Time-domain Technique Research Articles

Gravitational Wave Ringdown Analysis Using the F -statistic

After the final stage of the merger of two black holes (BHs), the ringdown signal takes an important role in providing information about the gravitational dynamics in strong field. We introduce a novel time-domain (TD) approach, predicated on the F -statistic, for ringdown analysis. This method diverges from traditional TD techniques in that its parameter space remains constant irrespective of the number of modes incorporated. This feature is achieved by reconfiguring the likelihood and analytically maximizing over the extrinsic parameters that encompass the amplitudes and reference phases of all modes. Consequently, when performing the ringdown analysis under the assumption that the ringdown signal is detected by the Einstein Telescope, the parameter estimation computation time is shortened by at most 5 orders of magnitude compared to the traditional TD method. We further establish that traditional TD methods become difficult when including multiple overtone modes due to close oscillation frequencies and damping times across different overtone modes. Encouragingly, this issue is effectively addressed by our new TD technique. The accessibility of this new TD method extends to a broad spectrum of research and offers flexibility for various topics within BH spectroscopy applicable to both current and future gravitational wave detectors.

Noise-robust modal parameter identification and damage assessment for aero-structures

PurposeGround vibration testing is critical for aircraft design and certification. Fast relaxed vector fitting (FRVF) and Loewner framework (LF), recently extended to modal parameter extraction in mechanical systems to address the computational challenges of time and frequency domain techniques, are applied for damage detection on aeronautically relevant structures.Design/methodology/approachFRVF and LF are applied to numerical datasets to assess noise robustness and performance for damage detection. Computational efficiency is also evaluated. In addition, they are applied to a novel damage detection benchmark of a high aspect ratio wing, comparing their performance with the state-of-the-art method N4SID.FindingsFRVF and LF detect structural changes effectively; LF exhibits better noise robustness, while FRVF is more computationally efficient.Practical implicationsLF is recommended for noisy measurements.Originality/valueTo the best of the authors’ knowledge, this is the first study in which the LF and FRVF are applied for the extraction of the modal parameters in aeronautically relevant structures. In addition, a novel damage detection benchmark of a high-aspect-ratio wing is introduced.

Electronic vs phononic thermal transport in Cr-doped V2O3 thin films across the Mott transition

Understanding the thermal conductivity of chromium-doped V2O3 is crucial for optimizing the design of selectors for memory and neuromorphic devices. We utilized the time-domain thermoreflectance technique to measure the thermal conductivity of chromium-doped V2O3 across varying concentrations, spanning the doping-induced metal–insulator transition. In addition, different oxygen stoichiometries and film thicknesses were investigated in their crystalline and amorphous phases. Chromium doping concentration (0%–30%) and the degree of crystallinity emerged as the predominant factors influencing the thermal properties, while the effect of oxygen flow (600–1400 ppm) during deposition proved to be negligible. Our observations indicate that even in the metallic phase of V2O3, the lattice contribution is the dominant factor in thermal transport with no observable impact from the electrons on heat transport. Finally, the thermal conductivity of both amorphous and crystalline V2O3 was measured at cryogenic temperatures (80–450 K). Our thermal conductivity measurements as a function of temperature reveal that both phases exhibit behavior similar to amorphous materials, indicating pronounced phonon scattering effects in the crystalline phase of V2O3.

Monitoring the gas metal arc additive manufacturing process using unsupervised machine learning

