¡@¡@In recent years, the trends in network communication towards mobile network .Traditional network cannot meet the need of new communication challenge. The 802.11 outperforms other new wireless network technique ¡@¡@In this paper, we assume user have wireless NIC devices with Mobile IPv6 protocol module. We use this model to build an environment and use this environment to design our Mobile IPv6 multicast simulation. We do these for two reasons, (1)To analyze how to use multicast over Mobile IPv6 wireless network. (2)To design an experimentation for multicast over wireless LAN. After we get results of the experimentation, we could know why wireless network does not have better quality than wired LAN. ¡@¡@During our implementation, we have a high latency problem and try to solve it by our program. After our improvement, average latency reduces to 27.77% and miss rate reduces to 33.07%. ¡@¡@Our implementation not only provide some useful information when some one need to build Mobile IPv6 environment but also give a solution for solving handover latency problem.

The goal of neuromorphic engineering is to create electronic systems that model the behavior of biological neural systems. Neuromorphic systems can leverage a combination of analog and digital circuit design techniques to enable computational modeling, with orders of magnitude of reduction in size, weight, and power consumption compared to the traditional modeling approach based upon numerical integration. These benefits of neuromorphic modeling have the potential to facilitate neural modeling in resource-constrained research environments. Moreover, they will make it practical to use neural computation in the design of intelligent machines, including portable, battery-powered, and energy harvesting applications. Floating-gate transistor technology is a powerful tool for neuromorphic engineering because it allows dense implementation of synapses with nonvolatile storage of synaptic weights, cancellation of process mismatch, and reconfigurable system design. A novel neuromorphic hardware system, featuring compact and efficient channel-based model neurons and floating-gate transistor synapses, was developed. This system was used to model a variety of network topologies with up to 100 neurons. The networks were shown to possess computational capabilities such as spatio-temporal pattern generation and recognition, winner-take-all competition, bistable activity implementing a "volatile memory", and wavefront-based robotic path planning. Some canonical features of synaptic plasticity, such as potentiation of high frequency inputs and potentiation of correlated inputs in the presence of uncorrelated noise, were demonstrated. Preliminary results regarding formation of receptive fields were obtained. Several advances in enabling technologies, including methods for floating-gate transistor array programming, and the creation of a reconfigurable system for studying adaptation in floating-gate transistor circuits, were made.