216results about How to "Strong robustness" patented technology

Obtaining spectral images of an eye includes taking an optical system that images eye tissue onto a digital sensor array and optically fitting a multi-spectral filter array and the digital sensor array, wherein the multi-spectral filter array is disposed between the digital sensor array and an optics portion of the optical system. The resulting system facilitates acquisition of a snap-shot image of the eye tissue with the digital sensor array. The snap shot images support estimation of blood oxygen saturation in a retinal tissue. The resulting system can be based on a non-mydriatic fundus camera designed to obtain the retinal images without administration of pupil dilation drops.

The invention relates to the technical field of satellite fault diagnosis and fault-tolerant control, and proposes a fault diagnosis method, which has very good robustness to external interference, isfree of influence of external interference in a fault diagnosis process, prevents generation of misinformation and missing report, and can effectively estimate fault severity. According to the satellite fault diagnosis and fault-tolerant control method based on a self-adaptive observer, first a satellite kinematics and dynamics model under an actuator fault and external interference influence isestablished to analyze influence of fault effective factors on a system; secondly, an observer is designed for a satellite fault system, a residual error evaluation function is established based on observer output and system actual output, an evaluation threshold value is obtained, and the purpose of fault detection is achieved; then, when occurrence of a fault is detected, a self-adaptive observer is designed to complete a fault diagnosis task; and finally, based on an estimated value of the fault, a finite time self-adaptive fault-tolerant controller is designed. The satellite fault diagnosis and fault-tolerant control method based on a self-adaptive observer is mainly applied to fault diagnosis and fault-tolerant control occasions.

The present invention relates to a binocular vision-based workpiece grabbing method. The method includes following steps that: (1) a binocular vision system is calibrated; (2) a left camera and a right camera simultaneously collect images and correct the images; (3) a template matching algorithm is adopted to identify and position a workpiece in the left image and right image; (4) the three-dimensional attitude of the workpiece is calculated according to the identification and positioning information of the workpiece of the images of the left camera and the right camera; and (5) an industrial robot automatically grabs the workpiece according to the three-dimensional attitude information. The binocular vision-based workpiece grabbing method of the invention has the advantages of high real-time performance, high applicability, accurate grabbing, high working efficiency and the like.

The invention belongs to the field of signal processing. The invention provides an improved Capon beamforming algorithm for the problem that the performance of a conventional Capon beamforming algorithm is significantly degraded, wherein the problem is caused by estimation errors of an array steering vector or inaccurate estimation of a data covariance matrix. On the basis that a reception data covariance matrix is reconstructed based on linear contraction estimation, a mismatch steering vector is projected to an updated subspace by updating the signal and interfering subspace, so as to acquire the optimal reception right. The algorithm maintains a high gain in the true wave incoming direction and assumed wave incoming direction of a desired signal, which avoids signal cancellation. Compared with other algorithms, the algorithm can more accurately point to the true direction of the desired signal. The algorithm is robust for steering vector mismatch caused by the observation error of the signal arrival direction.

A non-supervision clustering-based distributed cooperative spectrum sensing method for a cognitive self-organizing network relates to the field of cognitive radio in wireless communication technology. Aiming at solving the problems of difficult distributed cooperation of the cognitive self-organizing network and large overhead of whole-network cooperation, the method adopts the latest achievements of a non-supervision clustering theory and a co-recognition theory to achieve fully-distributed, steady and reliable distributed cooperative spectrum sensing under the condition of simplifying the overhead of network sensing; and users with potential optimal detection performance spontaneously gather only via information interaction between neighbors, further the users carry out cooperation spectrum sensing by utilizing an average co-recognition protocol, and a sensing result is broadcast to the whole network users. The method does not require local users to receive apriori information of noise-signal ratio, and does not need any central controllers, thereby greatly lowering sensing overhead and acquiring detection performances similar to optimal soft combination solution.