The study aimed to assess the performance of several unsupervised machine learning (ML) techniques in online anomaly (The term “anomaly” is used here to indicate a departure from expected process behavior which may indicate a quality issue which requires further investigation. The term “defect detection” has often been used previously but the specific imperfection is often indirectly inferred.) detection during surface tension transfer (STT)-based wire arc additive manufacturing. Recent advancements in quality monitoring for wire arc manufacturing were reviewed, followed by a comparison of unsupervised ML techniques using welding current and welding voltage data collected during a defect-free deposition process. Both time domain and frequency domain feature extraction techniques were applied and compared. Three analysis methodologies were adopted: ML algorithms such as isolation forest, local outlier factor, and one-class support vector machine. The results highlight that incorporating frequency analysis, such as fast Fourier transform (FFT) and discrete wavelet transform (DWT), for feature extraction based on general frequency response and defined bandwidth frequency response, significantly improves performance, reflected in a 14% increase in F2 score, compared with time-domain features extraction. Additionally, a deep learning approach employing a convolutional autoencoder (CAE) demonstrated superior performance by processing time-frequency domain data stored as spectrograms obtained through short-time Fourier transform (STFT) analysis. The CAE method outperformed frequency domain analysis and traditional ML approaches, achieving an additional 5% improvement in F2-score. Notably, the F2-score (The F2 score is the weighted harmonic mean of the precision and recall (given a threshold value). Unlike the F1 score, which gives equal weight to precision and recall, the F2 score gives more weight to recall than to precision.) increased significantly from 0.78 in time domain analysis to 0.895 in time-frequency analysis. The study emphasizes the potential of utilizing low-cost sensors to develop anomaly detection modules with enhanced accuracy. These findings underscore the importance of incorporating advanced data processing techniques in wire arc additive manufacturing for improved quality control and process optimization.

High-Performance Visible-to-SWIR Photodetector Based on the Layered WS2 Heterojunction with Light-Trapping Pyramidal Black Germanium.

This study presents a layered transition metal dichalcogenide/black germanium (b-Ge) heterojunction photodetector that exhibits superior performance across a broad spectrum of wavelengths spanning from visible (vis) to shortwave infrared (SWIR). The photodetector includes a thin layer of b-Ge, which is created by wet etching of germanium (Ge) wafer to form submicrometer pyramidal structures. On top of this b-Ge layer, the WS2 thin film is deposited using pulsed laser deposition. In comparison to conventional germanium, b-Ge absorbs about 25% more light between 850 and 1750 nm wavelengths. The WS2/b-Ge photodetector has a peak photoresponsivity of 0.65 A/W, which is more than twice the photoresponsivity of the WS2/Ge photodetector at 1540 nm. Additionally, it shows better responsivity and response speed compared with other similar state-of-the-art photodetectors. Such an improvement in the performance of the device is credited to the light-trapping effect enabled by the germanium pyramids. Theoretical simulations employing the finite-difference time-domain technique help validate the concept. This novel photodetector holds promise for efficient detection of light across the vis to SWIR spectrum.

New oscillatory features of electrodermal activity signals evaluated in automated mental workload monitoring

Oscillatory components are discriminative features of mental workload monitoring in electrodermal activity (EDA) signals. Currently, most feature extraction techniques mainly focus on time domain or linear techniques but rarely consider the non-stationary nature, individual differences, and recording noise. This study has proposed a new time–frequency feature extraction method based on Tunable Q-factor Wavelet Transform providing an effective quantification of oscillatory behavior of a single transient. The extracted features have been classified using a recurrent neural network with the advantage of dynamic mapping procedure. The proposed method has been evaluated using EDA data acquired during an arithmetic task of five workload levels with both subtle and distinct differences. In a comparative study, the effects of several decomposition parameters and cognitive factors have been explored. Experimental results have shown that it achieves an average accuracy rate of 98.52% for three workload levels discrimination by optimizing the number of features to 3 (features extracted from a low frequency sub-band) from 15 (features extracted from five frequency sub-bands), i.e., 80% feature reduction. The greatest contribution coming fromthe smallest frequency sub-bands has also been obtained. This method is superior to other existing methods in separating 3 to 5 workload levels with both distinct and subtle differences extracting effective oscillatory characteristics. Considering the merits of a simple feature extraction procedure and cost-effectiveness of a single lead EDA signal as well as high discriminability between several workload levels, it provides a trade-off between performance and computational complexity, thus demonstrating its potential for online human-error prevention systems.

