two-dimensional Gabor wavelet transform; step three, after the refined blood vessel framework is segmented, the image is subjected to path morphology filtering, the maximum path length is combined with geometrical characteristics of a blood vessel region, and an adjacent map is constructed; step four, a binaryzation process is performed with regional connectivity analysis and a hysteresis threshold technology for auxiliary segmentation of microvessels, and the segmentation is finished. Compared with the prior art, the method has the advantages as follows: the blood vessel location accuracy is higher, the adhesion phenomena are reduced, and a retinal blood vessel network of the eye fundus image is effectively segmented.

An embodiment of a method for solving the inverse problem of electrophysiology and determining a voltage distribution on a surface of a tissue may comprise receiving a plurality of voltages collected by a plurality of electrodes adjacent to the surface, discretizing the problem using a Finite Element Method (FEM) or a Boundary Element Method (BEM), introducing one or more regularization terms to an error minimization formulation, and solving, by a processor, the voltage distribution according to the plurality of voltages and according to the regularization terms. The regularization terms may comprise one or more of a Laplacian smoothness operator, a Tikhonov regularization matrix, a confidence matrix, and a linear operator that interpolates the plurality of electrode voltages to the tissue voltage distribution.

Methods, systems, and apparatuses for detecting seizure events are disclosed, having at least one accelerometer to be positioned on a patient and configured to collect acceleration data and a processor in communication with the at least one accelerometer and configured to receive acceleration data from the at least one accelerometer. The processor may apply at least one non-linear operator to the acceleration data to determine whether the acceleration data indicates an event, such application including calculation of a non-linear energy of the acceleration data and performance of at least one secondary analysis to determine whether the event is a seizure.

The invention belongs to the technical field of interference type array DOA estimation, and particularly relates to a two-dimensional DOA estimation method based on an L-shaped interference type linear array. The method comprises the following steps that a three-dimensional rectangular coordinate system is established by using the L-shaped interference type linear array as the datum; mutual correlation matrixes Rxz, xz and zx of x-axis receiving signals and z-axis receiving signals are obtained; in accordance with the Rxz, xz and zx, a linear operator Px and a linear operator Pz are obtained; according to the linear operator Px and the linear operator Pz, the coarse estimated value of each incidence signal azimuth angle and the coarse estimated value of each incidence signal pitch angle are searched for by using alpha as the search step size within the search angle range of 0-180 degrees; refined estimation values corresponding to the incidence signal azimuth angles are searched for by using beta as the search step size within each search region in azimuth angle refined estimation, and refined estimation values corresponding to the incidence signal pitch angles are searched for by using beta as the search step size within each search region in pitch angle refined estimation; according to the refined estimation value of each incidence signal pitch angle, and the refined estimation values of the paired incidence signal azimuth angles are acquired.

System and method for compensating distortions introduced by an optical channel and by at least one directly modulated transmitting laser that transmits a modulated optical signal that carries data to a receiving end over the optical channel. At the receiving end, the transmitted signal is received by a coherent receiver that processes the received signal by compensating the distortions introduced by the optical channel by applying, on the received signal, an operator being capable of essentially compensating for all fiber distortions in the optical channel; compensating the non-linear phase distortions and / or statistical phase distortions introduced by the directly modulated transmitting laser by applying, on the processed signal obtained after compensating the fiber distortion, a non-linear operator being the extraction of the absolute value of the compensated signal; and extracting the data carried by the processed signal, from the absolute value of the processed signal obtained after compensating the non-linear phase distortions and / or statistical phase distortions, thereby demodulating the processed signal.

The present invention is one light controlled linear operator of trackless electric extension gate. Its infrared ray controller includes one infrared emitting and receiving unit on the outer side of one end of the extension gate; one infrared reflector in head of the extension agent; and one signal controller connected to the infrared emitting and receiving unit and the driving mechanism of the extension gate. Its laser controller includes one or several laser emitters on the outer side of one end of the extension agent; one in head of the extension gate; and one signal controller connected to the laser sensor and the driving mechainsm of the extension gate. By means of the light control, the electric extension gate is always in its linear operation state without track.