An imaging system (110) for imaging a scene with a detector array (104) having an array of imaging elements is provided. The imaging system (110) includes an image estimation module (202) for generating a plurality estimates of uncorrupted images based upon a plurality of noisy images generated by the detector array (104). The imaging system (110) further includes a parameter determination module (204) for determining one or more nonuniformity correction parameters based upon the estimates of uncorrupted images.

A 3D face recognition method based on intermediate frequency information in a geometric image as follows: (1) preprocessing a library and test models of 3D faces, including 3D face area cutting, smoothing processing and point cloud thinning, and discarding the lower portion of the face; (2) mapping the remainder of the face to a 2D grid using grid parameters, and performing linear interpolation on the 3D coordinates of the grid top to acquire the 3D coordinate attributes and generating a geometric image of a 3D face model; (3) performing multi-scale filtering with a multi-scale Haar wavelet filter to extract horizontal, vertical, and diagonal intermediate frequency information image images as invariable facial features; (4) calculating the similarity between the test model and the library set model with a wavelet domain structuring similarity algorithm; and (5) judging the test and library set model models with the maximum similarity belong to the same person.

The invention relates to a human body detection method based on Gauss shape feature, belongs to the field of computer vision and mode identification, and solves the problem that the detection rate and detection speed of the prior detection method are low. The method comprises: a first step of extracting feature, namely extracting the Gauss shape feature of each rectangular area of each training sample; a second step of constructing cascade detectors, namely studying the current training sample to construct the cascade detectors; and a third step of detecting human body, namely using the cascade detectors to scan and detect an image to be detected to determine the position and size of the human body in the image. The method has strong robust property for changes of illumination, background and the like by using the constructed Gauss shape feature, and has low feature dimension; the construction of the Gauss shape feature is added with a mean value of areas so as to enhance the capability of identifying the human body and the background; therefore, the constructed cascade detectors can greatly improve the detection rate of the human body, and can be applied to intelligent monitoring, assistant driving and human-computer interaction systems.

The invention uses the idea of hierarchical structure control optimization to provide a manipulator actuator fault tolerant control system based on a double-layer structure and a method thereof, which belongs to the technical field of automatic control. In order to reduce the computational complexity and improve the instantaneity, a discrete system model under an actuator fault is established in an expansion manner through Taylor series. An FDD unit is designed to initiatively deal with a fault problem. The estimated fault information is introduced into a fault model to realize active fault tolerance. Uncertainties of the actual system are considered. A feedback correction mechanism is used for compensation. A manipulator fault tolerant controller is composed of a trajectory planning layer and a tracking control layer. According to different control objectives of each layer, a controller is designed separately, which is more specific to the problem. The fault tolerant control of the double-layer structure can deal with the existing system constraints, has strong robustness, can effectively solve the problem of the constant deviation fault of a complex manipulator actuator, and ensures the stability and the control performance of the whole system.

The existing technology focuses on the case of the same timing advance (TA) in carrier aggregation, not supporting randomly accessing a SCell in the multi-TA carrier aggregation. The invention provides a method of randomly accessing a SCell and receiving data. For a procedure of randomly accessing a secondary cell (SCell), the UE has accessed a primary cell (PCell), the PCell and the SCell belong to different timing advance groups, the first cell transmits (A, A′, B) a random access command to the UE, the command is used for instructing the UE to access the SCell; According to the random access command, the UE transmits (C) a random access preamble to the SCell. Preferably, the first cell comprises another cell which cross-carrier schedules the SCell or the SCell. The invention provides a technical solution of randomly accessing a SCell in a multi-TA scenario, which fills the gaps in the existing technology.

