In this paper, an entropy based associative memory model will be proposed and applied to memory retrievals with an orthogonal learning model to compare with the conventional model based on the quadratic Lyapunov functional to be minimized. In the present approach, the updating dynamics will be constructed on the basis of the entropy minimization strategy which may be reduced asymptotically to the above-mentioned autocorrelation dynamics as a special case. From numerical results, it will be found that the presently proposed novel approach realizes twice of the memory capacity in comparison with the autocorrelation based dynamics such as associatron.

In this work we propose a recursive electric circuit which has a fractal structure composed of resistances and capacitors. It is first confirmed in practice that such a device, which is referred as fractance, possesses a novel characteristics between the constituent elements, or the resistance and the capacitance. That is, the absolute value of the impedance shows such a characteristics as |Z|ω-1/2, whereas the phase angle does such a constant phase property as θ=-π/4 independent of the frequency. As an application of the presently proposed fractance device, we constructed a fractional integral circuit as well as a fractional differential circuit, and confirmed that the observed output wave forms agree well with those derived from a computer simulation.

An optical-layer adaptive restoration scheme is validated by a real-time experiment and numerical analyses. In this paper, it is assumed that this scheme can adaptively optimize the bitrate (up to 600Gb/s) and an optical reach with 100Gb/s granularity to maintain high-capacity optical signal transmission. The practicality of 600-Gb/s/carrier optical signal transmission over 101.6-km field-installed fiber is confirmed prior to the adaptive restoration experiment. After modifying the field setup, a real-time experiment on network recovery is demonstrated with bitrate adaptation for 600-Gb/s to 400-Gb/s signals. The results indicate that this scheme can restore failed connections with recovery times comparable to those of conventional restoration scheme; thus 99.9999% system availability can be easily attained even under double-link failures. Numerical analysis clarifies that adaptive restoration can recover >80% of double-link failures on several realistic topologies and improvement amount against conventional scheme is semi-statistically characterized by restoration path length.

Flexible resource utilization in terms of adaptive use of optical bandwidth with agile reconfigurability is key for future metro networks. To address this issue, we focus on optical subwavelength switched network architectures that leverage high-speed optical switching technologies and can accommodate dynamic traffic cost-effectively. Although optical subwavelength switched networks have been attracting attention, most conventional studies apply static (pre-planned) protection scenarios in the networks of limited sizes. In this paper, we discuss optical switch requirements, the use of transceivers, and protection schemes to cost-effectively create large-scale reliable metro networks. We also propose a cost-effective adaptive protection scheme appropriate for optical subwavelength switched networks using our fast time-slot allocation algorithm. The proposed scheme periodically re-optimizes the bandwidth of both working and protection paths to prevent bandwidth resources from being wasted. The numerical examples verify the feasibility of our proposed scheme and the impact on network resources.

In this work is first presented that a PDLC film with a ferroelectric alignment layer realizes a bistable switching in similar to the surface-stabilised ferroelectric liquid crystal display devices. Such a bistability is found to critically depend on the squareness parameter of the ferroelectric layer as well as such material properties of the dispersed nematics as the elastic and the dielectric constants. It is also found that there exists an appropriate elastic constant to improve the optical transmittance. The dependence of the distribution of the radii of the nematic droplets on the electro-optic bistability is also investigated in detail by means of the numerical computations assuming a fractal distribution. The fundamental electro-optic properties of the presently, proposed PDLC cells imply the advantage beyond the conventional PDLC without any ferroelectric alignment layer.

In this study we shall put forward a bidirectional synergetic neural network and investigate the crossassociation dynamics in an order parameter space. The present model is substantially based on a top-down formulation of the dynamic rule of an analog neural network in the analogy with the conventional bidirectional associative memory. It is proved that a complete association can be assured up to the same number of the embedded patterns as the number of neurons. In addition, a searching process of a couple of embedded patterns can be also realised by means of controlling attraction parameters as seen in the autoassociative synergetic models.

In this study, we propose a method of classifying a spontaneous electroencephalogram (EEG) approach to a brain-computer interface. Ten subjects, aged 21-32 years, volunteered to imagine left- and right-hand movements. An independent component analysis based on a fixed-point algorithm is used to eliminate the activities found in the EEG signals. We use a fractal dimension value to reveal the embedded potential responses in the human brain. The different fractal dimension values between the relaxing and imaging periods are computed. Featured data is classified by a three-layer feed-forward neural network based on a simple backpropagation algorithm. Two conventional methods, namely, the use of the autoregressive (AR) model and the band power estimation (BPE) as features, and the linear discriminant analysis (LDA) as a classifier, are selected for comparison in this study. Experimental results show that the proposed method is more effective than the conventional methods.

