Number Of Intersection Points Research Articles

Qualitative and quantitative comparisons of type 1 macular neovascularizations between pachychoroid neovasculopathy and neovascular age-related macular degeneration using optical coherence tomography angiography.

To compare qualitative and quantitative features of type 1 macular neovascularizations (MNV) in pachychoroid neovasculopathy (PNV) and neovascular age-related macular degeneration (nAMD). Forty-three treatment-naive eyes of 41 PNV patients and 40 treatment-naive eyes of 38 patients with nAMD were included. The patients were classified as PNV or nAMD according to the presence of pachychoroid features and soft/reticular drusen. Presence of central trunk and maturity of the MNV were evaluated on optical coherence tomography angiography (OCTA) images. MNV area, vessel density (VD), total vessel length (VL), number of intersection points (IPs), fractal dimension (FD), and lacunarity (LAC) were calculated using ImageJ software and FracLac plugin. The mean age was 56.8 ± 8.7 years in PNV and 70.4 ± 8.8 years in neovascular AMD groups (p < 0.001). Compared to nAMD, the presence of central trunk was less frequent in PNV (48.8% vs 77.5%, p = 0.007). Immature MNV pattern was observed more frequently in PNV eyes than nAMD (41.9% vs 20.0%, p = 0.009). PNV cases had significantly lower median MNV area [0.913(1.115) vs 2.542(3.273) mm²], total VL [14.84 (20.46) vs 36.34 (44.68) mm], number of IPs [104(140) vs 335(417.3)], and FD [1.56(0.10) vs 1.59(0.11)] comparing to nAMD cases (p < 0.001, p = 0.001, p < 0.001, p = 0.043 respectively). However, the mean VD (42.4 ± 6.8 vs 42.9 ± 9.0%) and the median LAC values [0.42 (0.09) vs 0.42 (0.09)] did not differ significantly between groups (p = 0.776, p = 0.526, respectively). Morphological and quantitative differences exist in type 1 neovascular lesions. Type 1 MNVs in the PNV group are characterized by a smaller and less complex structure.

TWC-EL: A multivariate prediction model by the fusion of three-way clustering and ensemble learning

Multivariate data analysis, as an important research topic in the field of machine learning, focuses on how to utilize the intrinsic connection between feature variables and target variables. However, in the face of complex multivariate prediction environments, existing single prediction models often fail to obtain ideal results. Meanwhile, existing ensemble prediction models are not always adapted to certain complex data. Moreover, the randomness in the clustering process cannot guarantee the clustering accuracy. Therefore, to improve the model’s prediction accuracy and ability to adapt to complex data and reduce the impact of randomness on clustering accuracy, this paper designs a multivariate prediction model utilizing three-way clustering (TWC) and ensemble learning, which is named the TWC-EL model. First, the initial division of the sample set is realized by k-means clustering algorithm, and further the sample set is divided again via the k-means clustering algorithm to solve the problem of clustering accuracy. Then, the results of clustering twice are combined according to the difference in the number of intersection points and the distance from the samples to the center point of each cluster, and the core and fringe regions of each cluster in the initial clustering results are obtained, forming a new TWC method. Next, based on the correlation between the regions, the obtained core and fringe regions are classified into low-correlation, medium-correlation and high-correlation regions, and an ensemble prediction model is designed by combining the advantages of the Elman neural network model, the Extreme Learning Machine (ELM) model and the back propagation neural network (BPNN) model. Finally, the experimental analysis results exhibit that the constructed TWC-EL model is efficient and feasible, and points out the excellent performance compared with the existing prediction models. The validity of the TWC method and the ensemble prediction model in the proposed TWC-EL model are verified by experiments, respectively.

Subquadratic algorithms for some 3Sum-hard geometric problems in the algebraic decision-tree model

We present subquadratic algorithms in the algebraic decision-tree model for several 3Sum-hard geometric problems, all of which can be reduced to the following question: Given two sets A, B, each consisting of n pairwise disjoint segments in the plane, and a set C of n triangles in the plane, we want to count, for each triangle Δ∈C, the number of intersection points between the segments of A and those of B that lie in Δ. We present solutions in the algebraic decision-tree model whose cost is O(n60/31+ε), for any ε>0.Our approach is based on a primal-dual range searching mechanism, which exploits the multi-level polynomial partitioning machinery recently developed by Agarwal et al. (2021) [3]. A key step in the procedure is a variant of point location in arrangements, say of lines in the plane, which is based solely on the order type of the lines, a “handicap” that turns out to be beneficial for speeding up our algorithm.

