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The extraction of geometric features such as holes, arcs, and surfaces of mechanical parts is crucial for quality control. The existing methods for geometrical feature segmentations on 3D point clouds still have limitations, especially for simultaneously extracting multiple types of geometric features from comprehensive workpieces. To this end, this study investigates segmentation methods that take 3D point cloud datasets of mechanical parts as inputs, and employs an improved PointNet++ deep learning model to solve this extraction difficulty. Firstly, the Set Abstraction module in PointNet++ was modified by incorporating Self-Attention mechanisms to increase interactivity and global correlation among data points. Then, the local feature extraction Multilayer Perceptron (MLP) from PointNet-Transformer was integrated to enhance the feature extraction accuracy. Due to the inherent class imbalance issue, the Focal Tversky Loss is employed as the loss function to ensure that geometric features with relatively lower proportions can be fully trained. Finally, the Statistical filtering algorithm is utilized to mitigate noise and attenuate subtle irregularities, such that the smoothness of geometric features can be substantially enhanced. The experimental results demonstrate that the proposed model achieves an accuracy of 86.6% on geometric feature segmentations and a mean Intersection over Union (mIoU) of 0.84. The comparison with the original PointNet++ proves that the proposed method can improve accuracy and mIoU by 3.7% and 0.03 respectively. [ABSTRACT FROM AUTHOR]

In the current landscape dominated by Artificial Intelligence, the integration of Machine Learning and Deep Learning within the realm of Cultural Heritage, particularly within architectural contexts, is paramount for the efficient processing and interpretation of point clouds. These advanced methods facilitate automated segmentation and classification, significantly improving both the clarity and practical use of data acquired from laser scanning and photogrammetry. The present study investigates the Palacio de Sástago—a prominent Renaissance palace in Zaragoza, Spain—and introduces a cutting-edge modus operandi for the automated recognition of architectural elements within the palace's inner courtyard. Employing the well-established Random Forest algorithm, implemented in a Python environment, the framework begins with a comprehensive evaluation of the geometric features identified in the LiDAR point cloud. This process employs the Mean Decrease in Impurity metric to evaluate the relevance of each variable. To boost the accuracy and efficiency of the final classifications, the features are refined post-assessment, enhancing both the training phase and the algorithm's later evaluation. The research's findings demonstrate significant potential, supporting advancements in CAD systems and HBIM that will enable more precise, automated modelling of architectural elements, thereby enhancing the accuracy of digital reconstructions and improving conservation planning for heritage sites. [ABSTRACT FROM AUTHOR]

Many aerodynamic shape optimization methods often focus on utilizing the end-to-end relationship between design variables and aerodynamic performance to find the optimal design, while overlooking the exploration of geometric knowledge of the shape itself. To fully use geometric knowledge to improve optimization efficiency, this paper proposes an efficient method by exploring the potential correlation between geometric features and aerodynamic performance at a low cost. We use unsupervised isometric feature mapping in manifold learning to capture geometric features that can distinguish the aerodynamic performance of different airfoils without embedding any tags. Then a filter criterion is establish based on the geometric features. During the optimization process, airfoils that deliver poor aerodynamic performance can be filtered out with a high probability before being precisely evaluated through computational fluid dynamics simulations. This helps improve samples quality to enhance the optimization efficiency. We applied the proposed method to the unconstrained and constrained optimizations of the Royal Aircraft Establishment (RAE) 2822 airfoil to validate its performance. The results demonstrate that the proposed method can improve the efficiency of optimization by over 50% compared with the original evolutionary optimization algorithm. It performs well across various optimization problems, demonstrating high engineering practical value. [ABSTRACT FROM AUTHOR]

Mobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems' comparative performance across different environments. [ABSTRACT FROM AUTHOR]

High electrical conductivity, along with high piezoresistive sensitivity and stretchability, are crucial for designing and developing nanocomposite strain sensors for damage sensing and on-line structural health monitoring of smart carbon fiber-reinforced polymer (CFRP) composites. In this study, the influence of the geometric features and loadings of carbon-based nanomaterials, including reduced graphene oxide (rGO) or carbon nanofibers (CNFs), on the tunable strain-sensing capabilities of epoxy-based nanocomposites was investigated. This work revealed distinct strain-sensing behavior and sensitivities (gauge factor, GF) depending on both factors. The highest GF values were attained with 0.13 wt.% of rGO at various strains. The stability and reproducibility of the most promising self-sensing nanocomposites were also evaluated through ten stretching/relaxing cycles, and a distinct behavior was observed. While the deformation of the conductive network formed by rGO proved to be predominantly elastic and reversible, nanocomposite sensors containing 0.714 wt.% of CNFs showed that new conductive pathways were established between neighboring CNFs. Based on the best results, formulations were selected for the manufacturing of pre-impregnated materials and related smart CFRP composites. Digital image correlation was synchronized with electrical resistance variation to study the strain-sensing capabilities of modified CFRP composites (at 90° orientation). Promising results were achieved through the incorporation of CNFs since they are able to form new conductive pathways and penetrate between micrometer-sized fibers. [ABSTRACT FROM AUTHOR]

Chip formation is considered one important indicator to evaluate machining processes. In particular, geometric features of chips may provide important insights for the assessment of machining stability and productivity. In fixed-abrasive machining processes, such as grinding and honing, chips are simultaneously produced by many of the geometrically undefined cutting edges. Despite being "undefined," geometric features of abrasive grains can still be statistically characterized or described. Accordingly, it can be assumed that, under stable machining conditions, the geometric features of abrasive chips may also conform to certain statistical patterns. However, statistical characterization of abrasive chips can be very challenging due to their large quantity, irregular shapes, minuscule size, and sometimes tangled condition. In this study, an analysis method combining metallographic preparation and micro-computed tomography (micro-CT) has been developed to characterize abrasive chips produced by a honing stone. The results regarding the geometric characteristics of the obtained massive abrasive chips, i.e., their sizes and shapes, were presented and statistically described. It was found most chips had a size around 50 µm and the shape being a slightly elongated and curved cone. Most of the geometric features could meet a positive skewness distribution. [ABSTRACT FROM AUTHOR]

