1215 results about "High spatial resolution" patented technology

Disclosed are an improved GNSS tropospheric delay (ZTD) real-time grid model construction method and system. Firstly, a global tropospheric delay elevation normalization grid model is constructed using global ERA5 reanalysis data from a plurality of years. Then, elevation normalization and grid horizontal interpolation are performed on GNSS real-time ZTDs to obtain a global tropospheric delay real-time grid model. A user only needs to provide position and time information and use the global tropospheric delay elevation normalization grid model and the global tropospheric delay real-time grid model to be able to obtain a ZTD at a target point. According to the present invention, the high spatial resolution of a numerical weather model (NWM) and the high precision of the GNSS tropospheric product are comprehensively used, and an elevation normalization function form is optimized, so that the construction of a tropospheric delay real-time grid model covering a larger height range is achieved, and high-precision tropospheric delay correction information can be provided for GNSS real-time high-precision data processing, thereby improving the data processing performance.

The invention relates to the technical field of corrosion monitoring, in particular to an intelligent pipeline corrosion monitoring and protection system which comprises a dynamic potential disturbance measurement module, a local microcell scanning module, a corrosion rate calculation module, a corrosion risk distribution analysis module and an environmental parameter compensation module. According to the invention, accurate measurement is realized, the spatial resolution is improved, and the current response change rate is calculated by measuring the reference potential of the surface of the pipeline, applying instantaneous disturbance, combining current change differential operation, dynamically tracking the corrosion process, improving the real-time response capability and arranging a microelectrode array to cover corrosion hot spots; the corrosion current density is solved by combining the electrochemical characteristics of the material, the corrosion rate is dynamically predicted, regional distribution is analyzed based on the change of the corrosion rate, the corrosion risk assessment precision is optimized, environmental factors are monitored, disturbance parameters are adjusted, interference is compensated, the monitoring accuracy under different working conditions is ensured, and data stability and protection measure optimization are guaranteed.

The invention discloses an atmospheric environmental pollution event emergency monitoring and early warning method and system, and belongs to the technical field of environmental management. The method comprises the following steps: acquiring multi-scale meteorological field data and chlorine concentration real-time data of at least five monitoring stations, extracting a wind field direction and a concentration change rate, and determining a position and a wind direction of a potential pollution hot spot area. The information is sent to the unmanned aerial vehicle, and the unmanned aerial vehicle automatically plans a path and collects supplementary concentration data from three angles. And fusing the monitoring data and the supplementary sampling data, and combining with a meteorological field input pollutant three-dimensional concentration distribution model to generate three-dimensional distribution data. And inputting the CALPUFF reverse model, and carrying out inversion to obtain the specific position and the emission intensity of the pollution source. According to the scheme, the accuracy and timeliness of source tracing of the pollution source are improved, high spatial resolution and flexible response capability are achieved, and efficient and intelligent technical support is provided for intelligent early warning, rapid source tracing and scientific response of sudden toxic gas leakage events.

The invention discloses an intelligent operation abnormity monitoring and positioning method for a polypropylene cable, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting multi-source physical signals in the operation process of the cable, and constructing a multi-dimensional feature matrix fusing multiple physical quantities through multi-scale time window division and space mapping processing; extracting space-time coupling characteristics among nodes by using a graph attention embedding network, and training a running state recognition model in combination with a label perception contrast learning mechanism; constructing a cable topological graph based on an identification result, introducing an improved Bayesian space reasoning network, and calculating abnormal probability distribution of each node; a weighted abnormal heat map is further generated, an abnormal propagation path is extracted through an abnormal state flow model and a directional propagation scoring algorithm, and abnormal node positioning and trend evolution prediction are achieved; the method has the advantages of high spatial resolution, high identification precision and good online adaptability, and is suitable for intelligent state perception and abnormity early warning of the polypropylene cable in a complex operation environment.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention relates to the field of remote sensing image processing, and discloses a high-quality HRHS image generation method based on a double-subnetwork structure, and the method comprises the following steps: receiving an input low-resolution hyperspectral image and a panchromatic image, which are respectively used for providing spectral information and spatial information; performing noise filtering on the low-resolution hyperspectral image through an anti-noise pre-fusion sub-network, and generating a pre-fused hyperspectral image by using the correlation between the high spatial resolution information of the panchromatic image and the spectral characteristics of the low-resolution hyperspectral image; performing high-frequency detail reconstruction and enhancement on the pre-fused image based on a conditional diffusion model through a high-frequency enhancement sub-network; and fusing the low-frequency information of the pre-fused image and the high-frequency detail information generated by the high-frequency enhancement sub-network through wavelet inverse transformation, and outputting a final high-resolution hyperspectral image. According to the method, spectrum and space information can be effectively fused in various noise environments, and a high-resolution hyperspectral image with hyperspectral fidelity and space details is generated.

