Time-frequency Analysis Framework Research Articles

Automated assessment of mental workload from PPG sensor data using cross-wavelet coherence and transfer learning.

Highly complex cognitive works require more brain power. The productivity of a person suffers due to this strain, which is sometimes referred to as a mental burden or psychological load. A person's mental health and safety in high-stress working conditions can be improved with the help of mental workload assessment. A photoplethysmogram (PPG) signal is a non-invasive and easily acquired physiological signal that contains information related to blood volume changes in the micro-vascular bed of tissues and can indicate psychologically relevant information to assess a person's mental workload (MW). An individual under a high MW possesses an increase in sympathetic nervous system activity, which results in morphological changes in the PPG waveform. In this work, a time-frequency analysis framework is developed to capture these distinguishing PPG features for the automatic assessment of MW. In particular, a cross-wavelet coherence (WTC) approach is proposed to extract simultaneous time-frequency information of the PPG during MW relative to the resting PPG. The suggested technique is validated on a publicly available data set of 22 healthy individuals who took part in an N-back task with PPG recording. Under three different fixed window lengths, images are obtained using WTC between PPG records during N-back task activity and rest. The images are used further to obtain PPG classification in two broad classes of low and high MW using a customized pre-trained Inception-V3 model. The best validation and test accuracy of 93.86% and 93.07%, respectively obtained in the window setting of 1200 samples used for WTC image creation.

Reduced-order variational mode decomposition to reveal transient and non-stationary dynamics in fluid flows

A novel data-driven modal analysis method, reduced-order variational mode decomposition (RVMD), is proposed, inspired by the Hilbert–Huang transform and variational mode decomposition (VMD), to resolve transient or statistically non-stationary flow dynamics. First, the form of RVMD modes (referred to as an ‘elementary low-order dynamic process’, ELD) is constructed by combining low-order representation and the idea of intrinsic mode function, which enables the computed modes to characterize the non-stationary properties of space–time fluid flows. Then, the RVMD algorithm is designed based on VMD to achieve a low-redundant adaptive extraction of ELDs in flow data, with the modes computed by solving an elaborate optimization problem. Further, a combination of RVMD and Hilbert spectral analysis leads to a modal-based time-frequency analysis framework in the Hilbert view, providing a potentially powerful tool to discover, quantify and analyse the transient and non-stationary dynamics in complex flow problems. To provide a comprehensive evaluation, the computational cost and parameter dependence of RVMD are discussed, as well as the relations between RVMD and some classic modal decomposition methods. Finally, the virtues and utility of RVMD and the modal-based time-frequency analysis framework are well demonstrated via two canonical problems: the transient cylinder wake and the planar supersonic screeching jet.

Time-frequency analysis framework for understanding non-stationary and multi-scale characteristics of sea-level dynamics

Rising sea level caused by global climate change may increase extreme sea level events, flood low-lying coastal areas, change the ecological and hydrological environment of coastal areas, and bring severe challenges to the survival and development of coastal cities. Hong Kong is a typical economically and socially developed coastal area. However, in such an important coastal city, the mechanisms of local sea-level dynamics and their relationship with climate teleconnections are not well explained. In this paper, Hong Kong tide gauge data spanning 68 years was documented to study the historical sea-level dynamics. Through the analysis framework based on Wavelet Transform and Hilbert Huang Transform, non-stationary and multi-scale features in sea-level dynamics in Hong Kong are revealed. The results show that the relative sea level (RSL) in Hong Kong has experienced roughly 2.5 cycles of high-to-low sea-level transition in the past half-century. The periodic amplitude variation of tides is related to Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO). RSL rise and fall in eastern Hong Kong often occur in La Niña and El Niño years, respectively. The response of RSL to the PDO and ENSO displays a time lag and spatial heterogeneity in Hong Kong. Hong Kong's eastern coastal waters are more strongly affected by the Pacific climate and current systems than the west. This study dissects the non-stationary and multi-scale characteristics of relative sea-level change and helps to better understand the response of RSL to the global climate system.

Efficient time–frequency approach for prediction of subway train-induced tunnel and ground vibrations

In this paper, an efficient time–frequency approach is presented for the prediction of subway train-induced tunnel and ground vibrations. The proposed approach involves two steps. In the first step, a time domain simulation of the vehicle–track subsystem is used to determine the track–tunnel interaction forces and, in the second step, the resulting forces are then applied to a 2.5 D FEM–PML model of the tunnel–soil system. There are two main aspects to the novelty and contribution of this work: First, the errors of the linearized Hertzian wheel–rail contact models in the calculation of the track–tunnel interaction forces are quantified by a comparison with the nonlinear Hertzian contact model. The results show that the relative errors are less than 2%. Second, an efficient time–frequency analysis framework is proposed, including the use of a strongly coupled model in the time domain solution and a 2.5 D FEM–PML model in the frequency–wavenumber domain solution. Finally, the accuracy and efficiency of the proposed approach are verified by comparison with a time-dependent 3 D approach, where three types of soil, i.e. soft, medium, and hard, are considered.