Mobile laser scanners are vital for intelligent transport infrastructure, capturing detailed 3D road representations, but their accuracy depends on factors like sensor positioning and environment. This study compares two van-mounted Mobile Laser Scanners (MLS): the dual head Lynx Mobile Mapper and the single head VUX-1 HA, along with the terrestrial laser scanner Faro Focus XX30. Using point cloud reference data from Faro Focus XX30 and GNSS data from Trimble R8, performance is assessed in road, urban, and semi-urban environments. Accuracy is measured by the difference between Trimble GNSS and MLS coordinates. Geometric features of each LiDAR are compared, and mapping tasks in road and urban areas are performed using a machine learning classifier. Results show the MLS-single head scanner achieves satisfactory accuracy in roads and semi-urban areas, while Faro performs better in urban settings for classification. MLS-single head excels in road environments, while Faro is superior in urban ones. This analysis aids researchers and professionals in selecting the appropriate mobile laser scanner for mapping transport infrastructure, providing valuable insights into MLS systems’ comparative performance across different environments.

Many aerodynamic shape optimization methods often focus on utilizing the end-to-end relationship between design variables and aerodynamic performance to find the optimal design, while overlooking the exploration of geometric knowledge of the shape itself. To fully use geometric knowledge to improve optimization efficiency, this paper proposes an efficient method by exploring the potential correlation between geometric features and aerodynamic performance at a low cost. We use unsupervised isometric feature mapping in manifold learning to capture geometric features that can distinguish the aerodynamic performance of different airfoils without embedding any tags. Then a filter criterion is establish based on the geometric features. During the optimization process, airfoils that deliver poor aerodynamic performance can be filtered out with a high probability before being precisely evaluated through computational fluid dynamics simulations. This helps improve samples quality to enhance the optimization efficiency. We applied the proposed method to the unconstrained and constrained optimizations of the Royal Aircraft Establishment (RAE) 2822 airfoil to validate its performance. The results demonstrate that the proposed method can improve the efficiency of optimization by over 50% compared with the original evolutionary optimization algorithm. It performs well across various optimization problems, demonstrating high engineering practical value.

Three-dimensional (3D) branch structures provide vital information for understanding tree phenotypic characteristics and for ecological studies related to carbon sequestration. Light detection and ranging (LiDAR) has been widely applied to capture the 3D structural information of individual trees. Wood–leaf separation and tree skeleton extraction are essential prerequisites for accurately estimating tree attributes (e.g., stem volume, aboveground biomass, and crown characteristics) and representing the tree branch network. Owing to the complex internal branch morphology and intercanopy component occlusion, precise extraction of the tree skeleton from point clouds remains a challenging issue. In this study, we propose an improved approach for extracting tree skeletons on the basis of the geometric features of point clouds. The approach consists of two steps: separation of the wood and leaves, followed by extraction of the tree skeleton. In the first step, the point clouds of the trees are sliced horizontally. The density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to cluster each layer of the point clouds and detect the main trunk. Subsequently, random sample consensus (RANSAC) circle feature detection and linear feature constraints are applied to achieve wood–leaf separation. In the second step, the wood point clouds are used to extract the initial tree skeleton via a minimum spanning tree (MST), and the initial tree skeleton is further optimized. Various comparative experiments are conducted on terrestrial-LiDAR-scanned data from nine trees across six species. The results show that the proposed method performs effectively, with overall wood–leaf separation accuracies ranging from 86% to 93%. Additionally, the extracted branch skeleton accurately reflects the natural geometric structure of the trees. The wood points and tree skeletons are further used to estimate tree attributes, demonstrating the potential of our method for the quantitative representation of trees and their ecological characteristics (e.g., carbon sequestration).

Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency. [ABSTRACT FROM AUTHOR]

The aim of the paper is to identify an efficient method for transforming the point cloud into parametric objects in the fields of architecture, engineering and construction by four main steps: 3D survey of the structure under investigation, generation of a new point cloud based on feature extraction and identification of suitable threshold values, geometry reconstruction by semi-automatic process performed in Rhinoceros/Grasshopper and BIM implementation. The developed method made it possible to quickly obtain geometries that were very realistic to the original ones as shown in the case study described in the paper. In particular, the application of ShrinkWrap algorithm on the simplify point cloud allowed us to obtain a polygonal mesh model without errors such as holes, non-manifold surfaces, compenetrating surfaces, etc. [ABSTRACT FROM AUTHOR]

The precise characterization of the rock microstructure is crucial for predicting the physical characteristics, flow behavior, and mechanical properties of rocks. This is particularly important for carbonate rocks, which depict a complex microstructure with multimodal pore radius distribution and natural fractures. Here, topological features that are typically ignored are taken into account to quantify the carbonate microstructure. Carbonate samples used are obtained from the Gaoshiti-Moxi block of the Sichuan Basin, which showed remarkable potential for oil and gas. Specifically, nuclear magnetic resonance (NMR), X-ray micro-computed tomography (micro-CT), and mercury injection capillary pressure (MICP) techniques are performed to describe the topological and geometric characteristics. The results indicate that NMR and MICP techniques can describe more rock pores than micro-CT. However, due to the presence of pore shielding in MICP tests, the pore radius obtained by MICP is smaller than that obtained by micro-CT and NMR. Furthermore, the effective method used for characterizing the pore structure is NMR technology. The hardest part is that the coefficient between the pore radius and T2 relaxation time is difficult to calculate. Therefore, a better calculation method must be found. In addition, micro-CT is an irreplaceable technique for obtaining a large number of topological and geometric features, and multi-phase or single-phase flow simulations can be conducted via digital rock models. However, for carbonates, micro-CT is not sufficient to describe the complete pore systems because macropores cannot be fully represented and sub-resolution micropores cannot be described. Those macropores and micropores have a very important effect on their seepage properties. Therefore, multi-scale digital rock modeling involving small and large pores is essential for complex rocks, which is of great significance for the analysis of pore systems and the simulation of rock physical properties. [ABSTRACT FROM AUTHOR]

