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.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Micro-electro-mechanical resonant tilt sensor

This paper reports a micro-electro-mechanical tilt sensor based on resonant sensing principles. The tilt sensor measures orientation by sensing the component of gravitational acceleration along a specified input axis. Design aspects of the tilt sensor are first introduced and a design trade-off between sensitivity, resolution and robustness is addressed. A prototype sensor is microfabricated in a foundry process. The sensor is characterized to validate predictive analytical and FEA models of performance. The prototype is tested over tilt angles ranging over ±90 degrees and the linearity of the sensor is found to be better than 1.4% over the tilt angle range of ±20°. The noise-limited resolution of the sensor is found to be approximately 0.00026 degrees for an integration time of 0.6 seconds. © 2012 IEEE.

Minimum sensor duty cycle with guaranteed estimator performance

In this paper, we consider Kalman filtering over a network and construct the optimal sensor data scheduling schemes which minimize the sensor duty cycle and guarantee a bounded error or a bounded average error at the remote estimator. Depending on the computation capability of the sensor, we can either give a closed-form expression of the minimum sensor duty cycle or provide tight lower and upper bounds of it. Examples are provided throughout the paper to demonstrate the results. © 2012 IEEE.

Sensor graphs for guaranteed cooperative localization performance

A group of mobile robots can localize cooperatively, using relative position and absolute orientation measurements, fused through an extended Kalman filter (ekf). The topology of the graph of relative measurements is known to affect the steady-state value of the position error covariance matrix. Classes of sensor graphs are identified, for which tight bounds for the trace of the covariance matrix can be obtained based on the algebraic properties of the underlying relative measurement graph. The string and the star graph topologies are considered, and the explicit form of the eigenvalues of error covariance matrix is given. More general sensor graph topologies are considered as combinations of the string and star topologies, when additional edges are added. It is demonstrated how the addition of edges increases the trace of the steady-state value of the position error covariance matrix, and the theoretical predictions are verified through simulation analysis.