Resonant Raman characterisation of ultra-thin nano-protective carbon layers for magnetic storage devices

Carbon thin films are very important as protective coatings for a wide range of applications such as magnetic storage devices. The key parameter of interest is the sp3 fraction, since it controls the mechanical properties of the film. Visible Raman spectroscopy is a very popular technique to determine the carbon bonding. However, the visible Raman spectra mainly depend on the configuration and clustering of the sp2 sites. This can result in the Raman spectra of different samples looking similar albeit having a different structure. Thus, visible Raman alone cannot be used to derive the sp3 content. Here we monitor the carbon bonding by using a combined study of Raman spectra taken at two wavelengths (514 and 244 nm). We show how the G peak dispersion is a very useful parameter to investigate the carbon samples and we endorse it as a production-line characterisation tool. The dispersion is proportional to the degree of disorder, thus making it possible to distinguish between graphitic and diamond-like carbon. © 2003 Elsevier B.V. All rights reserved.

Reconfigurable free-space optical switching technologies for storage area networks

Reconfigurable shutter-based free-space optical switching technologies using fiber ribbon and multiple wavelengths per fiber for Storage Area Networks (SANs) application are presented and demonstrated. ©2009 SPIE-OSA-IEEE.

Reconfigurable free-space optical switching technologies for storage area networks

Reconfigurable shutter-based free-space optical switching technologies using fiber ribbon and multiple wavelengths per fiber for Storage Area Networks (SANs) application are presented and demonstrated. ©2009 Optical Society of America.

Filtering via approximate Bayesian computation

Approximate Bayesian computation (ABC) has become a popular technique to facilitate Bayesian inference from complex models. In this article we present an ABC approximation designed to perform biased filtering for a Hidden Markov Model when the likelihood function is intractable. We use a sequential Monte Carlo (SMC) algorithm to both fit and sample from our ABC approximation of the target probability density. This approach is shown to, empirically, be more accurate w.r.t.~the original filter than competing methods. The theoretical bias of our method is investigated; it is shown that the bias goes to zero at the expense of increased computational effort. Our approach is illustrated on a constrained sequential lasso for portfolio allocation to 15 constituents of the FTSE 100 share index.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.