The classification of urban functional areas is important for understanding the characteristics of urban areas and optimizing the utilization of urban land resources. Existing related methods have improved accuracy. However, they neglect cognitive differences amongst humans in the different scales of regional functions. Moreover, how to build the correlations of cross-scale characteristics is still unresolved when realizing the classification of multiscale urban functional zones. To resolve these problems, a transportation analysis zone involving urban buildings as research units is created and these units are described by geometric and functional characteristics using multiple data sources. Then, a hierarchical clustering model is built for the recognition of urban functional areas at varying scales with landmark semantic constraints. In the experiments, Shanghai served as the study area, and multiscale zones were created using different levels of road networks considering the constraint correlation of the significance between cross-scale maps. The experiential results show the proposed method has excellent performance and optimizes the functional zone classification at different scales. This study not only enriches the multiscale urban functional area-recognition methods but also can be used in other aspects, like cartographic generalization or spatial analysis. [ABSTRACT FROM AUTHOR]

Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete (UHPC). This study detects crack evolution using a novel dynamic mode decomposition (DMD) method. In this method, the sparse matrix 'determined' from images is used to reconstruct the foreground that contains cracks, and the global threshold method is adopted to extract the crack patterns. The application of the DMD method to the three-point bending test demonstrates the efficiency in inspecting cracks with high accuracy. Accordingly, the geometric features, including the area and its projection in two major directions, are evaluated over time. The relationship between the geometric properties of cracks and load-displacement curves of UHPC is discussed. Due to the irregular shape of cracks in the spatial domain, the cracks are then transformed into the Fourier domain to assess their development. Results indicate that crack patterns in the Fourier domain exhibit a distinct concentration around a central position. Moreover, the power spectral density of cracks exhibits an increasing trend over time. The investigation into crack evolution in both the spatial and Fourier domains contributes significantly to elucidating the mechanical behavior of UHPC. [ABSTRACT FROM AUTHOR]

Recognizing human emotions simultaneously from multiple data modalities (e.g., face, and speech) has drawn significant research interest, and numerous research contributions have been investigated in the affective computing community. However, most methods concentrate less on facial alignment and keyframe selection for audio-visual input. Hence, this paper proposed a new audio-visual descriptor, mainly concentrating on describing the emotion through only a few frames. For this purpose, we proposed a new self-similarity distance matrix (SSDM), which computes the spatial, and temporal distances through landmark points on the facial image. The audio signal is described through an asset of composite features, including statistical features, spectral features, formant frequencies, and energies. A support vector machine (SVM) algorithm is employed to classify both models, and the final results are fused to predict the emotion. Surrey audiovisual expressed emotion (SAVEE) and Ryerson multimedia research lab (RML) datasets are utilized for experimental validation, and the proposed method has shown significant improvement from the state of art methods. [ABSTRACT FROM AUTHOR]

To explore the influence of fracture structure on mechanical properties of rock mass, compression simulation experiment were carried out on rock samples with prefabricated fractures. Based on SEM, NMR and Vic-3D technology, multi-scale study was carried out on rock mass in combination with acoustic and macro-meso-structural characteristics. The rock sample strength decreases with the increase of fracture length, penetration and number, and decreases first and then increases with the increase of fracture dip angle. The rock sample strength of 30 mm fracture is the lowest, and the rock sample strength of 90° fracture is closest to the intact rock sample. Tensile failure occurred in intact rock samples, 0° and 90° fracture samples and tensile and shear composite failure occurred in rock samples with different fracture penetration degrees. Shear failure occurred in rock samples with different fracture lengths, numbers, 30°, 45° and 60° fractures. After loading, the proportion of micro pores decreases while that of medium and large pores increases, indicating that the initial damage and loading caused by prefabricated fractures can change the internal pore structure of rock samples, thus affecting its macroscopic mechanical properties.

The aim of the paper is to identify an efficient method for transforming the point cloud into parametric objects in the fields of architecture, engineering and construction by four main steps: 3D survey of the structure under investigation, generation of a new point cloud based on feature extraction and identification of suitable threshold values, geometry reconstruction by semi-automatic process performed in Rhinoceros/Grasshopper and BIM implementation. The developed method made it possible to quickly obtain geometries that were very realistic to the original ones as shown in the case study described in the paper. In particular, the application of ShrinkWrap algorithm on the simplify point cloud allowed us to obtain a polygonal mesh model without errors such as holes, non-manifold surfaces, compenetrating surfaces, etc.

Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency.

The health services research network is showing a lot of interest in the Internet of Medical Things (IoMT). In IoMT, the Internet is used to help compile important health-related data. A brain tumor is caused by a mass of random cells inside the brain, which is dangerous and harmful to the brain. Today, it is difficult to accurately recognise brain images. In order to find and correctly categorize malignant cells in recognizing brain pictures, this research offers a support value-based deep neural network (SDNN) in e-Health care administration utilizing the IoMT innovation. As a starting point, a database of investigation is created using picture data based on IoT innovation and clinical images. The input brain picture is subjected to skull stripping during the preprocessing stage in order to isolate the desired brain area. The preprocessed output pictures are then used to extract the useful characteristics, such as entropy, geometric, and texture features. Finally, based on the collected characteristics, the proposed support value based adaptive deep neural network (SDNN) identification classifies the brain pictures as normal or abnormal. The results of the experiments are examined to show how the suggested recognition approach outperforms the ones already in use.

