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Titlebook: Location and Context Awareness; 4th International Sy Tanzeem Choudhury,Aaron Quigley,Koji Suginuma Conference proceedings 2009 Springer-Ver

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书目名称Location and Context Awareness
副标题4th International Sy
编辑Tanzeem Choudhury,Aaron Quigley,Koji Suginuma
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Location and Context Awareness; 4th International Sy Tanzeem Choudhury,Aaron Quigley,Koji Suginuma Conference proceedings 2009 Springer-Ver
描述These proceedings contain the papers presented at the 4th International S- posium on Location and Context Awareness (LoCA) during May 7–8, 2009 in Tokyo,Japan.Locationandcontextawarenessarefundamentstonext-generation mobile and pervasive computing systems. Pervasive computing is a model of computing in which computation is everywhere and computer functions are - tegrated into everything. The ultimate aim is to make information, applications and services available anywhere and at anytime in the human environment in a ?uid manner appropriate to our current context. Once away from the desktop, we ?nd ourselves in a wide variety of contexts and hence situations. For computing to be relevant and useful in these emerging situations we must rely on a range of contextual cues. Context includes phys- logical, environmental, and computational data, whether sensed or inferred. In addition, context includes details of a user’s activities, goals, abilities, pref- ences, a?ordances, and surroundings. With location and context awareness we can expect computers to deliver information, services, and entertainment in a way that maximizes convenience and minimizes intrusion.
出版日期Conference proceedings 2009
关键词GPS; Text; activity recognition; ambient controlling; communication; context aware computing; information;
版次1
doihttps://doi.org/10.1007/978-3-642-01721-6
isbn_softcover978-3-642-01720-9
isbn_ebook978-3-642-01721-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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Error Estimation for Indoor 802.11 Location Fingerprinting,ion error estimation, we present four novel algorithms that take different features into account. Advantages of the combination of these four algorithms are explored by using a machine-learning approach. We evaluate our proposed algorithms in two different real-world deployments by using real-world
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