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.

Temperature dependence of the Raman spectra of carbon nanotubes

We report on a temperature dependence of the frequency of all the major peaks in the Raman spectra of carbon nanotubes, using different excitation laser powers at the sample. The frequency decreases with increasing temperature for all peaks, and the shifts in Raman frequencies are linear in the temperature of the sample. In comparison, a similar dependence is found in active carbon, but no shift is observed for the highly ordered pyrolytic graphite within the same range of variation in laser power. A lowering of frequency at higher temperature implies an increase in the carbon-carbon distance at higher temperature. The relatively strong temperature dependence in carbon nanotubes and active carbon may be due to the enhanced increase in carbon-carbon distance. This enhancement may originate from the heavy defects and disorder in these materials. (C) 1998 American Institute of Physics. [S0021-8979(98)05219-0].

Effect of defects on optical phonon Raman spectra in SiC nanorods

Fabricated one-dimensional (1D) materials often have abundant structural defects. Experimental observation and numerical calculation indicate that the broken translation symmetry due to structural defects may play a more important role than the quantum confinement effect in the Raman features of optical phonons in polar semiconductor quantum wires such as SiC nanorods, (C) 1999 Elsevier Science Ltd. All rights reserved.

COMPUTATION OF SYNTHETIC SPECTRA FROM SIMULATIONS OF RELATIVISTIC SHOCKS

Particle-in-cell (PIC) simulations of relativistic shocks are in principle capable of predicting the spectra of photons that are radiated incoherently by the accelerated particles. The most direct method evaluates the spectrum using the fields given by the Lienard-Wiechart potentials. However, for relativistic particles this procedure is computationally expensive. Here we present an alternative method that uses the concept of the photon formation length. The algorithm is suitable for evaluating spectra both from particles moving in a specific realization of a turbulent electromagnetic field or from trajectories given as a finite, discrete time series by a PIC simulation. The main advantage of the method is that it identifies the intrinsic spectral features and filters out those that are artifacts of the limited time resolution and finite duration of input trajectories.

Analysis of Fixed-Speed Wind Farm Low-Frequency Power Pulsations using a Wavelet-Prony Method

The increasing penetration of wind generation on the Island of Ireland has been accompanied by close investigation of low-frequency periodic pulsations contained within the active power flow from different wind farms. A primary concern is excitation of existing low-frequency oscillation modes already present on the system, particularly the 0.75 Hz mode as a consequence of the interconnected Northern and Southern power system networks. Recently grid code requirements on the Northern Ireland power system have been updated stipulating that wind farms connected after 2005 must be able to control the magnitude of oscillations in the range of 0.25 - 1.75 Hz to within 1% of the wind farm's registered output. In order to determine whether wind farm low-frequency oscillations have a negative effect (excite other modes) or possibly a positive impact (damping of existing modes) on the power system, the oscillations at the point of connection must be measured and characterised. Using time - frequency methods, research presented in this paper has been conducted to extract signal features from measured low-frequency active power pulsations produced by wind farms to determine the effective composition of possible oscillatory modes which may have a detrimental effect on system dynamic stability. The paper proposes a combined wavelet-Prony method to extract modal components and determine damping factors. The method is exemplified using real data obtained from wind farm measurements.

A Wavelet Approach to the Selective Computation of Eigenvalues for Small Signal Stability Analysis

This paper proposes a method to assess the small signal stability of a power system network by selective determination of the modal eigenvalues. This uses an accelerating polynomial transform, designed using approximate eigenvalues
obtained from a wavelet approximation. Application to the IEEE 14 bus network model produced computational savings of 20%,over the QR algorithm.

A Wavelet Approach to the Selective Computation of Domninant Eigenvalues

This paper introduces an algorithm that calculates the dominant eigenvalues (in terms of system stability) of a linear model and neglects the exact computation of the non-dominant eigenvalues. The method estimates all of the eigenvalues using wavelet based compression techniques. These estimates are used to find a suitable invariant subspace such that projection by this subspace will provide one containing the eigenvalues of interest. The proposed algorithm is exemplified by application to a power system model.

Data Reduction of Power System Disturbances using Wavelet Transforms for Network Operation and Control

Wavelet transforms provide basis functions for time-frequency analysis and have properties that are particularly useful for compression of analogue point on wave transient and disturbance power system signals. This paper evaluates the reduction properties of the wavelet transform using real power system data and discusses the application of the reduction method for information transfer in network communications.

The use of an instrumented vehicle in a wavelet based damage detection approach for bridge structures

Periodic monitoring of structures such as bridges is necessary as their condition can deteriorate due to environmental conditions and ageing, causing the bridge to become unsafe. This monitoring - so called Structural Health Monitoring (SHM) - can give an early warning if a bridge becomes unsafe. This paper investigates an alternative wavelet-based approach for the monitoring of bridge structures which consists of the use of a vehicle fitted with accelerometers on its axles. A simplified vehicle-bridge interaction model is used in theoretical simulations to examine the effectiveness of the approach in detecting damage in the bridge. The accelerations of the vehicle are processed using a continuous wavelet transform, allowing a time-frequency analysis to be performed. This enables the identification of both the existence and location of damage from the vehicle response. Based on this analysis, a damage index is established. A parametric study is carried out to investigate the effect of parameters such as the bridge span length, vehicle speed, vehicle mass, damage level, signal noise level and road surface roughness on the accuracy of results. In addition, a laboratory experiment is carried out to validate the results of the theoretical analysis and assess the ability of the approach to detect changes in the bridge response.

Experimental investigation of a wavelet based drive-by bridge inspection system incorporating pattern recognition

This paper presents the results of an experimental investigation, carried out in order to verify the feasibility of a ‘drive-by’ approach which uses a vehicle instrumented with accelerometers to detect and locate damage in a bridge. In theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach in detecting damage in a bridge from vehicle accelerations. For this purpose, the accelerations are processed using a continuous wavelet transform and damage indicators are evaluated and compared. Alternative statistical pattern recognition techniques are incorporated to allow for repeated vehicle passes. Parameters such as vehicle speed, damage level, location and road roughness are varied in simulations to investigate the effect. A scaled laboratory experiment is carried out to assess the effectiveness of the approach in a more realistic environment, considering a number of bridge damage scenarios.

Wavelet Based Damage Detection Approach for Bridge Structures Utilising Vehicle Vibration

This paper investigates a wavelet-based damage detection approach for bridge structures. By analysing the continuous wavelet transform of the vehicle response, the approach aims to identify changes in the bridge response which may indicate the existence of damage. A numerical vehicle-bridge interaction model is used in simulations as part of a sensitivity study. Furthermore, a laboratory experiment is carried out to investigate the effects of varying vehicle configuration, speed and bridge damping on the ability of the vehicle to detect changes in the bridge response. The accelerations of the vehicle and bridge are processed using a continuous wavelet transform, allowing time-frequency analysis to be carried out on the responses of the laboratory vehicle-bridge interaction system. Results indicate the most favourable conditions for successful implementation of the approach.