Bayesian approach to signal modelling

In this paper, an introduction to Bayesian methods in signal processing will be given. The paper starts by considering the important issues of model selection and parameter estimation and derives analytic expressions for the model probabilities of two simple models. The idea of marginal estimation of certain model parameter is then introduced and expressions are derived for the marginal probability densities for frequencies in white Gaussian noise and a Bayesian approach to general changepoint analysis is given. Numerical integration methods are introduced based on Markov chain Monte Carlo techniques and the Gibbs sampler in particular.

Technique for wavelength converting a 2.488 Gb/s signal whilst simultaneously monitoring and replacing a wavelength identifying pilot tone

A wavelength conversion device was demonstrated at the bit rate of 2.488 Gb/s with 2R (reamplification and reshaping) regenerative properties. A low frequency pilot tone was removed during the conversion process and a new one added. The wavelength converter is shown to operate well at 10 Gb/s, and tone identification/replacement should also be possible at this data rate.

Quality enhancement of a WDM signal using dispersion-imbalanced loop mirror

The nonlinear filtering of a 10Gb/s data stream in a dispersion-imbalanced fibre loop mirror has been demonstrated over a wide spectral range of 28nm. A relative extinction ratio of - 30 dB for the cw background has been achieved across the whole spectral range.