Phrase linguistic classification and generalization for improving statistical machine translation

In this paper a method to incorporate linguistic information regarding single-word and compound verbs is proposed, as a first step towards an SMT model based on linguistically-classified phrases. By substituting these verb structures by the base form of the head verb, we achieve a better statistical word alignment performance, and are able to better estimate the translation model and generalize to unseen verb forms during translation. Preliminary experiments for the English - Spanish language pair are performed, and future research lines are detailed. © 2005 Association for Computational Linguistics.

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.

Comparison of manual and user guided methodologies for the classification and retrieval of construction site images

The amount of original imaging information produced yearly during the last decade has experienced a tremendous growth in all industries due to the technological breakthroughs in digital imaging and electronic storage capabilities. This trend is affecting the construction industry as well, where digital cameras and image databases are gradually replacing traditional photography. Owners demand complete site photograph logs and engineers store thousands of images for each project to use in a number of construction management tasks like monitoring an activity's progress and keeping evidence of the "as built" in case any disputes arise. So far, retrieval methodologies are done manually with the user being responsible for imaging classification according to specific rules that serve a limited number of construction management tasks. New methods that, with the guidance of the user, can automatically classify and retrieve construction site images are being developed and promise to remove the heavy burden of manually indexing images. In this paper, both the existing methods and a novel image retrieval method developed by the authors for the classification and retrieval of construction site images are described and compared. Specifically a number of examples are deployed in order to present their advantages and limitations. The results from this comparison demonstrates that the content based image retrieval method developed by the authors can reduce the overall time spent for the classification and retrieval of construction images while providing the user with the flexibility to retrieve images according different classification schemes.

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)

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.

Film bulk acoustic resonator pressure sensor with self temperature reference

A novel film bulk acoustic resonator (FBAR) with two resonant frequencies which have opposite reactions to temperature changes has been designed. The two resonant modes respond differently to changes in temperature and pressure, with the frequency shift being linearly correlated with temperature and pressure changes. By utilizing the FBAR's sealed back trench as a cavity, an on-chip single FBAR sensor suitable for measuring pressure and temperature simultaneously is proposed and demonstrated. The experimental results show that the pressure coefficient of frequency for the lower frequency peak of the FBAR sensors is approximately -17.4 ppm kPa-1, while that for the second peak is approximately -6.1 ppm kPa-1, both of them being much more sensitive than other existing pressure sensors. This dual mode on-chip pressure sensor is simple in structure and operation, can be fabricated at very low cost, and yet requires no specific package, therefore has great potential for applications. © 2012 IOP Publishing Ltd.

Sensor data scheduling for linear quadratic Gaussian control with full state feedback

Networked control systems (NCSs) have attracted much attention in the past decade due to their many advantages and growing number of applications. Different than classic control systems, resources in NCSs, such as network bandwidth and communication energy, are often limited, which degrade the closed-loop system performance and may even cause the system to become unstable. Seeking a desired trade-off between the closed-loop system performance and the limited resources is thus one heated area of research. In this paper, we analyze the trade-off between the sensor-to-controller communication rate and the closed-loop system performance indexed by the conventional LQG control cost. We present and compare several sensor data schedules, and demonstrate that two event-based sensor data schedules provide better trade-off than an optimal offline schedule. Simulation examples are provided to illustrate the theories developed in the paper. © 2012 AACC American Automatic Control Council).