The Experts below are selected from a list of 1011 Experts worldwide ranked by ideXlab platform
Amir G Aghdam - One of the best experts on this subject based on the ideXlab platform.
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distributed coverage control of mobile sensor networks subject to measurement error
IEEE Transactions on Automatic Control, 2016Co-Authors: Jalal Habibi, Hamid Mahboubi, Amir G AghdamAbstract:Deployment algorithms proposed to improve coverage in sensor networks often rely on the Voronoi diagram, which is obtained by using the position information of the sensors. It is usually assumed that all measurements are sufficiently accurate, while in a practical setting, even a small measurement error may lead to significant degradation in the coverage performance. This paper investigates the effect of measurement error on the performance of coverage control in mobile sensor networks. It also presents a distributed deployment strategy, namely the Robust Max-Area strategy, which uses information on error bounds in order to move the sensors to appropriate locations. To this end, two Polygons are obtained for each sensor, and it is shown that the exact Voronoi Polygon (associated with accurate measurements) lies between them. A local spatial probability function is then derived for each sensor, which translates the available information about the error bound into the likelihood of the points being inside the exact Voronoi Polygon. Subsequently, the deployment strategy positions each sensor such that the total covered area increases. The sensors’ movements are shown to be convergent under the proposed strategy.
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distributed coverage optimization in a network of mobile agents subject to measurement error
Advances in Computing and Communications, 2012Co-Authors: Jalal Habibi, Hamid Mahboubi, Amir G AghdamAbstract:The effect of localization error in mobile sensor networks is investigated in this paper. Localization is an essential building block in mobile sensor networks, and is achieved through information exchange among the sensors. Sensor deployment algorithms often rely on the Voronoi diagram, which is obtained by using the position information of the neighboring sensors. In the sensor network coverage problem, it is desired to place each sensor in a proper position in its Voronoi cell such that its local coverage increases. On the other hand, it is often assumed that all measurements are sufficiently accurate, while in a practical setup even a small localization error may lead to significant uncertainty in the resultant Voronoi diagram. This paper is concerned with the degrading effect of position measurement error in the sensor network coverage problem. To this end, the effect of localization error on the boundaries of the Voronoi Polygons is investigated. Two Polygons are obtained for each sensor, and it is shown that the exact Voronoi Polygon (corresponding to accurate localization) lies between them. The area between these two Polygons is directly related to the size of error. A sensor deployment strategy is presented based on these two Polygons, using a quantitative local density function which takes the uncertainty of the Voronoi Polygons into account to maximize the local coverage of each sensor.
Sezer Volkan - One of the best experts on this subject based on the ideXlab platform.
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A Novel Point Inclusion Test for Convex Polygons Based on Voronoi Tessellations
'Elsevier BV', 2021Co-Authors: Zengin, Rahman Salim, Sezer VolkanAbstract:The point inclusion tests for Polygons, in other words the point-in-Polygon (PIP) algorithms, are fundamental tools for many scientific fields related to computational geometry, and they have been studied for a long time. The PIP algorithms get direct or indirect geometric definition of a Polygonal entity, and validate its containment of a given point. The PIP algorithms, which are working directly on the geometric entities, derive linear boundary definitions for the edges of the Polygons. Moreover, almost all direct test methods rely on the two-point form of the line equation to partition the space into half-spaces. Voronoi tessellations use an alternate approach for half-space partitioning. Instead of line equation, distance comparison between generator points is used to accomplish the same task. Voronoi tessellations consist of convex Polygons, which are defined between generator points. Therefore, Voronoi tessellations have become an inspiration for us to develop a new approach of the PIP testing, specialized for convex Polygons. The equations, essential to the conversion of a convex Polygon to a Voronoi Polygon, are derived. As a reference, a very standard convex PIP testing algorithm, \textit{the sign of offset}, is selected for comparison. For generalization of the comparisons, \textit{the ray crossing} algorithm is used as another reference. All algorithms are implemented as vector and matrix operations without any branching. This enabled us to benefit from the CPU optimizations of the underlying linear algebra libraries. Experimentation showed that, our proposed algorithm can have comparable performance characteristics with the reference algorithms. Moreover, it has simplicity, both from a geometric representation and the mental model.Comment: 8 pages, 6 figures, "for the source code, see https://github.com/volimpro/Voronoi_pip
