Cross-wavelet Spectra Research Articles

Wet and dry spell induced changes in the soil CO2 effluxes of Pine and Oak ecosystems of Central Himalaya: a comparative assessment for monsoon and winter seasons.

Soil efflux of CO2( ) is known to be dependent on natural drying and rewetting of the soil. Although the central Indian Himalayan region is predominantly occupied with two ecosystems, i. e. Pine (Pinus roxburghii) and Oak (Quercus leucotrichophora), differences in their dynamics and responses of to varying wet and dry spells were hardly known. To address this knowledge gap, this study provides a comparative assessment of variability from Pine and Oakecosystems of central Himalaya as a response to rainfall induced wet and dry spells of monsoon and winter seasons. The data presented in this study are collected for242 days of 2021-22 that include monsoon and winter seasons from a Pine and an Oak sites. The mean sof Pine and Oak sites are found to be 3.95(± 0.02) and 3.61(± 0.01) μmol.m-2.s-1, respectively. We find that the winter reduction in the in comparison to monsoon at the Pine site (78%) is more substantial than at Oak site (64.6%). The cross wavelet spectra of and monsoon rainfall amount at the Oak site, unlike the Pine site, indicate a negative relationship. The rainfall spell duration and amount of monsoon wet spells are noted to have an inverse relationship with at both sites, although, increasing rainfall spell duration in winter is noted to increase at Pine and Oak sites. Similarly, increasing is observed with increasingdry spells of monsoon at both sites. Results of this study indicate that in comparison to Oak, variability at Pine ecosystem is primarily driven by abiotic factors wherein wet spell is a major determinant.

Comparing the stretching technique and the wavelet cross-spectrum technique for measuring stress-induced wave-velocity changes in concrete

Coda wave interferometry (CWI) holds promise as a technique for concrete stress monitoring. This is because the coda, which consists of multiply scattered arrivals, is the result of propagation through the medium over large distances. As such, it is sensitive to both minute structural changes and small velocity changes in that medium. Previous studies focusing on concrete have predominantly utilized the time-domain-based stretching technique to measure travel-time changes. There is, however, a lack of consensus on how to quantify these changes effectively. In this study, we conduct a systematic comparison between two techniques, namely the stretching technique and the wavelet cross-spectrum (WCS) technique, for measuring stress-induced velocity changes in a cylindrical concrete sample. Our comparison focuses on two key aspects: (i) stability against cycle skipping and (ii) consistency in retrieving velocity changes. Experimental results reveal that both the WCS technique and the stretching technique yield consistent velocity changes. In terms of stability, it is challenging to determine which technique performs better, due to differences in the mechanisms triggering cycle skipping. However, when considering waves with frequencies ranging from 50 kHz to 80 kHz, both techniques exhibit comparable performance. Based on our findings, we offer the following recommendations for utilizing these CWI techniques in concrete stress monitoring:For the stretching technique, selecting the time window length based on the wave frequency and the expected magnitude of velocity change.For the WCS technique, operating it in the frequency band where spectral decomposition shows sufficiently high energy in the signal and can accommodate the expected magnitude of velocity change.

A novel dynamic cardiorespiratory coupling quantification method reveals the effect of aging on the autonomic nervous system.

