926 resultados para distributed feedback lasers


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This paper demonstrates the respective roles that combined gain- and index-coupling play in the dynamic properties and overall link performance of DFB lasers. It is shown that for datacommunication applications, modest gain-coupling enables optimum transmission at 10Gbit/s.

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We report on an experimental and theoretical study of the transient flows which develop as a naturally ventilated room adjusts from one temperature to another. We focus on a room heated from below by a uniform heat source, with both high- and low-level ventilation openings. Depending on the initial temperature of the room relative to (i) the final equilibrium temperature and (ii) the exterior temperature, three different modes of ventilation may develop. First, if the room temperature lies between the exterior and the equilibrium temperature, the interior remains well-mixed and gradually heats up to the equilibrium temperature. Secondly, if the room is initially warmer than the equilibrium temperature, then a thermal stratification develops in which the upper layer of originally hot air is displaced upwards by a lower layer of relatively cool inflowing air. At the interface, some mixing occurs owing to the effects of penetrative convection. Thirdly, if the room is initially cooler than the exterior, then on opening the vents, the original air is displaced downwards and a layer of ambient air deepens from above. As this lower layer drains, it is eventually heated to the ambient temperature, and is then able to mix into the overlying layer of external air, and the room becomes well-mixed. For each case, we present new laboratory experiments and compare these with some new quantitative models of the transient flows. We conclude by considering the implications of our work for natural ventilation of large auditoria.

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A new form of ultrafast bistable polarization switching in twin-stripe injection lasers has been observed. For the first time, triggering between bistable states has been achieved by injecting light from a neighboring laser integrated on the same chip. Ultrafast switching times of 250 ps have been measured (detector limited).

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A bistable polarization switching element and optical triggering source has been produced by etching a facet in a twin stripe semiconductor laser. The switching element is formed by a pair of stripe segments at one end of the device and triggered with short light pulses from the other two segments. Detector limited switching risetimes have been measured at 250 ps.

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An n degree-of-freedom Hamiltonian system with r (1¡r¡n) independent 0rst integrals which are in involution is calledpartially integrable Hamiltonian system. A partially integrable Hamiltonian system subject to light dampings andweak stochastic excitations is called quasi-partially integrable Hamiltonian system. In the present paper, the procedures for studying the 0rst-passage failure and its feedback minimization of quasi-partially integrable Hamiltonian systems are proposed. First, the stochastic averaging methodfor quasi-partially integrable Hamiltonian systems is brie4y reviewed. Then, basedon the averagedIt ˆo equations, a backwardKolmogorov equation governing the conditional reliability function, a set of generalized Pontryagin equations governing the conditional moments of 0rst-passage time and their boundary and initial conditions are established. After that, the dynamical programming equations and their associated boundary and 0nal time conditions for the control problems of maximization of reliability andof maximization of mean 0rst-passage time are formulated. The relationship between the backwardKolmogorov equation andthe dynamical programming equation for reliability maximization, andthat between the Pontryagin equation andthe dynamical programming equation for maximization of mean 0rst-passage time are discussed. Finally, an example is worked out to illustrate the proposed procedures and the e9ectiveness of feedback control in reducing 0rst-passage failure.

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We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.