Novel dual transient temperature modulation technique for multi-vapour detection

Here we present a novel signal processing technique for a square wave thermally-modulated carbon black/polymer composite chemoresistor. The technique consists of only two mathematical operations: summing the off-transient and on-transient conductance signals; and subtracting the steady-state conductance signal. A single carbon black/polyvinylpyrrolidone composite chemo -resistor was fabricated and used to demonstrate the validity of the technique. Classification of water, methanol and ethanol vapours was successfully performed using only the peak time of the resultant curves. Quantification of the different vapours was also possible using the height of the peaks, because it was linearly proportional to concentration. This technique does not require zero-gas calibration and thus is superior to previously reported methods. ©2009 IEEE.

Fluxgate magnetic sensor with overload detection

A measurement system for magnetic fields and electric currents uses a single-core fluxgate device driven with a radio frequency excitation source and is provided with a means to indicate saturation of the core of the sensor. A means is provided for detecting overload of the sensor as the core approaches continuous saturation using a pair of demodulators and a comparator.

Detection of large-scale concrete columns for automated bridge inspection

There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame. This is because manually inspecting bridges is a time-consuming and costly task, and some state Departments of Transportation (DOT) cannot afford the essential costs and manpower. In this paper, a novel method that can detect large-scale bridge concrete columns is proposed for the purpose of eventually creating an automated bridge condition assessment system. The method employs image stitching techniques (feature detection and matching, image affine transformation and blending) to combine images containing different segments of one column into a single image. Following that, bridge columns are detected by locating their boundaries and classifying the material within each boundary in the stitched image. Preliminary test results of 114 concrete bridge columns stitched from 373 close-up, partial images of the columns indicate that the method can correctly detect 89.7% of these elements, and thus, the viability of the application of this research.

Large Scale Column Detection for Bridge Inspection

Manually inspecting bridges is a time-consuming and costly task. There are over 600,000 bridges in the US, and not all of them can be inspected and maintained within the specified time frame as some state DOTs cannot afford the essential costs and manpower. This paper presents a novel method that can detect bridge concrete columns from visual data for the purpose of eventually creating an automated bridge condition assessment system. The method employs SIFT feature detection and matching to find overlapping areas among images. Affine transformation matrices are then calculated to combine images containing different segments of one column into a single image. Following that, the bridge columns are detected by identifying the boundaries in the stitched image and classifying the material within each boundary. Preliminary test results using real bridge images indicate that most columns in stitched images can be correctly detected and thus, the viability of the application of this research.

A flexural crack model for damage detection in reinforced concrete structures

The use of changes in vibration data for damage detection of reinforced concrete structures faces many challenges that obstruct its transition from a research topic to field applications. Among these is the lack of appropriate damage models that can be deployed in the damage detection methods. In this paper, a model of a simply supported reinforced concrete beam with multiple cracks is developed to examine its use for damage detection and structural health monitoring. The cracks are simulated by a model that accounts for crack formation, propagation and closure. The beam model is studied under different dynamic excitations, including sine sweep and single excitation frequency, for various damage levels. The changes in resonant frequency with increasing loads are examined along with the nonlinear vibration characteristics. The model demonstrates that the resonant frequency reduces by about 10% at the application of 30% of the ultimate load and then drops gradually by about 25% at 70% of the ultimate load. The model also illustrates some nonlinearity in the dynamic response of damaged beams. The appearance of super-harmonics shows that the nonlinearity is higher when the damage level is about 35% and then decreases with increasing damage. The restoring force-displacement relationship predicted the reduction in the overall stiffness of the damaged beam. The model quantitatively predicts the experimental vibration behaviour of damaged RC beams and also shows the damage dependency of nonlinear vibration behaviour. © 2011 Published under licence by IOP Publishing Ltd.

Electrical detection of trapped charge displacements using an ultra-thin SOI percolation transistor

Electrical detection of solid-state charge qubits requires ultrasensitive charge measurement, typically using a quantum point contact or single-electron-transistor, which imposes strict limits on operating temperature, voltage and current. A conventional FET offers relaxed operating conditions, but the back-action of the channel charge is a problem for such small quantum systems. Here, we discuss the use of a percolation transistor as a measurement device, with regard to charge sensing and backaction. The transistor is based on a 10nm thick SOI channel layer and is designed to measure the displacement of trapped charges in a nearby dielectric. At cryogenic temperatures, the trapped charges result in strong disorder in the channel layer, so that current is constrained to a percolation pathway in sub-threshold conditions. A microwave driven spatial Rabi oscillation of the trapped charge causes a change in the percolation pathway, which results in a measurable change in channel current. © The Electrochemical Society.

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.