Time Domain Research Articles

In vivo optimization of the experimental conditions for the non-invasive optical assessment of breast density

In this study, time domain diffuse optical spectroscopy is performed in the range 600–1100 nm on 11 healthy volunteers with a portable system for the quantitative characterization of breast tissue in terms of optical properties and optically-derived blood parameters, tissue constituent concentrations, and scattering parameters. A measurement protocol involving different geometries (reflectance and transmittance), subject’s positions (sitting and lying down), probing locations (outer, lower, and inner breast quadrants), and source-detector distances (2 and 3 cm) allowed us to investigate the effect of tissue heterogeneity and different measurement configurations on the results with the aim of identifying the best experimental conditions for the estimate of breast density (i.e., amount of fibro-glandular tissue in the breast) as a strong independent risk factor for breast cancer. Transmittance results, that in previous studies correlated strongly with mammographic density, are used as a reference for the initial test of the simpler and more comfortable reflectance measurement configuration. The higher source-detector distance, which probes deeper tissue, retrieves optical outcomes in agreement with higher average density tissue. Similarly, results on the outer quadrants indicate higher density than internal quadrants. These findings are coherent with breast anatomy since the concentration of dense fibro-glandular stroma is higher in deep tissue and towards the external portion of the breast, where the mammary gland is located. The dataset generated with this laboratory campaign is used to device an optimal measurement protocol for a future clinical trial, where optical results will be correlated with conventional mammographic density, allowing us to identify a subset of wavelengths and measurement configurations for an effective estimate of breast density. The final objective is the design of a simplified, compact and cost-effective optical device for a non-invasive, routine assessment of density-associated breast cancer risk.

Mutual information-based feature selection for inverse mapping parameter updating of dynamical systems

AbstractA digital twin should be and remain an accurate model representation of a physical system throughout its operational life. To this end, we aim to update (physically interpretable) parameters of such a model in an online fashion. Hereto, we employ the inverse mapping parameter updating (IMPU) method that uses an artificial neural network (ANN) to map features, extracted from measurement data, to parameter estimates. This is achieved by training the ANN offline on simulated data, i.e., pairs of known parameter value sets and sets of features extracted from corresponding simulations. Since a plethora of features (and feature types) can be extracted from simulated time domain data, feature selection (FS) strategies are investigated. These strategies employ the mutual information between features and parameters to select an informative subset of features. Hereby, accuracy of the parameters estimated by the ANN is increased and, at the same time, ANN training and inference computation times are decreased. Additionally, Bayesian search-based hyperparameter tuning is employed to enhance performance of the ANNs and to optimize the ANN structure for various FS strategies. Finally, the IMPU method is applied to a high-tech industrial use case of a semi-conductor machine, for which measurements are performed in closed-loop on the controlled physical system. This system is modeled as a nonlinear multibody model in the Simscape multibody environment. It is shown that the model updated using the IMPU method simulates the measured system more accurately than a reference model of which the parameter values have been determined manually.

Real-Time Optical Fiber Salinity Interrogator Based on Time-Domain Demodulation and TPMF Incorporated Sagnac Interferometer.

A novel demodulation scheme for a point-type fiber sensor is designed for salinity concentration monitoring based on a Sagnac interferometer (SI) composed of a tapered polarization-maintaining fiber (TPMF) and optical time stretching technology. The SI, constructed using a PMF with a taper region of 5.92 μm and an overall length of 30 cm, demonstrated a notable enhancement in the evanescent field, which intensifies the interaction between the light field and external salinity. This enhancement allows for a direct assessment of salinity concentration changes by analyzing the variations in the SI reflection spectra and the experimental results indicate that the sensitivity of the sensor is 0.151 nm/‱. In contrast to traditional fiber optic sensors that depend on spectral demodulation with slower response rates, this work introduces a new approach where the spectral shift is translated to the time domain, utilizing a dispersion compensation fiber (DCF) with the demodulation rate reaching up to 50 MHz. The experimental outcomes reveal that the sensor exhibits a sensitivity of -0.15 ns/‱ in the time domain. The designed sensor is anticipated to play a pivotal role in remote, real-time monitoring of ocean salinity.