Traditional cardiopulmonary coupling (CPC) based on the Fourier transform shares an inherent trade-off between temporal and frequency resolutions with fixed window designs. Therefore, a cross-wavelet cardiorespiratory coupling (CRC) method was developed to highlight interwave cardiorespiratory dynamics and applied to evaluate the age effect on the autonomic regulation of cardiorespiratory function. The cross-wavelet CRC visualization successfully reflected dynamic alignments between R-wave interval signal (RR intervals) and respiration. Strong and continuous CRC was shown if there was perfect temporal coordination between consecutive R waves and respiration, while CRC becomes weaker and intermittent without such coordination. Using real data collected on electrocardiogram (ECG) and respiratory signals, the heart rate variability (HRV) and CRC were calculated. Subsequently, comparisons were conducted between young and elderly individuals. Young individuals had significantly higher partial time and frequency HRV indices than elderly individuals, indicating stronger control of parasympathetic regulation. The overall coupling strength of the CRC of young individuals was higher than that of elderly individuals, especially in high-frequency power, which was significantly lower in the elderly group than in the young group, achieving better results than the HRV indices in terms of statistical significance. Further analyses of the time-frequency dynamics of CRC indices revealed that the coupling strength was consistently higher in the high-frequency (HF) band (0.15-0.4 Hz) in young participants compared to elderly individuals. The dynamic CRC between respiration and HRV indices was accessible by integrating the cross-wavelet spectrum and coherence. Young participants had a significantly higher level of CRC in the HF band, indicating that aging reduces vagus nerve modulation.

Precipitation Variations in China’s Altay Mountains Detected from Tree Rings Dating Back to AD 1615

As the primary mountain range in Central Asia, the Altay Mountains receive water vapor carried by westerly circulation, resulting in relatively abundant local precipitation and lush pastures in all seasons. Consequently, it has become one of the important transportation routes between Asia and Europe. The exploration of long-term variations in precipitation is meaningful for understanding the ebb and flow of the Asia–Europe steppe trade routes. However, previous dendroclimatological studies of the Altay Mountains focused more on temperature changes than precipitations variations. We carried out a 404-year precipitation reconstruction based on the tree rings of Siberian larch growing on the south slopes of the Altay Mountains, which could explain 45.9% of the variance observed in the February–October precipitation. Our reconstruction demonstrated some severe drought events which could be found in the historical documents, such as the drought in the late Ming Dynasty (1640s) and the Ding-Wu Disaster (1870s). The spatial correlation analysis, cross-wavelet spectrum and wavelet coherency analysis indicated that the precipitation variations in the study area may be related to the ENSO and NAO. This study presents a robust precipitation reconstruction of the southern Altay Mountains, serving as a reference for future research on large-scale climatic forces acting on Altay precipitation.

Investigation of Phase Shifts Using AUC Diagrams: Application to Differential Diagnosis of Parkinson's Disease and Essential Tremor.

This study was motivated by the well-known problem of the differential diagnosis of Parkinson's disease and essential tremor using the phase shift between the tremor signals in the antagonist muscles of patients. Different phase shifts are typical for different diseases; however, it remains unclear how this parameter can be used for clinical diagnosis. Neurophysiological papers have reported different estimations of the accuracy of this parameter, which varies from insufficient to 100%. To address this issue, we developed special types of area under the ROC curve (AUC) diagrams and used them to analyze the phase shift. Different phase estimations, including the Hilbert instantaneous phase and the cross-wavelet spectrum mean phase, were applied. The results of the investigation of the clinical data revealed several regularities with opposite directions in the phase shift of the electromyographic signals in patients with Parkinson's disease and essential tremor. The detected regularities provide insights into the contradictory results reported in the literature. Moreover, the developed AUC diagrams show the potential for the investigation of neurodegenerative diseases related to the hyperkinetic movements of the extremities and the creation of high-accuracy methods of clinical diagnosis.

Investigation of Cerebral Autoregulation Using Time-Frequency Transformations.

The authors carried out the study of the state of systemic and cerebral hemodynamics in normal conditions and in various neurosurgical pathologies using modern signal processing methods. The results characterize the condition for the mechanisms of cerebral circulation Institute of Computer Science and Control, Higher School of Cyber-Physical Systems and Control regulation, which allows for finding a solution to fundamental and specific clinical problems for the effective treatment of patients with various pathologies. The proposed method is based on the continuous wavelet transform of systemic arterial pressure and blood flow velocity signals in the middle cerebral artery recorded by non-invasive methods of photoplethysmography and transcranial doppler ultrasonography. The study of these signals in real-time in the frequency range of Mayer waves makes it possible to determine the cerebral autoregulation state in certain diseases before and after surgical interventions. The proposed method uses a cross-wavelet spectrum, which helps obtain wavelet coherence and a phase shift between the wavelet coefficients of systemic arterial pressure signals and blood flow velocity in the Mayer wave range. The obtained results enable comparing the proposed method with that based on the short-time Fourier transform. The comparison showed that the proposed method has higher sensitivity to changes in cerebral autoregulation and better localization of changes in time and frequency.

