Cross Wavelet Transform Research Articles

Gait Recognition by Cross Wavelet Transform and Graph Model

In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is proposed. We use cross wavelet transform to extract dynamic feature and bipartite graph model to extract static feature which are coefficients of quadrature mirror filter U+0028 QMF U+0029-graph wavelet filter bank. Feature fusion is done after normalization. For normalization of features, min-max rule is used and mean-variance method is used to find weights for normalized features. Euclidean distance between each feature vector and center of the cluster which is obtained by k-means clustering is used as similarity measure in Bayesian framework. Experiments performed on widely used CASIA B gait database show that, the fusion of these two feature sets preserve discriminant information. We report 99.90 U+0025 average recognition rate.

The Coherency and Correlation between Sea Surface Temperature and Wind Velocity in Malacca Strait: Cross Wavelet Transform and Wavelet Coherency Application

This study tried to observe the correlation and coherency between sea surface temperature (SST) and wind velocity in the Malacca Strait at the year of 2015. The SST and wind velocity data with 6 hours interval step have been used in this study. S-Transform, the Cross Wavelet Transform, and the Wavelet Coherency were applied to observe the influence of the variation of sea surface temperature to the wind velocity in Malacca Strait. These methods could produce the phase lag and the time of occurrence between them. S-Transform was used to show the spectrum energy of the sea surface temperature variation. The strongest correlation between them has the period of 32 days during July to August and October to November at each point with significance level of 95 %. The coherency of them has the range of 4 to 64 days at each point. The last result is the spectrum energy of SST variation that has the period of 5 to 50 days at each point. It was similar to the result of the correlation and coherence period between the wind velocity and the SST data

Identification of Elevated ST Segment and Deep Q Type MI Variant Using Cross Wavelet Transform and Hierarchical Classification From ECG Signals

Coronary artery disease being one of the leading causes of mortality in the world requires prompt attention and for which technological intervention is helpful. The present manuscript proposes a cross wavelet transform based method for classification of Type I Inferior Myocardial Infraction (ST elevated and attenuated T wave) and Type II Inferior Myocardial Infarction (elongated Q wave and inverted T wave morphology). Cross-examination of cross-correlation between two time series reveals localised similarities in time-scale where distinguishing parameters for classification are extracted from wavelet cross spectrum and wavelet coherence. When ECG pattern in the QT region differ from the normal cardiac cycle and a variation is observed in the QT region of lead III, which gives an indication of Inferior MI. The WCS and WCOH of the ECG spectrum together show substantive spectral variations in the QRS complex region (R1) and over the T wave occurrences (R2). The classification of two types of MIs and normal parameters are identified from the corresponding regions R1 and R2. This method gives an accuracy of 99.43% for normal and abnormal class and 88.5 and 87.02% for Type I and Type II Inferior MI respectively.

Detection of heat and cold waves in Montevergine time series (1884–2015)

Abstract. In recent years, extreme events related to cooling and heating have taken high resonance, motivating the scientific community to carry out an intensive research activity, aimed to detect their variability and frequency. In this work, we have investigated about the frequency, the duration, the severity and the intensity of heat and cold waves in a Southern Italy high-altitude region, by analysing the climatological time series collected in Montevergine observatory. Following the guidelines provided by CLIVAR project (Climate and Ocean Variability, Predictability and Change), we have adopted indicators based on percentiles and duration to define a heat wave and cold event. Main results have highlighted a strong and significant positive trend in the last 40 years (1974–2015) in heat waves frequency, severity and intensity. On the contrary, in recent decades, cold wave events have exhibited a significant and positive trend only in intensity. Moreover, through the usage of two Wavelet Analysis tools, the Cross Wavelet Transform and the Wavelet Coherence, we have investigated about the connections between the extreme temperature events occurred in Montevergine and the large-scale atmospheric patterns. The heat wave events have exhibited relevant relationships with the Western European Zonal Circulation and the North Atlantic Oscillation, whereas the variability of cold wave events have shown linkages with the Eastern Mediterranean Pattern and the North Sea Caspian Pattern. In addition, the main features of synoptic patterns that have caused summer heat waves and winter cold waves in Montevergine site are presented.