In this paper a generalized recursive block coding (GRBC) scheme is put forward with a novel non-causal predictor free from the separable assumption on the original random field and then applied to an image data compression so as to compare with the conventional recursive block coding (RBC). In the presently proposed predictor to derive the residual random fields, the constraint on the separability of the original image is completely removed in general in contrast with the conventional 2-dimensional RBC based on non-causal predictive method which eventually leads to the one-dimensional RBC strategy. In addition the resultant characteristic KL functions for the residual errors in GRBC are confirmed to be substantially reduced to the same orthogonal discrete sine functions (DSFs) as RBC, whereas the corresponding eigen values are elucidated to be not expressed in the direct product form but in a somewhat generalized form. Also a novel bit allocation method for the transformed coefficients of the residuals is argued in connection with the eigen value problem for the residual random fields. Finally, introducing an adaptive zonal coding method, the presently proposed scheme is applied to the block codings to clarify a certain advantage beyond the conventional recursive block transform coding.

The traffic of the future aggregation network will dynamically change not only in volume but also destination to support the application of virtualization technology to network edge equipment to achieve cost-effectiveness. Therefore, future aggregation network will have to accommodate this traffic cost-effectively, despite dynamic changes in both volume and destination. To correspond to this trend, in this paper, we propose an optical layer 2 switch network based on bufferless optical time division multiplexing (TDM) and dynamic bandwidth allocation to achieve a future aggregation network cost-effectively. We show here that our proposed network architecture effectively reduced the number of wavelengths and optical interfaces by application of bufferless optical TDM technology and dynamic bandwidth allocation to the aggregation network.

Multi-band transmission technologies promise to cost-effectively expand the capacity of optical networks by exploiting low-loss spectrum windows beyond the conventional band used in already-deployed fibers. While such technologies offer a high potential for capacity upgrades, available capacity is seriously restricted not only by the wavelength-continuity constraint but also by the signal-to-noise ratio (SNR) constraint. In fact, exploiting more bands can cause higher SNR imbalance over multiple bands, which is mainly due to stimulated Raman scattering. To relax these constraints, we propose wavelength-selective band switching-enabled networks (BSNs), where each wavelength channel can be freely switched to any band and in any direction at any optical node on the route. We also present two typical optical node configurations utilizing all-optical wavelength converters, which can realize the switching proposal. Moreover, numerical analyses clarify that our BSN can reduce the fiber resource requirements by more than 20% compared to a conventional multi-band network under realistic conditions. We also discuss the impact of physical-layer performance of band switching operations on available benefits to investigate the feasibility of BSNs. In addition, we report on a proof-of-concept demonstration of a BSN with a prototype node, where C+L-band wavelength-division-multiplexed 112-Gb/s dual-polarization quadrature phase-shift keying signals are successfully transmitted while the bands of individual channels are switched node-by-node for up to 4 cascaded nodes.

In this paper we shall put forward a novel circularly connected synergetic neural network extending the previously studied auto-correlation or cross-correlation dynamics so as to realise a group memory retrieval. The present model is substantially based on a top-down approach of the dynamic rule of an analog neural network in the similar manner to the conventional synergetic dynamics early proposed by Haken. It will be proved that a complete association can be assured up to the same number of the embedded patterns as the minimal number of neurons of the linked synergetic neural networks. In addition, one finds that a searching process of a couple of embedded patterns can be also realised by means of controlling attraction parameters as was previously reported in the autoassociative synergetic models.

A virtual network edge using live migration of virtualized network functions (VNFs) can be expected to reduce computation time and save resources instead of conventional network edge routers that have complex functions. Wavelength-division-multiplexing/time-division-multiplexing (WDM/TDM) photonic switching technology for metro ring networks is proposed to provide fast bandwidth resource allocation for rapidly changing service-flow demand. However, there are no reports on the coexistence of high-speed path switching for live migration with fast bandwidth resource allocation, as far as we know. We propose an architecture that achieves both high-speed path switching and fast dynamic bandwidth allocation control for service flows with in-service live migration. The feature of this architecture is that the VNF for the virtual edge corresponds to each 10-gigabit Ethernet-passive optical network (10G-EPON) and fast route change can be achieved with a simple point-to-point path between VNFs and optical line terminals (OLTs). The second feature is that the live migration of a VNF is limited to a part of it that contains a larger number of subscribers. Owing to the reduction in the number of total paths, fast resource allocation can be provided.

