8112 results about "Microscope" patented technology

The invention provides a metal material defect identification and analysis method and system based on a multiple detection technology, and relates to the technical field of metal defect detection, and the method comprehensively utilizes microscope detection and ultrasonic detection technologies to obtain surface topography data and internal structure data of a target metal material. And performing feature alignment and fusion processing on the two types of data through a multi-grid method to generate a unified feature distribution model, and establishing a mathematical solution model of ultrasonic wave field propagation based on the model. And determining the position, shape and size information of the potential defect by using the solved data, and generating defect positioning data containing three-dimensional coordinates and contour features. And extracting key defect information in combination with an edge detection algorithm, judging defect types by adopting a classification algorithm, and generating a detection result. According to the method, the problems of insufficient detection precision, high data fusion difficulty and low classification efficiency in the prior art are solved, and the applicability and reliability of a defect detection technology are improved.

A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled / label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining and biomedical workflow.

In a method, for training an artificial neural network (ANN) to reduce noise and / or artefacts in an electron microscopy image, a plurality of training image pairs is generated. For each pair, an undistorted synthetic specimen image and a distorted image are created by simulating additional noise and / or artefact features. The ANN is trained, in which the distorted images are used as input and the corresponding undistorted images as output. An adversarial training strategy is used in which the ANN is trained, as a generator network, in conjunction with concomitantly training a further ANN, as a discriminator network, to differentiate output produced by the generator network from synthetic images in the training set. In training, parameters of the ANN and further ANN are optimized using a generator loss function and a discriminator loss function, in which a dependency exists between said loss functions to train the networks in an adversarial manner.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

A method of determining nanoparticle properties of nanoparticles (2) included in a sample (1), comprising the steps of collecting sequential frames of images by employing an interferometric microscope device (110), wherein the sample (1) is illuminated with illumination light (3) from a coherent light source device (111) and the images are created by scattering light (4) from the nanoparticles (2) superimposed with non-scattered reference light, said scattering light and reference light having a wavelength larger than a cross-sectional dimension of the nanoparticles (2), tracking the nanoparticles (2) in the sequential frames of images, wherein at least one interferometric point spread function (iPSF) feature of each of the nanoparticles (2) is established and nanoparticle trajectory motion data are determined for each nanoparticle (2), comprising the nanoparticle positions in each frame, for each nanoparticle (2), calculating a nanoparticle size d from the trajectory motion data of the nanoparticle and calculating an interferometric nanoparticle contrast from the at least one iPSF feature of the nanoparticle.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

The invention discloses a wafer carrying inspection machine. Comprising a main machine shell, a wafer stock bin, a stock bin lifting driving device, a bin entering and exiting arm, a bin entering and exiting arm translation driving device, a center suction cup, a suction cup lifting driving device, a suction cup horizontal adjusting mechanism, a suction cup turn-over driving device, a suction cup rotation driving device, an edge searching sensor, an edge searching sensor advancing and retreating driving device and a turn-over arm. The full-automatic wafer micro-detection device is compact in structure, small in size and high in efficiency, can be placed on a table or a workbench to be matched with a microscope to perform micro-detection on wafers, and is high in detection precision, high in detection precision and high in detection efficiency. The wafer surface detection device can complete operations such as automatic feeding, automatic overturning, automatic edge searching, automatic carrying of microscopic detection, automatic discharging and the like of wafers, and can assist an operator in carrying out wafer surface detection, wafer back edge detection, wafer back center detection and microscope microscopic detection on the wafers.

The invention discloses a multi-scale fatigue crack propagation prediction method and system based on a physical information neural network, and aims to improve the prediction precision of a crack propagation rate. The method comprises the following steps: S1, acquiring crack lengths and cycle cycles of a metal material under different scales by using a microscope; s2, calculating an initial small crack propagation rate based on the acquired data, performing fitting by adopting a Newman-Wu model, constructing an initial crack propagation physical model, further combining experimental data to identify physical characteristics influencing propagation behaviors, introducing a PSC-LC turning point to divide small crack and long crack stages, and constructing a crack propagation rate model fusing multiple characteristics; s3, preprocessing the expansion rate data; and S4, constructing a physical loss function in combination with a crack propagation rule in an experiment, fusing the physical loss function with data loss to form a total loss function, and training the neural network model to realize accurate prediction of the multi-scale fatigue crack propagation behavior of the metal material.

