Crack detection of steel girders using Brillouin optical time domain analysis

This article presents the results from an experimental program designed to evaluate the performance of a system consisting of a readout unit and a ribbon type Fiber Optic Sensor (FOS) based on Brillouin Optical Time Domain Analysis (BOTDA). The system is intended for the detection of cracks as well as the monitoring of long-term performance for steel bridge girders. The program consisted of introducing a crack at the center of a 3-m-long steel beam and monitoring its progression using static loading tests performed at ambient and sub-zero temperatures. For sensor lengths similar to those used in the field, the resonant frequency shifts per unit increase in crack width were found to decrease from 114 MHz/mm at ambient temperature (~25C) to 65 MHz/mm at -10C. Results also revealed nonlinearity and variability, which can be attributed to an incompatibility between the settings of the laser pump in the readout unit and the sensor length. Significant losses were detected along the bonded segments of the sensor and were attributed to the presence of ripples along the sensor. These undulations worsen with a reduction in temperature and are induced by the bonding procedure as well as the slack provided in the plastic sleeves containing the splices.

Comparison of Wavelet Transform and Time-Domain Analysis of Second Trimester Uterine Artery Doppler Waveforms in Screening for Pre-Eclampsia

The aim of this study was to compare time-domain waveform analysis of second-trimester uterine artery Doppler using the resistance index (RI) with waveform analysis using a mathematical tool known as wavelet transform for the prediction of pre-eclampsia (PE). This was a retrospective, nested case-cohort study of 336 women, 37 of whom subsequently developed PE. Uterine artery Doppler waveforms were analysed using both RI and waveform analysis. The utility of these indices in screening for PE was then evaluated using receiver operating characteristic curves. There were significant differences in uterine artery RI between the PE women and those with normal pregnancy outcome. After wavelet analysis, significant difference in the mean amplitude in wavelet frequency band 4 was noted between the 2 groups. The sensitivity for both Doppler RI and frequency band 4 for the detection of PE at a 10% false-positive rate was 45%. This small study demonstrates the application of wavelet transform analysis of uterine artery Doppler waveforms in screening for PE. Further prospective studies are needed in order to clearly define if this analytical approach to waveform analysis may have the potential to improve the detection of PE by uterine artery Doppler screening.

A Frequency Domain Algorithm for Wave Separation

We propose a frequency domain adaptive algorithm for
wave separation in wind instruments. Forward and backward travelling waves are obtained from the signals acquired by two microphones placed along the tube, while the
separation ?lter is adapted from the information given by a
third microphone. Working in the frequency domain has a
series of advantages, among which are the ease of design of
the propagation ?lter and its differentiation with respect to
its parameters.
Although the adaptive algorithm was developed as a ?rst
step for the estimation of playing parameters in wind instruments it can also be used, without any modi?cations, for
other applications such as in-air direction of arrival (DOA)
estimation. Preliminary results on these applications will
also be presented.

Monitoring Bridge Dynamic Behaviour using an Instrumented Two Axle Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing, loading and environmental factors. A rational transport policy must monitor and provide adequate maintenance to this infrastructure to guarantee the required levels of transport service and safety. Increasingly in recent years, bridges are being instrumented and monitored on an ongoing basis due to the implementation of Bridge Management Systems. This is very effective and provides a high level of protection to the public and early warning if the bridge becomes unsafe. However, the process can be expensive and time consuming, requiring the installation of sensors and data acquisition electronics on the bridge. This paper investigates the use of an instrumented 2-axle vehicle fitted with accelerometers to monitor the dynamic behaviour of a bridge network in a simple and cost-effective manner. A simplified half car-beam interaction model is used to simulate the passage of a vehicle over a bridge. This investigation involves the frequency domain analysis of the axle accelerations as the vehicle crosses the bridge. The spectrum of the acceleration record contains noise, vehicle, bridge and road frequency components. Therefore, the bridge dynamic behaviour is monitored in simulations for both smooth and rough road surfaces. The vehicle mass and axle spacing are varied in simulations along with bridge structural damping in order to analyse the sensitivity of the vehicle accelerations to a change in bridge properties. These vehicle accelerations can be obtained for different periods of time and serve as a useful tool to monitor the variation of bridge frequency and damping with time.

Analysis of Receiver Algorithms for LTE SC-FDMA Based Uplink MIMO Systems

This letter derives mathematical expressions for the received signal-to-interference-plus-noise ratio (SINR) of uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. An improved frequency domain receiver algorithm is derived for the studied systems, and is shown to be significantly superior to the conventional linear MMSE based receiver in terms of SINR and bit error rate (BER) performance.