An Alternative Low-Cost Embedded NILM System for Household Energy Conservation with a Low Sampling Rate

The measurement of the energy consumption of electrical appliances, where the meter is installed at a single point on the main input circuit of the building, is called non-intrusive load monitoring (NILM). The NILM method can distinguish the loads that are currently active and break down how the loads consume electricity. A microcontroller with embedded software was selected to read the data into the NILM method process at a low sampling rate every 1 s or 1 Hz. The measured data and the data obtained by the NILM algorithm were displayed via an internet platform. This article presents an alternative low-cost embedded NILM system for household energy conservation with a low sampling rate, which could identify electrical appliances such as an air conditioner, refrigerator, television, electric kettle, electric iron, microwave oven, rice cooker, and washing machine. Four features of symmetry pattern were extracted, containing information on the value of active power change, the value of reactive power change, the number of intersection points between the active power data and the reference line, and an estimation of an equation for the starting characteristics of the electrical equipment. The proposed NILM system was tested in a selected test house that used a single-phase power system. A typical meter was also installed to compare the results with the proposed NILM. The validity of the tests was checked for 1 month in 3 houses to analyze the results. The proposed method was able to detect 91.3% of total events. The accuracy of the average ability of the system to disaggregate devices was 0.897. The accuracy value for total power consumption was 0.927. The continuous data recording of the NILM method provides information on the behavior of electrical appliances that can be used for maintenance and warnings.

A combined feature set for automatic diaphyseal Tibial fracture classification from X-Ray images

In Orthopaedics, fracture detection is considered one of the challenging tasks with x-ray images. The proposed methodology uses a novel feature-set to classify diaphyseal Tibial fractures using an Artificial Neural Network. The task of classification is carried out in two levels. The first level involves the classification of images into normal and fractured. The second level comprises of classification into three types of fractures, namely, simple, wedge and complex type. Around 12,000 X-Ray images are used as a dataset, collected from local hospitals and publicly available musculoskeletal radiographs. The local features such as Hough lines, texture values, number of intersection points, number of fragments and local binary patterns are deployed in the work. Performance-based feature reduction is carried out. The experimentation performed with individuals, a combination of two, three, four and five features, has revealed an average classification accuracy of 98.59%. Along with BPNN, other classifiers, namely, k-NN and DT are used. Results show that the method outperforms the state-of-the-art works and are found encouraging. The work is useful for Orthopaedic practitioners and extendable to other types of bones.

Intersections and circuits in sets of line segments

Sets of straight line segments with special structures and properties appear in various applications of geometric modeling, such as scientific visualization, computer-aided design, and medical image processing. In this paper, we derive sharp upper and lower bounds on the number of intersection points and closed regions that can occur in sets of line segments with certain structure, in terms of the number of segments. In particular, we consider sets of segments whose underlying planar graphs are Halin graphs, cactus graphs, maximal planar graphs, and triangle-free planar graphs, as well as randomly produced segment sets.

Recognition of Handwritten English Numerals Based on Combining Structural and Statistical Features

Generally, pattern recognition considered a strong challenge in many information processing research fields. The aim of this paper is to propose a highly accurate model for recognizing a handwritten English numeral through efficiently extracting the most valuable features of a certain handwritten numeral or digit. The handwritten English Numerals Recognition Model (HENRM) is proposed in this paper. The features extraction of the proposal based on combining both statistical and structural features of the certain numeral sample image. Mainly, the proposed HENCM has four phases which are image acquisition, image preprocessing, features extraction, and classification. In fact, four feature extraction approaches are utilized in this paper, which are the number of intersection points, the number of open-end points, calculation of density feature, and determining the chain code for each of the English numerals. The latter phase gives a features vector of 26-element size to be fed into the classifier that uses the Multi-class Support Vector Machine (MSVM) for the classification process. The experimental results showed that the proposed HENCM exhibits an average recognition rate equals to 97%. Index Terms—Chain Code, Density feature, MSVM, Recognition.