The invention discloses a forest region cross-season change detection method and system and a computer program. The method comprises the following steps: acquiring forest region remote sensing time sequence data and multi-temporal high-spatial resolution remote sensing image data; constructing a forest phenology and environment disturbance detection model based on a convolutional neural network and a time attention mechanism; constructing a forest vegetation type change detection model based on a convolutional neural network and a multi-task learning method; a forest phenology and environment disturbance detection result is obtained, fine adjustment and retraining are performed on the forest vegetation type change detection model, and a forest cross-season change detection model is obtained; and obtaining a forest region cross-season change detection result. According to the technical scheme provided by the invention, on the basis of forest region vegetation type change detection, forest region phenology and environment disturbance change information are integrated, and vegetation coverage fine-scale detailed change information is extracted, so that accurate extraction of annual multi-stage vegetation coverage fine change information is realized; and carrying out comprehensive evaluation and fine tuning improvement on the network model based on multiple indexes.

The invention relates to a multispectral image and hyperspectral image fusion method and system, and belongs to the field of deep learning and signal processing, and the method comprises the steps: carrying out the coding and feature extraction of a multispectral image and a hyperspectral image through employing a coder based on a spectrum self-attention mechanism; the hyperspectral image features and the multispectral image features obtained by the encoder are input into a space-spectrum fusion module, deep fusion of the spatial dimension and the spectral dimension is carried out, and unified feature representation is obtained; inputting unified feature representation obtained by fusion into a decoder based on a spectrum self-attention mechanism, inputting layered features passing through a graph-based spectrum sensing module into a corresponding decoder symmetric layer, and then decoding and outputting to obtain a spectrum image with high spatial resolution and a spectrum image with high spectral resolution; through the above strategy, full fusion and complementation of spectral image space information and spectral information are ensured, and the method has important value for research and application of MHIF.

The invention provides a high-speed defect identification method and system based on optical diffraction imaging, and relates to the technical field of optics, and the method comprises the following steps: constructing a composite optical diffraction unit comprising a programmable optical metasurface and a quantum phase modulator, optimizing and determining modulation characteristics through a multi-target constraint function, and collecting a quantum coding diffraction image. The quantum state phase reconstruction network is used for processing the image and extracting characteristic parameters, and a result is obtained through analysis of a defect identification model. According to the invention, rapid defect identification with high spatial resolution is realized, and the detection efficiency and accuracy are improved.

The invention discloses a modeling method for inverting surface high-precision three-dimensional deformation through InSAR and GNSS data fusion, and belongs to the technical field of surface deformation monitoring. The method includes the steps of resolving InSAR line-of-sight deformation, unifying InSAR and GNSS data reference systems, establishing a stress model based on an elastic theory, calculating a three-dimensional deformation component for the first time based on a least square theory, obtaining an optimal weight matrix by applying a variance component estimation method, calculating final three-dimensional deformation and the like. According to the method, three-dimensional deformation and strain parameters are estimated by fusing InSAR measurement data and GNSS measurement data based on the elastic theory, the stress relation between adjacent points is considered, and unnecessary spatial interpolation of GNSS measurement values is avoided. The variance component estimation method solves the problem of unreasonable weight ratio of different types of observation values, and according to the adjusted optimal weight matrix, a function model is solved based on the least square method, and high-precision and high-spatial-resolution earth surface three-dimensional deformation information within the research range is obtained.

The invention discloses a near-shore sea ice remote sensing extraction method based on FY-3 / MERSI satellite data, and the method comprises the following steps: S1, carrying out the preprocessing of an obtained MERSI image, and obtaining an MERSI image only containing sea ice and seawater; s2, establishing a coastal sea ice remote sensing recognition algorithm according to the difference of the spectral characteristics of sea ice and seawater in each channel of the MERSI image, and evaluating the precision and accuracy of the coastal sea ice remote sensing recognition algorithm; s3, according to a coastal sea ice remote sensing recognition algorithm, extracting coastal sea ice information from the MERSI image of each day; and carrying out 10-day averaging and monthly averaging on the daily sea ice data extracted in each month to generate a sea ice ten-day product and a monthly product. According to the invention, automatic identification of high-spatial-resolution sea ice information of a high-latitude offshore area in winter can be realized.

