147results about How to "Improve registration accuracy" patented technology

The invention discloses a large-scale point cloud registration method, system and device based on a power grid power transmission line and a storage medium, and the method comprises the steps: collecting point cloud data of the power transmission line under different visual angles, and forming a source point cloud set and a target point cloud set; down-sampling point cloud data in the source point cloud set and the target point cloud set, dynamically adjusting the sampling granularity of different density regions, and obtaining representative point cloud data from the source point cloud set and the target point cloud set; extracting point cloud feature points, accelerating neighborhood search through a KD-Tree structure, and adaptively adjusting a neighborhood search radius; constructing a geometric topology model; performing coarse registration based on an improved RANSAC algorithm, selecting non-collinear feature points, and optimizing a transformation model; an improved ICP algorithm is adopted to carry out fine registration, and normal vector features are utilized to introduce weighting factors to optimize registration precision. According to the invention, large-scale power transmission line point cloud data can be efficiently and accurately processed, and the method is especially suitable for power inspection, three-dimensional modeling and maintenance scenes.

The invention discloses an unsupervised registration method and system for pelvic reduction surgical navigation, and the method comprises the steps: constructing a three-dimensional reconstruction network model, and completing the training based on a generative adversarial loss function; inputting the intra-operative two-dimensional X-ray image into the three-dimensional reconstruction network model to obtain a three-dimensional intra-operative CT image; respectively segmenting the preoperative CT image and the intraoperative CT image by adopting a MedSAM segmentation model to obtain a preoperative segmented image and an intraoperative segmented image of the pelvis region; key points of the preoperative segmented image and the intra-operative segmented image are determined through a convolutional neural network, the distance between the key points is subjected to minimization calculation according to a closed-form function, and a rigid transformation matrix is obtained through derivation; and registering the intraoperative CT image according to the rigid transformation matrix and the preoperative CT image. Through the technical scheme of the invention, the registration precision and generalization ability are improved, the prediction range of the pose is wider, and good technical support is provided for the rapidness and convenience of surgical navigation.

The invention discloses a large-scale actual measurement point set generated for ship body segmented large components, which is a coarse registration method based on an RANSAC principle and a significant structure and a point cloud-model fine registration method considering various information, and provides a fast and efficient coarse registration and fine registration method. The point cloud registration based on the deep learning method can fully consider wide information such as process constraint, measurement point cloud inherent information and point cloud local semantic information, the intelligence and rationality of a registration process are greatly improved, meanwhile, the engineering guidance of a registration result is improved, rapid error evaluation of a machined part is achieved, and the precision of the machined part is improved. And a basis is provided for fitting assembly parameters.

The invention discloses an electric power tower point cloud registration method and device fused with normal vector clustering, and relates to the technical field of point cloud registration or other related fields, and the method comprises the steps: scanning an electric power tower to obtain point cloud data, dividing the point cloud data into different voxels, and carrying out the point cloud registration; calculating normal vectors of all the point cloud data in each voxel by adopting a principal component analysis algorithm, dividing all the point cloud data in each voxel into different clusters by adopting a clustering algorithm, determining representative point clouds of each cluster, selecting target point clouds and source point clouds from the representative point clouds, and carrying out point cloud clustering on the target point clouds and the source point clouds; the method comprises the steps of determining a matching point cloud of each source point cloud in target point clouds by adopting a binary tree search algorithm to obtain a plurality of matching pairs, calculating transformation parameters of the matching pairs by adopting an iterative nearest point algorithm, calculating a registration error based on the matching pairs and the transformation parameters, and merging the source point clouds and the target point clouds under the condition that the registration error reaches a preset convergence error range. According to the invention, the technical problems of low point cloud registration precision and poor algorithm convergence in the prior art are solved.

The invention provides a thyroid ablation intraoperative monitoring method and system, a storage medium and electronic equipment. The system comprises a data acquisition unit; the image segmentation unit is used for obtaining a thyroid organ segmentation region and a focus region, and generating an ablation safety region based on the spatial information of the focus region; the three-dimensional reconstruction unit is used for acquiring reconstructed organ volume data; and the dynamic monitoring unit is used for carrying out real-time registration on the preoperative image volume data and the ultrasonic image sequence obtained in the operation so as to observe the position of the focus, obtaining thyroid deformation and the ablation state of the focus, and dynamically monitoring the ablation process and the ablation completion condition of the focus area. According to the method, through one-time registration and multiple times of rapid secondary precise registration, the 3d reconstruction process of data collected in the operation is omitted, deformation of the thyroid gland in the ablation state is conveniently monitored, the problem that positioning is not clear enough in the ultrasonic focus operation is solved through registration in the operation and before the operation, and a better ablation result is obtained.