Facile modification method for the controlled synthesis of dumbbell-like gold nanoparticles (AuNDBs) for application in detecting glucose using the SERS method

Dumbbell-like nanoparticles, previously known for their excellent light absorption and dispersion, have diverse applications, especially in SERS. Herein, we have explored a fast and novel controlled synthesis approach to achieve a high formation of AuNDBs. Precise control of precursor amounts and seed addition order halted seed growth at the AuNDB formation stage, preventing further elongation into rod-like structures. The AuNDBs were functionalized with 4-mercaptobenzoic acid (4-MBA) to create SERS nanosubstrates and deposited on the glass slide through 3-mercaptopropyl (dimethoxy)methylsilane by the spin-coating technique. The detection of d-glucose was achieved by measuring the signal decrease of the 4-MBA Raman probe molecules on the nanosubstrates at different trace concentrations of the analyte. These novel SERS substrates have the potential to provide an enhancement factor (EF) of 2.41 × 107 and a limit of detection (LOD) as low as 1.17 nmol/L. The electromagnetic field distribution of AuNDBs with many arrangements surrounded by air and d-glucose solutions was also determined by using the finite-difference time-domain technique (FDTD). The FDTD results confirmed that d-glucose on the SERS substrates caused the local electromagnetic field to degrade, resulting in the degree of the Raman signal. Our findings suggest that dumbbell-shaped SERS substrates can effectively detect very low levels of glucose, making them ideal for designing novel anisotropic nanoparticle-functionalized surfaces with ease.

Tungsten-Doped ZnO as an Electron Transport Layer for Perovskite Solar Cells: Enhancing Efficiency and Stability.

This study delves into enhancing the efficiency and stability of perovskite solar cells (PSCs) by optimizing the surface morphologies and optoelectronic properties of the electron transport layer (ETL) using tungsten (W) doping in zinc oxide (ZnO). Through a unique green synthesis process and spin-coating technique, W-doped ZnO films were prepared, exhibiting improved electrical conductivity and reduced interface defects between the ETL and perovskite layers, thus facilitating efficient electron transfer at the interface. High-quality PSCs with superior ETL demonstrated a substantial 30% increase in power conversion efficiency (PCE) compared to those employing pristine ZnO ETL. These solar cells retained over 70% of their initial PCE after 4000 h of moisture exposure, surpassing reference PSCs by 50% PCE over this period. Advanced numerical multiphysics solvers, employing finite-difference time-domain (FDTD) and finite element method (FEM) techniques, were utilized to elucidate the underlying optoelectrical characteristics of the PSCs, with simulated results corroborating experimental findings. The study concludes with a thorough discussion on charge transport and recombination mechanisms, providing insights into the enhanced performance and stability achieved through W-doped ZnO ETL optimization.

Modal group refractive index measurement of few-mode fibers based on time-domain cross-correlation

We propose a measurement method based on the time-domain cross-correlation technique, combined with the cut-back method, enabling the measurement of group refractive indices (n g ) in few-mode fibers (FMF). A Mach–Zehnder interferometric system, equipped with high-precision and extensive range delay devices, is established. The system records off-axis holograms of spatial reference light at various delays interfering with the emitted light from the fiber under test. The interference energy is extracted from these holograms, and a time-domain mode energy curve is developed utilizing the principle of cross correlation. Optimal holograms at each of the curve peaks are used to reconstruct the modal field distribution, effectively separating and accurately identifying each mode within the FMF. By integrating the cut-back method, the n g corresponding to each mode is calculated based on the changes in group delay before and after fiber cutting. The n g of modes in the two-mode fibers was measured and the differential group delay calculated from the measurement agrees with the manufacturer’s specifications. The measured n g of a standard single-mode fiber aligns with the manufacturer’s specifications. Furthermore, the n g of the higher-order modes in four-mode fibers were measured by exciting them at different angles and validating the wave optics theory that the n g of the fiber modes is independent of the excitation angle. This method can simultaneously measure the n g of several modes in a fiber, providing support for the development and application of FMFs.