The invention discloses a fragmented cultivated land remote sensing extraction method based on space-time deep learning, and the method comprises the following steps: 1, data preparation: selecting a high-spatial-resolution remote sensing image, and capturing the space-time change of cultivated land in different growth cycles and environment conditions in combination with multi-temporal data of different times and seasons; a second step of model construction: constructing a deep learning model fusing ConvNeXt and a U-Net framework, namely a ConvNeXt-U model; 3, model training is carried out; 4, crushing cultivated land extraction; and 5, analyzing and outputting a result. Through multi-dimensional technical innovation, remarkable performance breakthrough and application value improvement are realized in the field of fragmented cultivated land remote sensing monitoring, and the method is specifically embodied in the following three aspects: 1, high precision and robustness improvement are realized; 2, optimizing calculation efficiency and generalization ability; and 3, dynamic evolution analysis and decision support capability.

The invention discloses a high-resolution hyperspectral image reconstruction method based on wavelet domain feature fusion and contrast learning, and belongs to the technical field of image fusion and super-resolution reconstruction. The method comprises the following steps: constructing a fusion network model comprising a wavelet transformation module, a cross-modal feature fusion module, a high-frequency contrast learning module and an image reconstruction module; performing end-to-end supervised training by using a training data set containing the low-resolution hyperspectral image and the high-resolution multispectral image; and after training is completed, inputting a test image pair to realize image reconstruction. According to the method, the detail retention capability is improved by combining wavelet decomposition and a directional fusion mechanism, the cross-modal high-frequency feature alignment capability is enhanced through comparative learning, a fusion image with high spatial resolution and high spectral consistency is finally generated, and the method is suitable for multi-modal image reconstruction tasks such as remote sensing, medical and natural images.

The invention relates to a spaceborne laser radar wind field data correction method based on a ground-based laser wind finding radar, and belongs to the field of radio, and the method comprises the steps: calculating horizontal wind speed components U1 and V1, and horizontal wind speed components U2 and V2 of different height layers according to collected data; determining an optimal nonlinear regression correction model; generating U and V wind fields with high spatial resolution; constructing a power spectrum correction model, and generating power spectrum signals of the corrected high-spatial-resolution grids U and V; generating a satellite-borne laser wind finding radar antenna direction weighting function, and obtaining a weight coefficient corresponding to each grid point in the high-spatial-resolution grid; and constructing a satellite-borne laser wind-finding radar wind field data correction model, and finally obtaining corrected satellite-borne laser wind-finding radar wind field data. According to the invention, accurate simulation of the power spectrums of the U and V components of the satellite-borne laser wind-finding radar with low spatial resolution is realized, and accurate and efficient correction of the U and V components of the satellite-borne laser wind-finding radar is realized.

The invention discloses a multi-curvature high-resolution bionic miniature visual tactile sensor for fingertips, which relates to the technical field of sensors and comprises a fingertip structure, a transparent support structure, a flexible silica gel sensing layer and an image acquisition module. A cavity is formed in the fingertip structure; the transparent supporting structure is clamped in the cavity; the flexible silica gel sensing layer is adhered to the transparent supporting structure, and black circular array mark points are attached to the surface of the flexible silica gel sensing layer; the image acquisition module is arranged on the fingertip structure, the acquisition end of the image acquisition module is arranged above the transparent supporting structure, and the view field area of the acquisition end covers the whole flexible silica gel sensing layer area; compared with other fingertip tactile sensors, the visual tactile sensor is a human fingertip simulated visual tactile sensor with the highest spatial resolution, the smallest overall size and the lowest manufacturing cost.

Methods and systems are provided to generate and use pairs of highly nondegenerate frequency-entangled photons for Hong-Ou-Mandel interferometric measurement of local or remote samples. The use of highly nondegenerate frequency-entangled photon pairs enables ultra-high spatial resolutions even in the presence of background noise, dispersive intermediate media and / or dispersive or multi-interface targets, and high probe photon losses. The use of highly nondegenerate, narrow-band, frequency-entangled photon pairs for interferometric measurement of distance also allows the interferometer to be calibrated more easily for the two (or more) discrete narrow bands of wavelengths represented by the photon pairs. The use of narrow-band nondegenerate frequency-entangled photon pairs also permits improved noise rejection and increased fidelity in coincidence detection.