The invention discloses an optical three-dimensional space converter. The optical three-dimensional space converter comprises a long-focus lens, a short-focus micro-lens array and a relative position regulator, one of the two lenses is used for receiving light from an object space and imaging the light into an intermediate image; one is used for converting the intermediate image into a virtual image or a real image of a three-dimensional space; the intermediate image horizontal turner turns the intermediate image left and right in the horizontal direction for real image space conversion; the relative position adjuster is used for adjusting the relative position of the long-focus lens and the short-focus micro lens array. The optical three-dimensional space converter can provide a wide visual field, and the size of an image obtained through compression or amplification of the three-dimensional space converter can be adjusted. The lens can be applied to a wide-view-field real image and virtual image telescope for naked eye observation, a wide-view-field real image and virtual image microscope for naked eye observation, a wide-view-field three-dimensional camera or a wide-view-field three-dimensional projector, an artificial eye and the like.

The invention discloses an optimized preparation method of a high-strength flame-retardant polyamide-based composite material in the technical field of information, which comprises the following steps: in the preparation process of the composite material, acquiring online dispersed state monitoring data of a modified filler by using a laser particle size analyzer and an optical microscope, including particle size distribution, agglomeration degree and dispersion uniformity of the filler; according to online dispersion state monitoring data, parameter ranges of melt flow property characterization are determined, the parameter ranges comprise the shearing rate and the temperature, particle size distribution data are used for determining the shearing rate range, small particle size distribution corresponds to the high shearing rate range, and agglomeration degree data are used for determining the temperature range; the higher degree of agglomeration corresponds to a higher temperature range to promote the dispersion of the filler, and these parameters are directly used to guide the setting of the subsequent rheological test.

The invention provides an electron microscope automatic focusing method and system based on a deep learning model, and relates to the technical field of electron microscope automatic focusing, and the method comprises the steps: collecting a plurality of images at different focusing distances through an electron microscope, and recording the focusing distance of each image; calculating the characteristic value of each collected image, combining the focusing distance and the characteristic value of the image into a characteristic vector, and generating a characteristic sequence according to the characteristic vectors obtained under different focusing distances; training a pre-constructed neural network model by using the feature sequence to obtain a trained neural network model; and inputting a to-be-tested image into the trained neural network model, calculating a difference value between the predicted optimal focusing distance and the focusing distance of the collected image, and judging whether the difference value is within a set range or not. According to the invention, the microscopic image acquisition efficiency and quality are obviously improved.

A method for automated, artificial-intelligence-based COC retrieval includes using an artificial intelligence / machine learning system (AI / ML system) to optically scan a follicular fluid sample to produce an image object using an imaging system that includes a microscopy system, a camera system, and a lighting system. The method includes comparing the image object to a predetermined threshold using an AI / ML system, wherein the predetermined threshold is at least in part an optical pattern with a probability of corresponding to a cumulus-oocyte-complex (COC). The method includes identifying a COC within the follicular fluid sample based at least in part on the image object satisfying the predetermined threshold. The method includes determining a COC location within the follicular fluid sample based at least in part on identifying a region of the image object corresponding to an optical pattern within the image object that satisfies the predetermined threshold.

A microscopy method includes a trained deep neural network that is executed by software using one or more processors of a computing device, the trained deep neural network trained with a training set of images comprising co-registered pairs of high-resolution microscopy images or image patches of a sample and their corresponding low-resolution microscopy images or image patches of the same sample. A microscopy input image of a sample to be imaged is input to the trained deep neural network which rapidly outputs an output image of the sample, the output image having improved one or more of spatial resolution, depth-of-field, signal-to-noise ratio, and / or image contrast.

The magnetic tape includes a non-magnetic support, and a magnetic layer containing a ferromagnetic powder. A magnetic tape cartridge and a magnetic tape apparatus include the magnetic tape. CS VIVA 0.1, which is a height that is obtained by measuring a surface of the magnetic layer with an atomic force microscope and at which a cutout length is 0.1% with a length of one side of a measurement region as 100%, is 4 nm or more and 10 nm or less, and standard deviation a of the CS VIVA 0.1 in a width direction of the surface of the magnetic layer is 1.0 nm or less.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

A system and method for image-guided microscopic illumination are provided. A processing module controls an imaging assembly such that a camera acquires an image or images of a sample in multiple fields of view, and the image or images are automatically transmitted to a processing module and processed by the first processing module automatically in real-time based on a predefined criterion so as to determine coordinate information of an interested region in each field of view. The processing module also controls an illuminating assembly to illuminate the interested region of the sample according to the received coordinate information regarding to the interested region, with the illumination patterns changing among the fields of view.

