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.

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.

Image-based localization and pose recovery using scale invariant features

In this paper, we propose a vision based mobile robot localization strategy. Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. The local characteristics of the features we use prove to be robust to occlusion and outliers. In addition, the invariance of the features to viewpoint change makes them suitable landmarks for mobile robot localization. Scale-invariant features detected in the first exploration are indexed into a location database. Indexing and voting allow efficient recognition of global localization. The localization result is verified by epipolar geometry between the representative view in database and the view to be localized, thus the probability of false localization will be decreased. The localization system can recover the pose of the camera mounted on the robot by essential matrix decomposition. Then the position of the robot can be computed easily. Both calibrated and un-calibrated cases are discussed and relative position estimation based on calibrated camera turns out to be the better choice. Experimental results show that our approach is effective and reliable in the case of illumination changes, similarity transformations and extraneous features. © 2004 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 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.