Explore the vast range of titles available.

Summary For cable bridges, the cable tension force plays a crucial role in their construction, assessment and long‐term structural health monitoring. Cable tension forces vary in real time with the change of the moving vehicle loads and environmental effects, and this continual variation in tension force may cause fatigue damage of a cable. Traditional vibration‐based cable tension force estimation methods can only obtain the time‐averaged cable tension force and not the instantaneous force. This paper proposes a new approach to identify the time‐varying cable tension forces of bridges based on an adaptive sparse time‐frequency analysis method. This is a recently developed method to estimate the instantaneous frequency by looking for the sparsest time‐frequency representation of the signal within the largest possible time‐frequency dictionary (i.e. set of expansion functions). In the proposed approach, first, the time‐varying modal frequencies are identified from acceleration measurements on the cable, then, the time‐varying cable tension is obtained from the relation between this force and the identified frequencies. By considering the integer ratios of the different modal frequencies to the fundamental frequency of the cable, the proposed algorithm is further improved to increase its robustness to measurement noise. A cable experiment is implemented to illustrate the validity of the proposed method. For comparison, the Hilbert–Huang transform is also employed to identify the time‐varying frequencies, which are then used to calculate the time‐varying cable‐tension force. The results show that the adaptive sparse time‐frequency analysis method produces more accurate estimates of the time‐varying cable tension forces than the Hilbert–Huang transform method. Copyright © 2016 John Wiley & Sons, Ltd.

Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time–frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne. [Display omitted] •The MNE software provides a complete pipeline for MEG and EEG data analysis.•MNE covers preprocessing, forward modeling, inverse methods, and visualization.•MNE supports advanced analysis: time-frequency, statistics, and connectivity.•MNE-Python enables fast and memory-efficient processing of large data sets.•MNE-Python is an open-source software supporting a collaborative development effort.

Signal processing techniques play an important role in seismic data analysis. Variational mode decomposition (VMD), as a powerful signal processing method, has been extensively applied in seismic signal processing. A large number of papers on the application of VMD in seismic data analysis have appeared in various journals, conference proceedings, and technical communications. The paper aims to investigate and summarize the recent advancements of VMD and its application in seismic data analysis and give a comprehensive reference for scholars that may be interested in this topic so that researchers can select a more in-depth research direction. Firstly, the VMD principle is briefly introduced, and the advantage and limitations of this approach are illustrated in detail. Secondly, recent applications of the VMD in seismic data analysis are summarized in terms of specific scenarios, such as seismic time–frequency analysis (TFA), seismic denoising, and other applications. Finally, the key problems of VMD in seismic data analysis are discussed, and the potential research directions are listed. It is expected that the review would be constructive to the basic understanding of the VMD concept for beginners and insightful exploration of VMD’s applications in seismic data analysis for advanced researchers.Article HighlightsSeismic data analysis plays an important role in extracting valuable information from seismic recordsThis paper surveys the VMD and its applications in the field of seismic data analysis in a comprehensive wayPromising research prospects of VMD in seismic data analysis are proposed

Many approaches for estimating functional connectivity among brain regions or networks in fMRI have been considered in the literature. More recently, studies have shown that connectivity which is usually estimated by calculating correlation between time series or by estimating coherence as a function of frequency has a dynamic nature, during both task and resting conditions. Sliding-window methods have been commonly used to study these dynamic properties although other approaches such as instantaneous phase synchronization have also been used for similar purposes. Some studies have also suggested that spectral analysis can be used to separate the distinct contributions of motion, respiration and neurophysiological activity from the observed correlation. Several recent studies have merged analysis of coherence with study of temporal dynamics of functional connectivity though these have mostly been limited to a few selected brain regions and frequency bands. Here we propose a novel data-driven framework to estimate time-varying patterns of whole-brain functional network connectivity of resting state fMRI combined with the different frequencies and phase lags at which these patterns are observed. We show that this analysis identifies both broad-band cluster centroids that summarize connectivity patterns observed in many frequency bands, as well as clusters consisting only of functional network connectivity (FNC) from a narrow range of frequencies along with associated phase profiles. The value of this approach is demonstrated by its ability to reveal significant group differences in males versus females regarding occupancy rates of cluster that would not be separable without considering the frequencies and phase lags. The method we introduce provides a novel and informative framework for analyzing time-varying and frequency specific connectivity which can be broadly applied to the study of the healthy and diseased human brain. •Design of a framework for time–frequency analysis of coherence in rest fMRI data•We study time–frequency coherence in form of functional network connectivity (FNC).•Enables us to jointly study temporal dynamics spectral power and phase profiles of FNCs•Identification of clusters formed by such FNCs in the time–frequency domain•Reveals significant gender differences based on occupancy measures of each cluster

Targets with rotating components generate micro‐motion (MM) modulation effect in addition to the main body. Extracting MM parameters is challenging due to interference from the target's main body, necessitating the separation of modulation signals. This letter proposes a robust complex local mean decomposition (RCLMD) method with self‐adaptive sifting stopping, aiming at the problem of component redundancy due to multiple iterations during break and the loss of modulation components during the separation process. The proposed method sets the objective function and self‐adaptive stopping criterion, combined with the modulation signal characteristics, enhancing the accuracy and efficiency of MM component extraction. Simulation experiments show that compared with the complex local mean decomposition method, the complex empirical mode decomposition method, and its improved method, the RCLMD method can achieve the highest decomposition effect of 96.57%, and the separation time consumed has a significant advantage over the above methods, performance is less fluctuating by the change of signal‐to‐noise ratio with good robustness. The measured data in real scenarios also verify the effectiveness of the proposed method. This letter proposes a robust complex local mean decomposition method with self‐adaptive sifting stopping, aiming at the problem of component redundancy due to multiple iterations during break and the loss of modulation components during the separation process. The proposed method sets the objective function and self‐adaptive stopping criterion, combined with the modulation signal characteristics, enhancing the accuracy and efficiency of micro‐motion component extraction.