Vehicle Seat Detection Based on Improved RANSAC-SURF Algorithm

In order to detect the type of vehicle seat and the missing part of the spring hook, this paper proposes an improved RANSAC-SURF method. First, the image is filtered by a Gauss filter. Second, an improved RANSAC-SURF algorithm is used to detect the types of vehicle seats. Extract the feature points of vehicle seats. The feature points are matched according to the improved RANSAC-SURF algorithm. Third, the image distortion of the vehicle seat is corrected by the method of perspective transformation. Determine whether the seat’s spring hook is missing or not according to the absolute value of the gray difference between the image collected by the camera and the image of the normal installation. The experimental results show that the MSE of the Gauss filter under a 5 [Formula: see text] 5 template is 19.0753, and the PSNR is 35.3261, which is better than that of the mean filter and the median filter. The total matching logarithm of feature points and the number of intersection points are 188 and 18, respectively, in the improved RANSAC-SURF matching algorithm.

Modeling and analysis of melanoblast motion.

Melanoblast migration is important for embryogenesis and is a key feature of melanoma metastasis. Many studies have characterized melanoblast movement, focusing on statistical properties and have highlighted basic mechanisms of melanoblast motility. We took a slightly different and complementary approach: we previously developed a mathematical model of melanoblast motion that enables the testing of biological assumptions about the displacement of melanoblasts and we created tests to analyze the geometric features of cell trajectories and the specific issue of trajectory interactions. Within this model, we performed simulations and compared the results with experimental data using geometric tests. In this paper, we developed the associated mathematical model and the main focus is to study the crossings between trajectories with new theoretical results about the variation of number of intersection points with respect to the crossing times. Using these results it is possible to study the random nature of displacements and the interactions between trajectories. This analysis has raised new questions, leading to the generation of strong arguments in favor of a trail left behind each moving melanoblast.

The Least Number of Intersection Points in Currency Fluctuation and Well- Approximating Line Graph under Constraints of the Elliott Wave Principle

Recently producing profit by exchanging currency of one country with that of the other has been conducted among traders. This activity is called “FX” – the abbreviation of Foreign eXchange. The fluctuation of the price of currency is caused by the psychology of traders so that it apparently varies at random. However, after so many traders take part in FX, the trend of currency expectedly shows several patterns and is said to be subject to a certain principle which was firstly suggested by R.N.Elliott (1871-1948). In our recent research, we have tried to construct an algorithm relying on this Elliott wave principle and establish an automated transaction of FX by a computer. One of the aims of this PC-algorithm is to determine the line graph which well-approximates a real currency fluctuation under the constraints of the Elliott principle. In this paper, we will survey how to construct an algorithm for well-approximating line graph, and theoretically discuss the least number of intersection points in currency fluctuation and the well-approximating line graph, which enhances the validity of the algorithm.&#x0D;

The Least Number of Intersection Points in Currency Fluctuation and Well- Approximating Line Graph under Constraints of the Elliott Wave Principle

Recently producing profit by exchanging currency of one country with that of the other has been conducted among traders. This activity is called “FX” – the abbreviation of Foreign eXchange. The fluctuation of the price of currency is caused by the psychology of traders so that it apparently varies at random. However, after so many traders take part in FX, the trend of currency expectedly shows several patterns and is said to be subject to a certain principle which was firstly suggested by R.N.Elliott (1871-1948). In our recent research, we have tried to construct an algorithm relying on this Elliott wave principle and establish an automated transaction of FX by a computer. One of the aims of this PC-algorithm is to determine the line graph which well-approximates a real currency fluctuation under the constraints of the Elliott principle. In this paper, we will survey how to construct an algorithm for well-approximating line graph, and theoretically discuss the least number of intersection points in currency fluctuation and the well-approximating line graph, which enhances the validity of the algorithm.&#x0D;

