Optical Frequency Division Multiplexing (OFDM) is an attractive multiplexing approach for exploiting optical communication technology. Although considerable progress has been made in this approach, it still suffers from numerous potential impairments, stemming from several phenomena. (i.e., laser unstability, residual temperature variations, linear and nonlinear cross talk.). Conventional serial coding technique is not practical in lightwave systems, as it changes the system's bit rate that is not desirable. In this paper a new Parallel Coded Optical Multicarrier Frequency Division Multiplexing (PCOM-FDM) technique has been investigated. The strategy of multicarriers, together with Parallel Forward Error Control (PFEC) coding, is a potentially novel approach as in this approach we have, 1) Investigated optical multicarrier communication that is effective in combating dispersion and increasing throughput, 2) Proposed PFEC coding which is different from conventional serial coding in respect that it does not change the system bit rate per carrier and prevents the effects of channel wandering. It is highly desirable in lightwave systems and thus holds a vital importance in practical high speed optical communication systems. Theoretical treatment shows that the proposed approach is promising and practical.

A minimal siphon (or alternatively a structural deadlock) of a Petri net is defined as a minimal set S of places such that existence of any edge from a transition t to a place of S implies that there is an edge from some place of S to t. The subject of the paper is to find a minimal siphon containing a given set of specified places of a general Petri net.

In this paper, we propose the distributed stable marriage problem and apply it to planning for cooperative works of autonomous mobile robots and battery charger stations. We develop and analyze a distributed algorithm to determine the partner by message communication.

We report on the formation technique and the first observation of visible light emission from silicon nanoparticles (<10nm) embedded in CaF22 Iayers grown on Si(111) substrates by using codeposition of Si and CaF2. It is shown that the size and density of silicon particles embedded in the CaF2 layer can be controlled by varying the substrate temperature and the evaporation rates of CaF2 and Si. The photoluminescence (PL) spectra of Si nanoparticles embedded in CaF2 thin films were investigated. The blue or green light emissions obtained using a He-Cd laser (λ=325nm) could be seen with the naked eye even at room temperature for the first time. It is shown that the PL intensity strongly depends on growth conditions such as the Si:CaF2 flux ratio and the growth temperature. The PL spectra were also changed by in situ annealing process. These phenomena can be explained qualitatively by the quantum size effect of Si nanoparticles embedded in CaF2 barriers.

A three-terminal quantum device utilizing photon-assisted tunneling in a multilayer structure is proposed and analyzed in terms of its high frequency amplification characteristics. The operation principle of this device includes photonassisted tunneling at the input, formation of a propagating charge wave due to the beat of tunneling electrons and its acceleration, and radiation of electromagnetic waves at the output. Analysis of these operations, discussion of similarities and dissimilarities to classical klystrons, and estimation of the power gain and its frequency dependence are given. A simple example demonstrates that amplification up to the terahertz frequency range is possible using this device.

A type of Lienard's equation +µf(x)+x=0, where f(x) is not an even function of x, is studied by Le Corbeiller as a model of various biological oscillations, such as breathing, and called two-stroke oscillators. A distinctive feature of this type of oscillators is that the parameter µ has the upper limit µ0 for the oscillator to have some stable limit cycle. This paper gives a numerical method for calculating this upper limit µ0.

We discuss fabrication of InGaAs quantum dot structures using the self-assembling growth technique with the Stranski-Krastanow growth mode in MOCVD, including optical ploperties of the nano-structures. The formation process of the quantum dot islands was clarified by observing the samples grown under various conditions with an atomic force microscope. A trial for self-alignment of the quantum dots was also investigated. On the basis of these results, as the first step toward the ultimate semiconductor lasers in which both electrons and photons are fully quantized, a vertical microcavity InGaAs/GaAs quantum dot laser was demonstrated. Finally a perspective of the quantum dot lasers is discussed, including the bottleneck issues and the impact of the quantum dot structures for reducing threshold current in wide bandgap lasers such as GaN lasers.

This paper presents the device technology for monolithic integration of InP-based resonant tunneling diodes (RTDs) and high electron mobility transistors (HEMTs). The potential of this technology for applications in quantum functional devices and circuits is demonstrated in two integration schemes in which RTDs and FETs are integrated either in Parallel or in series. Based on the parallel integration scheme, we demonstrate an integrated device which exhibits negative differential resistance and modulated peak current. This integrated device forms the foundation of a new category of functional circuits featuring clocked supply voltage. Based on the series integration scheme, resonant-tunneling high electron mobility transistors (RTHEMTs) with novel current-voltage characteristics and useful circuit applications are demonstrated. The high-frequency characteristics of RTHEMTs are also reported.