The proposal of new signal representation techniques in time‐frequency domain can improve the visualization and acquisition of frequency components in various applications. In this sense, this article presents a novel and different representation of the short‐time Fourier transform (STFT) spectrogram for signal analysis, based on changing the visualization from a linear to a cylindrical representation by means of a linear transformation. Simulations show that this new representation focuses on the accuracy of the frequency components rather according to the angular position in which they occur; on the other hand it is easier to analyze high‐frequency components than those of low frequency. Finally, it is visually easier to identify fixed components along the test in this representation in a wide window STFT analysis. This article presents a novel and different representation of the short‐time Fourier transform (STFT) spectrogram for signal analysis, based on changing the visualization from a linear to a cylindrical representation by means of a linear transformation.

We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data-functional, time dependent, and multivariate components-we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework. The proposed methods identify a time-invariant functional basis for the functional observations, which is smooth and interpretable, and can be made common across multivariate observations for additional information sharing. The Bayesian framework permits joint estimation of the model parameters, provides exact inference (up to MCMC error) on specific parameters, and allows generalized dependence structures. Sampling from the posterior distribution is accomplished with an efficient Gibbs sampling algorithm. We illustrate the proposed framework with two applications: (1) multi-economy yield curve data from the recent global recession, and (2) local field potential brain signals in rats, for which we develop a multivariate functional time series approach for multivariate time-frequency analysis. Supplementary materials, including R code and the multi-economy yield curve data, are available online.

Event-related desynchronization (ERD) and synchronization (ERS) of electrocortical signals (e.g., electroencephalogram [EEG] and magnetoencephalogram) reflect important aspects of sensory, motor, and cognitive cortical processing. The detection of ERD and ERS relies on time–frequency decomposition of single-trial electrocortical signals, to identify significant stimulus-induced changes in power within specific frequency bands. Typically, these changes are quantified by expressing post-stimulus EEG power as a percentage of change relative to pre-stimulus EEG power. However, expressing post-stimulus EEG power relative to pre-stimulus EEG power entails two important and surprisingly neglected issues. First, it can introduce a significant bias in the estimation of ERD/ERS magnitude. Second, it confuses the contribution of pre- and post-stimulus EEG power. Taking the human electrocortical responses elicited by transient nociceptive stimuli as an example, we demonstrate that expressing ERD/ERS as the average percentage of change calculated at single-trial level introduces a positive bias, resulting in an overestimation of ERS and an underestimation of ERD. This bias can be avoided using a single-trial baseline subtraction approach. Furthermore, given that the variability in ERD/ERS is not only dependent on the variability in post-stimulus power but also on the variability in pre-stimulus power, an estimation of the respective contribution of pre- and post-stimulus EEG variability is needed. This can be achieved using a multivariate linear regression (MVLR) model, which could be optimally estimated using partial least square (PLS) regression, to dissect and quantify the relationship between behavioral variables and pre- and post-stimulus EEG activities. In summary, combining single-trial baseline subtraction approach with PLS regression can be used to achieve a correct detection and quantification of ERD/ERS. •Percentage baseline correction leads to an ERD underestimation and ERS overestimation.•Subtraction baseline correction does not introduce any bias in ERD/ERS estimation.•Pre-stimulus α-power varies from trial to trial, following a hyperbolic function.•ERD/ERS variability is influenced by the variability of pre-stimulus EEG power.•MVLR+PLS dissects the contribution of pre-/post-stimulus EEG on behavioral variables.

Due to the limitation from the uncertainty principle, the window size consistently affects the resolution of time–frequency distributions. Determining the optimal window size is crucial for time–frequency analysis. In this work, we propose the concept of the reliable window width range along time and frequency axes, which simplifies the process of determining the optimal window size and the search range for adaptive methods. Moreover, in existing work, adaptive time–frequency analyses apply either time- or frequency-varying windows. In this study, we utilize a multitaper technique to integrate time- and frequency-varying windows. The proposed concept of the reliable window width range can be applied to any window-based time–frequency analysis method and enhance post-processing methods in the time–frequency plane. Subsequently, together with the reliable window width range, we propose the multitaper adaptive time–frequency windowed synchroextracting transform. It can estimate the optimal window size for time and frequency axes simultaneously and achieve a more energy-concentrated time–frequency analysis result.

Estimating instantaneous frequencies (IFs) overlapped in both time and frequency domains is an important issue in time‐frequency (TF) analysis. In this letter, the TF information of the signal in fractional domain is combined with an optimization strategy to estimate the IF of multicomponent overlapping signals. The method converts the IF estimation into the problem of function fitting in the two‐dimensional plane. First, based on the fractional window function with a specific angle, the energy of the multicomponent signal is concentrated around a certain signal component to obtain a relatively high‐resolution TF representation in fractional domain. Second, the training data set is constructed by the obtained TF representation, which will be used to estimate the IF. At last, the IF curve of the signal component is fitted with an optimization function. Numerical experiments verify the effectiveness of the proposed method under the low SNR. We combine the TF information of the signal in the fractional domain with an optimization strategy to estimate the IF of multicomponent overlapping signals. The method converts the IF estimation into the problem of function fitting in the two‐dimensional plane.