The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm's efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature. [ABSTRACT FROM AUTHOR]

As a typical application of intelligent transportation systems (ITS), the electronic toll collection (ETC) system has been widely used on highways due to its excellent toll efficiency. In the actual traffic environment, there usually exists more than one vehicle in the radiation area of the road-side units (RSU) at the same time, and the front vehicle will affect the ray paths between the RSU and the on-board units (OBU) of the back vehicle. Here we studied the front vehicle’s impact on the ETC system’s path propagation loss by ray tracing technology and Uniform Theory of Diffraction (UTD). Firstly, we simplified the vehicle body structure into two equivalent geometric models. Four situations were considered according to the vehicles’ front and rear positions. We analyzed each scenario’s propagation loss models under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions based on the distance change among RSU, OBU, and the front vehicle. Finally, we developed an ETC comprehensive test equipment to measure the propagation loss of the four scenarios at a toll station. Both simulation and experiment results indicate that the propagation loss models proposed in this paper are valid.

Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations. [ABSTRACT FROM AUTHOR]

An algorithm providing information on the key geometric features of an aorta extracted from multi-slice computed tomography images is proposed. Using the numerical method, the aorta's geometric characteristics, such as vessel cross-sectional areas and diameters, as well as distances between arteries, can be determined. This step is crucial for training the meta-model necessary to construct an expert system with a significantly reduced volume of data and for identifying key relationships between diagnoses and geometric and hydrodynamic features. This methodology is expected to be part of an innovative decision-making software product for clinical implementation. Based on clinical and anamnestic data as well as calculations, the software will provide the shunt parameters (in particular, its diameter) and installation position to ensure regular blood flow. [ABSTRACT FROM AUTHOR]

The design of machine parts of different sizes and shapes has become relevant in the manufacturing industry which requires five-axis machine tools of high dynamic performance; different machining test pieces have been used to test and reflect the machine tool's performance. The S-shaped is still under development and consideration of which a new test piece better than the S-shaped part has been recommended to be put forward making the NAS979 the only standardized test piece; however, it has some limitations. Hence, this study proposes a new test piece to objectively satisfy the demand for machine tools with higher dynamic performance, which shows much improvement over the standard NAS979 and is the best alternative to the S-shaped test piece, and it combines the geometric and kinematic features of both test pieces. Geometrically, it has non-uniform surface continuity, variable twist angle, and variable curvature; and the cutting tool moves in close and opened angles along the tool path; there is sudden rise and fall of axes' velocity, acceleration, and jerk with much impact during machining which makes the S-cone test piece be machined by only five-axis machine tools with high dynamic performance, and has a better dynamic performance identification effect than the S-shaped test piece based on the trajectory test. Detailed work on the validation of the machine tool's dynamic performance using the S-cone part will be captured next part of this study. [ABSTRACT FROM AUTHOR]

Geometric map features, such as line segments and planes, are receiving increasing attention due to their advantages in simultaneous localization and mapping applications. However, large structures in different environments are very likely to appear repeatedly in several consecutive time steps, resulting in redundant features in the final map. These redundant features should be properly fused, in order to avoid ambiguity and reduce the computation load. In this paper, three criteria are proposed to evaluate the closeness between any two features extracted at two different times, in terms of their included angle, feature circle overlapping and relative distance. These criteria determine whether any two features should be fused in the mapping process. Using the three criteria, all features in the global map are categorized into different clusters with distinct labels, and a fused feature is then generated for each cluster by means of least squares fitting. Two competing methods are employed for comparative verification. The comparison results indicate that using the commonly used KITTI dataset and the commercial software PreScan, the proposed feature fusion method outperforms the competing methods in terms of conciseness and accuracy. [ABSTRACT FROM AUTHOR]

Monitoring biodiversity in forests is crucial for their management and preservation, especially in light of increasing climatic disturbances. However, traditional methods of surveying forest biodiversity, such as the inventory of tree-related microhabitats (TreMs), are costly and time-consuming. For many years, terrestrial laser scanning (TLS) was the main method for producing highly accurate 3D models of forests. However, with recent advancements in 3D scanning technologies, there are now numerous alternatives available on the market. The aim of this study was to evaluate the performance of four different 3D data acquisition methods, i.e. close-range photogrammetry (CRP), fish-eye photogrammetry (FEP), mobile laser scanning (MLS), and mixed reality depth camera (MRDC), in terms of accuracy and ability to measure biodiversity (TreMs) at tree-stem level, in comparison to TLS. Analysis was performed based on geometric accuracy and point neighbourhood relevance. CRP was the most accurate alternative to TLS for TreM measurement with a median error of 1.5 cm, while FEP provided a good balance between accuracy (median error 1.4 cm) and speed of data collection. Although MLS showed promising results (median error 1.6 cm), noise in the point cloud limited its ability to identify TreMs. MRDC, on the other hand, had lower quality (median error 3.6 cm) and lower point density, making it unsuitable for TreM segmentation. Nevertheless, the study demonstrated the feasibility of augmenting the real world with virtual content at single-tree-stem level using mixed reality technology. Overall, the 3D scanning technologies presented hold great promise for recording the evolution of biodiversity at stem level.

