In the future network, optical technology will play a stronger role not only for transmission but also for switching. Optical burst switching (OBS) emerged as a promising switching paradigm. It brings together the complementary strengths of optics and electronics. This paper presents the design and implementation of an overlay mode burst-switched photonic network testbed, including its architecture, protocols, algorithms and experiments. We propose a flexible "transceiver + forwarding" OBS node architecture to perform both electronic burst assembly/disassembly and optical burst forwarding. It has been designed to provide class of service (CoS), wavelength selection for local bursts, and transparency to cut-through bursts. The functional modules of OBS control plane and its key design issues are presented, including signaling, routing, and a novel scheduling mechanism with combined contention resolution in space and wavelength domains. Finally, we report the experimental results on functional verification, performance analysis and service demonstration.

We have proposed in [5] a practical blind channel identification algorithm for the white observation noise. In this paper, we examine the effectiveness of the algorithm given in [5] for the colored observation noise. The proposed algorithm utilizes Gram-Schmidt orthogonalization procedure and estimates (1) the channel order, (2) the noise variance and then (3) the channel impulse response with less computational complexity compared to the conventional algorithms using eigenvalue decomposition. It can be shown through numerical examples that the algorithm proposed in [5] is quite effective in the colored noise case.

The actual sound environment system exhibits various types of linear and non-linear characteristics, and it often contains an unknown structure. Furthermore, the observations in the sound environment are often in the level-quantized form. In this paper, a method for estimating the specific signal for stochastic systems with unknown structure and the quantized observation is proposed by introducing a system model of the conditional probability type. The effectiveness of the proposed theoretical method is confirmed by applying it to the actual problem of psychological evaluation for the sound environment.

This paper describes the contours of a Dutch monitoring and research site for climate change and related atmospheric processes. The station has large benefits for atmospheric science, both in The Netherlands and internationally. It provides a platform for collaboration in this important field, and will provide the routine observations needed to assess the impact of the different atmospheric parameters on the local climate. The station fits in directly in the selected group of global monitoring networks that are currently operational or being set up to address the problems of climate. In addition, the station can play a major role in supporting worldwide satellite measurements of climate related parameters. The only way to get a global picture of the essential climate change parameters can be found in the combination of satellite measurements and ground-based stations equipped with advanced remote sensing and in situ instrumentation. Furthermore, the combined expertise of European universities and research institutes, encompassing the whole field of atmospheric research, offers a unique chance for the training of young scientists. The research site is an attractive center for international young scientists to develop and deepen their skills.

In this paper, we propose a new reinforcement learning scheme called CHQ that could efficiently acquire appropriate policies under partially observable Markov decision processes (POMDP) involving probabilistic state transitions, that frequently occurs in multi-agent systems in which each agent independently takes a probabilistic action based on a partial observation of the underlying environment. A key idea of CHQ is to extend the HQ-learning proposed by Wiering et al. in such a way that it could learn the activation order of the MDP subtasks as well as an appropriate policy under each MDP subtask. The goodness of the proposed scheme is experimentally evaluated. The result of experiments implies that it can acquire a deterministic policy with a sufficiently high success rate, even if the given task is POMDP with probabilistic state transitions.

Optical Burst Switching (OBS) aims to provide higher utilization and greater flexibility at a lower cost and reduced complexity than current optical circuit switched networks. We introduce a new routing protocol for Optical Burst Switching, Shortest Path Prioritized Random Deflection Routing (SP-PRDR), that aims to lower burst loss probabilities while only using limited state information from traditional Internet Protocol technologies. We show, through analysis and simulation, that loss in OBS networks is significantly reduced by SP-PRDR for loads that previously gave moderate or low losses in the unmodified case. In the simulation examples studied, by using SP-PRDR we are able to increase the input load by approximately 15-20% while maintaining a constant burst loss probability of 10-3. Additionally, unlike other schemes, we show that the worst case burst loss probability of SP-PRDR is provably upper-bounded by the burst loss probability of standard OBS.