The invention relates to the technical field of remote sensing image processing, and particularly discloses a remote sensing hyperspectral image super-resolution fusion algorithm and system based on quaternion, and the method comprises the steps: employing a panchromatic image quaternion feature extraction module, a hyperspectral image quaternion feature extraction module and a self-attention fusion module, constructing a double-U-Net structure network based on a multi-scale residual link and a jump link; and inputting a panchromatic image and a low-resolution hyperspectral image, and calculating to obtain a high-spatial-resolution hyperspectral image. According to the method, a plurality of spectral channels of a hyperspectral image and high-resolution spatial information of a panchromatic image are embedded into four components of a quaternion, and spectral and spatial characteristics are modeled uniformly by means of algebra of the quaternion; and then feature extraction, fusion and reconstruction are carried out through a deep learning network, and finally a fusion image with both high spectral fidelity and high spatial resolution is generated, so that the spatial resolution of the hyperspectral image can be significantly improved, and the spectral consistency of the hyperspectral image can be maintained at the same time.

The invention discloses a marine environment pile foundation damage identification method based on optical fiber distributed strain monitoring, and belongs to the technical field of structural health monitoring and ocean engineering. The marine environment pile foundation damage identification method comprises the steps that distributed optical fiber sensors are evenly distributed in the axial direction of a pile foundation and used for strain monitoring; acquiring strain response data of the pile foundation along the pile length under the action of the wave force load through a distributed optical fiber demodulator; identifying modal characteristic parameters of the pile foundation structure by applying a random subspace identification algorithm, wherein the modal characteristic parameters comprise modal frequency and modal shape; calculating modal strain energy based on the modal shape, and obtaining a modal strain energy distribution diagram; identifying a damage area, judging whether a set threshold value is exceeded or not by comparing the change of the modal strain energy between the monitoring state and the reference state, judging the damage, and obtaining a damage identification result. The method provided by the invention has the advantages of high spatial resolution, high recognition precision, good environmental adaptability, accurate positioning, high automation, wide applicability and the like.

The invention discloses a panchromatic sharpening method and system based on cross-resolution adversarial learning and a Mama network. The method comprises the following steps: acquiring a low-resolution multispectral image LRMS and a high-spatial-resolution panchromatic image PAN; extracting features of the low-resolution multispectral image LRMS to obtain a first feature map; extracting features of the high-spatial-resolution panchromatic image PAN to obtain a second feature map; performing wavelet decomposition and weighted fusion on the first feature map and the second feature map to obtain a multi-channel feature map, and performing dynamic fusion on the multi-channel feature map through a cross attention gating mechanism to obtain a fused feature map; and carrying out residual connection on the fused feature map and the low-resolution multispectral image, and then carrying out feature reconstruction to obtain a high-resolution multispectral remote sensing image. According to the method, the panchromatic sharpening performance of the remote sensing image can be improved, and the high-quality and high-resolution multispectral remote sensing image is obtained.

The invention belongs to the technical field of image processing, and relates to a KAN-based space-frequency double-domain implicit guide sampling remote sensing image fusion method, which comprises the steps of constructing a training sample, creating a KAN-based space-frequency double-domain implicit guide sampling network, training the network and inputting an image to be fused into the trained network. And a fused image with high spatial resolution and high spectral resolution after fusion is generated through mapping. The network comprises a coding unit, a space implicit guiding unit, a frequency implicit guiding unit and a decoding unit, the coding unit is composed of parallel KANs and maps hyperspectral and multispectral remote sensing images to a depth potential space, and the space implicit guiding unit and the frequency implicit guiding unit conduct guiding implicit sampling on HSI in the space domain and the frequency domain respectively. And the decoding unit fuses the space-frequency domain sampling features and generates a final fused image. According to the invention, a pure KAN-driven architecture is adopted, the dependence on a multi-layer sensor in the prior art is replaced, and the lightweight degree is relatively high.

