Frequency Domain Research Articles

Unlocking the mind-body connection: An HRV-based assessment of cardiac autonomic responses to yogic postures

The cardiac autonomic nervous system (CANS) plays a crucial role in regulating cardiovascular health. While yoga is considered an effective lifestyle intervention for enhancing autonomic regulation, most existing studies focus on the effects of meditation or breathing practices. Limited research exists on the influence of specific yogic postures on heart rate variability (HRV), a sensitive indicator of cardiac autonomic function.: This study aimed to investigate the immediate impact of specific yoga asanas, namely Padahastasana (PD), Ardha Chakrasana (AD), Ardhkatichakrasan Right (AKRD), Ardhkatichakrasan Left (AKLD), Trikonasana Right (TRD), Trikonasana Left (TLD), and Sarvangasana (SD) on HRV in healthy individuals. This observational pilot study was conducted with 30 healthy male volunteers aged 18–30 years. HRV was recorded for 15 minutes for each participant during the pre, during-, and post-asana phases. The frequency and time-domain components of HRV were analysed using SPSS 21.0. Significant changes were observed in the frequency domain parameters such as the LF/HF ratio, LF power, and HF power post-intervention. Notably, backward and inversion asana significantly increased vagal modulation, suggesting a shift towards parasympathetic dominance. The findings of this study indicate that specific yoga asana significantly modulate cardiac autonomic regulation, underscoring their potential utility in cardiovascular health and rehabilitation. Further research is warranted to explore the long-term impact of these postures on cardiac autonomic function.

Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics

In tunnel engineering, joint shear slip caused by external disturbances is a key factor contributing to landslides, instability of surrounding rock masses, and related hazards. Therefore, accurately characterizing the macromechanical properties of joints is essential for ensuring engineering safety. Given the significant influence of rock joint morphology on mechanical behavior, this study employs the frequency spectrum fractal dimension (D) and the frequency domain amplitude integral (Rq) as quantitative descriptors of joint morphology. Using Fourier transform techniques, a reconstruction method is developed to model joints with arbitrary shape characteristics. The numerical model is calibrated through 3D printing and direct shear tests. Systematic parameter analysis validates the selected quantitative indices as effective descriptors of joint morphology. Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. Machine learning, recognized as a rapidly advancing field, plays a pivotal role in data-driven engineering applications due to its powerful analytical capabilities. In this study, six algorithms—Random Forest (RF), Support Vector Regression (SVR), BP Neural Network, GA-BP Neural Network, Genetic Programming (GP), and ANN-based MCD—are evaluated using 300 samples. The performance of each algorithm is assessed through comparative analysis of their predictive accuracy based on correlation coefficients. The results demonstrate that all six algorithms achieve satisfactory predictive performance. Notably, the Random Forest (RF) algorithm excels in rapid and accurate predictions when handling similar training data, while the ANN-based MCD algorithm consistently delivers stable and precise results across diverse datasets.

Cross-Validating the Electrophysiological Markers of Early Face Categorization.

Human face categorization has been extensively studied using event-related potentials (ERPs), positing the N170 ERP component as a robust neural marker of face categorization. Recently, the fast periodic visual stimulation (FPVS) approach relying on steady-state visual evoked potentials (SSVEPs) has also been used to investigate face categorization. FPVS studies consistently report strong bilateral SSVEP face categorization responses over the occipitotemporal cortex, with a right hemispheric dominance, closely mirroring the N170 scalp topography. However, it remains unclear whether SSVEP responses can be considered a proxy for the N170 or are driven by different components. To address this question, we recorded electrophysiological signals from observers viewing face and object images during FPVS and ERP paradigms. We quantified the FPVS response in the frequency domain and extracted ERP components, including the P1, N170, and P2, from both the FPVS time domain and ERP paradigms. Our results revealed little relationship between any single ERP component and the FPVS frequency response. Only the peak-to-peak differences between N170 and P2 components consistently explained the FPVS frequency response. Our data show that the FPVS frequency response reflects a later complex neural integration rather than any isolated ERP component, such as the N170. These findings raise important methodological and theoretical considerations regarding the relationship between SSVEPs and transient ERPs. While both markers are indicative of human face categorization, they appear to capture different stages of this cognitive process.