Harmonic Analysis of the Relationship between GNSS Precipitable Water Vapor and Heavy Rainfall over the Northwest Equatorial Coast, Andes, and Amazon Regions

This study finds the relationship between increases in precipitable water vapor (PWV), and intense rainfall events in four different climatological regions of South America’s equatorial northwest: the coast, Andes valley, high mountains, and Amazon. First, the PWV was derived from tropospheric zenith delay measured by Global Navigation Satellite System (GNSS) instrumentation located near meteorological stations within the regions of interest using hourly data from the year 2014. A harmonic analysis approach through continuous wavelet cross-spectrum and coherence, as well as discrete wavelets, was used to determine a measure of the lags found between PWV and specific heavy rain events and then compared with satellite IR images and meteorological anomalies. The link between PWV peaks and rainfall was the most evident on the coast, and less discernible in the other stations possibly due to local dynamic factors. The results showed a lag of 11 h between the preceding PWV increase and an intense rainfall event. This was apparent in all of the stations, except in Amazon where it was 6 h, with the highest precision at the coast and with the largest dispersion in the high mountains. The interpretation of this lag for each region is also discussed.

Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer’s disease

Objective. This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer’s disease (AD). Approach. The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert–Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs). Main results. With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. Significance. Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.

Epileptic-seizure onset detection using PARAFAC model with cross-wavelet transformation on multi-channel EEG.

Finding components from multi-channel EEG signal for localizing and detection of onset of seizure, is a new approach in biomedical signal analysis. Tensor-based approaches are utilized to fit the components into multi-dimensional arrays in recent works. We initially decompose EEG signals into Beta band using discrete wavelet transform (DWT). We compare patient templates with normal template for cross-wavelet analysis to obtain Wavelet cross spectrum (WCS) and Wavelet cross coherence coefficients. Next we apply parallel factorization (PARAFAC) modeling, a three-way tensor-based representation in channel, frequency and time-points dimensions on features. Finally, we utilize the ensemble classifier for detecting seizure-free, onset and seizure classes. The clinical dataset for this work comprises of 5 normal subjects and 6 epileptiform patients. The classification performances of WCS features on PARAFAC model for Seizure detection using Ensemble Bagged-Trees classifier obtains 82.21% accuracy, while for Wavelet Coherence features, it provides higher 84.76% accuracy. The results have been compared with well-known Fine Gaussian SVM, Weighted KNN and Ensemble Subspace KNN classifiers. The aim is to analyze data over three dimensions namely, time, frequency and space (channels). This EEG based analysis is significant and effective as an automatic method for detection of seizure before its actual manifestation.

A Remotely Sensed Framework for Spatially-Detailed Dryland Soil Organic Matter Mapping: Coupled Cross-Wavelet Transform with Fractional Vegetation and Soil-Related Endmember Time Series