Optical Burst Switching (OBS) has been developed as an efficient switching technique to exploit the capacity provided by Wavelength Division Multiplexing (WDM) transmission technology for the next generation optical Internet. One critical design issue in OBS is how to provide Quality-of-Service (QoS) on optical networks. In order to provide the service differentiation, we propose in this paper a buffer allocation algorithm to schedule bursts at the edge OBS nodes, a bandwidth allocation algorithm and a Fiber Delay Line (FDL) allocation algorithm to schedule bursts at the core OBS nodes. We also introduce a new burst assembly technique in which the burst is generated either when the sum of the collected packet sizes reaches the maximum threshold or when the burst assembling time reaches the timeout limit. Our simulation results show that the proposed algorithms achieve the controllable burst loss probability for different service classes. The bandwidth allocation algorithm performs very well at the core OBS nodes in terms of the low loss probability.

This paper discusses the least-squares linear filtering and fixed-lag smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of not necessarily independent Bernoulli variables. It is assumed that the observations are perturbed by white noise and the autocovariance function of the signal is factorizable. Using an innovation approach we obtain the filtering and fixed-lag smoothing recursive algorithms, which do not require the knowledge of the state-space model generating the signal. Besides the observed values, they use only the matrix functions defining the factorizable autocovariance function of the signal, the noise autocovariance function, the marginal probabilities and the (2,2)-element of the conditional probability matrices of the Bernoulli variables. The algorithms are applied to estimate a scalar signal which may be transmitted through one of two channels.

The problem of signal restoration in the presence of observation space noise has been tackled extensively. However, restoration of degraded signals in the presence of signal space noise leads to considerable complexity because it becomes difficult to distinguish between the original signal and the noise. In this paper, a partial projection filter has been devised for the restoration of signals degraded by both the signal space and the observation space noises. A closed form of the proposed filter has been derived and its performance has been verified experimentally.

This paper treats the least-squares linear filtering and smoothing problems of discrete-time signals from uncertain observations when the random interruptions in the observation process are modelled by a sequence of independent Bernoulli random variables. Using an innovation approach we obtain the filtering algorithm and a general expression for the smoother which leads to fixed-point, fixed-interval and fixed-lag smoothing recursive algorithms. The proposed algorithms do not require the knowledge of the state-space model generating the signal, but only the covariance information of the signal and the observation noise, as well as the probability that the signal exists in the observed values.

The partial projection filter gives optimal signal restoration in the presence of both the signal space and the observation space noises. In this paper, the filter has been characterized from the point of view of its signal restoration and noise suppression capabilities. The filter is shown to suppress the noise component in the restored signal while retaining the signal component, thus maximizing the signal-to-noise ratio. Further, a digital implementation of the filter is presented in matrix form in contrast to its original operator based derivation, for practical applications.

In this paper we consider an approximation method of a formal linearization which transform time-varying nonlinear systems into time-varying linear ones and its applications. This linearization is a kind of a coordinate transformation by introducing a linearizing function which consists of the Chebyshev polynomials. The nonlinear time-varying systems are approximately transformed into linear time-varying systems with respect to this linearizing functions using Chebyshev expansion to the state variable and Laguerre expansion to the time variable. As applications, nonlinear observer and filter are synthesized for time-varying nonlinear systems. Numerical experiments are included to demonstrate the validity of the linearization. The results show that the accuracy of the approximation by the linearization improves as the order of the Chebyshev and Laguerre polynomials increases.

We attempt to achieve corporative behavior of autonomous decentralized agents constructed via Q-Learning, which is a type of reinforcement learning. As such, in the present paper, we examine the piano mover's problem including a find-path problem. We propose a multi-agent architecture that has an external agent and internal agents. Internal agents are homogenous and can communicate with each other. The movement of the external agent depends on the composition of the actions of the internal agents. By learning how to move through the internal agents, avoidance of obstacles by the object is expected. We simulate the proposed method in a two-dimensional continuous world. Results obtained in the present investigation reveal the effectiveness of the proposed method.