Power system stability enhancement through optimal PSS design

Low-frequency oscillations (LFO) are generated in interconnected electric networks because of the weak tie lines between the parts of the networks. Such oscillations have been a significant challenge for engineers that may lead to system instability if appropriate measures are not taken. This article proposes two new metaheuristic algorithms, the jellyfish search algorithm (JSA) and the tunicate swarm algorithm (TSA), to design the power system stabilizers (PSS) for single machine infinite bus (SMIB) and multimachine power system (MPS) networks. The proposed method uses the JSA and TSA to dampen out the LFOs by modifying the vital parameters of lead-lag type conventional PSS (CPSS). A damping ratio-based objective function improves both models' system damping. These methods are tested on an SMIB system and two distinct MPS networks exposed to the three-phase fault under various loading conditions. The effectiveness of the suggested approach has been studied by comparing the system eigenvalues and minimum damping ratio (MDR) produced from JSA and TSA-tuned PSS and the CPSS. In addition, time domain simulation results are compared, demonstrating that the new approach outperforms the standard techniques, such as particle swarm optimization (PSO) and backtracking search algorithm (BSA). In the SMIB system, MDR produced by JSA and TSA-based PSS is 2.55 times higher than that of CPSS. Similarly, in comparison to PSO and BSA-based PSS, JSA, and TSA-tuned PSS offer >1.4 times greater MDR in the Two Area Four Machine network and up to 8.4 times larger MDR for the IEEE-39 Bus network. Furthermore, the efficacy of the suggested JSA and TSA approaches' prediction capacity is validated by satisfying the values of statistical performance metrics.

Correlation analysis and joint probability density function model of wind pressures: Focusing on multivariate wind loads field on low-rise building under typhoon climate

The characteristics of the multivariate wind loads field on the roof are crucial to the wind-resistant design of low-rise buildings, which contain the correlation characteristics in space and probability characteristics in the time domain. This paper proposes a framework for constructing a Joint Probability Density Function (Joint PDF) model for a multivariate wind loads field. It provides a detailed correlation analysis for the first time. This paper employs wind pressure data collected from the roof of a low-rise building during Typhoon Muifa. It was found that the correlation becomes more robust with increasing roof pitch and the wind pressures are strongly correlated with a correlation coefficient exceeding 0.50 when the roof pitch is above 15°. The mixture distribution model is applied to the probability density function fitting procedure of wind pressure time series under typhoon climate, and the fitting effect is significantly better than other classical probability density functions. The optimal copula function is determined according to the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for estimating the Joint PDF. The results reveal that Gumbel-copula and Student-copula have the highest proportion in optimal copula functions, accounting for over 90% of the total copula functions. Then, a bivariate Joint PDF for wind pressures are established with the optimal copula function. Additionally, the comparison between measured bivariate Joint PDF and that constructed using copula functions verifies the accuracy of the proposed framework for constructing Joint PDFs. The Joint PDF of wind pressures can enhance the understanding of the stochastic characteristics of local wind load fields on roofs, and the correlation characteristics in space provide crucial references for improving the accuracy of wind load random field simulation and saving the cost of wind resistance design.

A study of asymmetric guided wave propagation mechanism in elbow and prediction of elbow erosion degree based on SAFE and GA-LSSVM methods

Aiming at the problems of complex sensor layout, low detection accuracy when using guided wave to detect the elbow’s erosion degree, this research presents a new approach based on asymmetric guided wave and Genetic Algorithm-Least Support Vector Machine (GA-LSSVM). The program for calculating elbow’s dispersion curve was compiled using the semi-analytical finite element (SAFE) method. Through numerical simulation, it is found that the bending mode in the guided wave signal at the outer arch back position of the receiving end decreases with the increase of the erosion degree, which leads to the decrease of the signal’s time domain energy. Finally, The GA-LSSVM model were used to predict various elbow specimens’ residual wall thickness, and the results shows the high prediction accuracy. The average accuracy of predicting the new elbows is 87.63%. This research provides a feasible scheme for detecting elbow’s erosion degrees in engineering.