The invention discloses a flow field measurement system and method fusing event data and a blurred image, and the method comprises the steps: turning on a sheet-shaped light source, and enabling the sheet-shaped light source to illuminate tracer particles in a gas flow field; starting a gas flow field to enable the gas flow field to carry the tracer particles to flow through the transparent box body; capturing the motion change of the tracer particles in real time through an event camera, and collecting event data; blurred images of the tracer particles are synchronously collected through an industrial camera; based on the event data and the blurred image, reconstructing a clear moving image at any moment by using an image deblurring technology EDI; and carrying out time synchronization and space alignment on the clear moving image and the high-resolution static image, and extracting flow field information in the gas flow field in combination with the movement track and brightness change of the particles. According to the method, the high-speed dynamic capture capability of the event camera and the high spatial resolution of the industrial camera are combined, the bottleneck of the spatial-temporal resolution in the traditional flow field measurement technology can be effectively overcome, and meanwhile, the method has a relatively good effect in a complex flow field and a high-speed dynamic scene.

The invention discloses a high-resolution optical fiber integrated diamond color center sensor and a measuring method, the sensor comprises an optical fiber responsible for inputting exciting light and collecting fluorescence, the end face of the optical fiber is provided with a diamond color center, the diamond color center is excited by the output of the optical fiber, and a quartz capillary is arranged outside the optical fiber and the diamond color center. A microwave antenna is wound outside the quartz capillary tube, and a part of a bare wire of the microwave antenna is connected with the radio frequency line. The measuring method comprises the following steps: exciting a diamond color center; applying microwaves to the diamond color center; fluorescence generated by stimulated radiation of the NV color center is output by the optical fiber circulator and is input into the photoelectric detector through the filter; the upper computer controls the microwave generator to sweep frequency; the photoelectric detector performs photoelectric conversion, and the upper computer processes microwave frequency sweeping signals and electric signals. The magnetic field size and the temperature size of the sample to be measured in the micro space can be measured, and the device has the advantages of being high in spatial resolution, high in integration level, high in flexibility, high in controllability and the like.

The invention provides a multispectral image spectral super-resolution method based on adaptive learning and application, and belongs to the technical field of remote sensing image processing. The method solves the problems that in an existing spectrum super-resolution method, manual setting of prior information and complex parameter selection are carried out, in a deep learning method, pixel dependence between space long distances and self-similarity between spectrums are ignored, and deep network parameters cannot be dynamically adjusted in real time. Constructing a pseudo label based on the multispectral data set; constructing a spectrum super-resolution network, and performing supervised training on the spectrum super-resolution network based on a pseudo tag to generate a preliminary reconstructed image; inputting the preliminary reconstructed image into an adaptive learning module, learning the residual error of the preliminary reconstructed image and the real image, and performing residual error correction; constructing an adaptive learning network, and updating parameters of an adaptive learning module; and carrying out residual connection on the adaptive learning network after parameter updating and a to-be-processed image, and outputting a target high-spatial-resolution hyperspectral image.

The invention provides a forest-grain intercropping area fruit tree density extraction method based on remote sensing, and relates to the field of fruit tree density extraction. The method comprises the following main steps: firstly, based on an instance segmentation thought, utilizing a deep learning model to replace a traditional segmentation mode to extract a land parcel from an image with ultrahigh spatial resolution as a basic calculation unit; then, on the basis of a time sequence model, features of Sentinel-2 time sequence images are extracted to distinguish common farmland plots and forest-grain intercropping plots, and plot units for subsequent fruit tree density extraction tasks are determined; then, extracting spatial information features of the fruit tree from the GF-2 image based on a semantic segmentation model; and finally, designing a template filling strategy based on a semantic segmentation result, and obtaining the planting density of the fruit trees in the forest-grain intercropping area under the constraint of a land parcel result. The extraction method is high in accuracy, high in processing speed and good in demonstration application effect, and can provide support for scientifically adjusting the planting structure of the vegetation in the forest-grain intercropping area.

The invention relates to the technical field of medical elastography and material multi-scale mechanical measurement, in particular to an elastography method and system based on an instantaneous scattering displacement field, and the method comprises the steps: driving a passive / active method scattering displacement field excitation device through a frequency domain controllable sine wave signal, and generating a scattering displacement field on a target material, an instantaneous displacement field is obtained through high spatial resolution measurement, then sliding window local interception, two-dimensional autocorrelation calculation and nonlinear fitting based on the Rayleigh / shear wave spatial autocorrelation theory are performed on the single-frame instantaneous displacement field, shear wave velocity distribution is obtained, and then the viscoelasticity modulus of a target area is inverted to achieve comprehensive characterization of the viscoelasticity of the material. According to the scheme, the single-frame scattering displacement field is excited and collected, and the elasticity inversion is performed based on the instantaneous displacement field, so that the dependence of traditional elasticity imaging on high-frame-rate equipment is broken through, the material viscoelasticity evaluation can be efficiently completed at low cost, and the application prospect in the fields of clinical diagnosis and mechanical measurement is wide.