Enhanced photoacoustic signal processing using empirical mode decomposition and machine learning

ABSTRACT In this study, we propose a robust photoacoustic (PA) signal processing framework for a material independent defect detection using empirical mode decomposition (EMD) and machine learning algorithms. First, a database of the PA signals with 960 samples has been obtained from aluminium, iron, plastic and wood materials using a laser, microphone and data acquisition board-based PA apparatus. Second, the EMD based time and time-frequency domain techniques are proposed to extract robust cross-material feature space focusing on laser induced acoustic signal, and the decomposed intrinsic mode (IMF) with 14 extracted features are performed on totally 960 samples PA signals to evaluate k-nearest neighbour (k-NN), decision tree (DT) and support vector machine (SVM) classifiers. Inter- material and cross-material evaluations are performed, and the accuracy rates up to 100% for SVM and 97.77% for k-NN are yielded.

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: experimental validation in tissue-mimicking finger phantoms

Objective. Effective treatment within 3–5 months of disease onset significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 30% of RA patients fail their first treatment, and it takes 3–6 months to identify failure with current monitoring techniques. Time-domain diffuse optical imaging (TD-DOI) may be more sensitive to RA disease activity and could be used to detect treatment failure. In this report, we present the development of a TD-DOI hand imaging system and validate its ability to measure simulated changes in RA disease activity using tissue-mimicking finger phantoms. Approach. A TD-DOI system was built, based on a single-pixel camera architecture, and used to image solid phantoms which mimicked a proximal interphalangeal finger joint. For reference, in silico images of virtual models of the solid phantoms were also generated using Monte Carlo simulations. Spatiotemporal Fourier components were extracted from both simulated and experimental images, and their ability to distinguish between phantoms representing different RA disease activity was quantified. Main results. Many spatiotemporal Fourier components extracted from TD-DOI images could clearly distinguish between phantoms representing different states of RA disease activity. Significance. A TD-DOI system was built and validated using finger-mimicking solid phantoms. The findings suggest that the system could be used to monitor RA disease activity. This single-pixel TD-DOI system could be used to acquire longitudinal measures of RA disease activity to detect early treatment failure.

Time-domain diffuse optical imaging technique for monitoring rheumatoid arthritis disease activity: theoretical development and in silico validation

Objective. Effective early treatment—within 3–5 months of disease onset—significantly improves rheumatoid arthritis (RA) prognosis. Nevertheless, 1 in 3 patients experiences treatment failure which takes 3–6 months to detect with current monitoring techniques. The aim of this work is to develop a method for extracting quantitative features from data obtained with time-domain diffuse optical imaging (TD-DOI), and demonstrate their sensitivity to RA disease activity. Approach. 80 virtual phantoms of the proximal interphalangeal joint—obtained from a realistic tissue distribution derived from magnetic resonance imaging—were created to simulate RA-induced alterations in 5 physiological parameters. TD-DOI images were generated using Monte Carlo simulations, and Poisson noise was added to each image. Subsequently, each image was convolved with an instrument response function (IRF) to mimic experimental measurements. Various spatiotemporal features were extracted from the images (i.e. statistical moments, temporal Fourier components), corrected for IRF effects, and correlated with the disease index (DI) of each phantom. Main results. Spatiotemporal Fourier components of TD-DOI images were highly correlated with DI despite the confounding effects of noise and the IRF. Moreover, lower temporal frequency components ( ⩽ 0.4 GHz) demonstrated greater sensitivity to small changes in disease activity than previously published spatial features extracted from the same images. Significance. Spatiotemporal components of TD-DOI images may be more sensitive to small changes in RA disease activity than previously reported DOI features. TD-DOI may enable earlier detection of RA treatment failure, which would reduce the time needed to identify treatment failure and improve patient prognosis.

Frequency-Domain Low-Wavenumber Hyperspectral Stimulated Raman Scattering Microscopy.