Soil organic matter (SOM) plays pivotal roles in characterizing dryland structure and function; however, remotely sensed spatially-detailed SOM mapping in these regions remains a challenge. Various digital soil mapping approaches based on either single-period remote sensing or spectral indices in other ecosystems usually produce inaccurate, poorly constrained estimates of dryland SOM. Here, a framework for spatially-detailed SOM mapping was proposed based on cross-wavelet transform (XWT) that exploits ecologically meaningful features from intra-annual fractional vegetation and soil-related endmember records. In this framework, paired green vegetation (GV) and soil-related endmembers (i.e., dark surface (DA), saline land (SA), sand land (SL)) sequences were adopted to extract 30 XWT features in temporally and spatially continuous domains of cross-wavelet spectrum. We then selected representative features as exploratory covariates for SOM mapping, integrated with four state-of-the-art machine learning approaches, i.e., ridge regression (RR), least squares-support vector machines (LS-SVM), random forests (RF), and gradient boosted regression trees (GBRT). The results reported that SOM maps from 13 coupled filtered XWT features and four machine learning approaches were consistent with soil-landscape knowledge, as evidenced by a spatially-detailed gradient from oasis to barren. This framework also presented more accurate and reliable results than arithmetically averaged features of intra-annual endmembers and existing datasets. Among the four approaches, both RF and GBRT were more appropriate in the XWT-based framework, showing superior accuracy, robustness, and lower uncertainty. The XWT synthetically characterized soil fertility from the consecutive structure of intra-annual vegetation and soil-related endmember sequences. Therefore, the proposed framework improved the understanding of SOM and land degradation neutrality, potentially leading to more sustainable management of dryland systems.

Cross Spectrum Aided Surface Condition Assessment of Metal Oxide Surge Arrester Employing Convolutional Neural Network

This paper proposes an automated framework for surface condition assessment of metal oxide surge arresters (MOSA) employing cross-wavelet transform (XWT) of the leakage current and convolutional neural network (CNN). XWT is performed between different contaminated leakage currents with an uncontaminated leakage current. A set of cross-wavelet spectrum (XWS) images are obtained after XWT. The XWS images are fed to the proposed customized CNN model for classification of MOSA surface contamination levels. The performance of the proposed CNN model is also compared with two benchmark CNN architectures namely AlexNet, VGGNet16. It is observed that the proposed model delivers better performance and offers significantly reduced computational burden compared with the existing benchmark CNN models. The proposed method can be applied for surface condition monitoring of MOSA in real-life.

Spatiotemporal dynamics of dengue in Colombia in relation to the combined effects of local climate and ENSO

Dengue virus (DENV) is an endemic disease in the hot and humid low-lands of Colombia. We characterize the association of monthly series of dengue cases with indices of El Niño/Southern Oscillation (ENSO) at the tropical Pacific and local climatic variables in Colombia during the period 2007-2017 at different temporal and spatial scales. For estimation purposes, we use lagged cross-correlations (Pearson test), cross-wavelet analysis (wavelet cross spectrum, and wavelet coherence), as well as a novel nonlinear causality method, PCMCI, that allows identifying common causal drivers and links among high dimensional simultaneous and time-lagged variables. Our results evidence the strong association of DENV cases in Colombia with ENSO indices and with local temperature and rainfall. El Niño (La Niña) phenomenon is related to an increase (decrease) of dengue cases nationally and in most regions and departments, with maximum correlations occurring at shorter time lags in the Pacific and Andes regions, closer to the Pacific Ocean. This association is mainly explained by the ENSO-driven increase in temperature and decrease in rainfall, especially in the Andes and Pacific regions. The influence of ENSO is not stationary, given the reduction of DENV cases since 2005, and that local climate variables vary in space and time, which prevents to extrapolate results from one region to another. The association between DENV and ENSO varies at national and regional scales when data are disaggregated by seasons, being stronger in DJF and weaker in SON. Overall, the Pacific and Andes regions control the relationship between dengue dynamics and ENSO at national scale. Cross-wavelet analysis indicates that the ENSO-DENV relation in Colombia exhibits a strong coherence in the 12 to 16-months frequency band, which implies the frequency locking between the annual cycle and the interannual (ENSO) timescales. Results of nonlinear causality metrics reveal the complex concomitant effects of ENSO and local climate variables, while offering new insights to develop early warning systems for DENV in Colombia.