A bilinear modeling in counts time series with applications

This paper introduces a modified bilinear model for integer-valued time series in which thinning operators are applied in bilinear terms involving the product of the input and state process separately. The proposed model is able to consider overdispersion. Furthermore, it connects a feature of the integer-valued autoregressive conditional heteroskedasticity and integer-valued autoregressive processes. Important properties of the proposed model as well as the sufficient condition for stationarity are derived. After considering some estimation methods based on time domain and frequency domain approaches, simulation studies are conducted to check the performance of the estimates. The analysis of practical cases in social science is accomplished to highlight the usefulness of the proposed model in applications, and the model’s adequacy is provided. Further, the suggested model discusses the problem of data forecasting.

A variable modal decomposition and BiGRU-based tool wear monitoring method based on non-dominated sorting genetic algorithm II

ABSTRACT In the metal cutting process, it is important to realize the effective monitoring of tool wear to ensure the quality of parts machining. A variable modal decomposition (VMD) and bi-directional gated recurrent neural network (BiGRU) based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for tool wear monitoring (TWM) problem. The method takes the envelope entropy and kurtosis of the decomposed signal as the objective function, and iteratively optimizes the number of decomposition layers k and the penalty factor α of the VMD by NSGA-II to obtain the optimal solution set. Meanwhile, an evaluation method of entropy weight- technique for order preference by similarity to ideal solution (EW-TOPSIS) is proposed to obtain the optimal parameter combinations in VMD. The VMD decomposed signals are screened by calculating the correlation coefficients, the time domain and frequency domain features of the screened signals are extracted, and the screened signals and features are mirrored. To realize the in-depth mining of the feature information of time series data, a BiGRU deep learning monitoring model is established. By comparing three classification methods, it has been proven that this method has achieved better results in monitoring tool wear state.

A novel single mode fiber optic temperature sensor combined with the FLRDS technique

A single mode optical fiber loop was employed as a temperature sensor to observe changes in optical loss regarding to ringdown time (RDT) by high sensitive the fiber loop ringdown spectroscopy (FLRDS) technique which has real-time and fast response measurement capability due to allowing trapped light pulse multiple interactions with the measurands. Two different fiber loops of 45 ± 5 m and 120 ± 5 m lengths were embedded one by one into a copper, circular and closed housing. Continuous monitoring of RDTs was carried out by changing the temperature in the range of 25–200 °C with the steps of 25 °C for the first time by using bare fiber without any modification as a temperature sensor. The FLRDS system for temperature sensing has simple design without extra components such as an optical time domain reflectometer (OTDR), long-period fiber grating (LPFG) or fiber Bragg grating (FBG) as sensorhead. The FLRDS system was diligently optimized to achieve the lowest baseline as %0.49. Since the RDT of the FLRDS system was changed due to the thermal expansion of the fiber, continuous monitoring of the temperature was the first time recorded by utilizing this kind of FLRDS temperature sensor. These kind of FLRDS temperature sensors have high potential to be employed in mining, nuclear facilities, railways, underwater structures, biomedical, medicine, structural health monitoring, transportation and communication applications with simple system setup, lower cost, higher sensitivity, portability, real-time and continuous monitoring for early detection.

Autonomic cardiovascular control is unaffected in children referred for assessment of excessive daytime sleepiness.