The classification of urban functional areas is important for understanding the characteristics of urban areas and optimizing the utilization of urban land resources. Existing related methods have improved accuracy. However, they neglect cognitive differences amongst humans in the different scales of regional functions. Moreover, how to build the correlations of cross-scale characteristics is still unresolved when realizing the classification of multiscale urban functional zones. To resolve these problems, a transportation analysis zone involving urban buildings as research units is created and these units are described by geometric and functional characteristics using multiple data sources. Then, a hierarchical clustering model is built for the recognition of urban functional areas at varying scales with landmark semantic constraints. In the experiments, Shanghai served as the study area, and multiscale zones were created using different levels of road networks considering the constraint correlation of the significance between cross-scale maps. The experiential results show the proposed method has excellent performance and optimizes the functional zone classification at different scales. This study not only enriches the multiscale urban functional area-recognition methods but also can be used in other aspects, like cartographic generalization or spatial analysis.

The visible light positioning system is the hot spot of current indoor positioning technology research. Aiming at the problem of inaccurate azimuth angle acquisition in single-lamp positioning system, this paper proposes a single Light Emitting Diode (LED) indoor positioning system based on marked LED. The scheme uses a single LED light with a non-flickering lamp bead as the transmitter to transmit information while realizing the lighting function. The smartphone acts as the receiver to capture images with bright and dark stripes, and obtain the azimuth angle by combining the geometric features of the projection and processing the algorithm. Then the position of the receiver is obtained by further calculation. The results show that in the actual application scenario, when the experimental heights are 1.75 and 1.45 m. The system can achieve average positioning errors of 6.9 and 6.5 cm, which verifies the feasibility and effectiveness of the scheme.

The visible light positioning system is the hot spot of current indoor positioning technology research. Aiming at the problem of inaccurate azimuth angle acquisition in single-lamp positioning system, this paper proposes a single Light Emitting Diode (LED) indoor positioning system based on marked LED. The scheme uses a single LED light with a non-flickering lamp bead as the transmitter to transmit information while realizing the lighting function. The smartphone acts as the receiver to capture images with bright and dark stripes, and obtain the azimuth angle by combining the geometric features of the projection and processing the algorithm. Then the position of the receiver is obtained by further calculation. The results show that in the actual application scenario, when the experimental heights are 1.75 and 1.45 m. The system can achieve average positioning errors of 6.9 and 6.5 cm, which verifies the feasibility and effectiveness of the scheme.

The semantic segmentation of point clouds has significant applications in fields such as autonomous driving, robot vision, and smart cities. As LiDAR technology continues to develop, point clouds have gradually become the main type of 3D data. However, due to the disordered and scattered nature of point cloud data, it is challenging to effectively segment them semantically. A three-dimensional (3D) shape provides an important means of studying the spatial relationships between different objects and their structures in point clouds. Thus, this paper proposes a semi-supervised semantic segmentation network for point clouds based on 3D shape, which we call SBSNet. This network groups and encodes the geometric information of 3D objects to form shape features. It utilizes an attention mechanism and local information fusion to capture shape context information and calculate the data features. The experimental results showed that the proposed method achieved an overall intersection ratio of 85.3% in the ShapeNet dataset and 90.6% accuracy in the ModelNet40 dataset. Empirically, it showed strong performance on par or even better than state-of-the-art models. [ABSTRACT FROM AUTHOR]

[Display omitted] Molecular properties play a crucial role in material discovery, protein interaction and drug development. The appearance of Graph Neural Network (GNN) significantly improved the performance of molecular property prediction. However, nodes in GNN only update the features of neighbor nodes, resulting in insufficient ability to encode global feature information. The self- attention mechanism in transformer can encode the global information except for local information of molecules, while its spatial information is insufficient. Since molecules are three-dimensional spatial structures, spatial geometry information is an important attribute for molecules properties. To consider these factors, a network model of Space Graph Neural Network coupled Transformer (SGNN-T) is proposed in this paper which can combine global and local molecule information with three-dimensional spatial structures for molecular properties prediction. In this model, Graph neural network Geometric Feature Fusion Module (GGFF) and Transformer Spatial Geometric Feature Enhancement Module (TSGFE) are included to enhance the spatial geometry learning ability of the network. The GGFF module constructs a parallel graph neural network by thinking over atoms, bonds and bond angles at the same time which effectively complements the spatial information of the network by leading into bond angles than normal GNN. The TSGFE module introduces the coordinates and centrality degree features coupled with the features by GGFF into transformer to further enhance the geometric expression ability of the module. Through these two parts, SGNN-T model can encode local and global information of molecules at the same time. Property prediction experiments are executed on the QM9, OMDB and MEGNet dataset. The results of MAE show the proposed model has the best performance than the popular models. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) printing technology for continuous fiber reinforced thermoplastic composites (C-FRTP), capable of rapid manufacturing of lightweight components with intricate geometric features, has emerged as one of the most promising technologies in the field of advanced composite manufacturing. Path planning is a crucial step for determining the fabrication quality of C-FRTP components. This study proposed a new 3D printing path planning strategy driven by the geometric features and tensile properties of C-FRTP components. The strategy employed the properties of the Euler graph to generate the continuous full-field filling paths, ensuring the geometric features of the target components. The intersections were scattered along the printing path to enhance the tensile strength. The feasibility and advantages of the new path planning strategy were validated by comparative experiments with different printing paths. The results indicated that the new strategy not only achieved the geometric features of the target components but significantly enhanced their tensile strength. Using the printing path generated by the new path planning strategy, the tensile strength of specimens featuring mounting holes reached 349.4 MPa, which was only about 4.1 % lower than the tensile strength of continuous fibers at straight paths. Compared to the existing contour-parallel path, the new strategy in this work improved the tensile properties by about 40.9 %. The new path planning strategy proposed in this study shows great potential to design and fabricate C-FRTP components with enhanced mechanical properties for practical applications. [Display omitted] • A path planning strategy driven by geometry and tensile properties was proposed. • Euler graph properties were used to create a continuous full-field filling path. • All intersections were scattered to enhance the tensile strength of C-FRTP. • This strategy exhibited advantages in improving the fabrication quality of C-FRTP. • C-FRTP components printed with this strategy showed an improvement of about 40.9 %. [ABSTRACT FROM AUTHOR]