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A Novel Point Inclusion Test for Convex Polygons Based on Voronoi Tessellations
2020Co-Authors: Zengin, Rahman Salim, Sezer VolkanAbstract:The point inclusion tests for Polygons, in other words the point in Polygon (PIP) algorithms are fundamental tools for many scientific fields related to computational geometry and they have been studied for a long time. PIP algorithms get direct or indirect geometric definition of a Polygonal entity and validate its containment of a given point. The PIP algorithms which are working directly on the geometric entities derive linear boundary definitions for the edges of the Polygon. Moreover, almost all direct test methods rely on the two point form of the line equation to partition the space into half-spaces. Voronoi tessellations use an alternate approach for half-space partitioning. Instead of line equation, distance comparison between generator points is used to accomplish the same task. Voronoi tessellations consist of convex Polygons which are defined between generator points. Therefore, Voronoi tessellations have become an inspiration for us to develop a new approach of PIP testing specialized for convex Polygons. Essential equations to the conversion of a convex Polygon to a Voronoi Polygon are derived along this paper. As a reference, a very standard convex PIP testing algorithm, the sign of offset, is selected for comparison. For generalization of the comparisons the ray crossing algorithm is used as another reference. All algorithms are implemented as vector and matrix operations without any branching. This enabled us to benefit from the CPU optimizations of the underlying linear algebra libraries. All algorithms are tested for three different Polygon sizes and varying point batch sizes. Overall, our proposed algorithm has performed better with varying margin between 10% to 23% compared to the reference methods.Comment: 9 pages, 6 figures, "for the source code, see https://doi.org/10.5281/zenodo.3966996
Jalal Habibi - One of the best experts on this subject based on the ideXlab platform.
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distributed coverage control of mobile sensor networks subject to measurement error
IEEE Transactions on Automatic Control, 2016Co-Authors: Jalal Habibi, Hamid Mahboubi, Amir G AghdamAbstract:Deployment algorithms proposed to improve coverage in sensor networks often rely on the Voronoi diagram, which is obtained by using the position information of the sensors. It is usually assumed that all measurements are sufficiently accurate, while in a practical setting, even a small measurement error may lead to significant degradation in the coverage performance. This paper investigates the effect of measurement error on the performance of coverage control in mobile sensor networks. It also presents a distributed deployment strategy, namely the Robust Max-Area strategy, which uses information on error bounds in order to move the sensors to appropriate locations. To this end, two Polygons are obtained for each sensor, and it is shown that the exact Voronoi Polygon (associated with accurate measurements) lies between them. A local spatial probability function is then derived for each sensor, which translates the available information about the error bound into the likelihood of the points being inside the exact Voronoi Polygon. Subsequently, the deployment strategy positions each sensor such that the total covered area increases. The sensors’ movements are shown to be convergent under the proposed strategy.
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distributed coverage optimization in a network of mobile agents subject to measurement error
Advances in Computing and Communications, 2012Co-Authors: Jalal Habibi, Hamid Mahboubi, Amir G AghdamAbstract:The effect of localization error in mobile sensor networks is investigated in this paper. Localization is an essential building block in mobile sensor networks, and is achieved through information exchange among the sensors. Sensor deployment algorithms often rely on the Voronoi diagram, which is obtained by using the position information of the neighboring sensors. In the sensor network coverage problem, it is desired to place each sensor in a proper position in its Voronoi cell such that its local coverage increases. On the other hand, it is often assumed that all measurements are sufficiently accurate, while in a practical setup even a small localization error may lead to significant uncertainty in the resultant Voronoi diagram. This paper is concerned with the degrading effect of position measurement error in the sensor network coverage problem. To this end, the effect of localization error on the boundaries of the Voronoi Polygons is investigated. Two Polygons are obtained for each sensor, and it is shown that the exact Voronoi Polygon (corresponding to accurate localization) lies between them. The area between these two Polygons is directly related to the size of error. A sensor deployment strategy is presented based on these two Polygons, using a quantitative local density function which takes the uncertainty of the Voronoi Polygons into account to maximize the local coverage of each sensor.
Yi Chang - One of the best experts on this subject based on the ideXlab platform.