There is conflicting evidence for impaired autonomic control of heart rate (HR) in adults with narcolepsy and idiopathic hypersomnolence (IH). Despite these chronic hypersomnia conditions primarily being diagnosed around the age of puberty, there are limited studies in children. The present study investigated cardiovascular control using heart rate variability (HRV) and the extent of nocturnal HR dipping during sleep in children and adolescents with narcolepsy and IH. Children having an overnight polysomnographic study followed by a multiple sleep latency test (MSLT) for investigation of excessive daytime sleepiness (EDS) between May 2010 to December 2023 were included: 28 children diagnosed with narcolepsy, 11 with IH, and 26 subjectively sleepy children who did not meet the diagnostic criteria for either narcolepsy or IH. Each clinically referred child was matched for age and sex with a control. Time domain and frequency domain HRV were calculated from ECG recorded at 512 Hz. There were no differences in either time domain or spectral analysis of HRV between clinical groups or between clinical groups and their control group. The expected sleep state differences in HRV were observed in all groups. There was also no difference in HR nocturnal dipping between groups. Despite evidence for abnormal autonomic function in adults with narcolepsy and IH, our study did not identify any abnormalities in HR, HR control, or nocturnal dipping of HR in children referred for assessment of EDS. This suggests that autonomic dysfunction may be a feature of these conditions that develops in later life.

The point scatterer approximation for wave dynamics

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The effects of four hours of normobaric hypoxia on the vestibular control of balance.

Whole-body vestibular-evoked balance responses decrease following ~ 55min of normobaric hypoxia. It is unclear how longer durations of hypoxia affect the vestibular control of balance at the muscle and whole-body levels. This study examined how four hours of normobaric hypoxia influenced the vestibular control of balance. Fifteen participants (4 females; 11 males) stood on a force plate with vision occluded and head rotated rightward while subjected to three blocks of binaural, bipolar stochastic electrical vestibular stimulation (EVS; 0-25Hz, root mean square amplitude = 1.1mA) consisting of two, 90-s trials. The relationship between EVS and anteroposterior (AP) forces or medial gastrocnemius (MG) electromyography (EMG) was estimated in the time and frequency domains at baseline (BL; 0.21 fraction of inspired oxygen-FIO2) and following two (H2) and four (H4) hours of normobaric hypoxia (0.11 FIO2). The EVS-MG EMG short-latency peak and peak-to-peak amplitudes were smaller than BL at H2 and H4, but the medium-latency peak amplitude was only lower at H4. The EVS-AP force medium-latency peak amplitude was lower than BL at H4, but the short-latency peak and peak-to-amplitudes were unchanged. The EVS-MG EMG coherence and gain were reduced compared to BL at H2 and H4 across multiple frequencies ≥ 7Hz, whereas EVS-AP force coherence was blunted at H4 (≤ 4Hz), but gain was unaffected. Overall, the central nervous system's response to vestibular-driven signals during quiet standing wasdecreased forup to four hours of normobaric hypoxia, and vestibular-evoked responses recorded within postural muscles may be more sensitive than the whole-body response.

Germanium metasurface near-infrared high-q absorber with symmetry-protected bound states in the continuum

The all-dielectric germanium nanohole (GNH) metasurface with a sub-wavelength thickness supports simultaneous excitation of quasi bound state in the continuum (BIC) and super radiant mode. By selecting the different hole depths in a germanium slab, we present a trade-off metasurface between high Q-factor and high absorption in the photonic system. The presented device demonstrated absorption of super-radiant mode ∼98.5% and quasi-BIC ∼93% without back-metal reflector at the telecommunication wavelength. The numerical results, obtained by the finite difference time domain (FDTD) method are explained in the framework of temporal coupled mode theory (TCMT).

Heart Rate Variability in Military Pilots During Flight: A Scoping Review.