Transonic aeroelastic simulation for instability searches and uncertainty analysis

In this paper the use of eigenvalue stability analysis of very large dimension aeroelastic numerical models arising from the exploitation of computational fluid dynamics is reviewed. A formulation based on a block reduction of the system Jacobian proves powerful to allow various numerical algorithms to be exploited, including frequency domain solvers, reconstruction of a term describing the fluid–structure interaction from the sparse data which incurs the main computational cost, and sampling to place the expensive samples where they are most needed. The stability formulation also allows non-deterministic analysis to be carried out very efficiently through the use of an approximate Newton solver. Finally, the system eigenvectors are exploited to produce nonlinear and parameterised reduced order models for computing limit cycle responses. The performance of the methods is illustrated with results from a number of academic and large dimension aircraft test cases.

Wavelet analysis of bender element signals

Accurate determination of shear wave arrival time using bender elements may be severely affected by a combination of near-field effects and reflected waves. These may mask the first arrival of the shear wave, making its detection difficult in the time domain. This paper describes an approach for measuring the shear wave arrival time through analysis of the output signal in the time-scale domain using a multi-scale wavelet transform. The local maxima lines of the wavelet transform modulus are observed at different scales, and all singularities are mathematically characterised, allowing subsequent detection of the singularity relating to the first arrival. Examples of the use of this approach on typical synthetic and experimental bender element signals are also supplied, and these results are compared with those from other time and frequency domain approaches. The wavelet approach is not affected by near-field effects, but instead is characterised by a relatively consistent singularity related to the shear wave arrival time, across a range of frequencies and noise levels.

Using Brain Data for Sentiment Analysis

We present the results of exploratory experiments using lexical valence extracted from brain using electroencephalography (EEG) for sentiment analysis. We selected 78 English words (36 for training and 42 for testing), presented as stimuli to 3 English native speakers. EEG signals were recorded from the subjects while they performed a mental imaging task for each word stimulus. Wavelet decomposition was employed to extract EEG features from the time-frequency domain. The extracted features were used as inputs to a sparse multinomial logistic regression (SMLR) classifier for valence classification, after univariate ANOVA feature selection. After mapping EEG signals to sentiment valences, we exploited the lexical polarity extracted from brain data for the prediction of the valence of 12 sentences taken from the SemEval-2007 shared task, and compared it against existing lexical resources.

Wavelets for the Analysis and Compression of Power System Disturbances

Wavelets introduce new classes of basis functions for time-frequency signal analysis and have properties particularly suited to the transient components and discontinuities evident in power system disturbances. Wavelet analysis involves representing signals in terms of simpler, fixed building blocks at different scales and positions. This paper examines the analysis and subsequent compression properties of the discrete wavelet and wavelet packet transforms and evaluates both transforms using an actual power system disturbance from a digital fault recorder. The paper presents comparative compression results using the wavelet and discrete cosine transforms and examines the application of wavelet compression in power monitoring to mitigate against data communications overheads.

Absence of the 7-repeat variant of the DRD4 VNTR is associated with drifting sustained attention in children with ADHD but not in controls

Many genetic studies have demonstrated an association between the 7-repeat (7r) allele of a 48-base pair variable number of tandem repeats (VNTR) in exon 3 of the DRD4 gene and the phenotype of attention deficit hyperactivity disorder (ADHD). Previous studies have shown inconsistent associations between the 7r allele and neurocognitive performance in children with ADHD. We investigated the performance of 128 children with and without ADHD on the Fixed and Random versions of the Sustained Attention to Response Task (SART). We employed timeseries analyses of reaction-time data to allow a fine-grained analysis of reaction time variability, a candidate endophenotype for ADHD. Children were grouped into either the 7r-present group (possessing at least one copy of the 7r allele) or the 7r-absent group. The ADHD group made significantly more commission errors and was significantly more variable in RT in terms of fast moment-to-moment variability than the control group, but no effect of genotype was found on these measures. Children with ADHD without the 7r allele made significantly more omission errors, were significantly more variable in the slow frequency domain and showed less sensitivity to the signal (d') than those children with ADHD the 7r and control children with or without the 7r. These results highlight the utility of time-series analyses of reaction time data for delineating the neuropsychological deficits associated with ADHD and the DRD4 VNTR. Absence of the 7-repeat allele in children with ADHD is associated with a neurocognitive profile of drifting sustained attention that gives rise to variable and inconsistent performance. (c) 2008 Wiley-Liss, Inc.