The invention discloses a relocation method and device based on a 3D laser radar and a prior map, and a medium, and the method comprises the steps: carrying out the point cloud preprocessing and point cloud segmentation of laser radar data and inertial measurement unit data, and obtaining a to-be-registered point cloud; establishing a multi-resolution pyramid grid map according to the 2.5 D grid map; step 4, judging whether initial registration needs to be performed on the to-be-registered point cloud, and if yes, entering step 4; otherwise, obtaining an initial relocation result, and entering the step 5; performing 2D coarse registration based on branch and bound search according to the pyramid grid map and the to-be-registered point cloud, performing 3D fine registration based on ICP registration according to the 2D coarse registration result, the to-be-registered point cloud and the 3D point cloud map, and completing initial relocation after iterative optimization convergence to obtain an initial relocation result; and performing incremental frame-graph registration according to the initial relocation result and the 3D point cloud map to obtain a continuous relocation result. The robustness is ensured, and the calculation efficiency is improved.

The invention discloses a point cloud image registration method and apparatus, an electronic device and a storage medium. The point cloud image registration method comprises the steps of obtaining a laser point cloud, an image and an image initial pose; carrying out feature point extraction and matching on the image; 3D points corresponding to the matching feature points are obtained based on the initial pose of the image; laser points corresponding to the 3D points in the laser point cloud are obtained through laser point back projection, and depth values of the 3D points and the corresponding laser points are determined respectively; taking the depth value of the laser point as assistance to carry out BA optimization so as to obtain an optimized image pose; and the depth values of the 3D points and the corresponding laser points meet preset conditions. According to the technical scheme, the registration precision of the point cloud image is improved.

The invention discloses a self-adaptive unmanned aerial vehicle remote sensing image intelligent registration method. The method comprises the steps of data collection, basic enhancement processing, registration precision optimization and self-adaptive intelligent registration. The invention relates to the technical field of image registration, in particular to a self-adaptive unmanned aerial vehicle remote sensing image intelligent registration method, which adopts two aspects of basic enhancement and self-adaptive registration optimization to enhance self-adaptive performance, and optimizes data quality through self-adaptive basic attribute enhancement. The intelligence and the adaptivity of rigid transformation parameters are improved by combining a registration precision optimization model trained by multiple parameter data; a self-adaptive image registration optimization method based on combination of teaching learning and reinforcement learning is adopted, through combination of reinforcement learning and teaching learning, a parameter optimal solution can be intelligently optimized, and self-adaptive parameter adjustment can be performed according to provided image flight parameter information, so that the registration precision of an intelligent registration result can be optimized.

The invention discloses an electronic map road information extraction method and device, equipment and a medium, and relates to the field of mapping and mapping in the surveying and mapping and geographic information industry, and the method comprises the steps: employing a progressive registration method, carrying out the registration rectification of a target map slice set, and obtaining a to-be-processed road grid and a to-be-processed annotation grid; extracting a road surface of the target area based on a road surface extraction model; inputting the to-be-processed annotation grids into the annotation information extraction model to obtain single-character discrete characters of the target area; determining an annotation library of the target area; extracting road elements of the target area; and matching the annotation library of the target area with the road elements of the target area, and determining a road entity of the target area according to the name attributes of the road elements of the target area and the road elements of the target area. According to the invention, accurate extraction of electronic map road entities is realized.

The invention discloses a visible light image and infrared image fusion method and device, electronic equipment and a medium, and relates to the technical field of unmanned aerial vehicles. The fusion method comprises the following steps: acquiring an original visible light image and an original infrared image; respectively filtering the original visible light image and the original infrared image to obtain a filtered visible light image and a filtered infrared image; determining a matching position of the original visible light image and the original infrared image according to a maximum information metric value between a first sub-image corresponding to the filtered visible light image and a second sub-image corresponding to the filtered infrared image; and taking the matching position as a reference, respectively intercepting an overlapping region from the original visible light image and the original infrared image to obtain a visible light image to be fused and an infrared image to be fused, and carrying out fusion calculation on the visible light image to be fused and the infrared image to be fused to obtain a fused image. By adopting the fusion method provided by the invention, the visible light image and the infrared image can be automatically and quickly fused, and the registration precision is high.