Over the past decades, medical image segmentation methods have been intensively developed, but there are still a number of unsolved challenging problems in vascular image segmentation, including broken vessels, insufficient vessel branches, and missing small vessels. In order to optimize the topology and accuracy of segmented vessels, we propose a novel Edge Morphology Attention Network (EMA-Net) for the segmentation of vessel-like structures, and an Optimal Geometric Matching Connection (OGMC) model to connect the broken vessel fragments. The EMA-Net has an edge attention module that is able to improve segmented performances of edges and small branches by morphology operation extracting boundary voxels on multi-scales. The OGMC model uses the geometry concept of curve touching to filter out fragmented vessel endpoints, and then employs minimal surfaces to determine the optimal connection order between blood vessels. Finally, we adopt geodesics to repair missing vessels under a Riemannian metric. Our method achieves superior or competitive results compared to state-of-the-art methods on four datasets of 3D vascular segmentation tasks, both effectively reducing vessel broken and increasing vessel branch richness, yielding blood vessels with more precise topological structures. • A new two-stage approach fuses deep learning and geometric priors to repair vascular fractures and topology. • The first stage has a novel edge morphology attention mechanism to discriminate between fuzzy vessel boundaries. • The second stage uses an optimal geometric matching connection model to connect the broken vessels. [ABSTRACT FROM AUTHOR]

Cubic boron nitride (CBN) composites frequently serve as tool materials in abrasive machining for surface finishing. The geometric features of individual CBN grains are crucial for both machining stability and the resulting surface quality of workpieces. While the grains, whether embedded in the matrix or exposed on the working surfaces, showcase seemingly 'random' geometric characteristics, they can typically be described through statistical analysis. In this study, three crucial three-dimensional geometric features of the CBN abrasive grains exposed on the working surface of a commercial CBN honing stone were characterized. These features include the protrusion height, inclination angle, and projection angle. In total, 116 abrasive grains identified within the processing zone were individually measured and inspected using a digital microscope and scanning electron microscope. The obtained results underwent statistical analysis and were subsequently compared. The results revealed that the abrasive grains exhibited an average protrusion height of 18.56 μm, an average inclination angle of 35.32°, and an average projection angle of 120.70°. Utilizing the measurement and inspection results, the abrasive grains were categorized into five distinct groups, each corresponding to a different phase in their service life cycle. • CBN grain parameters measured and characterized on an in-service honing stone. • Service life categorized into five phases based on protrusion height and surface integrity. • Morphological characteristics and wear features summarized for each phase. • Protrusion height and inclination angle linked to grain wear; projection angle stable. [ABSTRACT FROM AUTHOR]

Node localization in two-dimensional (2D) and three-dimensional (3D) space for wireless sensor networks (WSNs) remains a hot research topic. To improve the localization accuracy and applicability, we first propose a quantum annealing bat algorithm (QABA) for node localization in WSNs. QABA incorporates quantum evolution and annealing strategy into the framework of the bat algorithm to improve local and global search capabilities, achieve search balance with the aid of tournament and natural selection, and finally converge to the best optimized value. Additionally, we use trilateral localization and geometric feature principles to design 2D (QABA-2D) and 3D (QABA-3D) node localization algorithms optimized with QABA, respectively. Simulation results show that, compared with other heuristic algorithms, the convergence speed and solution accuracy of QABA are greatly improved, with the highest average error of QABA-2D reduced by 90.35% and the lowest by 17.22%, and the highest average error of QABA-3D reduced by 75.26% and the lowest by 7.79%. [ABSTRACT FROM AUTHOR]

As the technology of high-precision 3D laser scanning becomes increasingly prevalent in the fields of hydraulic building modeling and deformation monitoring, the quality of point clouds plays an increasingly crucial role in data processing. This paper investigates an automatic extraction method of anchor rod points based on geometric features, which focuses on the influence of anchor rod points and mixed pixels in the data of an underground powerhouse of a pumped storage power station on modeling and deformation monitoring during the construction period. This workflow consists of two steps that can automatically extract anchor rod points from high-density point cloud data. Triangular mesh features in the local neighborhood and the parameters of the anchor rods are used to locate the anchor rod in downsampled data, and curvature features are used to extract anchor rod points precisely. The experiment of extracting anchor rods shows that the accuracy of this method of initial identification is 97.2%. Furthermore, precise extraction based on curvature curve fitting is applicable. This method can accurately separate the three types of anchor rods from the dense point cloud on the rough surface of a cavern roof; the false-extraction rate of anchor rod points is about 0.11% to 5.09%. This method can provide high-quality and dependable data sources for the precise registration, modeling and deformation analysis of point clouds in a construction cavern. [ABSTRACT FROM AUTHOR]

