Strong flexoelectric behavior in bimesogenic liquid crystals

In this paper, we demonstrate strong flexoelectric coupling in bimesogenic liquid crystals. This strong coupling is determined via the flexoelectro-optic effect in chiral nematic liquid crystals based on bimesogenic mixtures that are doped with low concentrations of high twisting power chiral additive. Two mixtures were examined: one had a pitch length of p∼300nm, the other had a pitch length of p∼600nm. These mixtures exhibit enantiotropic chiral nematic phases close to room temperature. We found that full-intensity modulation, that is, a rotation of the optic axis of 45° between crossed polarizers, could be achieved at significantly lower applied electric fields (E<5Vμm -1) than previously reported. In fact, for the condition of full-intensity modulation, the lowest electric-field strength recorded was E=2Vμm-1. As a result of a combination of the strong flexoelectric coupling and a divergence in the pitch, tilt angles of the optic axis up to 87°, i.e., a rotation of the optic axis through 174°, were observed. Furthermore, the flexoelastic ratios, which may be considered as a figure-of-merit parameter, were calculated from the results and found to be large, ranging from 1.3to2C/Nm for a temperature range of up to 40°C. © 2006 American Institute of Physics.

Convergence of the auxiliary particle implementation of the PHD filter

Optimal Bayesian multi-target filtering is in general computationally impractical owing to the high dimensionality of the multi-target state. The Probability Hypothesis Density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed Sequential Monte Carlo (SMC) implementations of the PHD filter. However, these implementations are the equivalent of the Bootstrap Particle Filter, and the latter is well known to be inefficient. Drawing on ideas from the Auxiliary Particle Filter (APF), a SMC implementation of the PHD filter which employs auxiliary variables to enhance its efficiency was proposed by Whiteley et. al. Numerical examples were presented for two scenarios, including a challenging nonlinear observation model, to support the claim. This paper studies the theoretical properties of this auxiliary particle implementation. $\mathbb{L}_p$ error bounds are established from which almost sure convergence follows.