Experimental Emulator of Pulse Dynamics in Fractional Nonlinear Schrödinger Equation

AbstractA nonlinear optical platform is presented to emulate a nonlinear Lévy waveguide that supports the pulse propagation governed by a generalized fractional nonlinear Schrödinger equation (FNLSE). This approach distinguishes between intra‐cavity and extra‐cavity regimes, exploring the interplay between the effective fractional group‐velocity dispersion (FGVD) and Kerr nonlinearity. In the intra‐cavity configuration, stable fractional solitons enabled by an engineered combination of the fractional and regular dispersions in the fiber cavity are observed. The soliton pulses exhibit their specific characteristics, viz., “heavy tails” and a “spectral valley” in the temporal and frequency domain, respectively, highlighting the effective nonlocality introduced by FGVD. Further investigation in the extra‐cavity regime reveals the generation of spectral valleys with multiple lobes, offering potential applications to the design of high‐dimensional data encoding. To elucidate the spectral valleys arising from the interplay of FGVD and nonlinearity, an innovative “force” model supported by comprehensive numerical analysis is developed. These findings open new avenues for experimental studies of spectral‐temporal dynamics in fractional nonlinear systems.

AECuration: Automated event curation for spike sorting.

Spike sorting is a commonly used analysis method for identifying single-units and multi-units from extracellular recordings. The extracellular recordings contain a mixture of signal components, such as neural and non-neural events, possibly due to motion and breathing artifacts or electrical interference. Identifying single and multi-unit spikes using a simple threshold-crossing method may lead to uncertainty in differentiating the actual neural spikes from non-neural spikes. The traditional method for classifying neural and non-neural units from spike sorting results is manual curation by a trained person. This subjective method suffers from human error and variability and is further complicated by the absence of ground truth in experimental extracellular recordings. Moreover, the manual curation process is time consuming and is becoming intractable due to the growing size and complexity of extracellular datasets. To address these challenges, we, for the first time, present a novel automatic curation method based on an autoencoder model, which is trained on features of simulated extracellular spike waveforms. The model is then applied to experimental electrophysiology datasets, where the reconstruction error is used as the metric for classifying neural and non-neural spikes. As an alternative to the traditional frequency domain and statistical techniques, our proposed method offers a time-domain evaluation model to automate the analysis of extracellular recordings based on learned time-domain features. The model exhibits excellent performance and throughput when applied to real-world extracellular datasets without any retraining, highlighting its generalizability. This method can be integrated into spike sorting pipelines as a pre-processing filtering step or a post-processing curation method.

Multiscale Convolution-Based Efficient Channel Estimation Techniques for OFDM Systems

With the advancement of wireless communication technology, the significance of efficient and accurate channel estimation methods has grown substantially. Recently, deep learning-based methods are being adopted to estimate channels with higher precision than traditional methods, even in the absence of prior channel statistics. In this paper, we propose two deep learning-based channel estimation models, CAMPNet and MSResNet, which are designed to consider channel characteristics from a multiscale perspective. The convolutional attention and multiscale parallel network (CAMPNet) accentuates critical channel characteristics by utilizing parallel multiscale features and convolutional attention, while the multiscale residual network (MSResNet) integrates information across various scales through cross-connected multiscale convolutional structures. Both models are designed to perform robustly in environments with complex frequency domain information and various Doppler shifts. Experimental results demonstrate that CAMPNet and MSResNet achieve superior performance compared to existing channel estimation methods within various channel models. Notably, the proposed models show exceptional performance in high signal-to-noise ratio (SNR) environments, achieving up to a 48.98% reduction in mean squared error(MSE) compared to existing methods at an SNR of 25dB. In experiments evaluating the generalization capabilities of the proposed models, they show greater stability and robustness compared to existing methods. These results suggest that deep learning-based channel estimation models have the potential to overcome the limitations of existing methods, offering high performance and efficiency in real-world communication environments.