A dissimilar metal tube joint fabricated by magnetic pulse welding (MPW) usually has uneven mechanical properties and an uneven morphology along the welding direction due to the small discontinuous matching range of the collision parameters. In this work, inclined, concave, and convex wall features were prefabricated on the target tube to improve the uniformity of the mechanical properties. The effects of the types of features and the prefabricated angle on the collision parameters, the axial distribution of the strength, and the interfacial morphology were investigated by simulations, shear tests, and microscopic observations, respectively. The results show that the collision parameters were increased with the increase of the prefabricated angle under the three types of features. Compared with the case without features, the increment of the collision parameters was found to be the greatest under the convex wall feature, and the smallest under the concave wall feature. With the increase of the prefabricated angle under the three types of features, the average shear strength tended to be reduced. When the prefabricated feature was the concave wall with a prefabricated angle of 5°, the quality of the joint was the best; it had an average shear strength of 46.46 MPa, an increase of 87%, and a low standard deviation of 4.96 MPa, a decrease of by 81%, as compared with that without geometric features. The transition layer was found to be the largest under the concave wall feature, and the maximum thickness decreased with the increase of the prefabricated angle. [ABSTRACT FROM AUTHOR]

Hemodialysis is an important means to maintain life in patients with end-stage renal disease (ESRD). Approximately 76.8% of patients who begin hemodialysis do so through catheters, which play vital roles in the delivery of hemodialysis to patients. During the past decade, the materials, structures, and surface-coating technologies of catheters have constantly been evolving to ameliorate catheter-related problems, such as recirculation, thrombosis, catheter-related infections, and malfunction. In this study, based on the commercial GDK catheter, six catheter models (GDK, GDK1, GDK2, GDK3, GDK4, and GDK5) with different lumen diameters and different geometric features of side holes were established, and computational flow dynamics (CFD) were used to measure flow rate, shear stress, residence time (RT), and platelet lysis index (PLI). These six catheters were then printed with polycarbonate PC using 3D printing technology to verify recirculation rates. The results indicated that: (1) the catheter with a 5.5 mm outer diameter had the smallest average shear stress in the arterial lumen and the smallest proportion of areas with shear stress > 10 pa. With increasing catheter diameter, the shear stress in the tip volume became lower, the average RT increased, and the PLI decreased due to larger changes in shear stress; (2) the catheters with oval-shaped side holes had smaller shear stress levels than those with circular-shaped holes, indicating that the oval design was more effective; (3) the catheter with parallel dual side holes had uniformly distributed flow around side holes and exhibited lower recirculation rates in both forward and reverse connections, while linear multi-side holes had higher shear stress levels due to the large differences in flow around side holes. The selection of the material and the optimization of the side holes of catheters have significant impacts on hemodynamic performances and reduce the probability of thrombosis, thus improving the efficiency of dialysis. This study would provide some guidance for optimizing catheter structures and help toward the commercialization of more efficient HD catheters. [ABSTRACT FROM AUTHOR]

Owing to its vibration reduction and indirect signal amplification characteristics, a nonlinear energy sink (NES) is used for crack identification of structures. The equation of a cracked simply supported beam with an NES is derived and the influences of the crack properties on the dynamics of the system are investigated. A pixel method is applied to estimate phase diagram areas and crack parameters are identified based on the geometric features of the phase diagram. It is shown that the phase diagram of the NES varies significantly with crack parameters. This is because of the higher amplitudes generated by the absorbing NES with respect to the primary structure. In addition, the NES can reduce the amplitude of the cracked beam without hindering crack identification. Moreover, through introducing the NES, the system can produce a strongly modulated response, which can be utilized for convenient crack detection. Two sets of crack identification indices are defined for the system, generating a single-period response and a strongly modulated response. Contour maps of identification indices based on the area of phase diagrams are obtained by varying crack location and depth. The crack parameters are identified through the intersection of contour lines. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and β), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection. [ABSTRACT FROM AUTHOR]

Within engineering and computer specializations, image processing is the most important study topic. It is one of today's fastest-growing technologies, with applications in a variety of biological sectors, including cancer sickness. According to the latest data from throughout the world, breast cancer is the most lethal of all cancer kinds. It is the most frequent cancer in women and the second leading cause of cancer mortality in women. In this study, we advocate using a watershed transformation to create a fast segmentation technique. This allows for the blending of updated information about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique. The method requires a mechanism to express the test picture in terms of the amount of change around every given pixel before it can begin the watershed modification. Each pixel in the greyscale representation of the original picture is subjected to the Sobel operator. According to the form, the tumors identified are round or semicircular and the light of the tumor dims as we travel away from its core. The complement for this previous data may be seen as a local minimum that necessitated the start of the watershed process. As a result, each tumor picture may be represented as a lake, with the center in the complement tumor picture being the least value. The identification of tumor percentage gets more reliable after using the approach. As a consequence, our computer-aided diagnostic method for mammographic breast cancer detection has improved significantly thanks to the novel methodology. The method was written in MATLAB and tested on a Windows computer. The strategy was put to the test using photos from MIAS (Mammogram Image Analysis Society, UK), which offers a consistent categorization system for mammographic examinations. In this study, we advocate using a watershed transformation to create a rapid segmentation technique. This allows for the blending of data about picture objects, extending the partitioning of the dividing waterline and therefore the standard watershed technique.

Semantic segmentation of mobile LiDAR point clouds is an essential task in many fields such as road network management, mapping, urban planning, and 3D High Definition (HD) city maps for autonomous vehicles. This study presents an approach to improve the evaluation metrics of deep-learning-based point cloud semantic segmentation using 3D geometric features and filter-based feature selection. Information gain (IG), Chi-square (Chi2), and ReliefF algorithms are used to select relevant features. RandLA-Net and Superpoint Grapgh (SPG), the current and effective deep learning networks, were preferred for applying semantic segmentation. RandLA-Net and SPG were fed by adding geometric features in addition to 3D coordinates (x, y, z) directly without any change in the structure of the point clouds. Experiments were carried out on three challenging mobile LiDAR datasets: Toronto3D, SZTAKI-CityMLS, and Paris. As a result of the study, it was demonstrated that the selection of relevant features improved accuracy in all datasets. For RandLA-Net, mean Intersection-over-Union (mIoU) was 70.1% with the features selected with Chi2 in the Toronto3D dataset, 84.1% mIoU was obtained with the features selected with the IG in the SZTAKI-CityMLS dataset, and 55.2% mIoU with the features selected with the IG and ReliefF in the Paris dataset. For SPG, 69.8% mIoU was obtained with Chi2 in the Toronto3D dataset, 77.5% mIoU was obtained with IG in SZTAKI-CityMLS, and 59.0% mIoU was obtained with IG and ReliefF in Paris. [ABSTRACT FROM AUTHOR]