The invention relates to the technical field of image processing, in particular to a multi-mode retina fundus image registration method and system. The reference image structural body passes through a first feature encoder, and reference rotation isovariant features of four stages and a total reference rotation isovariant feature graph are extracted; the floating image structural body passes through a second feature encoder, and floating rotation isovariant features of four stages and a total floating rotation isovariant feature graph are extracted; respectively inputting the reference rotation equivariant features and the floating rotation equivariant features of the four stages into a key point detection decoder for feature fusion, and outputting a reference key point prediction map and a floating key point prediction map; respectively inputting the total reference rotation equivariant feature pattern and the total floating rotation equivariant feature pattern into a key point descriptor decoder, and outputting a descriptor of each reference key point and a descriptor of each floating key point; and aligning the floating image to the reference image through a registration module to obtain a registered image. According to the invention, the multi-modal image registration precision is improved.

The invention provides an image generation method and device and a storage medium, and relates to the technical field of medical treatment. The specific implementation scheme is as follows: acquiring a bone image block and at least one type of soft tissue image block in an original image of a first modal; determining a dictionary matrix corresponding to at least one type of soft tissue image block from pre-stored dictionary matrixes corresponding to a plurality of soft tissue types; based on the dictionary matrix corresponding to the at least one type of soft tissue image blocks, converting the at least one type of soft tissue image blocks of the first mode into at least one type of soft tissue image blocks of a second mode; and generating a target image based on the at least one type of soft tissue image blocks of the second modality and the bone image blocks in the original image. Therefore, the soft tissue image blocks in the original modal image are converted into the soft tissue image blocks in the new modal, and the target image is generated by combining the bone image blocks in the original modal image, so that the image quality can be effectively improved, and the image registration precision is improved.

The invention discloses a multi-scale attention and accumulated learning-based multi-task registration and segmentation method, which realizes mutual promotion and enhancement between registration and segmentation tasks. According to the method, efficient feature coding between tasks is realized through a double-flow sharing encoder, a multi-scale attention module guides a model to pay attention to a challenging area, the accuracy of registration and segmentation is further improved through an accumulated learning strategy, multi-task registration and segmentation of medical images are efficiently realized, and the accuracy of registration and segmentation is improved. And the registration and segmentation precision superior to that of the current mainstream method is obtained. The method has important clinical application significance and reference value for medical image-assisted disease diagnosis, focus monitoring and surgical navigation.

The invention discloses an image registration method, an image registration device, electronic equipment and a storage medium, and the image registration method comprises the steps: obtaining a preoperative image and an intraoperative image; performing rigid registration on the preoperative image and the intraoperative image to obtain a corresponding initial narrow field angle rigid matrix; performing transformation processing on the preoperative image according to the initial narrow field angle rigid matrix to obtain a corresponding rigid registration preoperative image; according to the rigid registration preoperative image and the intraoperative image, obtaining a corresponding initial narrow field angle elastic deformation field; and according to the initial narrow field angle rigid matrix and the initial narrow field angle elastic deformation field, performing transformation processing on the preoperative image to obtain a target registration image. According to the method, rigid registration and elastic registration based on the preoperative image and the intraoperative image are realized, the preoperative image is registered to the intraoperative image, and the registration accuracy of image registration is improved.

The invention belongs to the technical field of geographic data registration, and discloses an urban map batch geographic registration method and system based on map feature classification. The method comprises the following steps: accurately classifying collected target maps based on map features, wherein the classification comprises dividing drawings with high repetition rate of drawing contents into the same class; unified batch processing of similar modern city maps is completed for the classified same-group maps by means of picture editor drawing software, and external reference grid cross points are loaded; and completing batch geographical registration of the same group of maps by utilizing the established external reference grid intersection points. The method is different from a thought starting from internal elements of a map, the thought of adding an external reference control point to the map is changed to carry out geographic registration, a batch high-efficiency geographic registration mode can be considered at the same time, the learning threshold is low, and the method has wide popularization and promotion value in the field of modern city maps.