Stabilization for Coupled Hyperbolic System With Memory Effects Via Minimal State Variable

ABSTRACTIn this work, we investigate the stabilization of a coupled PDE's system consisting of one Kirchhoff plate and one wave equation with memory effects. Three different cases are considered where the frictional infinite memory occurs in both equations or in one of the equations. First, we achieve the existence and uniqueness of the solution, utilizing the concept of minimal state variable. Moreover, it is shown that the coupled system is forced to polynomially decay. And by frequency domain analysis, the explicit decay rates are established, which only depend on the place of memory effect.

High AC field-induced polarization switching unraveled in frequency domain: Enhanced dielectric responses in lanthanum-doped Pb(Ni1/3Nb2/3)O3-Pb(Zr,Ti)O3 relaxor-ferroelectrics

In this study, we delve into the complex dielectric behaviors of lanthanum (La)-doped PNN-PZT relaxor-ferroelectric ceramics under the influence of high AC fields. Our approach involves a meticulous design of dielectric measurements to scrutinize the decoupling phenomenon between local polarization oscillation and global polarization switching. Remarkably, the application of high AC fields (>0.5 kV/mm) causes a dramatic increase in the dielectric permittivity (2x), alongside pronounced frequency dispersion (>65 °C) and a permittivity hump below Tm in 7% La-doped relaxor compositions. For relaxor-ferroelectric ceramics doped with lower La (<=5%) that are featured with tweed-like submicron domains as imaged in in situ transmission electron microscopy, the significantly enhanced dielectric permittivity and dielectric loss (>1) are induced under high AC fields (<0.5 kV/mm). A comparative study with a polarization loop in the time domain under various AC fields and DC bias demonstrates that the dielectric anomaly in the frequency domain is associated with global polarization switching, co-existing with polarization oscillation mechanism in various domains. This frequency domain method reveals threshold AC fields (0.25–0.5 kV/mm) above which polarization switching occurs in relaxor-FE compositions at elevated temperatures, complements the dynamic behaviors of P–E hysteresis, and cautions the control of AC fields in dealing with relaxor-ferroelectric materials for advanced electronic applications.

Numerical Investigation and Experimental Verification of Vibration Behavior for a Beam with Cantilever-Hertzian Contact Boundary Conditions

The simple spring structure, with detachable electrical contacts, is a very suitable solution for many applications, such as electromechanical relays and connectors. However, they are prone to exhibit instantaneous interruption faults under mechanical vibration environments. In this paper, the governing equations of the modal analysis of a beam with cantilever-Hertzian contact boundary conditions are presented. Then, the time domain analysis method and frequency domain analysis method for solving the forced vibration response are described explicitly. Next, the effect of the axial force on the modal frequency of a detailed model sourced from the practical relay is investigated by using commercial ANSYS Workbench 2021R1 software. Afterward, the harmonic response of the beam is numerically solved individually by using the transient analysis model and the harmonic analysis model in ANSYS Workbench 2021R1 software. Then, the influences of the damping coefficient and excited frequency on the contact force response are investigated. The experimental results of transient displacement and contact resistance of the beam structure agree well with the simulation outcomes. It is proven that there is a linear relationship between the stiffness coefficient and the mass coefficient, which are used for characterizing the damping of the structures in the time domain method and frequency domain methods.

A Review of Health Monitoring and Model Updating of Vibration Dissipation Systems in Structures

Given that numerous countries are located near active fault zones, this review paper assesses the seismic structural functionality of buildings subjected to dynamic loads. Earthquake-prone countries have implemented structural health monitoring (SHM) systems on base-isolated structures, focusing on modal parameters such as frequencies, mode shapes, and damping ratios related to isolation systems. However, many studies have investigated the dissipating energy capacity of isolation systems, particularly rubber bearings with different damping ratios, and demonstrated that changes in these parameters affect the seismic performance of structures. The main objective of this review is to evaluate the performance of damage detection computational tools and examine the impact of damage on structural functionality. This literature review’s strength lies in its comprehensive coverage of prominent studies on SHM and model updating for structures equipped with dampers. This is crucial for enhancing the safety and resilience of structures, particularly in mitigating dynamic loads like seismic forces. By consolidating key research findings, this review identifies technological advancements, best practices, and gaps in knowledge, enabling future innovation in structural health monitoring and design optimization. Various identification techniques, including modal analysis, model updating, non-destructive testing (NDT), and SHM, have been employed to extract modal parameters. The review highlights the most operational methods, such as Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI). The review also summarizes damage identification methodologies for base-isolated systems, providing useful insights into the development of robust, trustworthy, and effective techniques for both researchers and engineers. Additionally, the review highlights the evolution of SHM and model updating techniques, distinguishing groundbreaking advancements from established methods. This distinction clarifies the trajectory of innovation while addressing the limitations of traditional techniques. Ultimately, the review promotes innovative solutions that enhance accuracy, reliability, and adaptability in modern engineering practices.