Piloting an aircraft is a complex multitasking activity that involves managing information in a nonautomatic way and generates a high workload (psychological, cognitive, and physical) for the pilot. The excess of these demands can result in decreased performance and may impair flight safety. Heart rate variability (HRV) has been used in recent studies as a method to investigate operator's workload in complex environments. This measure can assess the stress and recovery ability of the autonomic nervous system. However, a better understanding of flight influence on the pilot's autonomic modulation is necessary. Therefore, this scoping review aims to systematically map the studies related to changes in the autonomic modulation in military pilots during flight, in order to characterize their workload at different times and flight profiles. A literature search was conducted using MEDLINE (by PubMed), SCOPUS, and LILACS databases. Recent records (2002-2022) that analyzed HRV in military pilots during flight were included. Meanwhile, studies in which piloting activity was not performed were excluded. The study was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline extension for Scoping Reviews and the Updated Methodological Guidance for the Conduct of Scoping Reviews. A total of 298 records were obtained, of which 19 were included in the scoping review. The studies analyzed flights performed in high- and low-performance aircrafts, helicopters, and flight simulators, using time domain, frequency domain, and nonlinear HRV indices. Real and simulated flights produced significant autonomic changes. Some flights elicited autonomic nervous system responses that persisted up to 5 h after landing. During real flight, the most sensitive indices for identifying variations in pilot workload were root mean square of successive differences between normal heartbeats, standard deviation of interbeat interval of normal sinus beat (SDNN), ratio of low-frequency to high-frequency power, and Poincaré plot standard deviation perpendicular to the line of identity (SD1), whereas the interbeat interval (RR), SDNN, SD1, and Poincaré plot standard deviation along the line of identity (SD2) were the most sensitive indices when comparing segments of simulated flight with different levels of difficulty. This scoping review provided insight into the influence of flight on autonomic modulation in military pilots. Some key themes were highlighted: Increased sympathetic activity during flight, sensibility of different domains of HRV to flight demands, and autonomic changes during recovery time. Future research efforts may allow us to enhance the understanding of pilot's workload limits and to elucidate the optimal postflight recovery time.

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

Study on Vibration and Noise of Railway Steel–Concrete Composite Box Girder Bridge Considering Vehicle–Bridge Coupling Effect

A steel–concrete composite beam bridge fully exploits the mechanical advantages of the concrete structure and steel structure, and has the advantages of a fast construction speed and large stiffness. It is of certain research value to explore the application of this bridge type in the field of railway bridges. However, with the rapid development of domestic high-speed railway construction, the problem of vibration and noise radiation of high-speed railway bridges caused by train loads is becoming more and more serious. A steel–concrete composite beam bridge combines the tensile characteristics of steel and the compressive characteristics of concrete perfectly. At the same time, it also has the characteristics of a steel bridge and concrete bridge in terms of vibration and noise radiation. This feature makes the study of the vibration and noise of the bridge type more complicated. Therefore, in this paper, the characteristics of vibration and noise radiation of a high-speed railway steel–concrete composite box girder bridge are studied in detail from two aspects: the theoretical basis and a numerical simulation. The main results obtained are as follows: Relying on the idea of vehicle–rail–bridge coupling dynamics, a structural dynamics analysis model of a steel–concrete combined girder bridge for a high-speed railroad was established, and numerical program simulation of the vibration of the vehicle–rail–bridge coupling system was carried out based on the parametric design language of ANSYS 18.0 and the language of MATLAB R2021a, and the structural vibration results were analyzed in both the time domain and frequency domain. By using different time-step loading for the vehicle–rail–bridge coupling vibration analysis, the computational efficiency can be effectively improved under the condition of guaranteeing the accuracy of the result analysis within 100 Hz. Based on the power flow equilibrium equation, a statistical energy method of calculating the high-frequency noise radiation is theoretically derived. Based on the theoretical basis of the statistical energy method, the high-frequency noise in the structure is numerically simulated in the VAONE 2021 software, and the average contribution of the concrete roof plate to the three acoustic field points constructed in this paper is as high as 50%, which is of great significance in the study of noise reduction in steel–concrete composite girders.

FTMLP: MLP with Feature-Temporal Block for multivariate time series forecasting

Multivariate time series (MTS) forecasting is extensively applied in real-world scenarios. Recent studies introduced the concept of channel independence, which has achieved significant results. However, this approach often misses the intricate interrelations among features and lacks physical interpretation. To address this problem, we propose a new framework, the Feature-Temporal block, designed to extract both temporal and feature information. Each block contains two modules: a feature module using a gating mechanism to understand competitive interactions among features, and a temporal module using learnable filters for frequency domain filtering. This innovative design allows FTMLP to combine information across feature and sequence dimensions, as well as time and frequency domains. The computational efficiency of FTMLP is enhanced due to its exclusive use of Multi-Layer Perceptron (MLP). Comprehensive experiments on several real-world datasets demonstrate that FTMLP achieves state-of-the-art results in terms of overall performance. Code is available at https://github.com/whxlearning/FTMLP.

