查看“智能硬件与智能系统”的源代码

=版权申明=

CC BY-NC-SA

=教学团队=

[[互联网+实验室]] 

助教：郑文勋 王晗

=教学背景=

随着计算机应用的日益普及和计算机网络的快速发展，互联网+的社会经济浪潮日益显现，以互联网连接的物理硬件系统和以大数据云计算为基础的信息系统，使得具有智能的机器人、自动驾驶的无人机等等智能系统成为新的技术发展浪潮，并催生了“智能硬件”的新生态。

=课程内容=

== 云＋端 ==

===智能端===

移动设备：Android / iOS

嵌入式设备：Raspberry Pi 2 / Arduino

可穿戴式硬件：手环 / Apple Watch

===嵌入式开发平台===

====NVIDIA JETSON TK1====

[http://www.nvidia.cn/object/jetson-tk1-embedded-dev-kit-cn.html Jetson TK1]

====Raspberry Pi====

[https://www.raspberrypi.org Raspberry Pi]

===云计算与大数据===

====数据中心==== 

Google公司拥有分布全球的十几个数据中心，上百万台机器的集群计算，具体数目是保密数字

# L.A. Barroso, J. Clidaras, and U. Holzle, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines, 2nd ed., 2013.

====云计算平台====

[http://cloud.icenter.tsinghua.edu.cn iCenter-Cloud]

[http://portal.azure.com Azure]

# Fox, Armando, et al. "Above the clouds: A Berkeley view of cloud computing." Dept. Electrical Eng. and Comput. Sciences, University of California, Berkeley, Rep. UCB/EECS 28 (2009): 13.

====大数据平台====

# Jeffrey Dean and Sanjay Ghemawat, MapReduce: Simplified Data Processing on Large Clusters, OSDI 2004.
# Chang F, Dean J, Ghemawat S, et al. Bigtable: A distributed storage system for structured data. OSDI 2006.
# Yang, Fangjin, et al. "Druid: a real-time analytical data store." ACM sigmod, 2014.

====机器学习====

# Jeffrey Dean et al. "Large scale distributed deep networks." Advances in Neural Information Processing Systems. 2012.

==智能系统==

[[大数据智能]]

=项目分组=
[[智能硬件-学生分组]]

=课程研究=

[[智能硬件-课程研究]]

=课程项目=

[[智能硬件-课程项目]]

=致谢=

本课程获得微软Azure云计算与机器学习捐赠支持。

感谢微软公司 杨滔经理，章艳经理，刘士君工程师，闫伟工程师。

=参考文献=

# Stuart Russell and Peter Norvig, Artificial Intelligence: A modern approach. Prentice-Hall, 2005.
# Ferrucci, David A. "Introduction to “this is watson”." IBM Journal of Research and Development 56.3.4 (2012): 1-1.
# Bradski, Gary, and Adrian Kaehler. Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc., 2008.
# Hauswald, Johann, et al. "Sirius: An open end-to-end voice and vision personal assistant and its implications for future warehouse scale computers." ASPLOS, ACM, 2015.
# Spectrogram, https://en.wikipedia.org/wiki/Spectrogram
# TensorFlow, https://www.tensorflow.org