The invention provides a three-dimensional point cloud registration method based on multiple convolutional networks. Firstly, bilateral filtering and voxel grid downsampling processing are carried out on point cloud data, discrete points of the point cloud data are removed, noise is filtered, point cloud redundant data can be screened under the condition that local details and edge features of the point cloud data are guaranteed, and quality defects are made up; then, constructing a point cloud edge position feature multi-convolution registration network model, and training the model by using the processed point cloud data; and finally, finishing point cloud registration by using a rotation transformation matrix output by the network model. Due to the fact that local information features of the point cloud data can be enhanced through the multiple convolutional networks, the scene learning and understanding ability of the network model to the point cloud data can be improved, and the precision of a point cloud registration task is improved.

The invention relates to the technical field of photogrammetry, and discloses a photogrammetry point cloud adjustment processing method and system based on satellite photon point clouds, and the method comprises the steps: obtaining photon point cloud data and photogrammetry point cloud data; dividing the photon point cloud data into grids of a two-dimensional plane, and screening out a first control point; calculating the topographic relief similarity between the first control point and the photogrammetry point cloud data, and calculating the offset between the first control point and the photogrammetry point cloud data by using a simulated annealing algorithm; performing registration processing on the first control points according to the offset, and screening out second control points meeting a preset condition from the registered first control points; obtaining original RPC parameters of the photogrammetry point cloud data, and carrying out iterative adjustment optimization on the original RPC parameters to obtain RPC parameters meeting precision conditions; and according to the RPC parameter meeting the precision condition, calculating to obtain the adjusted photogrammetric point cloud. The method has the advantage of high universality, and the precision of photogrammetric point cloud is improved.

The invention provides an MRI-TRUS image registration method and device based on weak supervised learning, and the method comprises the following steps: firstly, obtaining a to-be-registered MRI image and a to-be-registered TRUS image, inputting the to-be-registered MRI image and the to-be-registered TRUS image into a pre-trained segmentation sub-network, and obtaining a corresponding mask; and inputting the masks and the original image into a pre-trained registration sub-network to obtain a registration result. The registration sub-network comprises a transformation field and a registration unit, the transformation field unit generates a dense transformation field, the registration unit takes the MRI mask and the image as a moving object and takes the TRUS mask and the image as a fixed object, and the moving object is registered into the fixed object according to the transformation field. A to-be-registered MRI mask and a to-be-registered TRUS mask are obtained by segmenting a sub-network, and then accurate registration is carried out in a registration sub-network by taking the to-be-registered MRI mask and the to-be-registered TRUS mask as guidance.

The invention discloses an auricle positioning method of an otology surgical robot based on point cloud registration, and relates to the field of surgical robots. The problem that auricle positioning is difficult to achieve rapidly and accurately in the existing otology operation process is solved. The method comprises the following steps of: 1, performing registration preparation work: planning a target point before an operation, acquiring a three-dimensional image of the auricle of a patient through CT scanning, marking an operation target point in the three-dimensional image of the auricle, and acquiring a three-dimensional point cloud, namely a source point cloud, of the auricle part through a structured light camera; 2, noise reduction and down-sampling are carried out on the point cloud data, and features are extracted; 3, performing coarse registration on the target point cloud and the source point cloud by using an improved RANSAC algorithm; 4, performing fine registration of the target point cloud and the source point cloud by using an LM improved NDT algorithm; and 5, obtaining a rigid body transformation matrix from the source point cloud to the target point cloud, and completing registration positioning. The method is used for point cloud-based registration of the otology surgical robot.

The invention relates to the technical field of seismic data processing, in particular to a seismic waveform inversion method and system based on deep learning image registration, and the method comprises the steps: carrying out the least square migration based on small offset observation data and a background velocity field, and constructing a reflection coefficient model; performing Born forward modeling according to the reflection coefficient and the background velocity field to obtain a simulated seismic record image; acquiring a seismic record deformation field by adopting deep learning image registration based on the simulated seismic record image and the seismic observation image; calculating a normalized deformation field, and judging whether the normalized deformation field meets a preset condition or not; if yes, terminating the iteration process; otherwise, executing the next step; and according to the seismic record deformation field and the accompanying seismic source, updating the background velocity field through a conjugate gradient algorithm, and repeating the steps. According to the method, high efficiency and high precision of deep learning are utilized, and the problem that a cycle jump problem which is prone to occurring in waveform matching falls into a local minimum value is avoided.