Historical geopolitical risk acts and world crude oil production: evidence from the international level

Purpose The purpose of this study is to empirically investigates the influence of geopolitical risk acts (GPR) on world crude oil production. GPRs are a proxy for waging wars and military actions, thus it is expected to have a clear impact on world crude oil production (WCOP). Design/methodology/approach The current study formulates a theoretical model, and its parameters are estimated using three methodologies – traditional autoregressive distributed lag (ARDL) model, Dynamic ARDL model, and Frequency domain causality test – across the period 1973–2023 to ensure robust findings. Findings The results illustrate that GPRs constrain the global crude oil supply. The final impact relies on the positive interaction term between GPRs and international economic activity from one side and the negative interaction term with crude oil prices from the other. Likewise, the findings show that geopolitical threats reduce producing crude oil. Furthermore, this research deliberates on policy implications stemming from these findings. Originality/value Examining existing literature reveals a gap in understanding the relationship between geopolitical risk and crude oil production. Most of the studies in this research strand focus on investigating the impact on oil price volatility.

Iatrogenic aorto-coronary dissection triggered by contrast injection during coronary evaluation with optical frequency domain imaging.

Iatrogenic aorto-coronary dissection triggered by contrast injection during coronary evaluation with optical frequency domain imaging.

The Dominant Source Mechanism of Infrasound Generation in Powder Snow Avalanches

AbstractPowder snow avalanches (PSAs) radiate infrasound energy, yet the source mechanism remains unclear, limiting hazard monitoring and mitigation with infrasound‐based technologies. Here, we analyze a unique data set from a large PSA to improve the understanding of the source mechanism. Through comparison of cluster activity within the airborne layers of the PSA and the recorded infrasound signal in the frequency domain, we demonstrate that infrasound is mainly generated from particle clusters suspended by turbulent eddies or ejected from the denser basal layer. Further correlating infrasound amplitudes with radar‐derived spatial distributions of these clusters, we reveal a distributed source extending hundreds of meters behind the avalanche front. Additionally, we establish a relationship between infrasound and kinetic energy of suspended particles. These findings deepen our understanding of the complex dynamics of infrasound generation, offering valuable insights for avalanche detection and early warning strategies, and fundamental comprehension of PSA dynamics.

Least squares Marchenko imaging

Multiple reflections significantly influence seismic migration because they can lead to spurious events in traditional methods, including reverse time migration and Kirchhoff migration. Marchenko imaging represents a transformative advancement in the field of seismic migration that relocates seismic data containing internal multiples to accurate subsurface locations. Through the continuous pursuit of methodological refinement, we developed an innovative approach: incorporation of the Marchenko method within a least-squares migration framework, named least-squares Marchenko imaging (LSMI), based on the steepest descent method to minimize the L1 norm of gradient images. In the LSMI, to solve the problem that the conventional Born approximation can only simulate primary waves, we used the Marchenko Green’s function and the Born approximation to construct a Marchenko demigration operator, which can successfully simulate synthetic seismic data containing internal multiples. However, Marchenko demigration based on the Born approximation requires a large amount of calculation, and multiple iterations increase the computation time. Therefore, we introduce a parallel-computing framework implemented in the frequency domain to improve the computational efficiency of the Marchenko demigration. We partition the frequency-domain-transformed Green’s function into discrete chunks and implement parallel computation. This parallel processing significantly accelerates the Marchenko demigration process. The effectiveness of the proposed method is validated by applying it to a layered model. The experimental results show that LSMI converges with the L1 norm. Moreover, the superiority of our method over conventional Marchenko imaging is evident from its notable enhancement related to artifact suppression, resolution improvement, and illumination compensation. To further validate the effectiveness of our proposed method, we conducted additional experiments on the Sigsbee2A model, demonstrating that our method is also effective for this complex model. This is a novel approach to seismic migration, particularly in areas with internal multiples.

