Higher Order Spectral Analysis Research Articles

Local bispectral characteristics of nonlinear vibro-acoustic modulations for structural damage detection

Higher order spectral analysis is used for structural damage detection based on nonlinear vibro-acoustic modulations. A combined low-frequency vibration and high-frequency ultrasonic excitation is used, leading to nonlinear vibro-acoustic modulated responses in the presence of structural damage. The response data are analysed using a modulation signal bispectrum and bicoherence. The analysis of local amplitude and phase of these characteristics is proposed for structural damage detection. The method is illustrated using numerical simulations and experimental data. The latter involves impact damage detection in composite plates. The results show that – in contrast to the classical higher order spectral analysis – the method identifies all nonlinear damage-related coupled components correctly, distinguishes these components from undesired non-damage related modulations, and therefore shows a potential for reliable structural damage detection.

Magnetic Turbulence and Current Drive during Local Helicity Injection.

Magnetic measurements during dc helicity injection tokamak startup indicate Alfvénic turbulence in the injected current streams mediates magnetic relaxation and results in macroscopic plasma current drive. Localization of such activity to the injected current streams, a bias voltage dependence to its onset, and higher-order spectral analysis indicate super-Alfvénic electrons excite instabilities that drive the observed turbulence. Measured fluctuation helicity is consistent with an α-dynamo electromotive force driving net current comparable to the macroscopic equilibrium current density. These results imply new constraints for scaling local helicity injection to larger devices.

Higher-Order Spectral Analysis of Vibrating Systems with a Gap

Higher-Order Spectral Analysis of Vibrating Systems with a Gap

IoT-Fog-Cloud Centric Earthquake Monitoring and Prediction

Earthquakes are among the most inevitable natural catastrophes. The uncertainty about the severity of the earthquake has a profound effect on the burden of disaster and causes massive economic and societal losses. Although unpredictable, it can be expected to ameliorate damage and fatalities, such as monitoring and predicting earthquakes using the Internet of Things (IoT). With the resurgence of the IoT, an emerging innovative approach is to integrate IoT technology with Fog and Cloud Computing to augment the effectiveness and accuracy of earthquake monitoring and prediction. In this study, the integrated IoT-Fog-Cloud layered framework is proposed to predict earthquakes using seismic signal information. The proposed model is composed of three layers: (i) at sensor layer, seismic data are acquired, (ii) fog layer incorporates pre-processing, feature extraction using fast Walsh–Hadamard transform (FWHT), selection of relevant features by applying High Order Spectral Analysis (HOSA) to FWHT coefficients, and seismic event classification by K-means accompanied by real-time alert generation, (iii) at cloud layer, an artificial neural network (ANN) is employed to forecast the magnitude of an earthquake. For performance evaluation, K-means classification algorithm is collated with other well-known classification algorithms from the perspective of accuracy and execution duration. Implementation statistics indicate that with chosen HOS features, we have been able to attain high accuracy, precision, specificity, and sensitivity values of 93.30%, 96.65%, 90.54%, and 92.75%, respectively. In addition, the ANN provides an average correct magnitude prediction of 75%. The findings ensured that the proposed framework has the potency to classify seismic signals and predict earthquakes and could therefore further enhance the detection of seismic activities. Moreover, the generation of real-time alerts further amplifies the effectiveness of the proposed model and makes it more real-time compatible.