The invention belongs to the technical field of ecological monitoring, and discloses a forest ULS-TLS point cloud automatic registration method based on ground context description, and the method employs a ground point as a registration element, constructs a ground context, can directly achieve the registration of TLS single scanning data to a ULS point cloud, and does not need the pre-registration of different scanning point clouds of a TLS. The extraction process of the ground points is relatively standardized, stable and reliable results can be obtained generally, and the method is not affected by distribution, density and shape complexity of trees in the forest. Compared with extraction of tree attributes, the ground points are used as registration primitives, so that the calculation cost is reduced, and high calculation overhead caused by extraction of the tree attributes is avoided. The invention aims to provide a forest ULS-TLS point cloud automatic registration method based on ground context description so as to quickly, accurately and automatically realize registration of forest ULS and TLS point clouds, comprehensively obtain forest structure information and promote application of a laser radar technology in forest investigation.

The invention provides a feature-based oil and gas field scene point cloud registration method and device and electronic equipment, and belongs to the technical field of point cloud registration. The method comprises the steps of obtaining a plurality of associated point pairs based on a first point cloud feature vector and a second point cloud feature vector; based on the first coordinates and the second coordinates, determining a target consistency matrix, the first coordinates being coordinates of points in the first point cloud in each associated point pair, and the second coordinates being coordinates of points in the second point cloud in each associated point pair; constructing a consistent set corresponding to each seed pair based on the plurality of seed pairs and the association point pairs respectively corresponding to the plurality of seed pairs; hypothesis testing is carried out on the transformation matrix corresponding to each consistent set, and a target transformation matrix is determined; and based on the target transformation matrix, registering the first point cloud and the second point cloud to obtain registered point cloud data. The feature-based oil and gas field scene point cloud registration method provided by the invention can improve the registration precision of the point cloud data of the oil and gas field scene.

The invention discloses a multi-modal image registration method and equipment based on deep learning. The method comprises the following steps: acquiring a 4DCT image and a plurality of other medical images with different modalities; preprocessing the 4DCT image, determining each phase of a respiratory cycle, and calculating the motion range of different organs in the respiratory cycle; the medical image of each of the other modals is preprocessed; selecting an expiration or inspiration image in one respiratory cycle from the medical images of other modalities; carrying out feature extraction on the selected 4DCT image and other medical images of each modal, and extracting feature points of each image; performing registration on different modal image feature points and 4DCT image feature points by using the trained deep learning model; and rendering the registration result. According to the method, the complex relation between different modal image feature points is learned through the deep learning model, a more accurate registration result can be achieved, registration errors are reduced, and cross-modal image registration precision is improved.

The invention provides a multi-slice registration method and device, electronic equipment and a storage medium. The multi-slice registration method comprises the steps of obtaining a first feature set of a source slice based on spatial transcription data of the source slice, and obtaining a second feature set of a target slice based on spatial transcription data of the target slice; determining feature difference data according to the first feature set and the second feature set; determining first distribution data and second distribution data; and constructing a target transmission plan according to the feature difference data and the distribution difference between the first distribution data and the second distribution data, and registering the source slice and the target slice according to the target transmission plan. According to the embodiment of the invention, the accuracy of unbalanced data 3D registration can be improved.

The invention provides a three-dimensional organ reconstruction registration fusion method based on a multi-modal medical image, and belongs to the technical field of medical images. Comprising the following steps: S1, collecting a multi-modal medical image of an ith organ of a target patient in real time, and constructing a preoperative data set; s2, after the first data set is constructed, continuously constructing an intra-operative data set through the intra-operative ultrasonic image; s3, constructing an AI model by using a convolutional neural network; s4, based on the preoperative data set and the intraoperative data set, constructing a stability coefficient and a crowding degree coefficient of the ith organ; and S5, associating the stability coefficient of the ith organ with the crowding degree coefficient of the ith organ, and constructing a comprehensive dynamic risk coefficient. According to the method, CT, MRI and intraoperative ultrasonic images are collected at the same time, the preoperative data set is constructed, the organ volume change rate and the body position adjustment displacement are updated in real time in the operation, the optimal operation path can be automatically predicted, and the subjectivity of depending on doctor experience in traditional operation path planning is reduced.