Su1591 Cross-Wavelet Transform for the Automated Analysis of High-Resolution Colonic Manometry Data

Su1591 Cross-Wavelet Transform for the Automated Analysis of High-Resolution Colonic Manometry Data

Application of the Cross Wavelet Transform to Solar Activity and Major Earthquakes Occurred in Chile

Historical earthquakes registered in Chile (from 1900 up to 2015) with epicenters located between 17?30'S and 56?0'S latitude and yearly mean total sunspot number have been considered in order to evaluate a significant linkage between them. The occurrence of strong earthquakes along Chile and the sunspots activity are analyzed to inspect possible influence of solar cycles on earthquakes. The cross wavelet transform and wavelet coherence analysis were applied for sequences of sunspots and earthquakes activity. An 8 - 12 years modulation of earthquakes activity has been identified.

Analysis of the ground vibration induced by high dam flood discharge using the cross wavelet transform method

As flood is discharged in the high dam, vibrations are generated and transmitted to the surrounding ground, causing vibrations in adjacent structures. The vibration can sometimes reach such a high level that can be harmful to the neighboring residents and buildings, which is an undesirable phenomenon and should be prevented. In this study, an attempt has been made to identify the vibration sources, analyze the law of vibration propagation, and establish the relationship between the vibration sources and ground vibration using the Time-Wavelet Power Spectrum and the Cross Wavelet Transform techniques. The results reveal that the flow fluctuating loads generated by different discharge structures are the main cause of the ground vibration. The contribution from each vibration source as well as its attenuation during propagation is different, based on the ways of flood discharge and composition of the grounds. The results lay a good foundation for controlling the ground vibration induced by flood discharge of high dams in the future.

Variability of rainfall over Lake Kariba catchment area in the Zambezi river basin, Zimbabwe

In this study, average monthly and annual rainfall totals recorded for the period 1970 to 2010 from a network of 13 stations across the Lake Kariba catchment area of the Zambezi river basin were analyzed in order to characterize the spatial-temporal variability of rainfall across the catchment area. In the analysis, the data were subjected to intervention and homogeneity analysis using the Cumulative Summation (CUSUM) technique and step change analysis using rank-sum test. Furthermore, rainfall variability was characterized by trend analysis using the non-parametric Mann-Kendall statistic. Additionally, the rainfall series were decomposed and the spectral characteristics derived using Cross Wavelet Transform (CWT) and Wavelet Coherence (WC) analysis. The advantage of using the wavelet-based parameters is that they vary in time and can therefore be used to quantitatively detect time-scale-dependent correlations and phase shifts between rainfall time series at various localized time-frequency scales. The annual and seasonal rainfall series were homogeneous and demonstrated no apparent significant shifts. According to the inhomogeneity classification, the rainfall series recorded across the Lake Kariba catchment area belonged to category A (useful) and B (doubtful), i.e., there were zero to one and two absolute tests rejecting the null hypothesis (at 5 % significance level), respectively. Lastly, the long-term variability of the rainfall series across the Lake Kariba catchment area exhibited non-significant positive and negative trends with coherent oscillatory modes that are constantly locked in phase in the Morlet wavelet space.

A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

Lean or ultralean combustion is one of the popular strategies to achieve very low emission levels. However, it is extremely susceptible to lean blow-out (LBO). The present work explores a Cross-wavelet transform (XWT) aided rule based scheme for early prediction of lean blowout. XWT can be considered as an advancement of wavelet analysis which gives correlation between two waveforms in time-frequency space. In the present scheme a swirl-stabilized dump combustor is used as a laboratory-scale model of a generic gas turbine combustor with LPG as fuel. Various time series data of CH chemiluminescence signal are recorded for different flame conditions by varying equivalence ratio, flow rate and level of air-fuel premixing. Some features are extracted from the cross-wavelet spectrum of the recorded waveforms and a reference wave. The extracted features are observed to classify the flame condition into three major classes: near LBO, moderate and healthy. Moreover, a Rough Set based technique is also applied on the extracted features to generate a rule base so that it can be fed to a real time controller or expert system to take necessary control action to prevent LBO. Results show that the proposed methodology performs with an acceptable degree of accuracy.

