Shock transmission in a coupled beam system

This paper investigates the circumstances under which high peak acceleration can occur in the internal parts of a system when subjected to impulsive driving on the outside. Motivating examples include the design of packaging for transportation of fragile items. The system is modelled in an idealised form using two beams coupled with point connections. A Rayleigh-Ritz model of such coupled beams was validated against measurements on a particular beam system, then the model was used to explore the acceleration response to impulsive driving in the time, frequency and spatial domains. This study is restricted to linear vibration response and additional mechanisms for high internal acceleration due to nonlinear effects such as internal impacts are not considered. Using Monte Carlo simulation in which the indirectly driven beam was perturbed by randomly placed point masses a wide range of system behaviour was explored. This facilitates identification of vulnerable configurations that can lead to high internal acceleration. The results from the study indicate the possibility of curve veering influencing the peak acceleration amplification. The possibility of veering within an ensemble was found to be dependent on the relative coupling strength of the modes. Understanding of the mechanism may help to avoid vulnerable cases, either by design or by preparatory vibration testing. © 2013 Elsevier Ltd.

Free space communications with beam steering a two-electrode tapered laser diode using liquid-crystal slm

A free space optical wireless communication system with 3 degree angular coverage and 1.25 GHz modulation bandwidth is reported, in which relatively narrow laser beam of a simultaneous high power, high modulation speed and ultra high modulation efficiency directly modulated two-electrode tapered laser diode is steered using a nematic phase-only Liquid-Crystal On Silicon Spatial Light Modulator (LCOS SLM) by displaying reconfigurable 256 phase level gratings. © 1983-2012 IEEE.

Bayesian Structured Prediction Using Gaussian Processes

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.

Electron-beam-induced direct etching of graphene

We present electron-beam-induced oxidation of single- and bilayer graphene devices in a low-voltage scanning electron microscope. We show that the injection of oxygen leads to targeted etching at the focal point, enabling us to pattern graphene with a resolution of better than 20 nm. Voltage-contrast imaging, in conjunction with finite-element simulations, explain the secondary-electron intensities and correlate them to the etch profile. © 2013 Elsevier Ltd. All rights reserved.

Computational analysis of liquid crystalline elastomer membranes: Changing Gaussian curvature without stretch energy

Liquid crystalline elastomers (LCEs) can undergo extremely large reversible shape changes when exposed to external stimuli, such as mechanical deformations, heating or illumination. The deformation of LCEs result from a combination of directional reorientation of the nematic director and entropic elasticity. In this paper, we study the energetics of initially flat, thin LCE membranes by stress driven reorientation of the nematic director. The energy functional used in the variational formulation includes contributions depending on the deformation gradient and the second gradient of the deformation. The deformation gradient models the in-plane stretching of the membrane. The second gradient regularises the non-convex membrane energy functional so that infinitely fine in-plane microstructures and infinitely fine out-of-plane membrane wrinkling are penalised. For a specific example, our computational results show that a non-developable surface can be generated from an initially flat sheet at cost of only energy terms resulting from the second gradients. That is, Gaussian curvature can be generated in LCE membranes without the cost of stretch energy in contrast to conventional materials. © 2013 Elsevier Ltd. All rights reserved.

Free energy surface reconstruction from umbrella samples using Gaussian process regression

We demonstrate how the Gaussian process regression approach can be used to efficiently reconstruct free energy surfaces from umbrella sampling simulations. By making a prior assumption of smoothness and taking account of the sampling noise in a consistent fashion, we achieve a significant improvement in accuracy over the state of the art in two or more dimensions or, equivalently, a significant cost reduction to obtain the free energy surface within a prescribed tolerance in both regimes of spatially sparse data and short sampling trajectories. Stemming from its Bayesian interpretation the method provides meaningful error bars without significant additional computation. A software implementation is made available on www.libatoms.org.

Free energy surface reconstruction from umbrella samples using Gaussian process regression II: Multiple collective variables

We demonstrate how a prior assumption of smoothness can be used to enhance the reconstruction of free energy profiles from multiple umbrella sampling simulations using the Bayesian Gaussian process regression approach. The method we derive allows the concurrent use of histograms and free energy gradients and can easily be extended to include further data. In Part I we review the necessary theory and test the method for one collective variable. We demonstrate improved performance with respect to the weighted histogram analysis method and obtain meaningful error bars without any significant additional computation. In Part II we consider the case of multiple collective variables and compare to a reconstruction using least squares fitting of radial basis functions. We find substantial improvements in the regimes of spatially sparse data or short sampling trajectories. A software implementation is made available on www.libatoms.org.