Numerical comparison of time-, frequency- and wavelet-domain methods for coda wave interferometry

SUMMARYTemporal changes in subsurface properties, such as seismic wave speeds, can be monitored by measuring phase shifts in the coda of two seismic waveforms that share a similar source–receiver path but that are recorded at different times. These nearly identical seismic waveforms are usually obtained either from repeated earthquake waveforms or from repeated ambient noise cross-correlations. The five algorithms that are the most popular to measure phase shifts in the coda waves are the windowed cross correlation (WCC), trace stretching (TS), dynamic time warping (DTW), moving window cross spectrum (MWCS) and wavelet cross spectrum (WCS). The seismic wave speed perturbation is then obtained from the linear regression of phase shifts with their respective lag times under the assumption that the velocity perturbation is homogeneous between (virtual or active) source and receiver. We categorize these methods into the time domain (WCC, TS, DTW), frequency domain (MWCS) and wavelet domain (WCS). This study complements this suite of algorithms with two additional wavelet-domain methods, which we call wavelet transform stretching (WTS) and wavelet transform DTW, wherein we apply traditional stretching and DTW techniques to the wavelet transform. This work aims to verify, validate, and test the accuracy and performance of all methods by performing numerical experiments, in which the elastic wavefields are solved for in various 2-D heterogeneous half-space geometries. Through this work, we validate the assumption of a linear increase in phase shifts with respect to phase lags as a valid argument for fully homogeneous and laterally homogeneous velocity changes. Additionally, we investigate the sensitivity of coda waves at various seismic frequencies to the depth of the velocity perturbation. Overall, we conclude that seismic wavefields generated and recorded at the surface lose sensitivity rapidly with increasing depth of the velocity change for all source–receiver offsets. However, measurements made over a spectrum of seismic frequencies exhibit a pattern such that wavelet methods, and especially WTS, provide useful information to infer the depth of the velocity changes.

Karst hydrological changes during the Late-Holocene in Southwestern China

The frequent alternation between droughts and floods in the karst regions of Southwestern China has a serious impact on the ecological environment and socio-economic development. Although some high-resolution records for this region have been published, there is a lack of multi-proxy geological records that could be used to reconstruct the relationships between the changes in the Asian summer monsoon (ASM) and the regional hydrology and ecological environment since the Late Holocene. In this study, the history of the ASM from 3109 to 694 yr BP with a mean temporal resolution of 2.5 yr is reconstructed based on 47 high-precision 230Th dating results (mean 2-sigma error of ±14 yr), 959 pairs of δ18O/δ13C data, and multiple trace element analyses of a stalagmite from Shijiangjun (SJJ) Cave in the karst area of Southwestern China. The positive δ18O and δ13C excursions accurately recorded eight interdecadal-centennial weak summer monsoon events at ∼779, 1013–911, 1282–1172, 1736–1638, 1961–1864, 2472–2375, 2931–2818, and 3050–3014 yr BP. The cross-wavelet spectrum analysis of the δ18O and δ13C of stalagmite SJJ7 indicates that they have similar periods. The 7 yr period of the δ18O record was determined to have the maximum contribution rate (36.8%) to the periods using ensemble empirical mode decomposition (EEMD) analysis. In the Late Holocene, the weak ASM events were dominated by the southward shift of the Intertropical Convergence Zone (ITCZ) and the frequent El Niño events on an interdecadal-centennial timescale. The southward migration of the ITCZ lead to frequent El Niño events, and the resultant Hadley Circulation and Walker Circulation were weakened, leading to a weak ASM and changes in the hydrological conditions in the monsoon region. The δ13C values of the stalagmite changed relatively slowly compared with the δ18O values, which may indicate that the degradation and restoration of the regional ecological environment caused by abrupt changes in the climate is a relatively slow process. When the summer monsoon decreased, the stalagmite’s Mg/Ca and Sr/Ca ratios were relatively high due to CO2 degassing, and the prior calcite precipitation (PCP)/prior aragonite precipitation (PAP) increased in the karst zone. However, the Mg/Ca ratio increased and the Sr/Ca ratio rapidly decreased during the calcite deposition due to differences in the crystal structures and partition coefficients of aragonite and calcite. The dark layers in stalagmite SJJ7 correspond to transition from heavy to light δ18O values, reflecting the transition in the ASM. The pulsed increases in the elements (Mn, Fe, Al, and Si) in the dark layers reflect the stronger mechanical transport caused by more rainfall. The multi-proxy analysis of this stalagmite may reflect the interactions between the changes in the ASM and the atmosphere-hydrosphere-pedosphere-biosphere-lithosphere in the karst critical zone during the Late Holocene.