We develop a new class of stochastic Petri net: non-regenerative stochastic Petri net (NRSPN), which allows the firing time of its transitions with arbitrary distributions, and can automatically generate a bounded reachability graph that is equivalent to a generalization of the Markov renewal process in which some of the states may not constitute regeneration points. Thus, it can model and analyze behavior of a system whose states include some non-regeneration points. We show how to model a system by the NRSPN, and how to obtain numerical solutions for the NRSPN model. The probabilistic behavior of the modeled system can be clarified with the reliability measures such as the steady-state probability, the expected numbers of visits to each state per unit time, availability, unavailability and mean time between system failure. Finally, to demonstrate the modeling ability and analysis power of the NRSPN model, we present an example for a fault-tolerant system using the NRSPN and give numerical results for specific distributions.

This paper describes some dynamical properties of higher order neural networks with decreasing energy functions. First, we will show that for any symmetric higher order neural network which permits only one element to transit at each step, there are only periodic sequences with the length 1. Further, it will be shown that for any higher order neural network, with decreasing energy functions, which permits all elements to transit at each step, there does not exist any periodic sequence with the length being over k + 1, where k is the order of the network. Lastly, we will give a characterization for higher order neural networks, with the order 2 and a decreasing energy function each, which permit plural elements to transit at each step and have periodic sequences only with the lengh 1.

The two variational principles, the Maupertuis' and the Hamilton's principle, are discussed in conjunction with the Fermat's principle. These two principles are shown to describe two different aspects of waves, thus resulting in the different geometry of wave propagation, the treatment of which is thus called the stationary optics or the dynamical optics, respectively. Comparisons for the results obtained from these geometrical optics are given. Another new variational principle valid for the dynamical waves reflected/refracted at the inter-faces, which has not yet been discovered so far, is also derived.

The performance improvement of the partial response maximum-likelihood (PRML) system for (1, 7) run-length limited (RLL) code is studied. As a new PRML system, PR (1, 1, 0, 1, 1) system called modified E2PR4 (ME2PR4 ) followed by Viterbi detector for (1, 7) RLL code is proposed. At first, a determination method of the tap weights in transversal filter to equalize to PR (1, 1, 0, 1, 1) characteristic taking account of a noise correlation is described. And the equalization characteristics of the transversal filter are evaluated. Then, a Viterbi detector for ME2PR4 utilizing the constraint of run-length of (1, 7) RLL code is presented. Finally, the bit-error rate is obtained by computer simulation and the performance is compared with that of the conventional PRML systems called PR4, EPR4 and E2PR4 systems with Viterbi detector. The results show that among these systems our system exhibits the best performance and the SNR improvement increases with the increase in the linear density.

In this paper, we present an analysis of microstrip line with a trapezoidal dielectric ridge in multilayered media. The method employed in this characterization is called partial-boundary element method (p-BEM) which provides an efficient technique to the analysis of the structures with multilayered media. To improve the convergence of the Green's function used in the analysis with the P-BEM, we employ a technique based on a combination of the Fourier series expansion and the method of images. Treatment on convergence for the boundary integrals is also described. After this treatment, it requires typically one tenth or one hundredth of Fourier terms to obtain the same accuracy compared with the original Green's function. Numerical results are presented for two microstrip lines that have a trapezoidal dielectric ridge placed on a one-layered substrate and a two-layered substrate. These numerical results demonstrate the effects on the characteristics of the microstrip line due to the existence of the dielectric ridge as well as the second layer between the ridge and the fundamental substrate.

Deterministic nonlinear prediction is applied to both artificial and real time series data in order to investigate orbital-instabilities, short-term predictabilities and long-term unpredictabilities, which are important characteristics of deterministic chaos. As an example of artificial data, bimodal maps of chaotic neuron models are approximated by radial basis function networks, and the approximation abilities are evaluated by applying deterministic nonlinear prediction, estimating Lyapunov exponents and reconstructing bifurcation diagrams of chaotic neuron models. The functional approximation is also applied to squid giant axon response as an example of real data. Two metnods, the standard and smoothing interpolation, are adopted to construct radial basis function networks; while the former is the conventional method that reproduces data points strictly, the latter considers both faithfulness and smoothness of interpolation which is suitable under existence of noise. In order to take a balance between faithfulness and smoothness of interpolation, cross validation is applied to obtain an optimal one. As a result, it is confirmed that by the smoothing interpolation prediction performances are very high and estimated Lyapunov exponents are very similar to actual ones, even though in the case of periodic responses. Moreover, it is confirmed that reconstructed bifurcation diagrams are very similar to the original ones.