In recent years, stimulated Raman scattering (SRS) microscopy has experienced rapid technological advancements and has found widespread applications in chemical analysis. Hyperspectral SRS (hSRS) microscopy further enhances the chemical selectivity in imaging by providing a Raman spectrum for each pixel. Time-domain hSRS techniques often require interferometry and ultrashort femtosecond laser pulses. They are especially suited to measuring low-wavenumber Raman transitions but are susceptible to scattering-induced distortions. Frequency-domain hSRS microscopy, on the other hand, offers a simpler optical configuration and demonstrates high tolerance to sample scattering but typically operates within the spectral range of 400-4000 cm-1. Conventional frequency-domain hSRS microscopy is widely employed in biological applications but falls short in detecting chemical bonds with a weaker vibrational energy. In this work, we extend the spectral coverage of picosecond spectral-focusing hSRS microscopy to below 100 cm-1. This frequency-domain low-wavenumber hSRS approach can measure the weaker vibrational energy from the sample and has a strong tolerance to sample scattering. By expanding spectral coverage to 100-4000 cm-1, this development enhances the capability of spectral-domain SRS microscopy for chemical imaging.

Application of multi-objective optimization genetic algorithm to design terahertz metamaterials with fano resonances

In the prosperous development of terahertz (THz) metamaterials, Fano resonances have gained attention due to their potential applications in ultrasensitive systems. The performance of Fano resonance is directly influenced by the geometrical parameters of the element structure. However, the traditional design rules for Fano resonances in metamaterials rely on an empirical trial-and-error strategy, necessitating significant effort to achieve optimal results. To address this issue, we propose a design method in this study that utilizes the finite integration technique in time domain (FITD) along with a multi-objective optimization genetic algorithm for the intelligent design of metamaterial structures exhibiting the Fano resonance phenomenon. The FITD method is primarily used to calculate the Fano resonance with different metamaterial geometric structure parameters, while the genetic algorithm efficiently selects the optimal solution. Our method, characterized by high efficiency and complete independence from prior knowledge, could offer a new design technique for metamaterials with specific functions, thereby contributing to the development of THz applications.

Multiphysics simulations of a cylindrical waveguide optical switch using phase change materials on silicon

This work presents the design and multiphysics simulation of a cylindrical waveguide-based optical switch using germanium-antimony-tellurium (GST) as an active phase change material. The innovative cylindrical architecture is theoretically analyzed and evaluated at 1550 nm wavelength for telecommunication applications. The dispersion relation is derived analytically for the first time to model the optical switch, while finite element method (FEM) and finite difference time domain (FDTD) techniques are utilized to simulate the optical modes, light propagation, and phase change dynamics. The fundamental TE01 and HE11 modes are studied in detail, enabling switching between low-loss amorphous and high-loss crystalline GST phases. Increasing the GST thickness is found to increase absorption loss in the crystalline state but also slows down phase transition kinetics, reducing switching speeds. A 10 nm GST layer results in competitive performance metrics of 0.79 dB insertion loss, 13.47 dB extinction ratio, 30 nJ average power consumption, and 3.5 Mb/s bit rate. The combined optical, thermal, and electrical simulation provides comprehensive insights towards developing integrated non-volatile photonic switches and modulators utilizing phase change materials.

Adaptive event-triggered tracking control for nonlinear systems with prescribed performance: A time-domain mapping approach

This paper proposed an event-triggered adaptive fuzzy prescribed time control strategy for a class of nonlinear systems with uncertain external disturbances and unknown virtual control coefficients under prescribed performance. A novel time-domain mapping technique is proposed to transform the prescribed time tracking control problem into an asymptotic convergence problem. An observer is designed to estimate for the external disturbance and fuzzy approximation error of fuzzy logic systems (FLSs). The problem of “explosion of complexity” is addressed by applying dynamic surface control (DSC) technique to backstepping. An adaptive fuzzy event-triggered strategy is designed using the Lyapunov stability theorem. This strategy ensures that, after time-domain mapping, the tracking error asymptotically converges to zero. Additionally, there is no Zeno behavior and all system signals are bounded. Finally, the effectiveness of the proposed scheme is verified through three simulation examples.