Suicides and Mortality from Cardiovascular Diseases Due to Space-Weather Factors in High Latitudes

The aim of this study is to identify the impacts of the main space-weather factors on suicides and cardiovascular (CVD) mortality in Murmansk oblast (northwestern Russia). Data on a total of 908 suicides and 9057 CVD deaths in the town of Kirovsk (67.6 N; 33.7 E) have been analyzed for the period from 1948 to 2010. The main space-weather factors we consider are solar proton events (SPEs) and geomagnetic disturbance determined by the geomagnetic Ap index. As a result of the application of modern methods of spectral and wavelet analysis (MTM spectra, wavelet decomposition, and cross-wavelet spectra), periodicities corresponding to the main 11-year cycle of solar activity and its 2nd harmonic (5.5 years) are revealed. The results of calculating the wavelet coherence indicate the existence of a close relationship between variations in mortality rates and the main factors of space weather. In the case of CVD, although the influence of geomagnetic disturbance on the mortality rate is present, it is not significant compared with socioeconomic factors.

Health monitoring of rolling element bearings using improved wavelet cross spectrum technique and support vector machines

An improved wavelet cross spectrum (IWCS) scheme is proposed in this paper for the health monitoring of the rolling element bearings. The parameters under investigation are the axial preload, the lubricant condition and the surface roughness of the contact surfaces. These parameters determine the performance and the life of ball-bearings, especially in the space application which demands low torque noise during the mission life and operating under varying conditions of temperature and speed. The developed method takes the advantages of the wavelet cross spectrum technique for the feature extraction from the non-stationary vibration signatures. The vibration signals of the Rolling Element Bearings (REB) are first analysed by a continuous wavelet transform (CWT) over the selected scales corresponding to the bearing fundamental fault frequencies. Further, cross-correlation is utilised to enhance the defect-related periodic features. In this improved scheme, the contributive bandwidth selection from the Jarque-Bera (JB) statistic index is carried out with the assistance of an outlier technique. This method removes any outliers in the JB index data by using the linear interpolation and thereby enhancing the index value of the other cross-correlated scales. Experiments are conducted to verify the validity of the IWCS and found to be very effective in diagnosing the bearing health conditions. Using the support vector machines (SVM), the classification of the health conditions is obtained based on the novel improved wavelet cross spectrum analysis.

Cross-wavelet assisted convolution neural network (AlexNet) approach for phonocardiogram signals classification

The exponential growth of a multitude of cardiovascular diseases, leading to life frightening conditions, makes fast and accurate computer-aided techniques that are relevant and important to identify these lethally medical anomalies. The authors aim to build up an accurate scheme that analyzes the Phonocardiogram (PCG) signal and find out whether the patient’s heart works normally or required any special intervention for further diagnosis. This paper has a universal, object – to aid doctors and medical personnel, and an indispensable technique like a Cross-wavelet transform (XWT) assisted Convolution neural network (CNN) utilizing the AlexNet model to detect abnormal heart sounds which are the symbol of cardiovascular disease. A pre-trained AlexNet model has been used and fine-tuned to improve system performance. Convolution neural network (Alex Net architecture) utilizes the Cross-wavelet spectrum image as an input, to prevent and protect individuals from fatal medical conditions. The proposed method is applied both on the raw PCG data and on the PCG data after removing the noise. The results of the study show that our technique attained an accuracy of 98% and 97.89% when processed with the raw and the de-noised PCG dataset to distinguish abnormal heart sound from normal ones and outperformed all existing methods. The authors used the same dataset and evaluated three classic classifiers – LVQ, LS-SVM and PNN to compare the results with the proposed classifier. Also, the works which have been published by the previous researchers are sited and compared with our proposed approach.

