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 Layer (PHY) specifications--Amendment 6: Medium Access Control (MAC) Security Enhancements
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 Operation in Japan
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
Languages and foundations: separating concerns, filter technologies, improving modularity, integrating new features, and more
Using key AOSD tools, including AspectJ, Hyper/J, JMangler, and Java Aspect Components
Engineering aspect-oriented systems: UML, concern modeling and elaboration, dependency management, and aspect composition
Developing more secure applications with AOSD techniques
Applying aspect-oriented programming to database systems
Building dynamic aspect-oriented infrastructure
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 (active) in the band 5 470-5 570 MHz
within the 5 460 5 725 MHz range
* 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 libraries. It features multilayer
* feedforward perceptron neural networks, sigmoidal activation function
* with bias, backpropagation training with settable learning rate and
* momentum, and backpropagation training in batches.
％ 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,
％ [W1,W2,critvec,iteration,lambda]=marq(NetDef,W1,W2,PHI,Y,trparms)
％ trains the network with the Levenberg-Marquardt method.
％
％ The activation functions can be either linear or tanh. The
％ network architecture is defined by the matrix NetDef which
％ has two rows. The first row specifies the hidden layer and the
％ second row specifies the output layer.
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 , which has of two
％ rows. The first row specifies the hidden layer while the second
％ specifies the output layer.
