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.

Removal of surface states and recovery of band-edge emission in InAs nanowires through surface passivation.

Surface states in semiconductor nanowires (NWs) are detrimental to the NW optical and electronic properties and to their light emission-based applications, due to the large surface-to-volume ratio of NWs and the congregation of defects states near surfaces. In this paper, we demonstrated an effective approach to eliminate surface states in InAs NWs of zinc-blende (ZB) and wurtzite (WZ) structures and a dramatic recovery of band edge emission through surface passivation with organic sulfide octadecylthiol (ODT). Microphotoluminescence (PL) measurements were carried out before and after passivation to study the dominant recombination mechanisms and surface state densities of the NWs. For WZ-NWs, we show that the passivation removed the surface states and recovered the band-edge emission, leading to a factor of ∼19 reduction of PL linewidth. For ZB-NWs, the deep surface states were removed and the PL peaks width became as narrow as ∼250 nm with some remaining emission of near band-edge surface states. The passivated NWs showed excellent stability in atmosphere, water, and heat environments. In particular, no observable changes occurred in the PL features from the passivated NWs exposed in air for more than five months.

Sparse recovery of complex phase-encoded velocity images using iterative thresholding

In this paper we propose a new algorithm for reconstructing phase-encoded velocity images of catalytic reactors from undersampled NMR acquisitions. Previous work on this application has employed total variation and nonlinear conjugate gradients which, although promising, yields unsatisfactory, unphysical visual results. Our approach leverages prior knowledge about the piecewise-smoothness of the phase map and physical constraints imposed by the system under study. We show how iteratively regularizing the real and imaginary parts of the acquired complex image separately in a shift-invariant wavelet domain works to produce a piecewise-smooth velocity map, in general. Using appropriately defined metrics we demonstrate higher fidelity to the ground truth and physical system constraints than previous methods for this specific application. © 2013 IEEE.