It is shown that five optimal and one quasioptimal binary codes with respect to the Griesmer bound can be obtained from cyclic codes over GF(2fm). An [m(2em - 1), em, 2em-1m] code, a [3(22e - 1), 2e, 322e-1] code, a [2(22e - 1), 2, (22e+2 - 4)/3] code, a [3(22e - 1), 2, 22e+1 - 2] code, and a [3(22e - 1), 2(e+1), 322e-1 - 2] code are optimal and a [2(22e - 1), 2(e + 1), 22e - 2] code is quasi-optimal.

The Guaranteed Bandwidth Protocol (GBW) is an access scheme being proposed for implementation of connection oriented services in DQDB networks. Connection oriented services are expected to handle both constant bit rate (CBR) and variable bit rate (VBR) traffic that have delay and jitter constraints. It has been reported that the GBW protocol can provide guaranteed bandwidth and lower delays compared to the ordinary DQDB protocol. However, the intensity of the jitter introduced by this scheme has not been made clear. This paper compares the jitter results for the GBW scheme to those obtained by a new access method called Modified Guaranteed Bandwidth (MOD_GBW) protocol, which is proposed here. It is shown through simulation that MOD_GBW also provides guaranteed bandwidth and that its delay and jitter characteristics are significantly better than those of the GBW protocol. In the simulation model, the DQDB stations are divided into two groups: 1)Real-Time (RT) stations that generate either CBR or VBR real-time traffic; and 2)Data stations that generate memoryless type of traffic. Data stations operate according to the ordinary DQDB protocol only. The main performance measure adopted here for the real-time traffic is the interdeparture time distribution of consecutive segments from an RT-station. We define the variance of this distribution as jitter. This paper also shows the impact of GBW/MOD_GBW on the performance of the data stations by evaluating their throughput and average bus access delay. Finally, we show that the network performance is weakly related to the number of RT-stations under MOD_GBW, but it depends strongly on the overall loading.

In the paper a new type of Multilayer Perceptron, developed in Quaternion Algebra, is adopted to realize short-time prediction of chaotic time series. The new introduced neural structure, based on MLP and developed in the hypercomplex quaternion algebra (HMLP) allows accurate results with a decreased network complexity with respect to the real MLP. The short term prediction of various chaotic circuits and systems has been performed, with particular emphasys to the Chua's circuit, the Saito's circuit with hyperchaotic behaviour and the Lorenz system. The accuracy of the prediction is evaluated through a correlation index between the actual predicted terms of the time series. A comparison of the performance obtained with both the real MLP and the hypercomplex one is also reported.

In this paper, Chay's bursting pancreatic β-cell model is updated to include a role for [Ca2+]ER, the luminal calcium concentration in the endoplasmic reticulum (ER). The model contains a calcium current which is activated by voltage and inactivated by [Ca2+]i. It also contains a cationic nonselective current (INS) that is activated by depletion of luminal Ca2+ in the ER. In this model, [Ca2+]ER oscillates slowly, and this slow dynamic drives electrical bursting and the [Ca2+]i oscillations. This model is capable of providing answers to some puzzling phenomena,which the previous models could not (e. g., why do single pancreatic β-cells burst with a low frequency while the cells in an islet burst with a much higher frequency ?). Verification of the model prediction that [Ca2+]ER is a primary oscillator that drives electrical bursting and [Ca2+]i oscillations in pancreatic β-cells awaits experimental testing. Experiments using fluorescent dyes such as mag-fura-2-AM could provide relevant information.

The capability of generalization is the most desirable property of a learning system. It is well known that to achieve good generalization, the complexity of the system should match the intrinsic complexity of the problem to be learned. In this work, introduction of fractal connection structure in nonlinear learning systems like multilayer perceptrons as a means of improving its generalization capability in classification problems has been investigated via simulation on sonar data set in underwater target classification problem. It has been found that fractally connected net has better generalization capability compared to the fully connected net and a randomly connected net of same average connectivity for proper choice of fractal dimension which controlls the average connectivity of the net.

In this paper, after the introduction of the definition of State Controlled Cellular Neural Networks (SC-CNNs), it is shown that they are able to generate complex dynamics of circuits showing strange behaviour. Theoretical propoitions are presented to fix the templates of the SC-CNNs in such a way as to exactly match the dynamic behaviour of the circuits considered. The easy and cheap implementation of the proposed SC-CNN devices is illustrated and a gallery of experimentally obtained strange attractors are shown to confirm the practical suitability of the outlined strategy.