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.

The 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 probabilitiy 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.

Molecular evolution of CXCR1, a G protein-coupled receptor involved in signal transduction of neutrophils

Human neutrophils are a type of white blood cell, which forms an early line of defense against bacterial infections. Neutrophils are highly responsive to the chemokine, interleukin-8 (IL-8) due to the abundant distribution of CXCR1, one of the IL-8 receptors on the neutrophil cell surface. As a member of the GPCR family, CXCR1 plays a crucial role in the IL-8 signal transduction pathway in neutrophils. We sequenced the complete coding region of the CXCR1 gene in worldwide human populations and five representative nonhuman primate species. Our results indicate accelerated protein evolution in the human lineage, which was likely caused by Darwinian positive selection. The sliding window analysis and the codon-based neutrality test identified signatures of positive selection at the N-terminal ligand/receptor recognition domain of human CXCR1.

Bayesian signal processing

The application of Bayes' Theorem to signal processing provides a consistent framework for proceeding from prior knowledge to a posterior inference conditioned on both the prior knowledge and the observed signal data. The first part of the lecture will illustrate how the Bayesian methodology can be applied to a variety of signal processing problems. The second part of the lecture will introduce the concept of Markov Chain Monte-Carlo (MCMC) methods which is an effective approach to overcoming many of the analytical and computational problems inherent in statistical inference. Such techniques are at the centre of the rapidly developing area of Bayesian signal processing which, with the continual increase in available computational power, is likely to provide the underlying framework for most signal processing applications.