The invention relates to a visible light / short-wave infrared dual-band common-caliber optical system, which comprises a common-caliber part, a plane beam splitter, a visible light channel subsystem and a short-wave infrared channel subsystem, and is characterized in that the common-caliber part is positioned at the foremost end of the optical system and consists of a primary reflector and a secondary reflector which are coaxially arranged; the plane beam splitter is located on a light path behind the secondary reflector, full-wave-band light from an object side is reflected by the primary reflector and the secondary reflector in sequence, then reaches the plane beam splitter to be split, and then enters the visible light channel subsystem and the short-wave infrared channel subsystem to be imaged in different wave bands, and the optical system achieves dual-wave-band common caliber. According to the invention, the design of long focal length and large aperture of the dual-band system is realized, the target detection / identification capability of the system is improved, the system has the advantages of high spatial resolution, long operating distance and high identification probability, meanwhile, the system structure is effectively simplified, the system volume is reduced, the installation and adjustment difficulty is reduced, and a relatively low obscuration ratio and relatively high imaging quality are realized.

The invention discloses a multi-frame photoacoustic image reconstruction method based on optical flow alignment and depth feature fusion. The method comprises the following steps: S1, obtaining photoacoustic signal data; s2, reconstructing a photoacoustic cross-sectional image; s3, performing optical flow calculation and image alignment; and S4, training the deep feature fusion network. According to the method, through optical flow motion correction and a potential space learning mechanism, space-time information and complementary features in the aligned multiple frames of images are dynamically integrated, complementary information in the multiple frames of images is adaptively fused, noise is suppressed, and finally reconstruction of high signal-to-noise ratio and high spatial resolution images of biological tissues is achieved. Experimental results show that the method can significantly improve image quality, recover image distortion and detail loss caused by motion and noise, and provide a new effective scheme for promoting robust clinical application of a photoacoustic imaging technology.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

The invention discloses a laser ultrasonic defect imaging method based on sampling-imaging double-order compressed sensing, and relates to the technical field of laser ultrasonic detection and material defect detection, and the method comprises the following steps: carrying out sparse scanning on a to-be-detected sample through a random sparse sampling strategy; according to a compressed sensing principle, global ultrasonic intensity distribution of full-matrix scanning under a high sampling rate is reconstructed, and global sparse positioning is realized through extreme value detection; then, on the basis of global sparse positioning, carrying out local fine scanning by adopting high spatial resolution; and generating a sparse imaging result with low spatial resolution by using global sparse positioning and local fine scanning data through a synthetic aperture focusing technology, and performing high-resolution compressed sensing reconstruction imaging by using a compressed sensing principle again. Therefore, by adopting the laser ultrasonic defect imaging method based on sampling-imaging double-order compressed sensing, efficient imaging of tiny defects can be realized, and scanning points are reduced while the imaging precision is improved.

A high-density tactile sensing array based on a bionic microstructure comprises a flexible substrate for providing mechanical flexibility and packaging protection; the bionic microstructure layer is composed of a pyramid array or a hemisphere array and is used for enhancing sensitivity and spatial resolution; the carbon nanotube (CNT) sensitive layer uniformly covers the surface of the microstructure and converts pressure change into an electric signal; according to the thin film transistor signal reading system, active matrix design is adopted, pixel-level signal extraction is achieved through a row-column addressing strategy, and electric signals are rapidly and accurately processed. According to the preparation method, photoetching, wet etching and hot reflux processes are combined, precision machining of a microstructure is achieved, and a uniform CNT sensitive layer is formed through a spraying technology. According to the design method, microstructure parameters and sensitive layer characteristics are optimized through equivalent circuit model construction and simulation verification, and collaborative optimization of high sensitivity and wide dynamic range is achieved. The invention has the advantages of high sensitivity, high spatial resolution, low crosstalk, quick response and the like.