Robust children’s speech recognition in zero resource condition

User applications such as voice-based web search, online learning, and video gaming require an effective speech recognition module to take user commands. Nowadays, even children are frequently using such tools, especially for online learning and gaming. This has increased the demand for developing a noise-robust automatic speech recognition (ASR) system that can effectively transcribe children’s data under varied ambient conditions. However, automatic recognition of children’s speech is extremely challenging due to the insufficiency of data from child speakers in the majority of the languages across the world. Consequently, in this zero-resource condition, we are forced to decode children’s speech on systems trained using adults’ data. However, the acoustic mismatch between adults’ and children’s speech, such as differences in pitch, formant frequencies, and speaking-rates, leads to highly degraded recognition performance. To enhance the recognition rate under zero-resource conditions, we have explored the role of formant and duration-modification-based out-of-domain data augmentation in this paper. For that purpose, the formant frequencies of the adults’ speech data are upscaled using warping of linear predictive coding coefficients. On pooling original and formant modified adults’ speech data into training, the mismatch in formant locations is reduced leading to better recognition performance. Further improvement in recognition rate can be achieved by simultaneously modifying the duration as well as the formant frequencies of the training data. This case of out-of-domain data augmentation has also been studied in this work and found to yield added gains. In addition to data augmentation, a noise- and pitch-robust front-end acoustic feature extraction approach exploiting higher-order spectral analysis (simple and cross-bispectrum) is also proposed in this paper. The proposed features are noise-robust due to the inherent immunity of the bispectrum towards additive noises. An added advantage of bispectrum is reduced pitch sensitivity as demonstrated in this work. This, in turn, helps alleviate the aforementioned pitch-induced acoustic mismatch. The experimental evaluations presented in this paper demonstrate that the use of proposed acoustic features, as well as the out-of-domain data augmentation techniques, are highly suited for zero-resource children’s speech recognition tasks under clean and noisy conditions.

Aeroacoustic characteristics of slat finlets

An experimental study was performed on a 30P30N three-element high-lift airfoil fitted with different types of slat finlets and its noise reduction capabilities were assessed. To develop a better understanding of the noise reduction mechanism, simultaneous measurements of the unsteady surface pressure were taken at various locations at the vicinity of the slat cove and at far-field locations. While there was a small reduction in far-field noise for the fundamental peak, the use of slat finlets showed a substantial reduction in surface pressure fluctuations. The reduction of vortex shedding energy by the slat finlets also resulted in the reduction of nonpropagating hydrodynamic field between the slat and the main-element substantially reducing the near-field pressure spectra. Fourier and wavelet-based analysis along with high-order spectral analysis were provided for further confirmation of the observations and hypothesis. Finally, the wavelet coefficient findings showed spectral peaks as well as amplitudes modulated in time in the baseline case; however, these peaks were substantially reduced for the finlet configuration with larger spacing.

Impact of image segmentation techniques on celiac disease classification using scale invariant texture descriptors for standard flexible endoscopic systems

Celiac disease (CD) is quite common and is a proximal small bowel disease that develops as a permanentintolerance to gluten and other cereal proteins in cereals. It is considered as one of the most di?icult diseases to diagnose.Histopathological evidence of small bowel biopsies taken during endoscopy remains the gold standard for diagnosis.Therefore, computer-aided detection (CAD) systems in endoscopy are a newly emerging technology to enhance thediagnostic accuracy of the disease and to save time and manpower. For this reason, a hybrid machine learning methodshave been applied for the CAD of celiac disease. Firstly, a context-based optimal multilevel thresholding technique wasemployed to segment the images. Afterward, images were decomposed into subbands with discrete wavelet transform(DWT), and the distinctive features were extracted with scale invariant texture recognition. Classification accuracy,sensitivity and specificity ratio are 94.79%, 94.29%, and 95.08%, respectively. The results of the proposed models arecompared with the results of other state-of-the-art methods such as a convolutional neural network (CNN) and higherorder spectral (HOS) analysis. The results showed that the proposed hybrid approaches are accurate, fast, and robust.

Low data regimes in extreme climates: Foliage penetration personnel detection using a wireless network-based device-free sensing approach