Neuromuscular Disease Detection Employing Deep Feature Extraction from Cross Spectrum Images of Electromyography Signals.

In this paper, a deep learning framework for detection and classification of EMG signals for diagnosis of neuromuscular disorders is proposed employing cross wavelet transform. Cross wavelet transform which is a modification of continuous wavelet transform is an important tool to analyze any non-stationary signal in time scale and in time-frequency frame. To this end, EMG signals of healthy, myopathy and Amyotrophic lateral sclerosis disorders were procured from an online existing database. A healthy EMG signal was chosen as reference and cross wavelet transform of the rest of the healthy as well as the disease EMG signals was done with the reference. From the resulting cross wavelet spectrum images of EMG signals, a convolution neural network (CNN) based automated deep feature extraction technique was implemented. The extracted deep features were further subjected to feature ranking employing one way analysis of variance (ANOVA) test. The extracted deep features with high degree of statistical significance were fed to several benchmark machine learning classifiers for the purpose of discrimination of EMG signals. Two binary classification problems are addressed in this paper and it has been observed that the highest mean classification accuracy of 100% is achieved using the statistically significant extracted deep features. The proposed method can be implemented for real-time detection of neuromuscular disorders.

Characterization of twist of fancy yarns using wavelet analysis of sensor signal

Yarn twist variations may cause stripes in the direction of weft yarns or local defects on a fabric surface. Since fast Fourier transform and time analysis cannot directly detect local frequency variations of yarn signal and defect sensors are designed to detect the diameter decreases without considering frequency analysis, no data associated with twist-related frequency changes can be obtained when inspecting Chenille yarn (Cy) defects. This study proposes the prediction of twist level ( T) and twist variations ( ΔT) of Cy whose twist changes in accordance with the spatial period of pile density by using wavelet analysis, allowing localized frequency variations to be obtained. Complex-valued Paul wavelet was used to determine the ΔT of signals with small frequency fluctuations, while Morlet wavelet was addressed for signals with high frequency change. The relation of the signal frequency to the pile yarn density and, correspondingly, twist was modeled by equations. To prevent discontinuities in wavelet cross-spectrum (WCS), the twist simulation signal was generated by equalizing twist oscillation amplitudes without changing their phase. To compare ideal twist to the local twist, another simulation signal demonstrating the ideal twist at sample-specific frequency was generated. The WCS of the simulation signals allowing the segmentation of variation intervals was used for determining ΔT and yarn portions, where the twist is compatible with ideal twist, by establishing correlation between scales and twists. For yarn samples with various T and ΔT types, the T and ΔT results obtained by the proposed wavelet-based algorithm showed the mean absolute relative percentage errors of 1.617% and 37.062%, respectively.

Wavelet-based transmissibility for the analysis of time-variant systems

The wavelet-based transmissibility – that extends the concept of direct transmissibility - is proposed for the analysis of the time-variant system. The method is based on auto- and cross-wavelet spectra. The proposed approach is demonstrated using a simulated lumped parameter system and experimental data from a time-variant frame-like structure. The wavelet-based transmissibility and associated with it the wavelet-based coherence are presented and compared with the classical analysis based on frozen spectra and the input-output analysis that involves input-output characteristics, i.e. the wavelet-based Frequency Response Function and coherence. The results show that the proposed output-only approach can be used to reveal the time-variant behaviour of analysed systems.