The invention provides an aluminium alloy hot rolled strip transverse thickness distribution modeling method based on a spectral method. The influence of the thermal coupling effect of rolling force, roll bending force and a temperature field in the rolling process on roller deformation and strip transverse thickness distribution is studied, and a partial differential equation of work roller deformation with a thermal coupling effect is obtained; a characteristic function of a space linear operator corresponding to the equation is chosen as a space primary function, and the spectral method is used for conducting low dimensional approximation modeling to obtain a finite dimension nonlinear ordinary differential equation set; the linear part of the ordinary differential equation set is subjected to dimensionality reduction processing by a balanced truncation method or an optimization method, the nonlinear part and the non-modeled part in the approximate rolling process of a neural network are used for obtaining an intelligent hybrid module with the dimension being very low, and therefore the purpose of quickly forecasting strip transverse thickness distribution is achieved. The aluminium alloy hot rolled strip transverse thickness distribution modeling method based on the spectral method has the advantages that from the aspect of the mechanism of the rolling process, little computing amount is used, the model with the dimension being very low is obtained, accordingly, the real-time performance of forecasting of the strip transverse thickness distribution in the rolling process is improved, and a foundation of system optimization and control system design can be laid.

A linear operators or axes is disclosed which comprises a fixed linear body, a body guided and movable alternately on the fixed one with interposition of bearings, and at least one pneumatic or electric actuator to control the alternating movements of the moving body on the fixed body. The fixed and movable bodies (11, 12), are made up of extruded profiled elements, they are of the same width and along each of the longitudinal opposite sides of each of them a profiled groove (22, 23) is provided so that the collateral grooves of the two joined bodies are the same, parallel, positioned on the same plane and made to receive the fixing brackets for at least one control actuator.

The embodiment of the invention provides a flow matrix estimation method and device. The problems that the accuracy rate is low, the calculation complexity is high, and efficiency is low when a flow matrix is solved can be solved. According to the specific scheme, network flow in a preset range is obtained, and a constraint matrix B is obtained according to the obtained flow value; according to the preset algorithm and the constraint condition, a target function min(rank(LR<T>)) is solved so as to obtain L and R, wherein the flow matrix X=LRT, L is the left feature vector obtained by decomposing the flow matrix X through the non-negative matrix decomposition algorithm, R is the right feature vector obtained by decomposing the flow matrix X through the non-negative matrix decomposition algorithm, the constraint condition is lambda (LR<T>)=B, lambda ( ) is the linear operator, rank (LR<T>) is the rank of the flow matrix X, and the min function is the minimization function.

In an environment of temporally and spatially uncorrelated white additive noise, the direction estimation apparatus for coherent signals calculates (M−1) instantaneous cross-correlations between some array data, and selecting multiple pairs each of which is consisted of p instantaneous correlations from the (M−1) instantaneous cross-correlations to form a Hankel correlation matrix. Next, the apparatus divides that Hankel matrix into an upper submatrix and a lower submatrix and then calculates a linear operator at time n by using the adaptive LMS algorithm with a fixed or time-varying step-size parameter and or using the adaptive NLMS algorithm from the two submatrices. Then a noise subspace is estimated from that linear operator. Finally the directions of incident signals at time n is estimated and tracked by using that noise subspace with Newton approximation.

A method of transmitting a transmit vector comprising symbols to be delivered within a resource unit from a transmitting station with one or more transmit antennas to one or more receiving stations with one or more receive antennas. The method comprises obtaining information on the state of channels between the one or more transmit antennas and the one or more receive antennas; and computing, on the basis of the channel states between the one or more transmit antennas and the one or more receive antennas, a covariance matrix Rnn, a combined channel matrix H, and a block-diagonal linear operator V of decoding matrices of the receiving stations. The transmit vector is multiplied with a precoding matrix P fulfilling the equations(HHVHVH=λI)P=HHVH Vi(HiPPHHiH+Rnn,i)=PiHHiH, i=1 . . . Ktrace(PHP)=Ptr and corresponding performing filtering in the receiving station using a receiver filter corresponding to the block-diagonal linear operator V.