As far as low-cost deployment is concerned, wireless network-based device-free sensing (DFS) is of great interest and has successfully demonstrated the feasibility in Foliage penetration (FOPEN) target recognition. The classification accuracy of this technology is known to dramatically decrease in extreme climates where the received signals tend to be severely attenuated; while deep learning approaches have boosted performance, they only perform effectively when trained with large amounts of labeled data. Consequently, it is still unknown how to ensure reasonable detection accuracy in extreme climates where sufficient samples are difficult to obtain. To address this concern, we adopt two special measures for performance enhancement in this paper. One measure is to employ higher-order spectral (HOS) analysis to transform the time-domain signals into the bispectrum image representations, so that the shift to an image classification task could provide the advantage of using the existing Convolutional Neural Network (CNN) models. More importantly, the immunity of the approach against the unwanted clutters in foliage environments can be improved. The other one is to present an end-to-end Deep Learning Data Augmentation and Classification (DLDAC) model comprised of a Deep Convolutional Generative Adversarial Network (for data augmentation) and a SqueezeNet CNN backbone (for target classification), which can improve the classifier performance by using the augmented data on-the-fly. Thus, the negative impacts of low data regimes in extreme climates can be considerably accommodated. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a real FOPEN dataset collected by impulse-radio ultra-wideband (IR-UWB) transceivers under three severe weather conditions. The experimental results demonstrate that even when only 300 training samples are taken for each type of target under every weather condition, the average classification accuracy of the proposed approach is still better than 92% in terms of distinguishing between human and other targets.

Cloud based ensemble machine learning approach for smart detection of epileptic seizures using higher order spectral analysis.

The present paper proposes a smart framework for detection of epileptic seizures using the concepts of IoT technologies, cloud computing and machine learning. This framework processes the acquired scalp EEG signals by Fast Walsh Hadamard transform. Then, the transformed frequency-domain signals are examined using higher-order spectral analysis to extract amplitude and entropy-based statistical features. The extracted features have been selected by means of correlation-based feature selection algorithm to achieve more real-time classification with reduced complexity and delay. Finally, the samples containing selected features have been fed to ensemble machine learning techniques for classification into several classes of EEG states, viz. normal, interictal and ictal. The employed techniques include Dagging, Bagging, Stacking, MultiBoost AB and AdaBoost M1 algorithms in integration with C4.5 decision tree algorithm as the base classifier. The results of the ensemble techniques are also compared with standalone C4.5 decision tree and SVM algorithms. The performance analysis through simulation results reveals that the ensemble of AdaBoost M1 and C4.5 decision tree algorithms with higher-order spectral features is an adequate technique for automated detection of epileptic seizures in real-time. This technique achieves 100% classification accuracy, sensitivity and specificity values with optimally small classification time.

Serrated slat cusp for high-lift device noise reduction

Noise reduction capabilities of slat cusp serration were experimentally assessed and demonstrated for a 30P30N three-element high-lift airfoil fitted with two different types of serrated slat cusps. Aerodynamic characteristics were evaluated with the aid of surface pressure distribution, and insignificant differences were found among all the tested configurations. The unsteady flow characteristics of the slat serrations were examined using near- and far-field measurements to gain a deeper understanding of the noise generation mechanism. Although increased surface pressure fluctuations were demonstrated by the slat serrations, a substantial reduction in the far-field noise was observed. The increase in the near-field energy levels was attributed to the non-propagating hydrodynamic energy field within the slat cove and the main element, whereas the far-field noise reduction was attributed to the elimination of vortex shedding by the slat serration. Further reaffirmation of the observations and hypothesis was provided with higher-order spectral analysis and wavelet analysis.

A Multitask-Aided Transfer Learning-Based Diagnostic Framework for Bearings under Inconsistent Working Conditions.

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions.

A Study on Higher-Order Spectral Analysis and Identification Method in Continuously Differentiable Damping Model

A Study on Higher-Order Spectral Analysis and Identification Method in Continuously Differentiable Damping Model