A deep learning-based digital / virtual staining method and system enables the creation of digitally / virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally / virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital / virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally / virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

The invention discloses a quantum computing chip based on a vertical micromirror, a system on a crystal and a simulator correction method thereof, a vertical micromirror structure with the roughness of less than 5nm can be formed by performing side wall smoothing on a deep etching vertical structure, and the loss of light irradiated on a side wall interface of a silicon-based optical device can be greatly reduced; a quantum computing chip is developed on a silicon wafer, another optical computing chip structure except macroscopic space optics and silicon-based optoelectronics is realized, the technological process is completely compatible with an advanced packaging technology, and photoelectric wafer level system integration of sensing, storage and computing can be realized. For an on-crystal system integrated with a quantum computing chip and an integrated circuit chip (and an optical sensing chip), crosstalk, loss and delay of TSV transmission signals are reduced through a vertical micro-mirror technology, indirect measurement of side wall roughness is realized through Fabry-Perot interference, an ideal on-crystal system simulator is corrected, and the measurement precision of the on-crystal system simulator is improved. And the simulation result of the simulator is closer to the real measurement result of the on-chip system.

The invention discloses a display screen module detection device, and relates to the technical field of display screen module detection, the display screen module detection device comprises a detection table, a laser detection module is arranged on the detection table, and the laser detection module comprises a laser confocal microscope unit executing rotation and translation motion, an extrusion module and a detection module arranged at the side end of the laser detection module, comprising an extrusion part capable of vertically moving and rotating and a pressing tool assembly arranged at the side end of the extrusion part, and an extrusion station is arranged below the extrusion part; the extrusion module can automatically adjust the pressure head to the extrusion station according to needs, the time for replacing the pressure head is saved, after the extrusion piece adjusts the position of the pressure head of the detection station, the hold-down clamp assembly rotates to carry out arc scratch testing on the display screen module, testing is diversified, scratch and friction testing data of different modes can be provided, and the testing efficiency is improved. The accuracy of the whole test is improved through different data, and the test efficiency and accuracy are improved.

The invention discloses an electrostatic induction and optical imaging principle-fused online monitoring device and method for wear particles of a water-lubricated bearing, and belongs to the field of state monitoring of mechanical equipment. The device comprises a square insulation detection cavity, a detection electrode array, an electromagnetic shielding shell, a high-speed optical imaging module and a signal processing unit, the square insulation detection cavity is installed in a bearing lubrication water loop. The detection electrode array is fixed to the outer wall of the cavity and used for capturing electrostatic signals generated when the abrasion particles pass through. The electromagnetic shielding shell wraps the detection cavity and is grounded through a wire to eliminate environmental electromagnetic interference. The high-speed optical imaging module integrates a high-magnification microscope lens and a CMOS sensor, is coaxially mounted with the detection cavity, and obtains the morphology characteristics of the wear particles in real time; and the signal processing unit performs multi-modal data fusion analysis on the electrostatic signal and the optical image. Through cooperative detection of electrostatic induction and optical imaging, the monitoring precision and reliability of the wear particles of the water-lubricated bearing are remarkably improved.

A system (100) comprising one or more processors (110) and one or more storage devices (120) is configured to obtain biology-related image-based input data (107) and generate a high-dimensional representation of the biology-related image-based input data (107) by a trained visual recognition machine-learning algorithm executed by the one or more processors (110). The high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to at least one of store the high-dimensional representation of the biology-related image-based input data (107) together with the biology-related image-based input data (107) by the one or more storage devices (120) or output biology-related language-based output data (109) corresponding to the high-dimensional representation.

Phase contrast images are calculated by digitally post-processing microscope images acquired at different angled illumination configurations and defocus values.

Systems, components, and methods for detecting characteristic signals are described. A detector includes a detector cell. The detector cell can be configured to generate an electrical signal in response to a particle incident on an active layer of the detector cell, The active layer can define an absorption surface. The detector can include a filter. The filter can include a membrane of carbon material. The filter can be disposed relative to the detector cell to shield the absorption surface from a subset of the incident particles. The subset of the incident particles can include electrons, ions, and photons. The photons can have an energy less than about 40 eV.

A composition of matter is provided comprising hierarchically ordered crystalline microporous material having well-defined long-range mesoporous ordering of lamellar symmetry. The composition possesses mesopores having walls of crystalline microporous material and a mass of mesostructure between mesopores of crystalline microporous material. Long-range ordering is defined by presence of secondary peaks in an X-ray diffraction (XRD) pattern and / or lamellar symmetry observable by microscopy.

