In existing window time-frequency analysis methods, its resolution is commonly limited by the uncertainty principle. The time resolution and the frequency resolution restrict each other and cannot ...reach the maximum at the same time. For this reason, we employ the secondary processing technology of the time-frequency spectrum to enhance the time resolution of the S-transform (ST). By performing multiband filtering with a filter bank, the ST is capable of receiving the signal under different frequencies and its time-frequency coefficient. The window size is different, that is, the low-frequency time window is long, resulting in low time resolution and high-frequency resolution. The high-frequency time window is short, which makes the time resolution high and the frequency resolution low; however, the window size for the time component of each frequency is fixed. Based on the above idea, we perform "de-window" processing for each frequency component signal. We assume that the time-frequency spectrum is sparse without "windowing," so the window effect can be removed by sparse inversion. Based on the alternating direction method of multipliers (ADMM), we use the nonnegative penalty terms and sparse terms for the joint constraints to solve the optimization problem, and obtain the sparse time-frequency spectrum to enhance the time resolution. This time sparse ST (TSST) obtained by the proposed method is suitable for improving the ability of thin-layer identification and also for the analysis of transient signals.
The accurate detection and classification of power quality (PQ) disturbances in power systems is a key step to determine the causes of these events before any proper countermeasure could be taken. ...This paper presents a new algorithm for detection and classification of PQ disturbances based on the combination of double-resolution S-transform (DRST) and directed acyclic graph support vector machines (DAG-SVMs). The proposed method first employs DRST for an effective feature extraction from power signals. Then, the DAG-SVMs are used to predict the classes of PQ disturbances. The DRST not only has better time-frequency localization and stronger robustness but also reduces the computational complexity without losing the useful information of the original signal in comparison with the traditional S-transform. Through the combined use of DRST and DAG-SVMs, the algorithm can be easily implemented in embedded real-time applications. Finally, the implementation of the proposed algorithm in a digital signal processor + advanced reduced instruction set computing machine-based hardware test platform is introduced. The effectiveness of the proposed method is demonstrated by means of computer simulations and practical experiments with single and combined PQ disturbances.
Time-frequency (TF) analysis is essential for industrial engineering applications. However, the conventional TF analysis methods suffer from blurry TF energy. This article proposes a new unified ...sparse TA analysis (STFA) framework to concentrate the blurry energy, restrain noise, separate condition-related components, and retain the signal reconstruction property. The STFA framework leverages the weighted elastic net sparse regularization for sparsity- inducing and energy concentration and uses the reconstruction error term for condition-related component separation and signal reconstruction, which bridges the gaps among sparsity, decomposition, transformation, and reassignment. Theoretical analysis and comprehensive investigation of the proposed framework are performed in practical cases. Comparison results with the state-of-the-art methods demonstrate that the proposed framework has superior properties for TF feature energy concentration, denoising, component separation, and reconstruction, especially for the signals with the fast-varying features.
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
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
In the realm of expanding unmanned aerial vehicle (UAV) applications and types, the precision of UAV target classification is of paramount importance. Deep learning has emerged as the linchpin of ...such endeavors. A new approach based on deep learning fusion technique is proposed by our team, which integrates frequency modulated continuous wave (FMCW) radar micro-Doppler signals, cadence-velocity diagram (CVD) signals and cepstrum (CEP) signals. This synthesis culminates in UAV classification with exceptional accuracy, surpassing 97%. In this letter, two deep learning fusion approaches leveraging the ResNet34 network were employed: data-level fusion and feature-level fusion. Empirical results unequivocally highlight the potency of deep learning information fusion-most notably, the fusion of the three spectrograms-exceeding 97% accuracy. This firmly underscores the pivotal role that deep learning fusion techniques play in amplifying precision in UAV target classification.
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.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
We report on time-frequency analysis (TFA) of dispersion in a 1-D magneto-inductive waveguide (MIW). The short-time Fourier transform (STFT) and Wigner-Ville distribution (WVD) are used to localize ...the frequency content of the dispersed wave as a function of time and to estimate the group velocity in experiments using a 1-D MIW constructed with planar resonators. Time-domain reflectometry (TDR) measurements are performed after deliberately introducing defects along the waveguide; and the results are analyzed with TFA, demonstrating the potential for using such MIW-TDR configurations as sensors. The WVD-TFA is shown to provide precise time-frequency information, accurate defect localization, and group velocity estimation, confirming the slow wave long-range nature of dispersive MIWs. The adaptability of the MIW-TDR setup and the accuracy of the WVD for localization make the technique a suitable candidate for sensing applications using engineered dispersive media.
Linear transform has been widely used in time-frequency analysis of rotational machine vibration. However, the linear transform and its variants in current forms cannot be used to reliably analyze ...rotational machinery vibration signals under nonstationary conditions because of their smear effect and limited time variability in time-frequency resolution. As such, this paper proposes a new time-frequency method, named velocity synchronous linear chirplet transform (VSLCT). The proposed VSLCT is an extended version of the current linear transform. It can effectively alleviate the smear effect and can dynamically provide desirable time-frequency resolution in response to condition variations. The smearing problem is resolved by using linear chirplet bases with frequencies synchronous with shaft rotational velocity, and the time-frequency resolution is made responsive to signal condition changes using time-varying window lengths. To successfully implement the VSLCT, a kurtosis-guided approach is proposed to dynamically determine the two time-varying parameters, i.e., window length and normalized angle. Therefore, the VSLCT does not require the user to provide such parameters and hence avoids the subjectivity and bias of human judgment that is often time-consuming and knowledge-demanding. This method can also analyze normal monocomponent frequency-modulated signal.
With the fast development of smart terminals and emerging new applications (e.g., real-time and interactive services), wireless data traffic has drastically increased, and current cellular networks ...(even the forthcoming 5G) cannot completely match the quickly rising technical requirements. To meet the coming challenges, the sixth generation (6G) mobile network is expected to cast the high technical standard of new spectrum and energy-efficient transmission techniques. In this article, we sketch the potential requirements and present an overview of the latest research on the promising techniques evolving to 6G, which have recently attracted considerable attention. Moreover, we outline a number of key technical challenges as well as the potential solutions associated with 6G, including physical-layer transmission techniques, network designs, security approaches, and testbed developments.
Time-frequency analysis (TFA) is a powerful tool for describing time-frequency (TF) features of seismic data, such as short-time Fourier transform (STFT) and <inline-formula> <tex-math ...notation="LaTeX">S </tex-math></inline-formula>-transform (ST). Recently, sparse TFA (STFA) is proposed for enhancing TF readability of commonly used TFA tools. However, STFA is often solved via an optimal inverse problem with a prior regularization term, which is difficult to set in practice, where the key regularization parameters are sensitive to noise. Moreover, it often takes expensive calculation time, especially for 3-D field data application. We build a deep learning (DL)-based workflow for implementing STFA to obtain sparse TF (STF) spectra, termed the STF network with transfer learning (STFNTL). We first adopt a Marmousi II reflectivity model and Ricker wavelets with different dominant frequencies to generate synthetic training dataset. Then, we adopt a simplified STFA method with optimized parameters to generate synthetic training labels, i.e., sparse TF spectra. Afterward, we propose the STF network (STFN) based on a simplified Unet model, which is trained using synthetic training data and corresponding STF labels. Moreover, to enhance the generalization of STFN, we introduce an adaptive transfer learning (TL) strategy based on small samples of field data and their corresponding STF labels. Finally, synthetic and field data are utilized to illustrate the effectiveness and generalization ability of our proposed model.
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