Improved bounds for subband-adaptive iterative shrinkage/thresholding algorithms.

This paper presents new methods for computing the step sizes of the subband-adaptive iterative shrinkage-thresholding algorithms proposed by Bayram & Selesnick and Vonesch & Unser. The method yields tighter wavelet-domain bounds of the system matrix, thus leading to improved convergence speeds. It is directly applicable to non-redundant wavelet bases, and we also adapt it for cases of redundant frames. It turns out that the simplest and most intuitive setting for the step sizes that ignores subband aliasing is often satisfactory in practice. We show that our methods can be used to advantage with reweighted least squares penalty functions as well as L1 penalties. We emphasize that the algorithms presented here are suitable for performing inverse filtering on very large datasets, including 3D data, since inversions are applied only to diagonal matrices and fast transforms are used to achieve all matrix-vector products.

Novel passive launch scheme for ultimate bandwidth improvement of graded-index multimode fibers

We propose a new practical multimode fiber optical launch scheme, providing near single mode group excitation for >5 times transmission bandwidth improvement. Equalization-free transmission of a 10-Gb/s signal over 220-m fiber is achieved in experimental demonstrations. © 2010 Optical Society of America.

Polysiloxane surfactants for the dispersion of carbon nanotubes in nonpolar organic solvents.

We develop two new amphiphilic molecules that are shown to act as efficient surfactants for carbon nanotubes in nonpolar organic solvents. The active conjugated groups, which are highly attracted to the graphene nanotube surface, are based on pyrene and porphyrin. We show that relatively short (C18) carbon tails are insufficient to provide stabilization. As our ultimate aim is to disperse and stabilize nanotubes in siloxane matrix (polymer and cross-linked elastomer), both surfactant molecules were made with long siloxane tails to facilitate solubility and steric stabilization. We show that the pyrene-siloxane surfactant is very effective in dispersing multiwall nanotubes, while the porphyrin-siloxane makes single-wall nanotubes soluble, both in petroleum ether and in siloxane matrix.

Effect of inclusions and holes on the stiffness and strength of honeycombs

A finite element study has been performed on the effects of holes and rigid inclusions on the elastic modulus and yield strength of regular honeycombs under biaxial loading. The focus is on honeycombs that have already been weakened by a small degree of geometrical imperfection, such as a random distribution of fractured cell walls, as these imperfect honeycombs resemble commercially available metallic foams. Hashin-Shtrikman lower and upper bounds and self-consistent estimates of elastic moduli are derived to provide reference solutions to the finite element calculations. It is found that the strength of an imperfect honeycomb is relatively insensitive to the presence of holes and inclusions, consistent with recent experimental observations on commercial aluminum alloy foams.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.