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. However, most of mining algorithms or frequency approximation algorithms for a data stream are not able to extract the recent change of information in a data stream adaptively. This is because the obsolete information of old transactions which may be no longer useful or possibly invalid at present is regarded as important as that of recent transactions. This paper proposes an information decay method for finding recent frequent itemsets in a data stream. The effect of old transactions on the mining result of a data steam is gradually diminished as time goes by. Furthermore, the decay rate of information can be flexibly adjusted, which enables a user to define the desired life-time of the information of a transaction in a data stream.

This paper presents recursive algorithms for the least mean-squared error linear filtering and fixed-interval smoothing estimators, from uncertain observations for the case of white and white plus coloured observation noises. The estimators are obtained by an innovation approach and do not use the state-space model, but only covariance information about the signal and the observation noises, as well as the probability that the signal exists in the observed values. Therefore the algorithms are applicable not only to signal processes that can be estimated by the conventional formulation using the state-space model but also to those for which a realization of the state-space model is not available. It is assumed that both the signal and the coloured noise autocovariance functions are expressed in a semi-degenerate kernel form. Since the semi-degenerate kernel is suitable for expressing autocovariance functions of non-stationary or stationary signal processes, the proposed estimators provide estimates of general signal processes.

This paper considers the least-squares linear estimation problem of signals from randomly delayed observations when the additive white noise is correlated with the signal. The delay values are treated as unknown variables, modelled by a binary white noise with values zero or one; these values indicate that the measurements arrive in time or they are delayed by one sampling time. A recursive one-stage prediction and filtering algorithm is obtained by an innovation approach and do not use the state-space model of the signal. It is assumed that both, the autocovariance functions of the signal and the crosscovariance function between the signal and the observation noise are expressed in a semi-degenerate kernel form; using this information and the delay probabilities, the estimators are recursively obtained.

There exists a class of nonlinear systems which fail to have a well-defined relative degree but have a robust relative degree. We have removed the full relative degree assumption which the previous results required, and have provided a local state observer for nonlinear systems that have robust relative degree γ n and have detectability property in some sense. The proposed observer utilizes the coordinate change which transforms the system into an approximate normal form. Using the proposed method, we constructed an observer for the ball and beam system on a vibrating frame. Simulation results reveal that substantial improvement in the performance is achieved compared with other local observers.

AFM/STM observations were performed on sub nm thick C-Au-S film by co-operation process of plasma CVD and sputtering with using CH4, SF6 and Ar mixture gas and Au plate discharge electrode. From the refractive index values, the conductive granular molecules with a size of 0.4-0.6 nm were expected to exist in the film. For the film at thickness similar to the molecular size, Ra (arithmetic mean departures of roughness profile from the mean line) values were measured to be 0.712/6.10 nm by AFM/STM measurement, respectively. The one order large STM Ra value compared to the AFM Ra value suggests that the film contains conductive granular molecules.

This paper deals with a first evaluation of the efficiency and the robustness of the real-time "v-disparity" algorithm in stereovision for generic road obstacles detection towards various types of obstacles (vehicle, pedestrian, motorbike, cyclist, boxes) and under adverse conditions (day, night, rain, glowing effect, noise and false matches in the disparity map). The theoretical good properties of the "v-disparity" algorithm--accuracy, robustness, computational speed--are experimentally confirmed. The good results obtained allow us to use this stereo algorithm as the onboard perception process for Driving Safety Assistance: conductor warning and longitudinal control of a low speed automated vehicle (using a second order sliding mode control) in difficult and original situations, at frame rate using no special hardware. Results of experiments--Vehicle following at low speed, Stop'n'Go, Stop on Obstacle (pedestrian, fallen motorbike, load dropping obstacle)--are presented.

In this paper, we study reliable decentralized supervisory control of discrete event systems with a control architecture where certain controllable events are controlled under the conjunctive fusion rule, and certain others are controlled under the disjunctive fusion rule. We first introduce a notion of reliable co-observability with respect to such a partition of the controllable event set. We then prove that reliable co-observability together with Lm(G)-closure and controllability is a necessary and sufficient condition for the existence of a reliable decentralized supervisor under a given partition. Moreover, we present necessary and sufficient conditions for the existence of a partition of the controllable event set under which a given specification language is reliably co-observable.