This paper introduces the design of an Optical Switching Module (OSM) for our newly proposed Gigabit Ethernet Optical Switched Access Network (GE-OSAN) architecture that uses the Multi-Point Control Protocol (MPCP), defined in IEEE 802.3ah. We outline the GE-OSAN architecture to clarify OSM's role in the network. We offer an OSM configuration that has the basic functions needed to realize downstream and upstream high-speed data services in GE-OSAN. We clarify the OSM optical switching time that allows GE-OSAN to achieve the same throughput as GE-PON. Our survey of currently available optical switches identifies the optical packet switches that can meet this switching time requirement. We evaluate OSM insertion loss with these switches. We propose an OSM configuration that has a regeneration function as well as the basic ones to realize wider network configurations that can lead to a reduction in overall system costs. In addition, we present OSM configurations that have broadcast and multicast functions as well as the basic ones so that GE-OSAN can support broadcast and multicast video services to equal and exceed GE-PON.

This paper proposes a new optical access network architecture that differs from those of conventional Point-to-Point (PP) and Passive Optical Networks (PON). The proposed architecture, Optical Switched Access Network (OSAN), uses Optical Switching Modules (OSMs) that connect an Optical Line Terminal (OLT) to Optical Network Units (ONUs) in a virtual point to point configuration so that it offers the merits of both PP and PON while overcoming their demerits. Each OSM optically switches packets of variable length one by one under electrical control. To allow the elimination of optical buffers from OSM, OSAN uses the Multi-Point Control Protocol (MPCP) defined in IEEE 802.3ah. We evaluate the transmission distances between OLT and ONUs, and consider a network synchronization scheme and discovery mechanism that supports MPCP.

This peper proposes a simplified model of the well-known two-neuron neural oscillator. By eliminating one of the two positive feedback synapses in the neural oscillator, learning for the in-phase control of the oscillator is shown to be achievable via a very simple learning rule. The learning rule is devised in such a way that only the plasticity of two synaptic weights are required. We demonstrate some examples of the synchronization learning to validate the efficiency of the learning rule, and finally by illustrating the dynamics of the synchronization learning and by using computer simulation, we show the convergence behavior and the stability of the learning rule for the two-neuron simple neural oscillator.