Identifying Higher-Order Interactions in Wave Time-Series

Abstract Reliable design and reanalysis of coastal and offshore structures require, among other things, characterization of extreme crest elevation corresponding to long return periods. Extreme crests typically correspond to focused wave events enhanced by wave–wave interactions of different orders—third-order, four-wave interactions dominating in deep water (Janssen, P. A. E. M., 2003, “Nonlinear Four-Wave Interactions and Freak Waves,” J. Phys. Oceanogr., 33(4), pp. 863–884). Higher-order spectral (HOS) analysis can be used to identify wave–wave interactions in time-series of water surface elevation; trispectral analysis is needed to detect third-order, four-wave interactions. Four-wave interactions between Fourier components can involve interactions of the type where f1 + f2 + f3 = f4 and where f1 + f2 = f3 + f4, resulting in two definitions of the trispectrum—the T- and V-trispectrum (with corresponding tricoherences), respectively. It is shown that the T-tricoherence is capable of detecting phase-locked four-wave interactions of the type f4 = f1 + f2 + f3 when these are simulated with simple sinusoids, but such interactions were not detected in HOS model simulations and field data. It is also found that high V-tricoherence levels are detected at frequencies at which four-wave interactions of the type f1 + f2 = f3 + f4 are expected, but these may simply indicate combinations of independent pairs of Fourier components that happen to satisfy the frequency relationship. Preliminary analysis shows that using a cumulant-based trispectrum (Kravtchenko-Berejnoi, V., Lefeuvre, F., Krasnosel'skikh, V. V., and Lagoutte, D., 1995, “On the Use of Tricoherent Analysis to Detect Nonlinear Wave–Wave Interactions,” Signal Process., 42(3), pp. 291–309) may improve identification of wave–wave interactions. These results highlight that caution needs to be exercised in interpreting trispectra in terms of specific four-wave interactions occurring in sea states and further research is needed to establish whether this is in fact possible in practice.

Detection of Extreme and Exceptional Langmuir Wave Packets in Solar Type III Radio Bursts

AbstractWe report the STEREO observations of two extreme and exceptional Langmuir wave packets, which are the most intense wave packets ever detected in solar type III bursts; their peak intensities Et∼ 215 and ∼ 161 mVm−1 beat out the previous 107 mVm−1 record, reported by Thejappa and MacDowall (2018a; https://doi.org/10.3847/1538-4357/aaca3b). These observations provide new evidence for (1) the four‐wave interaction called the oscillating two‐stream instability (OTSI), which is the coupling of two beam‐excited Langmuir waves with an ion sound wave yielding downshifted and upshifted side bands, and (2) Langmuir solitons formed as a result of OTSI, and (3) density cavities created by their ponderomotive forces. The fast Fourier transform spectra of these wave packets provide what is believed to be the first evidence for harmonics of the electron plasma frequency, fpe up to the order of 5 with peak intensities falling off with increasing harmonic number. The higher order spectral analysis indicates that these harmonics probably correspond to the electromagnetic waves excited as a result of various three‐wave interactions. We argue that although the observed characteristics also indicate that these wave packets could be collapsing wave packets formed as a result of nucleation instability, the observed evidence for OTSI and Langmuir solitons trapped inside the self‐generated density cavities strongly favor the OTSI as the route for the spatial collapse of Langmuir waves in the present case. The implication of these findings for theories of solar type III radio bursts is discussed.

Higher-Order Spectral Analysis of Stray Flux Signals for Faults Detection in Induction Motors

Abstract This work is a review of current trends in the stray flux signal processing techniques applied to the diagnosis of electrical machines. Initially, a review of the most commonly used standard methods is performed in the diagnosis of failures in induction machines and using stray flux; and then specifically it is treated and performed the algorithms based on statistical analysis using cumulants and polyspectra. In addition, the theoretical foundations of the analyzed algorithms and examples applications are shown from the practical point of view where the benefits that processing can have using HOSA and its relationship with stray flux signal analysis, are illustrated.