The invention relates to a full-dimension and difference angle measurement method for zero setting conformal calibration of a planar phased array. The method comprises the following steps: evaluating to obtain an interference information matrix according to a block matrix and received data; obtaining a beam pointing Taylor sum weight vector and a direction/pitch Bayliss difference weight vector through Taylor and Bayliss functions; obtaining a direction/pitch full-dimension sum self-adaptive weight vector through a zero setting conformal calibration algorithm; obtaining a direction/pitch sum and difference beam directional diagram, and direction/pitch full-dimension sum beam output and difference output through the self-adaptive weight vector and the difference weight vector; obtaining a direction/pitch difference ratio sum resolvable angle curve and a direction/pitch difference ratio sum output value; counting the number of inflection points of the direction/pitch difference ratio sum resolvable angle curve, and adopting a nearest method to obtain a target direction/pitch angle estimation vector; and calculating to obtain a CAPON spectrum of a direction/pitch angle estimation value, searching a direction/pitch angle combination corresponding to a maximum value of the CAPON spectrum, and obtaining a target direction/pitch angle estimation value.

The invention discloses a method for estimating the jump cycle and the take-off time of a frequency hopping signal. The method comprises the following steps: step one: obtaining a data sequence; step two: initializing parameters; step three: calculating the power spectrum of data in the i-th short-time window; step four: estimating the peak frequency and the peak-to-average power ratio of the data in the short-time window through the power spectrum; step five: judging whether the processing for the data of all short-time windows is completed, if the processing is not completed, returning to the third step, and if the processing is completed, returning to the sixth step; step six: estimating the initial moment of each jump of the frequency hopping signal; step seven: utilizing an alpha-TM algorithm to estimate the jump cycle and the take-off time. The method simultaneously utilizes the frequency characteristics and the energy features of the frequency hopping signal, and the robustness is good; through short-time Fourier transform, the calculated amount is small, the engineering practicability is high, and the method is suitable for quick and robust estimation of the parameters of the frequency hopping signal.

The invention discloses an integrated circuit fault detection method based on feature extraction. The detection method comprises the following steps: obtaining a theoretical output order of a detected integrated circuit under nominal parameters of all components through theoretical calculation or simulation, obtaining an ideal noise characteristic output by the detected circuit through Monte Carlo simulation, obtaining an actual noise characteristic of a fault-free detected circuit through actual measurement, then calculating to obtain a fault-free characteristic value of the detected circuit; carrying out actual measurement on a detected circuit with an unknown fault, obtaining noise variance of the detected circuit with the unknown fault, then calculating to obtain a characteristic value of the detected circuit with the unknown fault; and finally comparing the characteristic value of the detected circuit with the unknown fault with the fault-free characteristic value of the detected circuit so as to achieve diagnosis for the fault of the integrated circuit. Compared with the prior art, the integrated circuit fault detection method based on the feature extraction is insensitive in noise, strong in robustness of circuit characteristic, low in probability of occurrence of fault aliasing and low in misjudgment rate.

The invention discloses a robust beamforming method with resistance to array system errors. The method comprises the following steps: first, carrying out eigen-decomposition of a sampled data covariance matrix received by an array antenna; estimating an eigenvector corresponding to a desired signal according to the correlation between the steering vector of the desired signal and the eigenvector corresponding to the desired signal, so as to further remove the eigenvector corresponding to the desired signal out of the covariance matrix; performing weighted combination to a subspace (formed by the rest of eigenvectors) and an interference plus noise covariance matrix estimated by Capon spectrum to obtain an interference plus noise covariance matrix used in the beamforming method disclosed by the invention; and carrying out a quadratically constrained quadratic optimization algorithm to obtain the corrected steering vector of the desired signal. The robust beamforming method provided by the invention has the advantages of effectively avoiding performance reduction caused by array system errors and greatly improving the robustness of a beamformer; compared with the most of current robust beamforming methods, the robust beamforming method provided by the invention is capable of getting closer to a theoretical output SINR (signal to interference plus noise ratio) and has better output performance.

A logical expression indicating a logical relation between arbitrary two or three objective functions, of a plurality of mathematically approximated objective functions is computed. A feasible region/sensitivity information display unit displays the feasible region in arbitrary objective space according to it. An inverse image computation unit computes a point or area in design space corresponding to arbitrary design parameters in relation to a point or area specified by a user in the feasible region of the objective space. A feasible region/sensitivity information display unit displays the distribution state of the corresponding point or area as sensitivity information in relation to the specified point or area in the feasible region.

