介绍贝叶斯网络的概念和相关算法以及概率图模型，有例子介绍怎样使用概率图模型来做决策的。

A wireless communication network can be viewed as a collection of nodes, located in some domain, which can in turn be transmitters or receivers (depending on the network considered, nodes may be mobile users, base stations in a cellular network, access points of a WiFi mesh etc.). At a given time, several nodes transmit simultaneously, each toward its own receiver. Each transmitter–receiver pair requires its own wireless link. The signal received from the link transmitter may be jammed by the signals received from the other transmitters. Even in the simplest model where the signal power radiated from a point decays in an isotropic way with Euclidean distance, the geometry of the locations of the nodes plays a key role since it determines the signal to interference and noise ratio (SINR) at each receiver and hence the possibility of establishing simultaneously this collection of links at a given bit rate. The interference seen by a receiver is the sum of the signal powers received from all transmitters, except its own transmitter.

A wireless communication network can be viewed as a collection of nodes, located in some domain, which can in turn be transmitters or receivers (depending on the network considered, nodes may be mobile users, base stations in a cellular network, access points of a WiFi mesh etc.). At a given time, several nodes transmit simultaneously, each toward its own receiver. Each transmitter–receiver pair requires its own wireless link. The signal received from the link transmitter may be jammed by the signals received from the other transmitters. Even in the simplest model where the signal power radiated from a point decays in an isotropic way with Euclidean distance, the geometry of the locations of the nodes plays a key role since it determines the signal to interference and noise ratio (SINR) at each receiver and hence the possibility of establishing simultaneously this collection of links at a given bit rate. The interference seen by a receiver is the sum of the signal powers received from all transmitters, except its own transmitter.

Neuronal Dynamics From Single Neurons to Networks and Models of Cognition.pdf Neuronal Dynamics From Single Neurons to Networks and Models of Cognition.pdf

云数据中心网络设计原则。介绍云网特征，云网演进，fabric技术，组网技术，已有的数据中心组网标准，服务器虚拟化与云网的结合，网络虚拟化技术，存储网络优化，以及高性能技术组网，未来云网发展预测。

The physical layer -- The data link layer -- The medium access control sublayer -- The network layer -- The transportation layer -- The application layer -- Network security.

vapnik的SVM论文 支持向量网络是一种针对两类问题的新学习机器.它的实现基于以下思想:将输入向量非线性地映射到一个很高维的特征空间.并在该特征空间中构造一个线性决策平面.该决策平面的特殊性质保证了学习机器具有很好的推广能力.支持向量网络的思想已在完全可分的训练数据集上得以实现,这里我们将它扩展到不完全可分的训练数据集

different phase in the way computer networks were used. When the first edition appeared in 1980, networks were an academic curiosity. When the second edition appeared in 1988, networks were used by universities and large businesses. When the third edition appeared in 1996, computer networks, especially the Internet, had become a daily reality for millions of people. By the fourth edition, in 2003, wireless networks and mobile computers had become commonplace for accessing the Web and the Internet. Now, in the fifth edition, networks are about content distribution (especially videos using CDNs and peer-to-peer networks) and mobile phones are small computers on the Internet. New in the Fifth Edition Among the many changes in this book, the most important one is the addition of Prof. David J. Wetherall as a co-author. David brings a rich background in networking, having cut his teeth designing metropolitan-area networks more than 20 years ago. He has worked with the Internet and wireless networks ever since and is a professor at the University of Washington, where he has been teaching and doing research on computer networks and related topics for the past decade. Of course, the book also has many changes to keep up with the: ever-changing world of computer networks. Among these are revised and new material on Wireless networks (802.12 and 802.16) The 3G networks used by smart phones RFID and sensor networks Content distribution using CDNs Peer-to-peer networks Real-time media (from stored, streaming, and live sources) Internet telephony (voice over IP) Delay-tolerant networks

使用神经网络进行预测，有BF，FF，GRNN，RBF网络等， 使用神经网络进行预测 (MATLAB版)Neural Networks predict

关于论文“Methods for interpreting and understanding deep neural networks”的学习摘要