Computer-Aided Classification of Breast Lesions Based on US RF Time Series Using a Novel Machine Learning Approach.

One of the most promising adjuncts for screening breast cancer is ultrasound (US) radio-frequency (RF) time series. It has the superiority of not requiring any supplementary equipment over other methods. This research aimed to propound a machine learning (ML) approach for automatically classifying benign, probably benign, suspicious, and malignant breast lesions based on the features extracted from the accumulated US RF time series. In this article, 220 data of the aforementioned categories, recorded from 118 patients, were analyzed. The dataset, named RFTSBU, was registered by a SuperSonic Imagine Aixplorer medical/research system equipped with a linear transducer. The regions of interest (ROIs) of the B-mode images were manually selected by an expert radiologist before computing the suggested features. Regarding time, frequency, and time-frequency domains, 291 various features were extracted from each ROI. Finally, the features were classified by a pioneering technique named the reference classification method (RCM). Furthermore, the Lee filter was applied to evaluate the effectiveness of reducing speckle noise on the outcomes. The accuracy of two-class, three-class, and four-class classifications were respectively calculated 98.59 ± 0.71%, 98.13 ± 0.69%, and 96.10 ± 0.66% (considering 10 repetitions) while support vector machine (SVM) and K-nearest neighbor (KNN) classifiers with 5-fold cross-validation were utilized. This article represented the proposed approach, named CCRFML, to distinguish between breast lesions based on registered in vivo RF time series employing an ML framework. The proposed method's impressive level of classification accuracy attests to its capability of effectively assisting medical professionals in the noninvasive differentiation of breast lesions.

Verification of time-temperature superposition principle for viscoplastic strains of asphalt mixtures obtained from creep-recovery tests in the low-strain regime

The creep compliances, total, and viscoplastic (VP) strains of various asphalt mixtures were determined from creep-recovery tests at different temperatures to construct their master curves. In order to ensure the reliability of the obtained results, the agreement between quantities in the time and frequency domain as well as sources of bias in testing were investigated. A viscoelastic and a VP model were used to respectively characterise the reversible and irreversible deformation of the materials. The behaviour of the specimens exerted by a constant stress at various temperatures were then predicted using independent values of the viscoelastic and VP reduced times. Owing to the good agreement between prediction and measurement, the time-temperature superposition principle in the VP domain was verified. In general, the shift factors were uncorrelated with each other. However, at elevated temperatures, the gaps between them became unimportant, enabling the extension of viscoelastic shift factors into the VP domain.

Singularly perturbed time-fractional convection–diffusion equations via exponential fitted operator scheme

In this paper, we proposed an accurate ϵ-uniformly convergent numerical method to solve singularly perturbed time-fractional convection–diffusion equations via exponential fitted operator scheme. The time-fractional derivative is defined in the sense of Caputo with order η∈(0,1). The time-fractional derivative is discretized by employing the Crank–Nicolson method on a uniform mesh, and an exponential fitted operator scheme along with the standard upwind method is used to mesh-grid the space domain. The truncation error and uniform stability of the discretized problems are examined in order to prove the parameter uniform convergence of the proposed scheme. It is demonstrated that the scheme is ϵ-uniformly convergent of order O((Δt)2−η+Δx), where Δt and Δx represent the step sizes of the time and space domains, respectively. Two numerical examples are provided in order to assess the accuracy of the suggested scheme and validate the theoretical concepts discussed. To demonstrate the efficiency of the numerical scheme presented, comparisons have been made with the numerical solution obtained by the finite difference method that exists in the literature. Consequently, it is observed that the results obtained by the present scheme are more accurate and have a better convergence rate.