ecosystems的留言板

ecosystems 2020-01-02 06:48:49

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Changes Terrestrial Ecosystems

Our lifestyle is changing dramatically with the ubiquity of mobile devices and network connectivity. By seamlessly collecting advanced data related to human activity, and providing responsive actions and services to users, developers can maximize the functionality of mobile devices, thereby improving livability, convenience and safety, and ultimately enabling a smart life. The spatial and contextual data, i.e., users’ locations as well as their interaction with the cyber and physical world, has been a decisive driver for the ongoing trend towards smart and connected applications to date. Context-aware location sensing is the cornerstone of this vision. Over the past few years, a broad variety of services have been targeted to revolution- ize how people sense and interact with everyday objects and locations to- wards a smart life. For example, sensor networks provide realtime activity data for heating/air conditioning systems and fire and smoke detectors, GPS and WLAN systems provide way-finding and coarse-grained location services, RFID and short-range communication devices provide proximity detection and awareness. However, these separate and usually proprietary systems are far from satisfactory. The major metrics of these spatial enabling technolo- gies, most notably accuracy, interoperability, and deployability performance, are far from satisfactory. Significant gaps exist in our understanding of how a scalable location sensing system design could meet a multitude of smart application demands. Moreover, no extensible and developer-friendly system frameworks are available for location and smart applications. Developers do not have any testbed or prototype system available for them to play with. Senior developers are reluctant to extend and debug their existing prototype infrastructures since the errors and software deficiencies are hard to identify in the distributed manner. Large-scale deployment is rarely available and hard to share with junior developers for partial or temporal development. xxiii xxiv Towards Mobile SmartLife via Sensing, Localization, & Cloud Ecosystems Our overarching goal is to develop an intellectual framework and a location infrastructure testbed to promote and guide the developers to realize the full- fledged smart applications for a smart lifestyle, to address societal challenges in local communities. We aim to offer a comprehensive book on a complete mobile system design for SmartLife applications. This book is structured to be a complete and updated guide for building the ecosystem step by step from the hardware to mobile apps, to the cloud processing and service back- end. Beginners can start from the initial introductory and tutorial chapters while advanced readers can learn directly from the algorithms and prototype design. Practitioners can find inspiration for utilizing the proposed localization techniques in a variety of mobile applications including shopping map, indoor navigation, visitor guide, augmented reality, and location-based social-aided sensing/sourcing. We have tested and verified the information in this book to the best of our ability, but you may find that features have changed (which may in fact resemble bugs). Please let us know about any errors you find, as well as your suggestions for future editions, by writing to the following address. Please get in touch (kaikai.liu@sjsu.edu) if you know of services that are missing, or have other questions or suggestions. I hope this book will give you a good head start and that you have fun in the process.

Introduction to the Modelling of Marine Ecosystems

2012年发表在Science的一篇论文，提出了收敛交叉映射算法，针对复杂生态系统进行因果推断,针对格兰杰检验进行的改进。用一句话来概述收敛交叉映射算法，变量Y的历史数据能够由变量X可靠的推出的程度越高，那么X到Y的因果关系就越强。

3D-ecosystems.zip,用3.js制作的小型低多边形3D世界,3D建模使用专门的软件来创建物理对象的数字模型。它是3D计算机图形的一个方面，用于视频游戏，3D打印和VR，以及其他应用程序。

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