The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform
Yong Xue - One of the best experts on this subject based on the ideXlab platform.
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Workload and task management of Grid-enabled quantitative aerosol retrieval from Remotely Sensed Data
Future Generation Computer Systems, 2010Co-Authors: Yong Xue, Wei Wan, Ying Wang, Jie Guang, Linlu Mei, Linyan BaiAbstract:As the quality and accuracy of remote sensing instruments improve, the ability to quickly process Remotely Sensed Data is in increasing demand. Quantitative retrieval of aerosol properties from Remotely Sensed Data is a Data-intensive scientific application, where the complexities of processing, modeling and analyzing large volumes of Remotely Sensed Data sets have significantly increased computation and Data demands. While Grid computing has been a prominent technique to tackle computational issues, little work has been done on making Grid computing adapted to remote sensing applications. In this paper, we intended to demonstrate the usage of Grid computing for quantitative remote sensing retrieval applications. A workload estimation and task partition algorithm was developed, and it executes a generic remote sensing algorithm in parallel over partitioned Datasets, which is embedded in a middleware framework for remote sensing retrieval named the Remote Sensing Information Service Grid Node (RSIN). A case study shows that significant improvement of system performance can be achieved with this implementation. It also gives a perspective on the potential of applying Grid computing practices to remote sensing problems.
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ISPA Workshops - Study on grid-based special Remotely Sensed Data processing node in grid GIS
Frontiers of High Performance Computing and Networking – ISPA 2006 Workshops, 2006Co-Authors: Jianqin Wang, Yong Xue, Jianping Guo, Lei Zheng, Ying Luo, Yi Xie, Yunling LiuAbstract:Grid Geospatial Information Service (Grid GIS) system aims to study and develop grid-based uniform spatial information access and analysis system. Data resources of Grid GIS include not only original and traditional GIS Data but Remotely Sensed Data. Everyday, space missions involve the download, from space to ground, of huge amount of raw images that are stored in the ground stations geographically distributed. It is a practical pressing task to process these Data resource in real time or almost real time and to effectively share spatial information among remote sensing community. Grid technology can provide access to a global distributed computing environment via authentication, authorization, negotiation and security tools. This paper discusses the key technologies of Grid-based special Remotely Sensed Data processing node. First, the concept of Grid-based special Remotely Sensed Data processing node is introduced. Following is the architecture and functions of this node. Based on this architecture, the tasks scheduling algorithm is presented. Finally we introduce the computing resource meta-module information registry and renewal mechanism of the Grid-based special Remotely Sensed Data processing node for Grid GIS.
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APWeb Workshops - A grid-based programming environment for Remotely Sensed Data processing
Lecture Notes in Computer Science, 2006Co-Authors: Yong Xue, Jianqin Wang, Ying LuoAbstract:Grid computing can provide significant computational power which can be applied to Remotely Sensed Data processing. But not all the users in the field of remote sensing have the required knowledge of the grid computing. In this paper, we introduce a Grid-based programming environment (GPE) for Remotely Sensed Data processing. User who doesn’t have the knowledge of how to deal with transactions of the Grid computing environment can program with it. Although GPE isn’t fit for the algorithms in which there exists strong correlation, it works well with the other algorithms and accelerates the computation evidently.
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GCC Workshops - Study on Remotely Sensed Data Access and Integration Grid-enabled Middleware
2006 Fifth International Conference on Grid and Cooperative Computing Workshops, 2006Co-Authors: Jianqin Wang, Yong Xue, Rui-zhi Sun, Guangli Liu, Yi Xie, Dingsheng Liu, Lu Yang, Ying Ding, Ya Ouyang, Yunling LiuAbstract:Every day, Space missions involve the download, from space to ground, of many raw images that are stored in the ground station. These Data are stored in geographically distributed and heterogeneous Database systems, which make these resources access and share difficult. Data grid technique can hide the difference of storage systems and makes the isolated Data resources be shared in the grid environment. This paper describes OGSA-DAI module, which is based on the Data grid technique and studies the Remotely Sensed Data access and integration middleware, especially the technology of developing middleware to deal with large binary Remotely Sensed Data.
