Full quadrature regeneration of QPSK signals using sequential phase sensitive amplification and parametric saturation.

We demonstrate all-optical regeneration of both the phase and the amplitude of a 10 GBaud quadrature phase shift keying (QPSK) signal using two nonlinear stages. First we regenerate the phase using a wavelength converting phase sensitive amplifier and then we regenerate the amplitude using a saturated single-pump parametric amplifier, returning the signal to its original wavelength at the same time. We exploit the conjugating nature of the two processing stages to eliminate the intrinsic SPM distortion of the system, further improving performance.

Optimisation of amplitude limiters for phase preservation based on the exact solution to degenerate four-wave mixing.

Adopting an exact solution to four-wave mixing (FWM), wherein harmonic evolution is described by the sum of two Bessel functions, we identify two causes of amplitude to phase noise conversion which impair FWM saturation based amplitude regenerators: self-phase modulation (SPM) and Bessel-order mixing (BOM). By increasing the pump to signal power ratio, we may arbitrarily reduce their impact, realising a phase preserving amplitude regenerator. We demonstrate the technique by applying it to the regeneration of a 10 GBaud QPSK signal, achieving a high level of amplitude squeezing with minimal amplitude to phase noise conversion.

Who will be a good employee in the future?

This article examines the attributes necessary for the successful employee in the future. Many of these are already familiar to the manager: flexibility and adaptability, a team approach, and the ability to see the bigger picture. Implications for the educational process and its development of successful employees are also presented.

Novel method for the display of multivariate data using neural networks.

A neural network has been used to reduce the dimensionality of multivariate data sets to produce two-dimensional (2D) displays of these sets. The data consisted of physicochemical properties for sets of biologically active molecules calculated by computational chemistry methods. Previous work has demonstrated that these data contain sufficient relevant information to classify the compounds according to their biological activity. The plots produced by the neural network are compared with results from two other techniques for linear and nonlinear dimension reduction, and are shown to give comparable and, in one case, superior results. Advantages of this technique are discussed.