Highly selective single-mode graphene bandpass filter based on Wilkinson power divider structure

In this paper, we introduce a novel graphene nanoribbon bandpass filter designed using the Wilkinson power divider structure for single-mode operation. Through the three-dimensional finite-difference time-domain (3D-FDTD) technique, we analyze transmission spectra and electromagnetic field distributions. Our primary goals are to achieve high transmission efficiency, improved adjustability, and an excellent quality factor (Q-factor). The proposed single-mode graphene nanoribbon bandpass filter exhibits outstanding performance characteristics, including a 45 THz free spectral range (FSR), 95 % transmission efficiency, and a full width at half maximum (FWHM) of 0.14 THz at the resonant frequency of 15 THz. By changing the chemical potential (or bias voltage) to manipulate the conductivity and dielectric constant of the graphene surface, we successfully tune the center frequency of the filter within the 10–20 THz range. This flexibility, combined with exceptional transmission efficiency and Q-factor, positions our filter as a prime candidate for seamless integration into compact optical devices. Additionally, by adjusting the refractive index of the substrate, we achieve a sensitivity exceeding 7 THz/RIU or 7500 nm/RIU, making our filter a promising sensor for applications requiring precise detection and measurement capabilities.

A Fast Factorized Back-Projection Algorithm Based on Range Block Division for Stripmap SAR

Fast factorized back-projection (FFBP) is a classical fast time-domain technique that has garnered significant success in spotlight synthetic aperture radar (SAR) signal processing. The algorithm’s efficiency has been extended to stripmap SAR through integral aperture determination and full-aperture data block processing while retaining its computational efficiency. However, the above method is only operated in the azimuth direction, and the computing efficiency needs to be urgently improved in the actual processing process. This paper proposes a fast factorized back-projection algorithm for stripmap SAR imaging based on range block division. The echo data are divided into multiple subblocks in the range direction, and FFBP processing is applied separately to each full-aperture subblock, further enhancing computational efficiency. The paper analyzes the algorithm’s principles, underscores the necessity of integral aperture determination and full-aperture data block processing, provides specific implementation steps, and applies the algorithm to point target simulation and experimental data from a vehicle-mounted ice radar. The experiments validate the algorithm’s efficiency in stripmap SAR imaging.

Surface-state-related carrier dynamics of GaAs determined by UV-visible pump-probe terahertz spectroscopy

The surface velocity and the bulk lifetime of photo-excited free carriers in GaAs were measured using an optical-pump and THz-probe time-domain technique. By varying the pump laser photon energy from 1.56 to 4.15 eV, we observe that the surface velocity drops abruptly from 0.7×106 cm/s down to 0.2×106 cm/s at 2.5 eV, while the bulk lifetime remains almost constant. We tentatively explain this step-like behavior of the surface velocity vs the photon energy by a trapping of the free carriers at surface states, whose density of states shows a maximum at 2.5 eV.

Cell wall water induced dimensional changes of beech and pine wood

Hygroexpansion in timber construction is a phenomenon that occurs as a result of changes in the cell wall structure of wood caused by water. In this study, beech (Fagus sylvatica) and pine (Pinus taeda L.) wood were conditioned at different relative humidities with saturated salt solutions and saturated through vacuum pressure impregnation. The moisture content and dimensions of the samples were evaluated under various adsorption equilibrium states, as well as during drying from water saturation to an air-dried state. The results obtained using the two-dimensional time-domain nuclear magnetic resonance technique revealed two distinct types of cell wall water in both wood species, designated as B-water and C-water, as they displayed different mobilities. With increasing moisture content, the mobility of cell wall water increased; however, B-water maintained a constant level of mobility and formed water clusters when it accumulated in the high humidity region and at slightly above the fiber saturation point (FSP), which is evidence of the occurrence of free-bound water. The B-water, located in the amorphous region of microfibrils and their surrounding hemicellulose, exerted a predominant influence on the dimensional changes of the wood both under equilibrium and nonequilibrium conditions, in comparison to the C-water which located in the matrix of hemicellulose and lignin.