Parameter estimation of thrust models of uninhabited airborne systems

This paper presents a method for the estimation of thrust model parameters of uninhabited airborne systems using specific flight tests. Particular tests are proposed to simplify the estimation. The proposed estimation method is based on three steps. The first step uses a regression model in which the thrust is assumed constant. This allows us to obtain biased initial estimates of the aerodynamic coeficients of the surge model. In the second step, a robust nonlinear state estimator is implemented using the initial parameter estimates, and the model is augmented by considering the thrust as random walk. In the third step, the estimate of the thrust obtained by the observer is used to fit a polynomial model in terms of the propeller advanced ratio. We consider a numerical example based on Monte-Carlo simulations to quantify the sampling properties of the proposed estimator given realistic flight conditions.

Object tracking via non-Euclidean geometry : a Grassmann approach

A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.

Computation of ECG signal features using MCMC modelling in software and FPGA reconfigurable hardware

Computational optimisation of clinically important electrocardiogram signal features, within a single heart beat, using a Markov-chain Monte Carlo (MCMC) method is undertaken. A detailed, efficient data-driven software implementation of an MCMC algorithm has been shown. Initially software parallelisation is explored and has been shown that despite the large amount of model parameter inter-dependency that parallelisation is possible. Also, an initial reconfigurable hardware approach is explored for future applicability to real-time computation on a portable ECG device, under continuous extended use.

Bioeconomic modelling and risk assessment of tiger prawn (Penaeus esculentus) stock enhancement in Exmouth Gulf, Australia

A bioeconomic model was developed to evaluate the potential performance of brown tiger prawn stock enhancement in Exmouth Gulf, Australia. This paper presents the framework for the bioeconomic model and risk assessment for all components of a stock enhancement operation, i.e. hatchery, grow-out, releasing, population dynamics, fishery, and monitoring, for a commercial scale enhancement of about 100 metric tonnes, a 25% increase in average annual catch in Exmouth Gulf. The model incorporates uncertainty in estimates of parameters by using a distribution for the parameter over a certain range, based on experiments, published data, or similar studies. Monte Carlo simulation was then used to quantify the effects of these uncertainties on the model-output and on the economic potential of a particular production target. The model incorporates density-dependent effects in the nursery grounds of brown tiger prawns. The results predict that a release of 21 million 1 g prawns would produce an estimated enhanced prawn catch of about 100 t. This scale of enhancement has a 66.5% chance of making a profit. The largest contributor to the overall uncertainty of the enhanced prawn catch was the post-release mortality, followed by the density-dependent mortality caused by released prawns. These two mortality rates are most difficult to estimate in practice and are much under-researched in stock enhancement.

Semiparametric estimation of count regression models

This paper develops a semiparametric estimation approach for mixed count regression models based on series expansion for the unknown density of the unobserved heterogeneity. We use the generalized Laguerre series expansion around a gamma baseline density to model unobserved heterogeneity in a Poisson mixture model. We establish the consistency of the estimator and present a computational strategy to implement the proposed estimation techniques in the standard count model as well as in truncated, censored, and zero-inflated count regression models. Monte Carlo evidence shows that the finite sample behavior of the estimator is quite good. The paper applies the method to a model of individual shopping behavior. © 1999 Elsevier Science S.A. All rights reserved.

Optimal Bayesian experimental design for models with intractable likelihoods using indirect inference applied to biological process models

This paper addresses the problem of determining optimal designs for biological process models with intractable likelihoods, with the goal of parameter inference. The Bayesian approach is to choose a design that maximises the mean of a utility, and the utility is a function of the posterior distribution. Therefore, its estimation requires likelihood evaluations. However, many problems in experimental design involve models with intractable likelihoods, that is, likelihoods that are neither analytic nor can be computed in a reasonable amount of time. We propose a novel solution using indirect inference (II), a well established method in the literature, and the Markov chain Monte Carlo (MCMC) algorithm of Müller et al. (2004). Indirect inference employs an auxiliary model with a tractable likelihood in conjunction with the generative model, the assumed true model of interest, which has an intractable likelihood. Our approach is to estimate a map between the parameters of the generative and auxiliary models, using simulations from the generative model. An II posterior distribution is formed to expedite utility estimation. We also present a modification to the utility that allows the Müller algorithm to sample from a substantially sharpened utility surface, with little computational effort. Unlike competing methods, the II approach can handle complex design problems for models with intractable likelihoods on a continuous design space, with possible extension to many observations. The methodology is demonstrated using two stochastic models; a simple tractable death process used to validate the approach, and a motivating stochastic model for the population evolution of macroparasites.

The effect of microscopic texture on the direct plasma surface passivation of Si solar cells

Textured silicon surfaces are widely used in manufacturing of solar cells due to increasing the light absorption probability and also the antireflection properties. However, these Si surfaces have a high density of surface defects that need to be passivated. In this study, the effect of the microscopic surface texture on the plasma surface passivation of solar cells is investigated. The movement of 105 H+ ions in the texture-modified plasma sheath is studied by Monte Carlo numerical simulation. The hydrogen ions are driven by the combined electric field of the plasma sheath and the textured surface. The ion dynamics is simulated, and the relative ion distribution over the textured substrate is presented. This distribution can be used to interpret the quality of the Si dangling bonds saturation and consequently, the direct plasma surface passivation.

Plasma enables edge-to-center-oriented graphene nanoarrays on Si nanograss

The formation of clearly separated vertical graphenenanosheets on silicon nanograss support is demonstrated. The plasma-enabled, two-stage mask-free process produced self-organized vertical graphenes of a few carbon layers (as confirmed by advanced microanalysis), prominently oriented in the substrate center–substrate edge direction. It is shown that the width of the alignment zone depends on the substrate conductivity, and thus the electric field in the vicinity of the growth surface is responsible for the graphene alignment. This finding is confirmed by the Monte Carlo simulations of the ion flux distribution in the silicon nanograss pattern.