The relevance of the cross-wavelet transform in the analysis of human interaction – a tutorial

This article sheds light on a quantitative method allowing psychologists and behavioral scientists to take into account the specific characteristics emerging from the interaction between two sets of data in general and two individuals in particular. The current article outlines the practical elements of the cross-wavelet transform (CWT) method, highlighting WHY such a method is important in the analysis of time-series in psychology. The idea is (1) to bridge the gap between physical measurements classically used in physiology – neuroscience and psychology; (2) and demonstrates how the CWT method can be applied in psychology. One of the aims is to answer three important questions WHO could use this method in psychology, WHEN it is appropriate to use it (suitable type of time-series) and HOW to use it. Throughout these explanations, an example with simulated data is used. Finally, data from real life application are analyzed. This data corresponds to a rating task where the participants had to rate in real time the emotional expression of a person. The objectives of this practical example are (i) to point out how to manipulate the properties of the CWT method on real data, (ii) to show how to extract meaningful information from the results, and (iii) to provide a new way to analyze psychological attributes.

The importance of studying all subgoals at once.

The importance of studying all subgoals at once.

Identification of Strong Karst Groundwater Runoff Belt by Cross Wavelet Transform

Karst aquifers are highly heterogeneous and exhibit hierarchical permeability structures or flow paths. Conduits and fractures typically account for less than 1 percent of the porosity of the aquifer, but more than 95 percent of the permeability. For the purposes of karst groundwater resources management, as well as of protection strategies against potential contamination, identifying the strong karst groundwater runoff belt of an entire aquifer system is generally more important than information about a specific spring. In this project, we introduce cross wavelet transform to analyze the relation between precipitation and spring discharge, and then identify the strong karst groundwater runoff belt. In highly concentrated karst areas, the precipitation signal can penetrate an aquifer relatively easily and will readily affect spring discharge. The precipitation and spring discharge are thus closely related, and the cross wavelet transform coefficients are large. Conversely, in areas of low karst concentration, the cross wavelet transform coefficients are small. We applied the method to Niangziguan Springs Basin in China to detect the strong karst groundwater runoff belt. Results showed that Pingding County and Yangquan City have a high degree of karstification (i.e. the strong karst groundwater runoff belt), Xiyang County and Shouyang County have a moderate degree of karstification, and Yuxian County, Heshun County, and Zuoquan County have a low degree of karstification. The results agree with the geological structure of Niangziguan Springs Basin.

Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification

In this paper, we use cross wavelet transform (XWT) for the analysis and classification of electrocardiogram (ECG) signals. The cross-correlation between two time-domain signals gives a measure of similarity between two waveforms. The application of the continuous wavelet transform to two time series and the cross examination of the two decompositions reveal localized similarities in time and frequency. Application of the XWT to a pair of data yields wavelet cross spectrum (WCS) and wavelet coherence (WCOH). The proposed algorithm analyzes ECG data utilizing XWT and explores the resulting spectral differences. A pathologically varying pattern from the normal pattern in the QT zone of the inferior leads shows the presence of inferior myocardial infarction. A normal beat ensemble is selected as the absolute normal ECG pattern template, and the coherence between various other normal and abnormal subjects is computed. The WCS and WCOH of various ECG patterns show distinguishing characteristics over two specific regions R1 and R2, where R1 is the QRS complex area and R2 is the T-wave region. The Physikalisch-Technische Bundesanstalt diagnostic ECG database is used for evaluation of the methods. A heuristically determined mathematical formula extracts the parameter(s) from the WCS and WCOH. Empirical tests establish that the parameter(s) are relevant for classification of normal and abnormal cardiac patterns. The overall accuracy, sensitivity, and specificity after combining the three leads are obtained as 97.6%, 97.3%, and 98.8%, respectively.

The Time-Delayed Correlation Analysis of the Several Factors which Affect “0801 Blizzard” Based on the Cross Wavelet and Wavelet Coherence Transform Methods

We think the " 0801 blizzard " is not only caused by La Nina events, but also caused by the combined effect of multiple factors just as "combination of abnormity" .So firstly in this paper, the mutative character of several primary affecting factors are elementarily analyzed. Then based on the cross wavelet and wavelet coherence transform methods, these factors how to affect rain and snow disasters is found in time-delayed characteristics and the correlation between them is confirmed, to further explain the partial reason of the “0801 blizzard”.