The Automatic Analytics Framework for Multiple Oscillations in the Coupled Control Loops via a New Variant of Slow Feature Analysis

Oscillation is a frequent type of control performance degradation in the process. Multiple oscillations may propagate in the coupled control loops, bringing challenges to detection and localization of oscillations. In this paper, a time-frequency analysis framework including detection, extraction, and localization of oscillations is proposed. The method is based on a new variant of slow feature analysis (SFA), termed multi-lag derivatives dynamic slow feature analysis (MDSFA), and a new indicator, termed oscillation matched degree (OMD). To detect and reveal the possible oscillation sources, MDSFA is proposed to extract features with different rates from the observed data and probe into the time-delay effect and multi-lag autocorrelations specific to control loops. To pinpoint the root loops and travel paths of oscillations, the OMD indicator is designed via the spectral analysis, which can measure the oscillation frequencies and amplitudes. The proposed method is verified to be able to detect and locate oscillations automatically and efficiently via the real thermal power process.

A novel approach framework based on statistics for reconstruction and heartrate estimation from PPG with heavy motion artifacts

One of the most important applications of photoplethysmography (PPG) signal is heartrate (HR) estimation. For its applications in wearable devices, motion artifact (MA) may be the most serious challenge for randomness both in format and temporal distribution. This paper proposes an advanced time-frequency analysis framework based on empirical mode decomposition (EMD) to select specific time slices for signal reconstruction. This framework operates with a type of pre-processing called variance characterization series (VCS), EMD, singular value decomposition (SVD), and a precise and adaptive 2-D filtration reported first. This filtration is based on Harr wavelet transform (HWT) and 3rd order cumulant analysis, to make it have resolution in both the time domain and different components. The simulation results show that the proposed method gains 1.07 in absolute average error (AAE) and 1.87 in standard deviation (SD); AAEs 1st and 3rd quartiles are 0.12 and 1.41, respectively. This framework is tested by the PhysioBank MIMIC II waveform database.

A refined Frequency Domain Decomposition tool for structural modal monitoring in earthquake engineering

Output-only structural identification is developed by a refined Frequency Domain Decomposition (rFDD) approach, towards assessing current modal properties of heavy-damped buildings (in terms of identification challenge), under strong ground motions. Structural responses from earthquake excitations are taken as input signals for the identification algorithm. A new dedicated computational procedure, based on coupled Chebyshev Type II bandpass filters, is outlined for the effective estimation of natural frequencies, mode shapes and modal damping ratios. The identification technique is also coupled with a Gabor Wavelet Transform, resulting in an effective and self-contained time-frequency analysis framework. Simulated response signals generated by shear-type frames (with variable structural features) are used as a necessary validation condition. In this context use is made of a complete set of seismic records taken from the FEMA P695 database, i.e. all 44 “Far-Field” (22 NS, 22 WE) earthquake signals. The modal estimates are statistically compared to their target values, proving the accuracy of the developed algorithm in providing prompt and accurate estimates of all current strong ground motion modal parameters. At this stage, such analysis tool may be employed for convenient application in the realm of Earthquake Engineering, towards potential Structural Health Monitoring and damage detection purposes.

CFAR Detection in the Framework of Time-Frequency Analysis

This paper deals with the resolution improvement of metric radars and proposes a new architecture that contains the constant false alarm rate (CFAR) technique conjointly with time-frequency analysis. The clutter map (CMAP) CFAR is used to detect the existence of targets; in this case, we search if there are one or more targets inside this cell using Gabor-based detector. Each target is considered to be fluctuating according to Swirling I model. The performance of the proposed system is evaluated and tested on simulated data using Monte-Carlo simulations under various clutter scenarios. In all cases, the obtained results show clearly that the new proposed detector outperforms the conventional OS-CFAR detector in terms of azimuth and range resolution.

Time-Frequency Analysis of Nonstationary Process Based on Multivariate Empirical Mode Decomposition

Currently, empirical mode decomposition (EMD) has become a popular data-driven time-frequency analysis method for nonstationary and nonlinear data. However, it is still limited to univariate data due to the number and/or scale misalignment for multivariate data. A newly developed multivariate EMD (MEMD) scheme decomposes multivariate data simultaneously and thus leads to mode alignment and minimizes mode mixing. In addition, an improved amplitude and frequency modulation (AM-FM) decomposition algorithm presented here provides an estimation of a more meaningful instantaneous amplitude and frequency than the widely used Hilbert transform (HT). Both of these facilitate development of a time-frequency analysis framework for multivariate nonstationary and nonlinear data analysis. In this paper, MEMD-based scalogram and coscalogram, and instantaneous frequency spectra and cospectra are proposed to characterize a multivariate nonstationary process. The scalogram and instantaneous frequency spectra capture spectral evolution of each component while the coscalogram and instantaneous frequency cospectra reveal embedded intermittent correlation between two components. Compared with scale-based scalogram and coscalogram, frequency-based instantaneous frequency spectra and cospectra provide a more detailed portrait of multivariate data. The effectiveness of the proposed MEMD-based time-frequency analysis framework is demonstrated by numerical examples of a thunderstorm downburst and an earthquake ground motion. Also, the results from the MEMD-based approach are compared with those based on a continuous wavelet transform, which further reinforces the effectiveness of the proposed framework.

