Analyzing laser plasma interferograms with a continuous wavelet transform ridge extraction technique: the method

Laser plasma interferograms are currently analyzed by extraction of the phase-shift map with fast Fourier transform (FFT) techniques [Appl. Opt. 18, 3101 (1985)]. This methodology works well when interferograms are only marginally affected by noise and reduction of fringe visibility, but it can fail to produce accurate phase-shift maps when low-quality images are dealt with. We present a novel procedure for a phase-shift map computation that makes extensive use of the ridge extraction in the continuous wavelet transform (CWT) framework. The CWT tool is flexible because of the wide adaptability of the analyzing basis, and it can be accurate because of the intrinsic noise reduction in the ridge extraction. A comparative analysis of the accuracy performances of them new tool and the FFT-based one shows that the CWT-based tool produces phase maps considerably less noisy and that it can better resolve local inhomogeneties. (C) 2001 Optical Society of America.

Regression analysis of spatial data

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Rapid design of discrete orthonormal wavelet transforms

A methodology which allows a non-specialist to rapidly design silicon wavelet transform cores has been developed. This methodology is based on a generic architecture utilizing time-interleaved coefficients for the wavelet transform filters. The architecture is scaleable and it has been parameterized in terms of wavelet family, wavelet type, data word length and coefficient word length. The control circuit is designed in such a way that the cores can also be cascaded without any interface glue logic for any desired level of decomposition. This parameterization allows the use of any orthonormal wavelet family thereby extending the design space for improved transformation from algorithm to silicon. Case studies for stand alone and cascaded silicon cores for single and multi-stage analysis respectively are reported. The typical design time to produce silicon layout of a wavelet based system has been reduced by an order of magnitude. The cores are comparable in area and performance to hand-crafted designs. The designs have been captured in VHDL so they are portable across a range of foundries and are also applicable to FPGA and PLD implementations.

The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source, then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are, however, considerably fewer techniques available if only a single-channel measurement is available and yet single-channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single-channel measurements. The EEMD technique is first used to decompose the single-channel signal into a multidimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second-order statistics. The new technique is tested against the currently available wavelet denoising and EEMD-ICA techniques using both electroencephalography and functional near-infrared spectroscopy data and is shown to produce significantly improved results. © 1964-2012 IEEE.

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.

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.

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.

A wavelet based drive-by bridge inspection system

This paper investigates a low-cost wavelet-based approach for the preliminary monitoring of bridge structures, consisting of the use of a vehicle fitted with accelerometers on its axles. The approach aims to reduce the need for direct instrumentation of the bridge. A time-frequency analysis is carried out in order to identify the existence and location of damage from vehicle accelerations. Firstly, in theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach. A number of damage indicators are evaluated and compared. A range of parameters such as the bridge span, vehicle speed, damage level and location, signal noise and road roughness are varied in simulations. Secondly, a scaled laboratory experiment is carried out to validate the results of the theoretical analysis and assess the ability of the selected damage indicators to detect changes in the bridge response from vehicle accelerations.