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ICCSA (3) - Study on grid-based special Remotely Sensed Data processing node
Lecture Notes in Computer Science, 1Co-Authors: Jianqin Wang, Yong Xue, Jianping Guo, Lei Zheng, Ying Luo, Rui-zhi Sun, Guangli Liu, Yunling LiuAbstract:Everyday, space missions involve the download, from space to ground, of huge amount of raw images that are stored in the ground stations geographically distributed. It is a practical pressing task to process these Data resource in real time or almost real time and to effectively share spatial information among remote sensing community. Grid technology can provide access to a global distributed computing environment via authentication, authorization, negotiation and security tools. This paper discusses the key technologies of Grid-based special Remotely Sensed Data processing node. First, the concept of Grid-based special Remotely Sensed Data processing node is introduced. Following is the architecture and functions of this node. Based on this architecture, the tasks scheduling algorithm is presented. Finally we introduce the computing resource meta-module information registry and renewal mechanism of the Grid-based special Remotely Sensed Data processing node for Spatial Information Grid (SIG).
Yunling Liu - One of the best experts on this subject based on the ideXlab platform.
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GCC Workshops - Study on Remotely Sensed Data Access and Integration Grid-enabled Middleware
2006 Fifth International Conference on Grid and Cooperative Computing Workshops, 2006Co-Authors: Jianqin Wang, Yong Xue, Rui-zhi Sun, Guangli Liu, Yi Xie, Dingsheng Liu, Lu Yang, Ying Ding, Ya Ouyang, Yunling LiuAbstract:Every day, Space missions involve the download, from space to ground, of many raw images that are stored in the ground station. These Data are stored in geographically distributed and heterogeneous Database systems, which make these resources access and share difficult. Data grid technique can hide the difference of storage systems and makes the isolated Data resources be shared in the grid environment. This paper describes OGSA-DAI module, which is based on the Data grid technique and studies the Remotely Sensed Data access and integration middleware, especially the technology of developing middleware to deal with large binary Remotely Sensed Data.
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ISPA Workshops - Study on grid-based special Remotely Sensed Data processing node in grid GIS
Frontiers of High Performance Computing and Networking – ISPA 2006 Workshops, 2006Co-Authors: Jianqin Wang, Yong Xue, Jianping Guo, Lei Zheng, Ying Luo, Yi Xie, Yunling LiuAbstract:Grid Geospatial Information Service (Grid GIS) system aims to study and develop grid-based uniform spatial information access and analysis system. Data resources of Grid GIS include not only original and traditional GIS Data but Remotely Sensed Data. Everyday, space missions involve the download, from space to ground, of huge amount of raw images that are stored in the ground stations geographically distributed. It is a practical pressing task to process these Data resource in real time or almost real time and to effectively share spatial information among remote sensing community. Grid technology can provide access to a global distributed computing environment via authentication, authorization, negotiation and security tools. This paper discusses the key technologies of Grid-based special Remotely Sensed Data processing node. First, the concept of Grid-based special Remotely Sensed Data processing node is introduced. Following is the architecture and functions of this node. Based on this architecture, the tasks scheduling algorithm is presented. Finally we introduce the computing resource meta-module information registry and renewal mechanism of the Grid-based special Remotely Sensed Data processing node for Grid GIS.
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ICCSA (3) - Study on grid-based special Remotely Sensed Data processing node
Lecture Notes in Computer Science, 1Co-Authors: Jianqin Wang, Yong Xue, Jianping Guo, Lei Zheng, Ying Luo, Rui-zhi Sun, Guangli Liu, Yunling LiuAbstract:Everyday, space missions involve the download, from space to ground, of huge amount of raw images that are stored in the ground stations geographically distributed. It is a practical pressing task to process these Data resource in real time or almost real time and to effectively share spatial information among remote sensing community. Grid technology can provide access to a global distributed computing environment via authentication, authorization, negotiation and security tools. This paper discusses the key technologies of Grid-based special Remotely Sensed Data processing node. First, the concept of Grid-based special Remotely Sensed Data processing node is introduced. Following is the architecture and functions of this node. Based on this architecture, the tasks scheduling algorithm is presented. Finally we introduce the computing resource meta-module information registry and renewal mechanism of the Grid-based special Remotely Sensed Data processing node for Spatial Information Grid (SIG).
