High temperature robust SOI ethanol sensor

In this paper we present a robust SOI-CMOS ethanol sensor based on a tungsten-doped lanthanum iron oxide sensing material. The device shows response to gas, has low power consumption, good uniformity, high temperature stability and can be manufactured at low cost and with integrated circuitry. The platform is a tungsten-based CMOS micro-hotplate that has been shown to be stable for over two thousand hours at a high temperature (600°C) in a form of accelerated life test. The tungsten-doped lanthanum iron oxide was deposited on the micro-hotplate as a slurry with terpineol using a syringe, dried and annealed. Preliminary gas testing was done and the material shows response to ethanol vapour. These results are promising and we believe that this combination of a robust CMOS micro-hotplate and a good sensing material can form the basis for a commercial CMOS gas sensor. © 2011 Published by Elsevier Ltd.

Zinc oxide nanowire based hydrogen sensor on SOI CMOS platform

Here we report on the successful low-temperature growth of zinc oxide nanowires (ZnONWs) on silicon-on-insulator (SOI) CMOS micro-hotplates and their response, at different operating temperatures, to hydrogen in air. The SOI micro-hotplates were fabricated in a commercial CMOS foundry followed by a deep reactive ion etch (DRIE) in a MEMS foundry to form ultra-low power membranes. The micro-hotplates comprise p+ silicon micro-heaters and interdigitated metal electrodes (measuring the change in resistance of the gas sensitive nanomaterial). The ZnONWs were grown as a post-CMOS process onto the hotplates using a CMOS friendly hydrothermal method. The ZnONWs showed a good response to 500 to 5000 ppm of hydrogen in air. We believe that the integration of ZnONWs with a MEMS platform results in a low power, low cost, hydrogen sensor that would be suitable for handheld battery-operated gas sensors. © 2011 Published by Elsevier Ltd.

Model structure and uncertainty for stochastic non-point source modelling applications

The uncertainty associated with a rainfall-runoff and non-point source loading (NPS) model can be attributed to both the parameterization and model structure. An interesting implication of the areal nature of NPS models is the direct relationship between model structure (i.e. sub-watershed size) and sample size for the parameterization of spatial data. The approach of this research is to find structural limitations in scale for the use of the conceptual NPS model, then examine the scales at which suitable stochastic depictions of key parameter sets can be generated. The overlapping regions are optimal (and possibly the only suitable regions) for conducting meaningful stochastic analysis with a given NPS model. Previous work has sought to find optimal scales for deterministic analysis (where, in fact, calibration can be adjusted to compensate for sub-optimal scale selection); however, analysis of stochastic suitability and uncertainty associated with both the conceptual model and the parameter set, as presented here, is novel; as is the strategy of delineating a watershed based on the uncertainty distribution. The results of this paper demonstrate a narrow range of acceptable model structure for stochastic analysis in the chosen NPS model. In the case examined, the uncertainties associated with parameterization and parameter sensitivity are shown to be outweighed in significance by those resulting from structural and conceptual decisions. © 2011 Copyright IAHS Press.

Steady-state modelling of the universal exhaust gas oxygen (UEGO) sensor

The universal exhaust gas oxygen (UEGO) sensor is a well-established device which was developed for the measurement of relative air fuel ratio in internal combustion engines. There is, however, little information available which allows for the prediction of the UEGO's behaviour when exposed to arbitrary gas mixtures, pressures and temperatures. Here we present a steady-state model for the sensor, based on a solution of the Stefan-Maxwell equation, and which includes a momentum balance. The response of the sensor is dominated by a diffusion barrier, which controls the rate of diffusion of gas species between the exhaust and a cavity. Determination of the diffusion barrier characteristics, especially the mean pore size, porosity and tortuosity, is essential for the purposes of modelling, and a measurement technique based on identification of the sensor pressure giving zero temperature sensitivity is shown to be a convenient method of achieving this. The model, suitably calibrated, is shown to make good predictions of sensor behaviour for large variations of pressure, temperature and gas composition. © 2012 IOP Publishing Ltd.

Near-ultraviolet zinc oxide nanowire sensor using low temperature hydrothermal growth.

Two near-ultraviolet (UV) sensors based on solution-grown zinc oxide (ZnO) nanowires (NWs) which are only sensitive to photo-excitation at or below 400 nm wavelength have been fabricated and characterized. Both devices keep all processing steps, including nanowire growth, under 100 °C for compatibility with a wide variety of substrates. The first device type uses a single optical lithography step process to allow simultaneous in situ horizontal NW growth from solution and creation of symmetric ohmic contacts to the nanowires. The second device type uses a two-mask optical lithography process to create asymmetric ohmic and Schottky contacts. For the symmetric ohmic contacts, at a voltage bias of 1 V across the device, we observed a 29-fold increase in current in comparison to dark current when the NWs were photo-excited by a 400 nm light-emitting diode (LED) at 0.15 mW cm(-2) with a relaxation time constant (τ) ranging from 50 to 555 s. For the asymmetric ohmic and Schottky contacts under 400 nm excitation, τ is measured between 0.5 and 1.4 s over varying time internals, which is ~2 orders of magnitude faster than the devices using symmetric ohmic contacts.

GYGAX: Multi-Sensor Software Platform API

CLI .NET 4.0 research prototype platform coded in C# and Windows Presentation Foundation (WPF)

Physical consequences of a nonparametric uncertainty model in structural dynamics

One of the main claims of the nonparametric model of random uncertainty introduced by Soize (2000) [3] is its ability to account for model uncertainty. The present paper investigates this claim by examining the statistics of natural frequencies, total energy and underlying dispersion equation yielded by the nonparametric approach for two simple systems: a thin plate in bending and a one-dimensional finite periodic massspring chain. Results for the plate show that the average modal density and the underlying dispersion equation of the structure are gradually and systematically altered with increasing uncertainty. The findings for the massspring chain corroborate the findings for the plate and show that the remote coupling of nonadjacent degrees of freedom induced by the approach suppresses the phenomenon of mode localization. This remote coupling also leads to an instantaneous response of all points in the chain when one mass is excited. In the light of these results, it is argued that the nonparametric approach can deal with a certain type of model uncertainty, in this case the presence of unknown terms of higher or lower order in the governing differential equation, but that certain expectations about the system such as the average modal density may conflict with these results. © 2012 Elsevier Ltd.

Uncertainty management for on-line optimisation of a POMDP-based large-scale spoken dialogue system

The optimization of dialogue policies using reinforcement learning (RL) is now an accepted part of the state of the art in spoken dialogue systems (SDS). Yet, it is still the case that the commonly used training algorithms for SDS require a large number of dialogues and hence most systems still rely on artificial data generated by a user simulator. Optimization is therefore performed off-line before releasing the system to real users. Gaussian Processes (GP) for RL have recently been applied to dialogue systems. One advantage of GP is that they compute an explicit measure of uncertainty in the value function estimates computed during learning. In this paper, a class of novel learning strategies is described which use uncertainty to control exploration on-line. Comparisons between several exploration schemes show that significant improvements to learning speed can be obtained and that rapid and safe online optimisation is possible, even on a complex task. Copyright © 2011 ISCA.