High Order Spectral Analysis of Local Field Potential Data on a Subgroup of Parkinson's Disease Patients Who Are Carriers of Mutations in the Glucocerebrosidase (GBA) Gene Undergoing DBS Electrode Placement

High Order Spectral Analysis of Local Field Potential Data on a Subgroup of Parkinson's Disease Patients Who Are Carriers of Mutations in the Glucocerebrosidase (GBA) Gene Undergoing DBS Electrode Placement

A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System

To investigate the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS), activated by spectral analysis features, for detection of abnormal cardiac valves sound signals. A dataset of 1837 heart sound signals were acquired from international PhysioNet Challenge 2016 databases (classes A, B and E). This included 1369 normal and 468 abnormal signals. The signals were de-noised using Notch and Butterworth filtering, fed to Discrete Fourier Transform, and 5 features using High Order Spectral (HOS) analysis were extracted from the third Cumulant. Later, the ANFIS neural network was trained and tested to discern abnormal signals. The results showed that the selected features were statistically significant ( $\text{p} ). The proposed method was tested and achieved classification of 63-89% accuracy, 63-100% sensitivity, and 62-100% specificity, respectively. The results were compared with reports utilizing different neural network techniques, indicating competitive performance. The HOS spectral features can be reliable to participate in neural network systems to sort heart sound (HS) signals as normal or abnormal. The bispectral matrix is a new presentation of attributes describing signals. The ANFIS is a suggestive successful tool, which has not been attempted in Physio-net challenge 2016. The HOS attributes and ANFIS can participate successfully in PhysioNet Challenge 2016.

Detection and diagnosis of Oscillation in Shell and Tube Heat Exchanger Process

Oscillations found in process control loops are the visible indications of process deterioration. The presence of oscillation in an industrial control loop may reduce quality of the product; increase the rejection rate and energy consumption which may result in reduced profitability. Many techniques stated in the literature for oscillation diagnosis has their own benefits and limitations. Performance assessment of a single control loop is a valued procedure to spot suddenly rising faults and slowly emerging degradation. In this paper, the oscillations present in the process variable of a Shell and Tube Heat eXchanger (STHX) process are diagnosed using the Higher Order Spectral Analysis (HOSA) in MATLAB. From HOSA, bispectrum, modified bispectrum and bicoherence analysis technique is applied to the laboratory STHX process to spot the probable root cause for oscillations.

Higher Order Statistical Analysis for Thyroid Texture Classification and Segmentation in 2D ultrasound Images.

Ultrasound (US) imaging is one of the most cost-effective imaging modality that utilizes sound waves for generating medical images of anatomical structure. However, the presence of speckle noise and low contrast in the US images makes it difficult to use for proper classification of anatomical structures in clinical scenarios. Hence, it is important to devise a method that is robust and accurate even in the presence of speckle noise and is not affected by the low image contrast. In this work, a novel approach for thyroid texture characterization based on extracting features utilizing higher order spectral analysis (HOSA) was used. A Support Vector Machine (SVM) was applied on the extracted features to classify the thyroid texture. Since HOSA is a well suited technique for processing non-Gaussian data involving non-linear dynamics, good classification of thyroid texture can be obtained in US images as they also contain non-Gaussian Speckle noise and nonlinear characteristics. A final accuracy of 93.27%, sensitivity of 0.92 and specificity of 0.62 were obtained using the proposed approach.

Rotor Fault Detection in Induction Motors Based on Time-Frequency Analysis Using the Bispectrum and the Autocovariance of Stray Flux Signals

The aim of this work is to find out, through the analysis of the time and frequency domains, significant differences that lead us to obtain one or several variables that may result in an indicator that allows diagnosing the condition of the rotor in an induction motor from the processing of the stray flux signals. For this, the calculation of two indicators is proposed: the first is based on the frequency domain and it relies on the calculation of the sum of the mean value of the bispectrum of the flux signal. The use of high order spectral analysis is justified in that with the one-dimensional analysis resulting from the Fourier Transform, there may not always be solid differences at the spectral level that enable us to distinguish between healthy and faulty conditions. Also, based on the high-order spectral analysis, differences may arise that, with the classical analysis with the Fourier Transform, are not evident, since the high order spectra from the Bispectrum are immune to Gaussian noise, but not the results that can be obtained using the one-dimensional Fourier transform. On the other hand, a second indicator based on the temporal domain that is based on the calculation of the square value of the median of the autocovariance function of the signal is evaluated. The obtained results are satisfactory and let us conclude the affirmative hypothesis of using flux signals for determining the condition of the rotor of an induction motor.