The invention discloses a radar anti-jamming efficiency assessment credibility measurement realization method. For a relationship between mode classification and efficiency assessment, the efficiency assessment is regarded as efficiency grade classification, and from classification credibility, the assessment credibility is researched. An uncertainty theory is introduced in the method to realize uncertainty processing of test data. A credibility measurement method for an assessment model is obtained through calculation according to performance assessment indexes of the classification model. Multiple single-sample classification credibility calculation methods are fused to obtain a widely used single-sample assessment credibility calculation method, and further through a credibility propagation model, a comprehensive efficiency assessment credibility measurement result is obtained. According to the method, the radar countermeasure efficiency assessment credibility comprehensive analysis can be processed, and a complete analysis process and a corresponding calculation formula are given; and the assessment result analysis demands can be met in most weapon system efficiency assessment.

The invention discloses an active fault-tolerant predictive control method for faults of multiple actuators in a discrete system, especially provides an active fault-tolerant control method which considers common partial faults of multiple actuators in the discrete control system and combines with a discrete sliding mode observer and a model predictive control. According to an initial state modelof the system, considering the step type disturbance and error, an augmented state model of an embedded integrator is designed and predictive control rules are designed. According to a fault model, asecond-order discrete sliding mode observer is designed to detect fault factors of the faults of the multiple actuator, compensate the predictive control rules, and finally form a complete active fault-tolerant predictive controller. The active fault-tolerant predictive control method for the faults of the multiple actuators in the discrete system accurately estimates the fault factors by designing the second-order sliding mode observer, and designs the predictive control rules by using the model predictive control algorithm, can realize an online optimization of the control rules and effectively improves the control precision of the controlled system, provides good fault tolerance to the fault control system with multiple actuators. The active fault-tolerant predictive control method forthe faults of the multiple actuators in a discrete system is used for the active fault-tolerant control of the faults of the multiple actuators in the discrete system.

The invention discloses a speech recognition method suitable for a noise environment. According to the speech recognition method, a beam-forming device which integrates minimum variance undistorted response beam-forming and diagonally loaded beam-forming is established for noisy speech signals collected by double micro microphone arrays, the computational complexity is reduced by using a recursivematrix inversion method, and after beam-forming is carried out, a speech signal which is preliminarily denoised is obtained; then a post-modulation domain spectral subtraction method further processes the preliminarily denoised speech signal, the residual noise is removed, the speech distortion is reduced, and the final denoised speech signal is obtained; and moreover, the speech recognition method adopts a convolution neural network to train speech models and extract deep features of speech. According to the speech recognition method, the problem of speech recognition rate decline in the noisy environment is solved, and the speech recognition method has the advantage of good robustness, can be applied to aspects such as household robots, intelligent speakers and speech devices working inthe noisy environment.

The present disclosure provides a method for object detection and recognition based on a neural network. The method includes: adding a detection layer following three detection layers of an existing YOLOv5 network model, to construct a new YOLOv5 network model; then, training the new YOLOv5 network model by considering an overlapping area between a predicted box and a ground truth box, a center-to-center distance between the two boxes, and an aspect ratio of the two boxes; and finally, inputting a to-be-detected image into the trained new YOLOv5 network model, outputting a predicted box of an object and probability values corresponding to a class to which the object belongs, and setting a class corresponding to a maximum probability value as a predicted class of the object in the to-be-detected image. This method can quickly and effectively detect multiple classes of objects. Especially, a detection effect for small objects is more ideal.