The invention discloses a semi-parameter number estimation method for coherent and incoherent mixed signals. The method comprises the following steps: firstly, determining ranks of a cross covariance matrix and a combined matrix, and estimating the number of incoherent signals and the number of coherent signal groups; secondly, estimating elevations of the incoherent signals by utilizing a linear operator and the cross covariance matrix of two uniform linear arrays, and further calculating an oblique projection operator according to the estimated elevations of the incoherent signals; finally, constructing a telescopic matrix sequence only containing coherent signal information by utilizing the oblique projection operator, and determining the number of signals in each coherent signal group according to the rank sequence of vector product matrixes of telescopic matrixes. According to the method, the number of the incoherent signals and the number of the signals in each coherent signal group are estimated respectively, the influence of superposition noise is reduced, and complicated characteristic decomposition is avoided. Under the condition of small snapshot number and / or low signal to noise ratio, the method is lower in calculation complexity and higher in result accuracy for the number of the incoherent and coherent mixed signals with similar detection distance.

An interactive multi-party system for collaboratively performing homomorphic operations, such that no party has access to unencrypted data or an unencrypted operator. A first party device may add noise to encrypted data and an encrypted linear operator to generate noisy encrypted data and a noisy encrypted operator, and transmit the noisy encrypted data and operator to a second party device possessing a secret decryption key for the encryption. The second party device may decrypt the noisy encrypted data and noisy encrypted operator to generate unencrypted noisy data and an unencrypted noisy operator, solve the linear operation using the unencrypted noisy data and an unencrypted noisy operator to generate a noisy solution, encrypt the noisy solution to the linear operation, and transmit it to the first party device. The first party device may then cancel the noise of the encrypted noisy solution to generate the encrypted solution to the linear operation.

Patterns (e.g., such as images, waveforms such as sounds, electromagnetic waves, or other signals, etc.) are synthesized, modulated and / or analyzed through the use of a computer programmed with a novel mathematical formula. The formula acts as a linear operator and can be used to create a variety of shapes, waveforms, and other representations. The formula greatly enhances ability in computer operations and provides a great savings in computer memory and a substantial increase in computing power.

The invention discloses an edge calculation-oriented reparametric neural network architecture search method. The method comprises the following steps of 1, designing a linear operator and multiple branch block structures; 2, constructing a super network by stacking the multiple branch block structures; 3, training the super network through a gradient-based first-stage search algorithm; 4, deleting redundant branches in the super network to construct an optimal sub-network; 5, converting the multi-branch optimal sub-network into a single-branch network; and 6, completing task reasoning by using the single-branch network. The method is used for searching the neural network structure capable of performing re-parameterization, and ensures the reasoning real-time performance and the high efficiency of model operation while ensuring the reasoning precision.

Computing device, computer instructions and method for deghosting seismic data related to a subsurface of a body of water. The method includes receiving seismic data recorded by seismic receivers that are towed by a vessel, wherein the seismic data is recorded in a time-space domain and the seismic receivers are located at different depths (zr) in the body of water; modeling the seismic data in a shot or common midpoint domain as a function of linear operators that re-ghost the receiver to derive a model; using the derived model to remove the receiver ghost from the seismic data to obtain deghosted seismic data; and generating a final image of the subsurface based on the deghosted seismic data.

In object registration and recognition based on a set of known templates, the tremendous set of possible transformations that may relate the template and an observed signature makes any detection and recognition problem ill-defined unless this variability is taken into account. The present invention estimates the deformation that transforms some pre-chosen representation of an object (template) into the current observation. The method employs a set of non-linear operators to replace a high dimensional problem by an equivalent linear problem, expressed in terms of the unknown parameters of the transformation model. The solution is applicable to any homeomorphic transformation regardless of its magnitude. In the special case where the transformation is affine the solution is shown to be exact.