The invention belongs to the related technical field of aero-engine manufacturing and precision repair, and discloses a blade repair path follow-up compensation method and device based on non-rigid matching, and the method comprises the steps: S1, reconstructing a machining error reference point cloud curved surface based on a scanning data point set and a repair region design model; s2, performing unified modeling on the static deviation of the machining error of the to-be-compensated blade to obtain an error transfer model, and simplifying the corresponding nonlinear comprehensive machining error into a calculation equation of linear pose deviation of a local workpiece coordinate system; s3, establishing a tangent contact approximation cost function based on non-rigid matching so as to register and approach a target curved surface under the constraint of retaining tool path features, and meanwhile, solving a calculation equation by adopting a least square method so as to obtain a linear pose deviation of a local workpiece coordinate system, solving to obtain a pose deviation of each tangent contact coordinate system so as to obtain a linear pose deviation of each tangent contact coordinate system; and furthermore, each cutting contact and each cutter location point are updated, and the shape follow-up compensation of the blade is realized. The compensation precision is improved.

This invention relates to the field of image registration technology, specifically to a dynamic monitoring system for pulmonary nodules using dose-dependent CT scans. The invention obtains an edge cluster encompassing all lung images based on the positional distances between corresponding contour points on edge lines in all lung images; it obtains multiple sets of isotopic contour points based on the positional distribution of all contour points in different lung images; it obtains the referenceability of each set of isotopic contour points by considering the positional relationship between each set of isotopic contour points and other sets of isotopic contour points with the same number of angles on the lung images, as well as the fluctuation characteristics of the corresponding angles of each set of isotopic contour points, and selects anchor points; it constructs a relative positional model of the nodule region in each lung image based on the positional distribution of anchor points and nodule regions in each lung image; and it achieves lung image registration for dynamic monitoring of pulmonary nodules. This invention improves the accuracy of dynamic monitoring of pulmonary nodules by flexibly registering the patient's lung images.

The invention discloses a histogram-based augmented reality registration method, system and device and a medium, and the method comprises the steps: obtaining a real image of a real world, carrying out the equalization of a plurality of channel histograms of the real image, obtaining an equalized image, calculating a self-adaptive dynamic threshold value according to the equalized image, and carrying out the calculation of the self-adaptive dynamic threshold value. Carrying out binarization on the balanced image according to an adaptive dynamic threshold to obtain a binarized image, extracting an edge of the binarized image, obtaining a voting matrix according to a Gaussian function, carrying out probability Hough transform detection on the edge according to the voting matrix to obtain an initial line segment, screening the initial line segment to obtain a frame of the target object, and obtaining a target object frame. Camera external parameters are calculated according to the frame, the virtual object is registered to a target object according to the camera external parameters, and the target object is located in the real image. The registration precision can be improved, and the method can be widely applied to the technical field of augmented reality registration.

The application discloses a CT-guided old brain PET image spatial standardization system, method and device, the system comprises a PET / CT image acquisition and format conversion module, a PET / CT preprocessing module, a PET / CT image coarse standardization module, a PET / CT image fine standardization module and an automatic SUV extraction module, the old brain PET molecular image spatial standardization is carried out by using a low-dose CT brain structure image as an auxiliary, and the SUV of a region of interest is automatically extracted by using a standard space brain atlas partitioning, the application adopts a two-step strategy from coarse to fine, and by means of an optimized process, an accurate, stable and user-friendly spatial standardization system is provided for the old brain PET image, and the application has important clinical application value and scientific research significance.

This invention discloses an adaptive template registration method based on image grayscale. First, FAST corner points are extracted from the image to be registered. Then, the corner points are clustered, and the minimum bounding rectangle of each set is extracted. The region enclosed by the minimum bounding rectangle in the image to be registered is used as the template to complete adaptive template extraction. Next, a normalized cross-correlation algorithm is used for matching in subsequent image sequences. Finally, the affine transformation coefficients of the image are calculated using the coordinates before and after template matching, and the image to be registered is subjected to an affine transformation to achieve image registration. This invention, through its grayscale-based adaptive template image registration method, provides an adaptive template selection method under different background conditions, solving the problems of fixed template position and limited template information in previous methods, improving the template matching accuracy, and showing good application prospects.