Increasing the length of single-wall carbon nanotubes in a magnetically enhanced arc discharge

It is demonstrated that a magnetic field has a profound effect on the length of a single-wall carbon nanotube (SWCNT) synthesized in the arc discharge. The average length of SWCNT increases by a factor of 2 in discharge with magnetic field as compared with the discharge without magnetic field, and the yield of long nanotubes with lengths above 5 μm also increases. A model of SWCNT growth on metal catalyst in arc plasma was developed. Monte-Carlo simulations confirm that the increase of the plasma density in the magnetic field leads to an increase in the nanotube growth rate and thus leads to longer nanotubes.

Deterministic nanoassembly : Neutral or plasma route?

It is shown that, owing to selective delivery of ionic and neutral building blocks directly from the ionized gas phase and via surface migration, plasma environments offer a better deal of deterministic synthesis of ordered nanoassemblies compared to thermal chemical vapor deposition. The results of hybrid Monte Carlo (gas phase) and adatom self-organization (surface) simulation suggest that higher aspect ratios and better size and pattern uniformity of carbon nanotip microemitters can be achieved via the plasma route. © 2006 American Institute of Physics.

Simulation and visualization of self-assembled nanodevice networks synthesized via plasma–surface interaction

Using advanced visualization techniques, a comprehensive visualization of all the stages of the self-organized growth of internetworked nanostructures on plasma-exposed surface has been made. Atomistic kinetic Monte Carlo simulation for the initial stage of deposition, with 3-D visualization of the whole system and half-tone visualization of the density field of the adsorbed atoms, makes it possible to implement a multiscale predictive modeling of the development of the nanoscale system.

Distribution of ion-current density on substrate between carbon nanotubes grown from low-temperature plasma

Using Monte Carlo simulation technique, we have calculated the distribution of ion current extracted from low-temperature plasmas and deposited onto the substrate covered with a nanotube array. We have shown that a free-standing carbon nanotube is enclosed in a circular bead of the ion current, whereas in square and hexagonal nanotube patterns, the ion current is mainly concentrated along the lines connecting the nearest nanotubes. In a very dense array (with the distance between nanotubes/nanotube-height ratio less than 0.05), the ions do not penetrate to the substrate surface and deposit on side surfaces of the nanotubes.

Pulsed i-PVD of dielectric nanodot arrays using a nanoporous template

The results of multi-scale numerical simulations of pulsed i-PVD template-assisted nanofabrication of ZnO nanodot arrays on a silicon substrate are presented. The ratios and spatial distributions of the ion fluxes deposited on the lateral and bottom surfaces of the nanopores are computed as a function of the external bias and plasma parameters. The results show that the pulsed bias plays a significant role in the ion current distribution inside the nanopores. The results of numerical experiments of this work suggest that by finely adjusting the pulse voltage, the pulse duration and the duty cycle of the external pulsed bias, the nanopore clogging can be successfully avoided during the deposition and the shapes of the deposited ZnO nanodots can be effectively controlled. A figure is presented.

Magnetic-field-enhanced synthesis of single-wall carbon nanotubes in arc discharge

The ability to control the properties of single-wall nanotubes (SWNTs) produced in the arc discharge is important for many practical applications. Our experiments suggest that the length of SWNTs significantly increases (up to 4000 nm), along with the purity of the carbon deposit, when the magnetic field is applied to arc discharge. Scanning electron microscopy and transmission electron microscopy analyses have demonstrated that the carbon deposit produced in the magnetic-field-enhanced arc mainly consists of the isolated and bunched SWNTs. A model of a carbon nanotube interaction and growth in the thermal plasma was developed, which considers several important effects such as anode ablation that supplies the carbon plasma in an anodic arc discharge technique, and the momentum, charge, and energy transfer processes between nanotube and plasma. It is shown that the nanotube charge with respect to the plasma as well as nanotube length depend on plasma density and electric field in the interelectrode gap. For instance, nanotube charge changes from negative to positive value with an electron density decrease. The numerical simulations based on the Monte Carlo technique were performed, which explain an increase in the nanotubes produced in the magnetic-field-enhanced arc discharge. © 2008 American Institute of Physics.

Microscopic ion fluxes in plasma-aided nanofabrication of ordered carbon nanotip structures

Three-dimensional topography of microscopic ion fluxes in the reactive hydrocarbon-based plasma-aided nanofabrication of ordered arrays of vertically aligned single-crystalline carbon nanotip microemitter structures is simulated by using a Monte Carlo technique. The individual ion trajectories are computed by integrating the ion equations of motion in the electrostatic field created by a biased nanostructured substrate. It is shown that the ion flux focusing onto carbon nanotips is more efficient under the conditions of low potential drop Us across the near-substrate plasma sheath. Under low- Us conditions, the ion current density onto the surface of individual nanotips is higher for higher-aspect-ratio nanotips and can exceed the mean ion current density onto the entire nanopattern in up to approximately five times. This effect becomes less pronounced with increasing the substrate bias, with the mean relative enhancement of the ion current density ξi not exceeding ∼1.7. The value of ξi is higher in denser plasmas and behaves differently with the electron temperature Te depending on the substrate bias. When the substrate bias is low, ξi decreases with Te, with the opposite tendency under higher- Us conditions. The results are relevant to the plasma-enhanced chemical-vapor deposition of ordered large-area nanopatterns of vertically aligned carbon nanotips, nanofibers, and nanopyramidal microemitter structures for flat-panel display applications. © 2005 American Institute of Physics.