Time-Frequency Analysis of Non-Stationary Ground Motions via Multivariate Empirical Mode Decomposition

In this paper, MEMD-based scalogram and coscalogram, and instantaneous frequency spectral are proposed to characterize the data derived from the multivariate non-stationary process. The scalogram and instantaneous frequency spectral capture spectral evolution of each component while the coscalogram reveals embedded intermittent correlation between two components. Compared with scale-based scalogram and coscalogram, frequency-based instantaneous frequency spectral provides more detailed portrayal for multivariate data. The effectiveness of the proposed MEMD-based time-frequency analysis framework is validated by numerical examples of uniformly and generally modulated ground motions.

FINGER KNUCKLE-PRINT IDENTIFICATION BASED ON LOCAL AND GLOBAL FEATURE EXTRACTION USING SDOST

Finger knuckle-print biometric system has widely us ed in modern e-world. The region of interest is needed as the key for the feature extraction in a g ood biometric system. The symmetric discrete orthonormal stockwell transform provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture feature s in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods. Thi s motivates us to propose a new local and global feature extractor. For the finger knuckle-print, th e local and global features are critical for an ima ge observation and recognition. For the finger knuckle -print, the local and global information are critic al for an image observation and recognition. The local features of an enhanced finger knuckle-print image are extracted using symmetric discrete orthon ormal stockwell transform. The Fourier transform of an image is obtained by increasing the scale of symmetric discrete orthonormal stockwell transform to infinity. The Fourier transform coefficients ext racted from the finger knuckle-print image is considered as the global information. The local and global information are physically linked by means of the framework of time frequency analysis. The gl obal feature is exploited to refine the arrangement of finger knuckle-print images in matching. The pro posed scheme makes use of the local and global features to verify finger knuckle-print images. The weighted average of the local and global matching distances is taken as the final matching distance o f two finger knuckle-print images. The investigational results indicate that the proposed work outperforms an existing works with an equal error rate of 0.0045 and 100% correct recognition r ate on the finger knuckle-print database.

Optimized frames and multi-dimensional challenges in time-frequency analysis

The aim of this introductory communication is to present some challenges of the emerging research topics related to uncertainties, optimizations and multi-dimensional settings in the framework of time-frequency analysis. On one hand we study problems related to multivariate high-D Gabor frame constructions and on the other hand we corelate them with the optimal window constructions.

Ensemble of local and global information for finger–knuckle-print recognition

Biometrics authentication is an effective method for automatically recognizing a person's identity. Recently, it has been found that the finger-knuckle-print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one's finger, has high capability to discriminate different individuals, making it an emerging biometric identifier. In this paper, based on the results of psychophysics and neurophysiology studies that both local and global information is crucial for the image perception, we present an effective FKP recognition scheme by extracting and assembling local and global features of FKP images. Specifically, the orientation information extracted by the Gabor filters is coded as the local feature. By increasing the scale of Gabor filters to infinite, actually we can get the Fourier transform of the image, and hence the Fourier transform coefficients of the image can be taken as the global features. Such kinds of local and global features are naturally linked via the framework of time-frequency analysis. The proposed scheme exploits both local and global information for the FKP verification, where global information is also utilized to refine the alignment of FKP images in matching. The final matching distance of two FKPs is a weighted average of local and global matching distances. The experimental results conducted on our FKP database demonstrate that the proposed local-global information combination scheme could significantly improve the recognition accuracy obtained by either local or global information and lead to promising performance of an FKP-based personal authentication system.

Wavelets: Theoretical Concepts and Vibrations Related Applications

In this paper we provide a review of wavelet analysis in the context of applications to vibrations problems. First, we give an introduction to the important concepts and mathematical properties of wavelets within the framework of time-frequency analysis of signals. Next, wavelet analysis is discussed as applied to relevant themes in vibrations, such as time-varying spectra estimation, random field synthesis, system identification, damage detection, and material characterization. In view of the large number of related books and journal articles published in recent decades, the list of selected references in the paper is not meant to be exhaustive. Nevertheless, the cited references aim to point out the salient features of wavelet analysis, and to identify significant contributions in each theme, with the goal of expediting additional research and development efforts.