Identification and Removal of Light Pollution in Maritime Night-Time Images Based on Spatial Frequency Blocks

The safety of ships during nighttime navigation has always been a major concern. With the widespread application of technologies such as intelligent recognition, intelligent detection, and unmanned ship navigation at night, nighttime maritime light pollution has significantly affected the effectiveness of these intelligent technologies and navigation safety. Therefore, effectively eliminating nighttime maritime light pollution has become an urgent challenge that needs to be addressed. This paper presents a model based on spatial frequency blocks (SFBs) to solve the problem of light pollution in nighttime sea images. The model includes ResNet-50, an encoder, a decoder, and a discriminator. To enable the model to better remove the influence of light pollution, this study designs a method of first detecting the light pollution area and then removing it. It extracts image information from the space–frequency domain to help eliminate light pollution and retain more image information. The experimental results show that on the nighttime light pollution dataset, the Peak Signal-to-Noise Ratio (PSNR) of the model is improved to 24.91 compared to the current state-of-the-art image restoration model, while the Frechet inception distance (FID) is reduced to 64.85. At the same time, in the real night environment, the model can better remove light pollution to recover the original nighttime information. It has excellent performance and provides a certain reference value for advancing the safety of nighttime maritime navigation.

A weighted frequency domain energy operator spectral method based on soft thresholding fast iterative filtering for rolling bearing incipient fault feature extraction

The incipient features in the vibration signal are a typical signature of rolling bearing faults, and their extraction is vital for early-stage bearing fault diagnosis, yet quite challenging given the complexity of the noisy vibration signal. Therefore, a weighted frequency domain energy operator (FDEO) spectral method based on soft thresholding fast iterative filtering (FIF) is proposed in this work. Firstly, a soft thresholding FIF technique is proposed to improve the equivalent filter properties of FIF such as the continuity and the transition band lengths. Secondly, an integrated sparsity measure named frequency domain average Shannon kurtosis entropy that incorporates randomness and impulse characteristics is constructed to estimate the incipient fault feature information of the noisy signal. Finally, the weighted FDEO spectrum is designed to complete the bearing fault feature identification, it suppresses the chaotic signal component in the frequency domain. The proposed incipient fault extraction framework is evaluated by simulation and experiment, and its performance in an intense noise background is verified through comparisons with other signal decomposition methods.

Analysis of GNSS-RTK Monitoring Background Noise Characteristics Based on Stability Tests.

GNSS-RTK offers numerous advantages and broad prospects in structural dynamic monitoring in civil engineering. However, in practical applications, GNSS-RTK accuracy is susceptible to the monitoring environments, causing actual monitoring accuracy to fall below its calibrated accuracy. This study investigates the monitoring accuracy and spectral characteristics of GNSS-RTK based on stability tests under different environments related to reflection and obstruction conditions (i.e., concrete, grass, an obstructed balcony, and a water area). The findings indicate that in open environments of grass, concrete, and water, the standard deviation (STD) of GNSS-RTK monitored displacement is below 8 mm, its accuracy meeting the specifications of structural health monitoring. In the obstructed balcony environments, GNSS-RTK signals exhibit amplitude jumps, resulting in lower accuracy; however, during non-jump intervals, the STD of monitored displacement is below 10 mm, satisfying the structural health monitoring accuracy requirements. Moreover, the amplitudes of GNSS-RTK displacements in the concrete, grass, and water areas are basically consistent with the calibration accuracy of ±10 mm in the horizontal direction and ±20 mm in the elevation direction, while the amplitudes of GNSS-RTK displacements in the obstructed balcony condition are far greater than the calibration accuracy. The spectral analysis of GNSS-RTK signals reveals that multipath errors in concrete, grass, and obstructed balcony environments are primarily concentrated in the low-frequency range within 0.04 Hz, while the internal white noise of the instrument is widely and evenly distributed across the whole frequency domain. Based on these findings, adaptive methods, such as filter methods and multipath error correction techniques, are proposed for the de-noising of GNSS-RTK background noise.