Jianqin Wang - One of the best experts on this subject based on the ideXlab platform.
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ISPA Workshops - Study on grid-based special Remotely Sensed Data processing node in grid GIS
Frontiers of High Performance Computing and Networking – ISPA 2006 Workshops, 2006Co-Authors: Jianqin Wang, Yong Xue, Jianping Guo, Lei Zheng, Ying Luo, Yi Xie, Yunling LiuAbstract:Grid Geospatial Information Service (Grid GIS) system aims to study and develop grid-based uniform spatial information access and analysis system. Data resources of Grid GIS include not only original and traditional GIS Data but Remotely Sensed Data. Everyday, space missions involve the download, from space to ground, of huge amount of raw images that are stored in the ground stations geographically distributed. It is a practical pressing task to process these Data resource in real time or almost real time and to effectively share spatial information among remote sensing community. Grid technology can provide access to a global distributed computing environment via authentication, authorization, negotiation and security tools. This paper discusses the key technologies of Grid-based special Remotely Sensed Data processing node. First, the concept of Grid-based special Remotely Sensed Data processing node is introduced. Following is the architecture and functions of this node. Based on this architecture, the tasks scheduling algorithm is presented. Finally we introduce the computing resource meta-module information registry and renewal mechanism of the Grid-based special Remotely Sensed Data processing node for Grid GIS.
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APWeb Workshops - A grid-based programming environment for Remotely Sensed Data processing
Lecture Notes in Computer Science, 2006Co-Authors: Yong Xue, Jianqin Wang, Ying LuoAbstract:Grid computing can provide significant computational power which can be applied to Remotely Sensed Data processing. But not all the users in the field of remote sensing have the required knowledge of the grid computing. In this paper, we introduce a Grid-based programming environment (GPE) for Remotely Sensed Data processing. User who doesn’t have the knowledge of how to deal with transactions of the Grid computing environment can program with it. Although GPE isn’t fit for the algorithms in which there exists strong correlation, it works well with the other algorithms and accelerates the computation evidently.
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GCC Workshops - Study on Remotely Sensed Data Access and Integration Grid-enabled Middleware
2006 Fifth International Conference on Grid and Cooperative Computing Workshops, 2006Co-Authors: Jianqin Wang, Yong Xue, Rui-zhi Sun, Guangli Liu, Yi Xie, Dingsheng Liu, Lu Yang, Ying Ding, Ya Ouyang, Yunling LiuAbstract:Every day, Space missions involve the download, from space to ground, of many raw images that are stored in the ground station. These Data are stored in geographically distributed and heterogeneous Database systems, which make these resources access and share difficult. Data grid technique can hide the difference of storage systems and makes the isolated Data resources be shared in the grid environment. This paper describes OGSA-DAI module, which is based on the Data grid technique and studies the Remotely Sensed Data access and integration middleware, especially the technology of developing middleware to deal with large binary Remotely Sensed Data.
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ICCSA (3) - Study on grid-based special Remotely Sensed Data processing node
Lecture Notes in Computer Science, 1Co-Authors: Jianqin Wang, Yong Xue, Jianping Guo, Lei Zheng, Ying Luo, Rui-zhi Sun, Guangli Liu, Yunling LiuAbstract:Everyday, space missions involve the download, from space to ground, of huge amount of raw images that are stored in the ground stations geographically distributed. It is a practical pressing task to process these Data resource in real time or almost real time and to effectively share spatial information among remote sensing community. Grid technology can provide access to a global distributed computing environment via authentication, authorization, negotiation and security tools. This paper discusses the key technologies of Grid-based special Remotely Sensed Data processing node. First, the concept of Grid-based special Remotely Sensed Data processing node is introduced. Following is the architecture and functions of this node. Based on this architecture, the tasks scheduling algorithm is presented. Finally we introduce the computing resource meta-module information registry and renewal mechanism of the Grid-based special Remotely Sensed Data processing node for Spatial Information Grid (SIG).