Human can easily see and read the document word by word, character by character whether it is written clearly or not. He can also easily guess the next word after the current word. But machine or computer cannot easily predict the handwritten words until they are not trained enough. Optical character recognition is such a system which recognizes the characters both printed and handwritten. It works very well for English characters but the results are not that good for handwritten Devanagari characters, that is why the experiments are going on to train the machine with new features and classification techniques. In this paper, handwritten Devanagari characters recognition system has been presented using discrete cosine transform zigzag features, geometric features (open end points, intersection points, number of horizontal lines, length of horizontal lines, number of right diagonal lines, length of right diagonal lines, number of vertical lines, length of vertical lines, number of left diagonal lines, length of left diagonal lines, foreground area) and the hue moments features. For classification purpose, linear discriminant analysis (LDA), support vector machine with Quadratic kernel, Subspace Discriminant analysis, Subspace K-nearest neighbours and Weighted K-nearest neighbours techniques are considered. Authors have created a database of 3877 Devanagari characters from the scanned handwritten documents of the Marathi notebooks of 5th–9th class students which is very young age group to get variety of characters. Authors have achieved 93.6% recognition accuracy using a combination of all the features with Linear Discriminant Analysis (LDA) classifier. [ABSTRACT FROM AUTHOR]

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The automatic recognition of sign language is very important to allow for communication by hearing impaired people. The purpose of this study is to develop a method of recognizing the static Mexican Sign Language (MSL) alphabet. In contrast to other MSL recognition methods, which require a controlled background and permit changes only in 2D space, our method only requires indoor conditions and allows for variations in the 3D pose. We present an innovative method that can learn the shape of each of the 21 letters from examples. Before learning, each example in the training set is normalized in the 3D pose using principal component analysis. The input data are created with a 3D sensor. Our method generates three types of features to represent each shape. When applied to a dataset acquired in our laboratory, an accuracy of 100% was obtained. The features used by our method have a clear, intuitive geometric interpretation. [ABSTRACT FROM AUTHOR]

Geometric map features, such as line segments and planes, are receiving increasing attention due to their advantages in simultaneous localization and mapping applications. However, large structures in different environments are very likely to appear repeatedly in several consecutive time steps, resulting in redundant features in the final map. These redundant features should be properly fused, in order to avoid ambiguity and reduce the computation load. In this paper, three criteria are proposed to evaluate the closeness between any two features extracted at two different times, in terms of their included angle, feature circle overlapping and relative distance. These criteria determine whether any two features should be fused in the mapping process. Using the three criteria, all features in the global map are categorized into different clusters with distinct labels, and a fused feature is then generated for each cluster by means of least squares fitting. Two competing methods are employed for comparative verification. The comparison results indicate that using the commonly used KITTI dataset and the commercial software PreScan, the proposed feature fusion method outperforms the competing methods in terms of conciseness and accuracy.

An algorithm providing information on the key geometric features of an aorta extracted from multi-slice computed tomography images is proposed. Using the numerical method, the aorta’s geometric characteristics, such as vessel cross-sectional areas and diameters, as well as distances between arteries, can be determined. This step is crucial for training the meta-model necessary to construct an expert system with a significantly reduced volume of data and for identifying key relationships between diagnoses and geometric and hydrodynamic features. This methodology is expected to be part of an innovative decision-making software product for clinical implementation. Based on clinical and anamnestic data as well as calculations, the software will provide the shunt parameters (in particular, its diameter) and installation position to ensure regular blood flow.

BACKGROUND: Mental arithmetic analysis based on electroencephalogram (EEG) signals can help to understand some disorders such as attention deficit hyperactivity disorder, arithmetic disorder, or autism spectrum disorder in which learning is difficult. Most mental computation detection and classification systems rely on the characteristics of a single channel, however, the understanding of the connections between EEG channels, which certainly contains valuable information, is still evolving. The methods presented in this paper are the result of a research project that introduces an alternative method for better and faster receipt of information from the EEG signals of individuals, which are generally complex and nonlinear. METHODS: The EEGs of 66 healthy individuals were recorded in two rest modes and mental task a designed, with a sampling frequency of 500 Hz. To classify these two modes, we extracted features from our recordings to differentiate the EEG signals of these two groups in a single channel as well as combine possible channels. The new method that was proposed was the extraction of several geometric features from Poincaré design analysis, which used the necessary comparison t-test to determine brain differences, with a significance level of less than 0.05 in the state of mental calculations and facial rest. Also, an artificial neural network (ANN) has been used for automatic learning and diagnosis in the two mentioned modes. RESULTS: The results of this paper show that by using a combination of geometric properties (sides, angles, shortest distance, slope, and coefficients of the third-degree equation) using selected channels (FP1, F7, C4, O1) can achieve 100 % accuracy. The sensitivity reached 100 %. As well as 100 % feature. CONCLUSIONS: With the help of mental calculation, it is possible to diagnose, treat, rehabilitate and rehabilitation people who have lost the function of a part of their brain due to a disease in this field (Tab. 6, Fig. 15, Ref. 45). [ABSTRACT FROM AUTHOR]