This paper gives an overall introduction to the basic concept of LAC(location-aware computing) and its development status, puts forward an integrated location-aware computing architecture which is useful for designing...This paper gives an overall introduction to the basic concept of LAC(location-aware computing) and its development status, puts forward an integrated location-aware computing architecture which is useful for designing the reasonable logical model of LBS(location-based service). Finally, a brief introduction is conducted on a LAC experimental prototype, which acts as a mobile urban tourism assistant.展开更多
This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigoro...This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigorous mathematical footing may not be efficient in extracting essential physical information from climate data;rather,adaptive and local analysis methods that agree well with fundamental physical principles are more capable of capturing key information of climate data. To illustrate the improved power of adaptive and local analysis of climate data,we also introduce briefly the empirical mode decomposition and its later developments.展开更多
文摘This paper gives an overall introduction to the basic concept of LAC(location-aware computing) and its development status, puts forward an integrated location-aware computing architecture which is useful for designing the reasonable logical model of LBS(location-based service). Finally, a brief introduction is conducted on a LAC experimental prototype, which acts as a mobile urban tourism assistant.
基金US National Science Foundation Grant(No.AGS-1139479)
文摘This paper focuses on how to extract physically meaningful information from climate data,with emphases placed on adaptive and local analysis. It is argued that many traditional statistical analysis methods with rigorous mathematical footing may not be efficient in extracting essential physical information from climate data;rather,adaptive and local analysis methods that agree well with fundamental physical principles are more capable of capturing key information of climate data. To illustrate the improved power of adaptive and local analysis of climate data,we also introduce briefly the empirical mode decomposition and its later developments.