Ying Luo - One of the best experts on this subject based on the ideXlab platform.
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ISPA Workshops - Study on grid-based special Remotely Sensed Data processing node in grid GIS
Frontiers of High Performance Computing and Networking – ISPA 2006 Workshops, 2006Co-Authors: Jianqin Wang, Yong Xue, Jianping Guo, Lei Zheng, Ying Luo, Yi Xie, Yunling LiuAbstract:Grid Geospatial Information Service (Grid GIS) system aims to study and develop grid-based uniform spatial information access and analysis system. Data resources of Grid GIS include not only original and traditional GIS Data but Remotely Sensed Data. Everyday, space missions involve the download, from space to ground, of huge amount of raw images that are stored in the ground stations geographically distributed. It is a practical pressing task to process these Data resource in real time or almost real time and to effectively share spatial information among remote sensing community. Grid technology can provide access to a global distributed computing environment via authentication, authorization, negotiation and security tools. This paper discusses the key technologies of Grid-based special Remotely Sensed Data processing node. First, the concept of Grid-based special Remotely Sensed Data processing node is introduced. Following is the architecture and functions of this node. Based on this architecture, the tasks scheduling algorithm is presented. Finally we introduce the computing resource meta-module information registry and renewal mechanism of the Grid-based special Remotely Sensed Data processing node for Grid GIS.
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APWeb Workshops - A grid-based programming environment for Remotely Sensed Data processing
Lecture Notes in Computer Science, 2006Co-Authors: Yong Xue, Jianqin Wang, Ying LuoAbstract:Grid computing can provide significant computational power which can be applied to Remotely Sensed Data processing. But not all the users in the field of remote sensing have the required knowledge of the grid computing. In this paper, we introduce a Grid-based programming environment (GPE) for Remotely Sensed Data processing. User who doesn’t have the knowledge of how to deal with transactions of the Grid computing environment can program with it. Although GPE isn’t fit for the algorithms in which there exists strong correlation, it works well with the other algorithms and accelerates the computation evidently.
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ICCSA (3) - Study on grid-based special Remotely Sensed Data processing node
Lecture Notes in Computer Science, 1Co-Authors: Jianqin Wang, Yong Xue, Jianping Guo, Lei Zheng, Ying Luo, Rui-zhi Sun, Guangli Liu, Yunling LiuAbstract:Everyday, space missions involve the download, from space to ground, of huge amount of raw images that are stored in the ground stations geographically distributed. It is a practical pressing task to process these Data resource in real time or almost real time and to effectively share spatial information among remote sensing community. Grid technology can provide access to a global distributed computing environment via authentication, authorization, negotiation and security tools. This paper discusses the key technologies of Grid-based special Remotely Sensed Data processing node. First, the concept of Grid-based special Remotely Sensed Data processing node is introduced. Following is the architecture and functions of this node. Based on this architecture, the tasks scheduling algorithm is presented. Finally we introduce the computing resource meta-module information registry and renewal mechanism of the Grid-based special Remotely Sensed Data processing node for Spatial Information Grid (SIG).
Guido Pasquariello - One of the best experts on this subject based on the ideXlab platform.
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Fuzzy logic and neural techniques integration: an application to Remotely Sensed Data
Pattern Recognition Letters, 1996Co-Authors: Palma Blonda, A. Bennardo, G. Satalino, Guido PasquarielloAbstract:Abstract The paper reviews the most recent proposals on the integration of fuzzy and neural networks techniques. First, it focuses on the strategies developed and employed for the fuzzification of neural network architectures. Then it applies an unsupervised fuzzy architecture to the analysis of Remotely Sensed Data and compares the results with those obtained by means of a conventional neural model.