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a method based on the centroid of segregation points a Voronoi Polygon application to solidification of alloys
Journal of Alloys and Compounds, 2018Co-Authors: Zibing Hou, Dongwei Guo, Jianghai Cao, Yi ChangAbstract:Abstract A new method for controlling segregation delicately based on the centroid of independent segregation points in an alloy macrostructure is established using Voronoi Polygon method. Voronoi Polygon area is found to nearly obey the log-normal distribution, which shows that a Polygon may reflect a new solidifying unit. The relationship between Voronoi Polygon characteristics and carbon segregation extent (segregation ratio) is investigated at different locations. In a pure columnar grain zone, the segregation extent worsens as the Voronoi Polygon size decreases; in an equiaxed grain zone, this extent worsens as the size increases. A near-equilibrium solidification model for Polygons under different conditions is studied, and the results are consistent with the aforementioned finding of segregation ratio. Compared with a secondary arm spacing, the fractal dimension of the solidification structure may be a better method to describe the characteristics of the solidification structure and the size change of Voronoi Polygons as to relevant segregation. Thus, this study proposes a new experimental method that depicts the macrostructure from a different perspective, i.e., the segregation point instead of the solidification structure. The formation of Voronoi Polygons created by segregation points during solidifying should be divided into two stages, i.e., appearance of the segregation point and determination of the Polygon's outline. The segregation extent can be controlled by altering the size of the relevant Voronoi Polygon, which is a unique alternative strategy for improving the uniformity of element distribution rather than simultaneously considering nucleus formation, solidification structure growth, and liquid flow as to the complex process of segregation formation.
Zengin, Rahman Salim - One of the best experts on this subject based on the ideXlab platform.
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A Novel Point Inclusion Test for Convex Polygons Based on Voronoi Tessellations
'Elsevier BV', 2021Co-Authors: Zengin, Rahman Salim, Sezer VolkanAbstract:The point inclusion tests for Polygons, in other words the point-in-Polygon (PIP) algorithms, are fundamental tools for many scientific fields related to computational geometry, and they have been studied for a long time. The PIP algorithms get direct or indirect geometric definition of a Polygonal entity, and validate its containment of a given point. The PIP algorithms, which are working directly on the geometric entities, derive linear boundary definitions for the edges of the Polygons. Moreover, almost all direct test methods rely on the two-point form of the line equation to partition the space into half-spaces. Voronoi tessellations use an alternate approach for half-space partitioning. Instead of line equation, distance comparison between generator points is used to accomplish the same task. Voronoi tessellations consist of convex Polygons, which are defined between generator points. Therefore, Voronoi tessellations have become an inspiration for us to develop a new approach of the PIP testing, specialized for convex Polygons. The equations, essential to the conversion of a convex Polygon to a Voronoi Polygon, are derived. As a reference, a very standard convex PIP testing algorithm, \textit{the sign of offset}, is selected for comparison. For generalization of the comparisons, \textit{the ray crossing} algorithm is used as another reference. All algorithms are implemented as vector and matrix operations without any branching. This enabled us to benefit from the CPU optimizations of the underlying linear algebra libraries. Experimentation showed that, our proposed algorithm can have comparable performance characteristics with the reference algorithms. Moreover, it has simplicity, both from a geometric representation and the mental model.Comment: 8 pages, 6 figures, "for the source code, see https://github.com/volimpro/Voronoi_pip
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A Novel Point Inclusion Test for Convex Polygons Based on Voronoi Tessellations
2020Co-Authors: Zengin, Rahman Salim, Sezer VolkanAbstract:The point inclusion tests for Polygons, in other words the point in Polygon (PIP) algorithms are fundamental tools for many scientific fields related to computational geometry and they have been studied for a long time. PIP algorithms get direct or indirect geometric definition of a Polygonal entity and validate its containment of a given point. The PIP algorithms which are working directly on the geometric entities derive linear boundary definitions for the edges of the Polygon. Moreover, almost all direct test methods rely on the two point form of the line equation to partition the space into half-spaces. Voronoi tessellations use an alternate approach for half-space partitioning. Instead of line equation, distance comparison between generator points is used to accomplish the same task. Voronoi tessellations consist of convex Polygons which are defined between generator points. Therefore, Voronoi tessellations have become an inspiration for us to develop a new approach of PIP testing specialized for convex Polygons. Essential equations to the conversion of a convex Polygon to a Voronoi Polygon are derived along this paper. As a reference, a very standard convex PIP testing algorithm, the sign of offset, is selected for comparison. For generalization of the comparisons the ray crossing algorithm is used as another reference. All algorithms are implemented as vector and matrix operations without any branching. This enabled us to benefit from the CPU optimizations of the underlying linear algebra libraries. All algorithms are tested for three different Polygon sizes and varying point batch sizes. Overall, our proposed algorithm has performed better with varying margin between 10% to 23% compared to the reference methods.Comment: 9 pages, 6 figures, "for the source code, see https://doi.org/10.5281/zenodo.3966996