Stabilizing a Bio-Inspired Rotating Empennage Fighter Aircraft in Multiple Trim Scenarios

This paper considers the problem of stabilizing a bio-inspired fighter aircraft at its Air Combat Maneuver Condition in steady-level and coordinated-turning flight. The aircraft equations of motion are linearized, and an infinite-horizon linear quadratic regulator design is conducted. The open-loop system is unstable in the short-period and Dutch roll modes. This is mitigated in the closed-loop system, which is analyzed in the time and frequency domains. Included in the simulation dynamics are first-order actuator models, actuator deflection limits, and actuator rate limits. These are particularly important for this bio-inspired aircraft because control actuation requires rotation of the empennage, which has relatively large inertia. Simulation responses to initial condition dispersions, aerodynamic model error, and atmospheric turbulence are analyzed to characterize time-domain properties: settling time, region of attraction, control saturation, and robustness. Due to poor singular values for the throttle setting and rotating tail inputs, analysis is dedicated to control designs with these inputs fixed at trim values. Within the scope of these analyses, fixing the rotating tail at trim does not significantly degrade system performance.

Speckle‐Free 3D Holography in the Wigner Domain

AbstractComputer‐generated hologram (CGH) provides an approach to modulate the 3D coherent wavefront and has been widely used in many optical applications. Over the past few decades, extensive efforts have been made to design optimization models for high‐quality reconstruction. However, the reconstruction quality is still limited due to the mismatch of the bandwidth between the reconstructed field of interest and the reconstructed complex amplitude field in 3D space. Here, an ideal numerical light field mapping physical model from the hologram plane to the 3D image plane for speckle‐free 3D holography in the Wigner domain is established. With the aid of the Wigner distribution function (WDF), which allows the analysis of a light field from not only the space domain but also the spatial frequency domain, the bandwidth properties of the reconstructed field in 3D space are analyzed, which provides a guideline for the sampling of the reconstructed field to efficiently describe the speckles and artifacts. Accordingly, a comprehensive CGH optimization method in the Wigner domain is proposed to constrain the reconstructed intensity fluctuations without errors and omissions for high‐quality reconstruction. As such, this method enables speckle‐free and artifact‐free reconstruction with a twice improvement in peak signal‐to‐noise ratio (PSNR) and a five times improvement in structural similarity index measure (SSIM) compared to conventional phase‐only holograms. The optical experimental results show that this method paves the road for the future implementation of speckle‐free color 3D holography harnessing the advanced integrated photonic devices, and also offers an efficient and practical route for various optical applications, such as 3D display, optical encryption, beam shaping, optical computing and so on.

Accurate Classification of Human CD4+ T, CD8+ T, and CD19+ B Cells Isolated from Splenocytes by Cross-Polarized Diffraction Image Pairs.

Diffraction imaging of cells allows rapid phenotyping by the response of intracellular molecules to coherent illumination. However, its ability to distinguish numerous types of human leukocytes remains to be investigated. Here, we show that accurate classification of three lymphocyte subtypes can be achieved with features extracted from cross-polarized diffraction image (p-DI) pairs. A deep neural network (DNN) of DINet-PS has been developed for feature extraction from and filtering of, in the angular frequency domain, p-DI pairs acquired from live lymphocytes isolated from human spleen tissues. We built the network in a dual-channel structure and incorporated two adaptive spectral filter blocks to actively suppress extracted features related to the noise component of light in p-DI pairs. The DINet-PS was trained with p-DI pairs acquired from 5311 CD4+ T, 3819 CD8+ T, and 4054 CD19+ B cells after preprocessing and rebelling of manually derived secondary labels and classification accuracy of 96.6 ± 0.40% has been achieved in hold-out test data sets among the three subtypes. Our results show the power of DNN to extract cell-related features from p-DI pairs and the potential of polarization diffraction imaging flow cytometry for accurate and label-free classification of lymphocyte subtypes in particular and leukocytes in general.