The invention relates to the technical field of oil exploration, and discloses a rock mechanical parameter logging intelligent evaluation method considering continental facies shale fabric characteristics. The method comprises the following steps: 1, dividing shale lithofacies, identifying and determining the bedding development thickness of a sample through an optical microscope, determining a granularity experiment of the sample through granularity analysis, and forming mineral composition and bedding factor lithofacies division; 2, constructing a typical logging facies based on a rock electricity response relationship, and dividing the imaging logging facies of the research area according to natural gamma curves of conventional logging and imaging logging, core deposition structure characteristics and imaging logging image characteristics; 3, testing the longitudinal and transverse wave velocities and the porosity of different shale lithofacies, and calculating the dynamic elastic modulus and the Poisson's ratio. Through fine division and description, the method for accurately evaluating the bedding development condition, the particle size characteristics and the mineral components of the shale sample is provided, the testing time is short, the cost is low, and accurate evaluation of shale can be achieved.

The invention relates to the technical field of semiconductors, and discloses a semiconductor sample defect detection system which comprises an imaging module, a data acquisition module, a data processing module, an automation and integration module and a user interface module. According to the defect detection system and method for the semiconductor sample, by integrating the imaging modules of the optical microscope, the electron microscope, the X-ray diffractometer and the super-resolution microscope, details of the semiconductor sample can be comprehensively captured from macroscopic to microscopic and from the surface to a crystal structure, so that precise recognition of tiny or deep defects is ensured; the application of the deep learning model further improves the accuracy of image processing, through feature extraction and intelligent identification, even defects difficult to perceive by naked eyes have no place to hide, and the high-precision detection capability greatly reduces the risk that quality problems are only found in subsequent production links, reduces the possibility of reworking and scrapping, and improves the production efficiency. Therefore, the product quality and the production yield are improved.

The invention belongs to the technical field of particle motion track recognition, and particularly relates to a method for tracking a large number of moving particles based on image recognition, the moving particles suitable for the method are particles undergoing active Brownian motion, the same color mark is made on each moving particle, and image acquisition is performed through a microscope or a video recorder; identifying a pixel point cluster corresponding to the color mark on each moving particle by setting an RGB value; determining moving particles corresponding to the color marks by setting a threshold value in the pixel point cluster; taking a pixel point corresponding to the average value position of all the position data as the position of the moving particle; and recording the motion trail of the moving particles in a period of time. According to the method, the marks are introduced to the particles, the algorithm is developed to identify the marks, calculate and record the average positions of the pixel points corresponding to the marks, the particles are tracked, the colored marks can exist stably and do not interfere with one another, and it can be ensured that a large number of particles can be tracked for a long time.

The invention provides a preparation method of a bionic microfluidic device for researching fluid migration in carbonate rock micro-nano fractures, which comprises the following steps: step 1, analyzing and classifying scanning electron microscope images of carbonate rock core slices through a random forest algorithm to generate a chip design drawing; step 2, based on the chip design drawing, re-etching a crack structure through a photoresist template and polydimethylsiloxane by adopting a soft lithography technology, and bonding with a glass substrate to form a micro-fluidic chip; and step 3, injecting a precursor liquid containing the calcite nano seed crystal into the micro-fluidic chip flow channel, fixing the seed crystal through ultraviolet irradiation, and injecting a saturated equilibrium liquid to realize calcium carbonate mineral precipitation so as to form the bionic micro-fluidic device. The method can be used for coupling research of all fluids and carbonate rock fractures, and a method for recovering the carbonate rock fracture structure to the maximum extent and a rock surface property cloning method are provided.

The invention provides an auxiliary pathological analysis method for a microscope system, and belongs to the technical field of pathological analysis, and the method comprises the steps: firstly carrying out the gray normalization, denoising and other preprocessing of an obtained pathological section image, and carrying out the illumination equalization processing through a region adaptive histogram equalization algorithm; then, color features of the image are extracted in an LAB color space, a color representation center is calculated, a feature mapping matrix is established, and meanwhile texture features are extracted and fused with the color features; and carrying out dimension reduction on the fused feature vector by using a principal component analysis algorithm, and reserving important feature information. And then, establishing a convolutional neural network model, inputting the feature vectors after dimension reduction, performing training and cross validation by using a labeling data set, and improving the model performance through iterative optimization. According to the invention, the technical problem that the diagnosis and judgment of a doctor are seriously influenced due to inconsistent color expression caused by the difference of shooting conditions of pathological section images observed by a microscope is solved.