An analysis of stochastic discrete fracture networks on shale gas recovery

Complex fracture networks are created by hydraulic fracturing in shale gas reservoirs. The well performance is largely affected by the geometrical properties of fracture networks. It is difficult to estimate well performance because a large number of parameters are used for characterization of a fracture network. In this study, only two parameters are screened to estimate the shale gas recovery by the method of design of experiments. Synthetic fracture networks are generated based on the stochastic theory to represent the secondary fractures in the hydraulic fracture network. Two numerical experiments are designed to correlate the fracture network properties to gas recovery estimated by a commercial software. Various fracture network parameters derived from the analyses of fracture intersection, fractal and percolation are screened to correlate the gas recovery. In terms of the internal structure of a fracture network, a dimensionless parameter called the effective fracture density is proposed to estimate shale gas recovery. It is a combination of the number of fractures, fracture length and orientation. In terms of the boundary of a fracture network, the shale gas recovery is determined by the number of intersection points between the primary fractures and secondary fractures (NIPS). The shale gas recovery predicted by the two parameters (the NIPS and the dimensionless fracture density) is accurate compared with that calculated by the commercial software, which provides us a practical method to estimate the gas recovery by eliminating the number of parameters used for fracture network properties.

English

In this paper, an Indian Number Handwriting Recognition Model (INHRM) is proposed. Mainly, the proposed model consists of four phases which are the image acquisition, image preprocessing, features extraction, and classification model. Initially, the captured images of the handwritten Indian numbers were enhanced and preprocessed to obtain the skeleton for the interested object. The extracted features of the handwritten Indian numbers were obtained by calculating four parameters for each captured number sample, these parameters are the number of starting points, the number of intersection points, the average zoning which consists of four values, and finally, the normalized chain vector of length of 10 elements. So, the resulted 16 values of the four parameters were arranged in a vectors of length of 16 elements. These features vectors were used in the training and testing processes of the proposed INHRM model. Multi-class SVM (MSVM) approach is suggested for the classification phase. An accumulation of 600 samples of various handwritten Indian numbers styles has been gathered from a group of 60 students. These samples were preprocessed, features extracted, then delivered to the classification phase by utilizing 500 samples of them for training while the remaining 100 samples were used for testing of the MSVM-classifier model. The results showed that the proposed INHRMachieved relatively high percentage of exactness of around 97%.

Indian Number Handwriting Features Extraction and Classification using Multi-Class SVM

In this paper, an Indian Number Handwriting Recognition Model (INHRM) is proposed. Mainly, the proposed model consists of four phases which are the image acquisition, image preprocessing, features extraction, and classification model. Initially, the captured images of the handwritten Indian numbers were enhanced and preprocessed to obtain the skeleton for the interested object. The extracted features of the handwritten Indian numbers were obtained by calculating four parameters for each captured number sample, these parameters are the number of starting points, the number of intersection points, the average zoning which consists of four values, and finally, the normalized chain vector of length of 10 elements. So, the resulted 16 values of the four parameters were arranged in a vectors of length of 16 elements. These features vectors were used in the training and testing processes of the proposed INHRM model. Multi-class SVM (MSVM) approach is suggested for the classification phase. An accumulation of 600 samples of various handwritten Indian numbers styles has been gathered from a group of 60 students. These samples were preprocessed, features extracted, then delivered to the classification phase by utilizing 500 samples of them for training while the remaining 100 samples were used for testing of the MSVM-classifier model. The results showed that the proposed INHRM achieved relatively high percentage of exactness of around 97%.

String of scrolls from a time-delayed chaotic circuit

Over the last few decades, complex multi-scroll chaotic attractors generated through electronic circuits have considerably been raised. Therefore, this paper concerns the design and implementation of multi-scroll chaotic attractors such as two-scrolls, three-scrolls and four-scrolls generated from a first-order delay differential equation. Modelling a time-delayed feed-back chaotic system considers a simple nonlinearity piecewise-linear (PWL) function to generate the chaotic attractors with an odd/even numbers of scrolls. The delayed feedback consists of a cascade of Bessel filters and the design of a circuit which generates a string of scrolls chaotic attractors depending on the number of intersection points of nonlinearity of PWL function with a state variable. The simulation results reveal that the multiple-scroll chaotic attractors can be successfully reproduced using PSpice. The implementation based on circuitswitching mode with simple components such as resistors, capacitors, and operational amplifiers is also presented.