The invention belongs to the real time positioning technical field of a distributed multi-point positioning monitoring system, and especially relates to a positive semi-definite relaxation time difference positioning method for the distributed multi-point positioning monitoring system; the method comprises the following steps: firstly building a time difference positioning equation; carrying out maximum likelihood estimation for a signal source position, i.e., a to-be-positioned object position; importing an auxiliary vector; converting a distance difference positioning equation into a constraint least square problem; carrying out weight least square solving for the auxiliary vector; using the weight least square solved auxiliary vector to primarily estimate relaxation equality constraint, and building a novel cost function; using a protruding positive semi-definite program to optimize and solve the auxiliary vector value and a variable value transpositioned by the auxiliary vector; using a characteristic constant to decompose the obtained auxiliary vector value; finally obtaining the signal source position information according to a relation between the solved auxiliary vector value and the signal source position. The method can prevent the local convergence and solving divergence problems caused by a conventional iteration algorithm, thus improving the positioning precision.

The invention discloses a museum monitoring system based on ZigBee. According to the system, in the complex communication environment of a museum, upper computers conduct real-time positioning on induction bracelets embedded with ZigBee radio frequency chips according to an optimized RSSI range finding algorithm and a mass center positioning model, with the help of the forwarding function of a ZigBee coordinator, the upper computers and various sensors on lower computers conduct mutual information feedback, and therefore internal environment variables of the museum are monitored and tourists, especially children and special staff are accurately positioned. The museum monitoring system based on the ZigBee wireless technology is free of wiring, easy to operate and large in network capacity, and has the advantages that staff filing is convenient, the tourist distribution condition inside the museum is accurately counted, the number of people simultaneously entering the museum is increased, the change of internal environmental parameters is accurately monitored, and safety of collected exhibits is guaranteed.

The present invention provides a method and a system for embedding and extracting an image digital watermark. The method includes: performing a DWT on an original carrier digital image to obtain two intermediate frequency coefficient matrixes; firstly performing a DCT on one of the two intermediate frequency coefficient matrixes, then performing the DWT, and then selecting an obtained low frequency coefficient matrix as a first low frequency coefficient matrix; performing the DWT on another intermediate frequency coefficient matrix, and then selecting an obtained low frequency coefficient matrix as a second low frequency coefficient matrix; and adjusting magnitude of a first frequency coefficient and a second frequency coefficient according to watermark values to be embedded at said embedment locations to make a magnitude relationship between two adjusted low frequency coefficients comply with a corresponding relationship between a preset embedded value and a magnitude relationship between the two low frequency coefficients.

The present invention provides an acoustic vector circular array mode-domain robust direction estimation method. The method includes the following steps that: the received signals P(t), Vx (t) and Vy (t) of an acoustic vector circular array acoustic pressure channel, an acoustic vector circular array vibration velocity x channel and an acoustic vector circular array vibration velocity y channel are obtained; the received signals Pe (t), Vex (t) and Vey(t) of a phase mode-domain acoustic pressure channel, a phase mode-domain vibration velocity x channel and a phase mode-domain vibration velocity y channel are obtained; Vex (t) and Vey(t) are rotated electronically, so that a combined vibration velocity Vec (t) can be obtained, a covariance matrix Repv is obtained based on Pe (t) and Vec (t), an unitary matrix Q is introduced to transform the matrix Repv, so that a real-value covariance matrix Rpv of mode-domain acoustic pressure and vibration velocity combined processing can be obtained; a steering vector a<~>r(Phi<~>) which has been subjected to mode-domain transformation and real-value processing is constrained, second-order cone programming is adopted to carry out solving, so that an optimal weight vector Omegarob can be obtained; and an output space spectrogram can be obtained based on the steering vector Omegarob which has been subjected to mode-domain transformation and unitary matrix real-value transformation, and a target direction can be obtained based on a spectrum peak position. With the method of the invention adopted, the problems of performance degradation and high signal-to-noise ratio processing threshold of a minimum variance distortionless response algorithm under a condition that related sound sources are difficult to distinguish and mismatching occurs can be solved. The method has the advantages of high resolution, high robustness, small amount of calculation, strong background noise suppression ability and the like.