The traffic of the future metro network will dynamically change not only in volume but also in destination to support the application of virtualization technology to network edge equipment such as cloud edges to achieve cost-effectiveness. Therefore, the future metro network will have to accommodate traffic cost-effectively, even though both the traffic volume and the traffic destination will change dynamically. To handle to this trend, in this paper, we propose a future metro network architecture based on Next-Generation Passive Optical Network Stage 2 systems that offers cost-effectiveness while supporting virtual machine migration of cloud edges. The basic idea of the proposed method is sharing a burst-mode receiver between the continuous-mode transmitters and burst-mode transmitters. In this paper, we show the feasibility and effectiveness of the proposed method with experiments on prototype systems, and simulations for the preliminary evaluation of network capital expenditure.

Improvement of conventional networks with an incremental approach is an important design method for the development of the future internet. For this approach, we are developing a future aggregation network based on passive optical network (PON) technology to achieve both cost-effectiveness and high reliability. In this paper, we propose a timeslot (TS) synchronization method for sharing a TS from an optical burst mode transceiver between any route of arbitrary fiber length by changing both the route of the TS transmission and the TS control timing on the optical burst mode transceiver. We show the effectiveness of the proposed method for exchanging TSs in bidirectional bufferless wavelength division multiplexing (WDM) and time division multiplexing (TDM) multi-ring networks under the condition of the occurrence of a link failure through prototype systems. Also, we evaluate the reduction of the required number of optical interfaces in a multi-ring network by applying the proposed method.

Adaptive and flexible network control technology is considered essential for efficient network resource utilization. Moreover, such technology is becoming a key to cost-effectively meet diverse service requirements and accommodate heavier traffic with limited network resources; demands that conventional static operation cannot satisfy. To address this issue, we previously studied dynamic network control technology for large-capacity network services including on-demand broad bandwidth provisioning services and layer-one VPN. Our previous study introduced a simple weighting function for achieving fairness in terms of path length and proposed two dynamic Make Before Break Routing algorithms for reducing blocking probability. These algorithms enhance network utilization by rerouting existing paths to alternative routes while completely avoiding disruption for highly reliable services. However, the impact of this avoidance of service disruption on blocking probability has not been clarified. In this paper, we propose modified versions of the algorithms that enhance network utilization while slightly increasing disruption by rerouting, which enable us to elucidate the effectiveness of hitless rerouting. We also provide extensive evaluations including a comparison of original and modified algorithms. Numerical examples demonstrate that they achieve not only a high degree of fairness but also low service blocking probability. Hitless rerouting is achieved with a small increase in blocking probability.

In this study we shall put forward a synergetic neural network and investigate the association dynamics. The present neuron model is substantially based on a top down formulation of the dynamic rule of an analog neural network in contrast to the conventional framework. It is proved that a complete association can be assured up to the same number of the embedded patterns as the number of neurons. In practice an association process is carried out for practical images with 256 gray scale levels and 256256 size. In addition, a searching process of the embedded patterns is also realised by means of controlling attraction parameters. Finally a stochastic model for the dynamic process is also proposed as an intermediate model between the association and the searching of the embedded patterns. Finally a stochastic property of the present model is characterized by fractal dimension of the excitation level of a neuron.

We are developing an optical layer-2 switch network that uses both wavelength-division multiplexing and time-division multiplexing technologies for efficient traffic aggregation in metro networks. For efficient traffic aggregation, path bandwidth control is key because it strongly affects bandwidth utilization efficiency. We propose a fast time-slot allocation method that uses hierarchical calculation, which divides the network-wide bandwidth-allocation problem into small-scale local bandwidth-allocation problems and solves them independently. This method has a much shorter computation complexity and enables dynamic path bandwidth control in large-scale networks. Our network will be able to efficiently accommodate dynamic traffic with limited resources by using the proposed method, leading to cost-effective metro networks.