Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.

Geometric optimization methods for the analysis of gene expression data

DNA microarrays provide such a huge amount of data that unsupervised methods are required to reduce the dimension of the data set and to extract meaningful biological information. This work shows that Independent Component Analysis (ICA) is a promising approach for the analysis of genome-wide transcriptomic data. The paper first presents an overview of the most popular algorithms to perform ICA. These algorithms are then applied on a microarray breast-cancer data set. Some issues about the application of ICA and the evaluation of biological relevance of the results are discussed. This study indicates that ICA significantly outperforms Principal Component Analysis (PCA).

Different methods of manufacturing FE-based oxygen carrier particles for reforming via chemical looping, and their effect on performance

Chemical looping combustion (CLC) is a means of combusting carbonaceous fuels, which inherently separates the greenhouse gas carbon dioxide from the remaining combustion products, and has the potential to be used for the production of high-purity hydrogen. Iron-based oxygen carriers for CLC have been subject to considerable work; however, there are issues regarding the lifespan of iron-based oxygen carriers over repeated cycles. In this work, haematite (Fe2O3) was reduced in an N2+CO+CO2 mixture within a fluidised bed at 850°C, and oxidised back to magnetite (Fe3O4) in a H2O+N2 mixture, with the subsequent yield of hydrogen during oxidation being of interest. Subsequent cycles started from Fe3O4 and two transition regimes were studied; Fe3O4↔Fe0.947O and Fe 3O4↔Fe. Particles were produced by mechanical mixing and co-precipitation. In the case of co-precipitated particles, Al was added such that the ratio of Fe:Al by weight was 9:1, and the final pH of the particles during precipitation was investigated for its subsequent effect on reactivity. This paper shows that co-precipitated particles containing additives such as Al may be able to achieve consistently high H2 yields when cycling between Fe3O4 and Fe, and that these yields are a function of the ratio of [CO2] to [CO] during reduction, where thermodynamic arguments suggest that the yield should be independent of this ratio. A striking feature with our materials was that particles made by mechanical mixing performed much better than those made by co-precipitation when cycling between Fe3O4 and Fe0.947O, but much worse than co-precipitated particles when cycling between Fe3O 4 and Fe.