Approximate Bayesian Computation for Smoothing

We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the observations. The quality of the approximation may be controlled to arbitrary precision through a parameter ε > 0. We provide theoretical results which quantify, in terms of ε, the ABC error in approximation of expectations of additive functionals with respect to the smoothing distributions. Under regularity assumptions, this error is, where n is the number of time steps over which smoothing is performed. For numerical implementation, we adopt the forward-only sequential Monte Carlo (SMC) scheme of [14] and quantify the combined error from the ABC and SMC approximations. This forms some of the first quantitative results for ABC methods which jointly treat the ABC and simulation errors, with a finite number of data and simulated samples. © Taylor & Francis Group, LLC.

Zinc oxide nanostructures and high electron mobility nanocomposite thin film transistors

This paper reports on the synthesis of zinc oxide (ZnO) nanostructures and examines the performance of nanocomposite thin-film transistors (TFTs) fabricated using ZnO dispersed in both n- and p-type polymer host matrices. The ZnO nanostructures considered here comprise nanowires and tetrapods and were synthesized using vapor phase deposition techniques involving the carbothermal reduction of solid-phase zinc-containing compounds. Measurement results of nanocomposite TFTs based on dispersion of ZnO nanorods in an n-type organic semiconductor ([6, 6]-phenyl-C61-butyric acid methyl ester) show electron field-effect mobilities in the range 0.3-0.6 cm2V-1 s-1. representing an approximate enhancement by as much as a factor of 40 from the pristine state. The on/off current ratio of the nanocomposite TFTs approach 106 at saturation with off-currents on the order of 10 pA. The results presented here, although preliminary, show a highly promising enhancement for realization of high-performance solution-processable n-type organic TFTs. © 2008 IEEE.

Methods for enhanced electrical transduction and characterization of micromechanical resonators

This paper details the design and enhanced electrical transduction of a bulk acoustic mode resonator fabricated in a commercial foundry MEMS process utilizing 2.5 μm gaps. The I-V characteristics of electrically addressed silicon resonators are often dominated by capacitive parasitics, inherent to hybrid technologies. This paper benchmarks a variety of drive and detection principles for electrostatically driven square-extensional mode resonators operating in air via analytical models accompanied by measurements of fabricated devices with the primary aim of enhancing the ratio of the motional to feedthrough current at nominal operating voltages. In view of ultimately enhancing the motional to feedthrough current ratio, a new detection technique that combines second harmonic capacitive actuation and piezoresistive detection is presented herein. This new method is shown to outperform previously reported methods utilizing voltages as low as ±3 V in air, providing a promising solution for low voltage CMOS-MEMS integration. To elucidate the basis of this improvement in signal output from measured devices, an approximate analytical model for piezoresistive sensing specific to the resonator topology reported here is also developed and presented. © 2010 Elsevier B.V. All rights reserved.

GROOVE FORMATION AND INTERACTION DURING ABRASION OF COPPER.

A pin on cylinder wear rig has been built with precision stepping motor drives to both rotary and axial motions which enable accurate positional control to be achieved. Initial experiments using sapphire indenters running against copper substrates have investigated the build up of a single wear groove by repeated sliding along the same track. An approximate three dimensional ploughing analysis is also presented and the results of theory and experiment compared.

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how sequential Monte Carlo methods provide a natural method for implementing our likelihood-based ABC procedures.