Method for an automatic alignment of imagery and vector data applied to cadastral information in Poland

Nowadays, an important problem in combining vector data and imagery is that they rarely align. This problem can become particularly acute in the case of cadastral systems. In this study, and as part of the partnership between the Universities of Jaén and Adam Mickiewicz (Poznań), we provide a methodological proposal to assess the conflation procedures between cadastral vector data and imagery, improving the alignment between both data sets. To do this, we use an automatic alignment algorithm which detects road intersections from both data sets as control points by using image texture characterisation. With this method, we first train the system on the imagery to learn the road texture distribution, then we can obtain its segmentation according to its texture, and finally the system locates road intersection points. The last step is to align vector data and imagery by using different techniques. This algorithm is based on an earlier one, detailed in [Ruiz, J.J., Rubio, T.J., and Ureña, M.A., 2011b. Automatic extraction of road intersections from images in conflation processes based on texture characterization. Survey review, 43 (321), 212–225.]. However, in the updated version we have solved the problem of not-well-defined intersection points, resulting in a substantial increase in the number of intersection points employed for the final adjustment to align both products and in a reduction of the computation time. On the other hand, the positional uncertainty assessment of parcel boundary lines both before and after applying our alignment procedure between them is provided. With regard to the experimental results, in the case of Polish cadastral data this procedure allows for significant improvement in the alignment between imagery and cadastral parcels boundaries.

Abstract Voronoi Diagrams from Closed Bisecting Curves

We present the first algorithm for constructing abstract Voronoi diagrams from bisectors that are unbounded or closed Jordan curves. It runs in expected [Formula: see text] many steps and [Formula: see text] space, where [Formula: see text] is the number of sites, [Formula: see text] denotes the average number of faces (connected components) per Voronoi region in any diagram of a subset of [Formula: see text] sites, and [Formula: see text] is the maximum number of intersection points between any two related bisectors.

On subRiemannian geodesics associated to a Grushin operator

SubRiemannian geodesics induced by the Grushin operator are obtained and completely classified. The classification is done with respect to the position of the endpoints of the geodesics. The number of geodesics starting at the origin is determined, for the finite case, and the number of intersection points of an arbitrary geodesic with the canonical submanifolds is found as well.

열림방향을 이용한 자동차번호판 숫자인식

본 논문에서는 열림부분에 기반을 둔 자동차 번호판의 숫자인식 기법을 제안한다. 제안된 인식기법에서 숫자를 상부와 하부로 나누고, 각각에 대해 열림부분을 판정하여 숫자를 인식한다. 제안된 기법에서, 상부와 하부의 분할은 정해진 절단선을 바탕으로 하며, 교점의 개수에 따라 절단선의 위치가 조절된다. 제안된 방법은 템플릿 매칭 방법에 비해 잡음이나 회전 등의 영향을 받지 않으며 강건하다. 모의실험에서 제안된 기법의 성능을 평가하기 위해 번호판에 사용되는 숫자들에 대해 처리를 하고, 그 결과를 제시하였다. 제안된 기법은 번호판의 숫자인식에서 매우 높은 인식률을 보인다. In this paper, we propose a new digit recognition method based on opened enclosure. In the proposed method, each digit is divided into two parts, an upper part and a lower part, which are based on a cutting line that is modified depending on the number of intersection points. In the simulation, the performance evaluation through the data acquisition and application of the proposed algorithm was carried out and the result was presented.

Extending some induced substructures of an inversive plane

Given a circle $$C$$C of an inversive plane $${\mathcal {I}}$$I of order $$n$$n, the remaining circles are partitioned into three types according to the number of intersection points with $$C$$C. Let $${\mathcal {S}}$$S be the incidence structure formed by the points of $${\mathcal {I}}$$I and any two types of circles. It is proved that with some additional requirements, $${\mathcal {I}}$$I is the only inversive plane of order $$n$$n having $${\mathcal {S}}$$S as an induced substructure.