Monitoring of inter-turn insulation failure in induction motor using advanced signal and data processing tools

Detection of stator winding inter-turn insulation failure at early stages is crucial for promoting safe and economical use of induction motors in industrial applications. Whereas major insulation failures involving larger percentages of winding are easily discernible from magnitude of supply current, minor inter-turn insulation failures involving less than 5% of turns often go undetected. The present contribution reports experimental results of minor faults due to inter-turn insulation failures in stator windings of induction motor under different loading conditions being analyzed using data and signal processing tools combining Park's Transform and Cross Wavelet Transform. Rough Set Theory (RST) based classifier has been used for fault severity monitoring.

Application of 3D wavelet transforms for crack detection in rotor systems

The dynamics and diagnostics of a cracked rotor have been gaining importance in recent years. The early detection of faults like fatigue cracks in rotor shafts are very important to prevent catastrophic failure of the rotor system. Vibration monitoring during start up or shut-down is as important as during steady state operation to detect cracks especially for machines such as aircraft engines which start and stop quite frequently and run at high speeds. So, the transient data of the cracked rotor has been transformed using the wavelet transforms for crack detection. Most of the works quoted in the literature used 1D wavelets or 2D wavelets (Continuous Wavelet Transform-CWT) for crack detection. The crack detectors in the signals are both time as well as frequency dependent. So, the use of 2D wavelets is also not enough to detect the crack. In the present work a 3D wavelet (CWT) has been utilized which clearly indicates both the time and frequency features of the crack. The presence of sub-criticals in the CWT may be a best crack indicator but it is not always reliable. The addition of noise to the signal may sometimes lead to inaccurate results. So, there is a need to identify a parameter in addition to the sub-criticals. The phase angle between the two signals (cracked and un-cracked) or two transverse vibrations can be a better crack indicator because it is very less sensitive to noise disturbance. So, to extract the above phase angle a new transform has been applied called Cross Wavelet Transform (XWT). The XWT is exploited for the first time to a rotor fault detection system in the present work. Some interesting results have been obtained using the same. The advantage of the XWT is that both, the phase angles between the transverse signals and also the amplitudes of sub-criticals are viewed in a single plot. Parametric analysis is also carried out by varying crack depth and crack position for diagnostic purposes. The inverse problem of crack identification (i.e. determining the crack parameters through known vibration data) has also been carried out using Artificial Neural Network (ANN).

Association of North Atlantic Oscillations with Aksu River runoff in China

The relationship between North Atlantic Oscillations (NAO) and Aksu River Runoff (ARR) was investigated by using the wavelet transform (WT), cross wavelet transform (CWT), correlation and linear trend analyses, and abrupt change test. The main results are as follows: the interannual/decadal variation and period analyses of ARR and NAO reveal that the both were close correlated each other; the CWT indicates that the correlation was good between ARR and NAO at all periods in the 1990s, because the significant correlation areas mainly concentrated in the 1990s; the variations in the trend strength of ARR and NAO were consistent; the abrupt change of NAO was also temporally consistent with that of ARR, which exerted impact on the Aksu River Basin (ARB) climate and then the ARR through atmospheric circulation variation.

Unintended interpersonal co-ordination: “can we march to the beat of our own drum?”

The main purpose of this article was to identify whether an interpersonal motor co-ordination emerges between two participants when they intentionally tried to not co-ordinate their movements between each other. To do this we conjointly examined both the emergence of the motor co-ordination (collective properties) between the two participants, and the maintenance of the individual intrinsic motor properties present in the interpersonal co-ordination. Six pairs of participants performed three situations where they freely moved their right forearm with no instructions (nor restrictions) on frequency or relative phase. The goal of the first two situations was for participants to intentionally co-ordinate ( P-on) or not co-ordinate ( P-off) their movements between each other. In order to determine his/her individual motor properties, each participant performed this task alone, in a third condition. The cross-wavelet transform (CWT) revealed in the P-off condition the emergence of an unintended co-ordination in the frequency domain. Moreover, random distribution of the relative phase appeared, in this condition, whereas in the P-on condition, we observed a collective organization especially at a relative phase around 0°. Conjointly, the wavelet transform (WT) showed the presence of individual intrinsic motor properties (motor signature) in the three conditions. These results indicate that, when there was information sharing, participants could not avoid (unintentionally) coordinating with someone. This study suggests that, in daily life, interactions with other people lead the individual motor behavior to adapt unintended motor co-ordination.