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IEEE 802.11i-2004 Amendment to IEEE Std 802.11, 1999 Edition (Reaff 2003). IEEE Standard for Information technology--Telecommunications and information exchange between system--Local and metropolitan area networks?Specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical ...

IEEE 802.11j-2004 IEEE Standard for Information technology—Telecommunications and information exchange between systems--Local and metropolitan area networks—Specific requirements—Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications—Amendment 7: 4.9 GHz–5 GHz ...

Aspect-Oriented Software Developement Coverage includes Using AOSD to streamline complex systems development without sacrificing flexibility or scalability How AOSD builds on the object-oriented paradigmand how it s different State-of-the-art best practices for the AOSD development process Lang ...

Apriori源代码，包含c++/java实现；<br>神经网络算法源程序，如SOM、HOPFIELD、CPN、BPN、BOLTZMAN、ART、ADALINE，同时提供针对不同算法的演示源程序；遗传C源代码；外加充实的数据挖掘的算法讲解ppt

RECOMMENDATION ITU-R M.1653*,** Operational and deployment requirements for wireless access systems including radio local area networks in the mobile service to facilitate sharing between these systems and systems in the Earth exploration-satellite service (active) and the space research service (ac ...

另一本介绍贝叶斯网络的经典教材，可以与Learning Bayesian Networks配合使用，相得益彰。

* Lightweight backpropagation neural network. * This a lightweight library implementating a neural network for use * in C and C++ programs. It is intended for use in applications that * just happen to need a simply neural network and do not want to use * needlessly complex neural network librar ...

％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is possible to use regularization by ％ weight decay. Also pruned (ie. not fully connected) networks can ％ be trained. ％ ％ Given a set of corresponding input-output pairs and an initial ％ network, ％ ...

This function applies the Optimal Brain Surgeon (OBS) strategy for ％ pruning neural network models of dynamic systems. That is networks ％ trained by NNARX, NNOE, NNARMAX1, NNARMAX2, or their recursive ％ counterparts.

Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully ％ connected) networks can be trained. ％ ％ The activation functions can either be linear or tanh. The network ％ architecture is defined by the matrix NetDef , w ...