This work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifi...
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This work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures. The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m(2) and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m(2). More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent. (c) 2015 The Authors. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
This paper presents a novel interprocessor communication network which has been designed to be integrated on a specialized low-cost massively parallel system, PAPRICA-3, featuring 256 single-bit processors arranged as...
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This paper presents a novel interprocessor communication network which has been designed to be integrated on a specialized low-cost massively parallel system, PAPRICA-3, featuring 256 single-bit processors arranged as a linear array. This communication network is of basic importance in the implementation of communications among processors not directly connected, and can solve efficiently problems involving non-local exchange of information such as image transforms, seed-propagation, labeling of connected components, or the handling of hierarchical data structures.A sample application, the implementation of an image transform for road markings detection, requiring global line-wise data transfers among processors is discussed and the computational complexity of its implementation on PAPRICA-3 is presented.
An unstructured grid adaptation technology has been developed for capturing flow features effectively. The face-based hierarchical data structure is used at grid refinement procedure. The difference of suitable flow p...
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An unstructured grid adaptation technology has been developed for capturing flow features effectively. The face-based hierarchical data structure is used at grid refinement procedure. The difference of suitable flow properties between cell and its neighbors for flow features detection. The results of different simulation cases show the strengths of unstructured grid adaptation technology. The flow features such as shock wave and vortex are resolved better with grids refinement. The grid adaptation also improves the computational accuracy of aerodynamic characteristics and loads.
Electron beam direct writing technologies for 0.3-mu-m devices are studied in this paper. In order to prevent charging effects, TQV, which is a varnish consisting of 7,7,8,8-tetracyano quino dimethane complex salt, ha...
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Electron beam direct writing technologies for 0.3-mu-m devices are studied in this paper. In order to prevent charging effects, TQV, which is a varnish consisting of 7,7,8,8-tetracyano quino dimethane complex salt, has been coated on the top layer of a trilayer resist system. The effectiveness of TQV coating is indicated by the experimental results obtained from test patterns. A proximity effect correction system with several strategies to reduce the correction time and output data volume has been developed. These technologies have been adopted for the fabrication of ULSI circuit patterns with the dimension of about 0.3 mu-m. The calculation time and the output data volume of a proximity effect correction are reduced considerably by using new methods. It is revealed that 0.3-mu-m ULSI patterns can be precisely fabricated by these new technologies.
Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemi...
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Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three fac
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