Time-domain Processing Research Articles

Time domain spectral LiDAR enabled by cascaded Raman in a hydrogen-filled fiber transmitter

We introduce what we believe to be novel spectral light detection and ranging (LiDAR) architectures that enable ultra-compact systems by a transition from spectral signal processing in space (gratings) to processing in time. The architectures leverage temporal dispersion and the unique spectro-temporal waveforms produced from the cascaded Raman scattering generated in the (H2) filled hollow core fiber. The characterized Raman source yields as many as six Raman orders from 1.06-1.70 μm; their unique spectro-temporal waveforms are measured. System performance simulations based on measured Raman waveforms show that high accuracy measurement of range and reflectivity are possible with proper selection of signal-to-noise ratio and detector bandwidth. Materials classification analysis based on the system performance analysis shows that near-optimal classification is feasible with time domain processing.

Pseudospin-polarized slow light waveguides with large delay-bandwidth product

Delay-bandwidth product (DBP) is a key metric in slow light waveguides, requiring a balance between a large group index and broad bandwidth—two parameters that often involve a trade-off. Here, we propose and demonstrate a slow light waveguide with large DBP using a pseudospin-polarized transverse electromagnetic mode. This waveguide features a folded edge configuration that supports a 200% relative bandwidth from quasistatic limit (zero frequency) and an arbitrarily large group index. Owing to the pseudospin-polarized design, the dense folding would not introduce backscattering and the associated group velocity dispersion (GVD). The resulting gapless linear dispersion and pulse transmission behavior in folded edge waveguide are observed in microwave experiments. Our scheme provides a way to overcome the trade-off between group index and working bandwidth in slow light waveguide, which has potential applications in broadband optical buffering, light-matter interaction enhancement, terahertz radiation source and time domain processing.

Optimization of efficiency of short-pulsed fast flow CO2 laser amplifier with dual-band and multispectral lines

In this paper, we optimize the amplification efficiency of a nanosecond pulse CO2 laser in a fast flow amplifier using dual-band and multispectral lines techniques. Utilizing a six-temperature model and a random rotational relaxation model, we simulate the time-domain amplification process of dual-band and multispectral lines short-pulse seeds in a fast flow CO2 laser amplifier, analyzing the effects of input pulse fluence, pulse width, and spectral line composition on amplification efficiency. Compared to single-line 10P(20) amplification, the extraction efficiency of 10.6-µm and 9.6-µm dual-band two-line 15-ns pulses is improved by approximately 70%, surpassing that of 10.6-µm single-band four-line amplification. This study is of great significance for the efficient amplification of short-pulse CO2 lasers and the generation of extreme ultraviolet light from laser produced plasma.

UniChromCD for Demultiplexing Time-Resolved Charge Detection-Mass Spectrometry Data.

Charge detection mass spectrometry (CD-MS) enables characterization of large, heterogeneous analytes through the analysis of individual ion signals. Because hundreds to thousands of scans must be acquired to produce adequate ion statistics, CD-MS generally requires long analysis times. The slow acquisition speed of CD-MS complicates efforts to couple it with time-dispersive techniques, such as chromatography and ion mobility, because it is not always possible to acquire enough scans from a single sample injection to generate sufficient ion statistics. Multiplexing methods based on Hadamard and Fourier transforms offer an attractive solution to this problem by improving the duty cycle of the separation while preserving retention/drift time information. However, integrating multiplexing with CD-MS data processing is complex. Here, we present UniChromCD, a new module in the open-source UniDec package that incorporates CD-MS time-domain data processing with demultiplexing tools. Following a detailed description of the algorithm, we demonstrate its capabilities using two multiplexed CD-MS workflows: Hadamard-transform size-exclusion chromatography and Fourier-transform ion mobility. Overall, UniChromCD provides a user-friendly interface for analysis and visualization of time-resolved CD-MS data.

Enhancing battery electrochemical-thermal model accuracy through a hybrid parameter estimation framework

Electrochemical-thermal models offer profound insight into the internal state of batteries, demonstrating significant potential in energy storage. However, model complexity makes accurate parameter acquisition challenging. This study proposes a novel hybrid parameter estimation framework for effective parameterisation of battery electrochemical-thermal models. A high-fidelity reduced-order electrochemical-thermal model was established to accelerate the simulation of the entire time-domain process. A global sensitivity analysis of the model parameters was conducted, comprehensively evaluating their impact on simulated voltage and temperature. Highly sensitive parameters were categorised into static and time-dependent dynamic estimation groups for hierarchical estimation. A deep learning method was applied to generate preliminary parameter estimates to accelerate the convergence of estimation process. Subsequently, a genetic algorithm was employed to optimise the preliminary estimates of static parameters. A full temporal domain estimation was conducted using Markov Chain Monte Carlo methods for time-dependent dynamic parameters. The experimental data covers six dynamic and nine constant current conditions to validate the proposed method's feasibility. After parameter estimation, the proposed method reduces the comprehensive normalised root mean square error of voltage and temperature by up to 80.36% compared to four conventional methods. The minimum average absolute errors of voltage and temperature under constant and dynamic current conditions are 10.63 mV and 0.10 °C, respectively. The proposed method provides new insights for the parameterisation of battery models.

Signature of anyonic statistics in the integer quantum Hall regime

Anyons are exotic low-dimensional quasiparticles whose unconventional quantum statistics extend the binary particle division into fermions and bosons. The fractional quantum Hall regime provides a natural host, with the first convincing anyon signatures recently observed through interferometry and cross-correlations of colliding beams. However, the fractional regime is rife with experimental complications, such as an anomalous tunneling density of states, which impede the manipulation of anyons. Here we show experimentally that the canonical integer quantum Hall regime can provide a robust anyon platform. Exploiting the Coulomb interaction between two copropagating quantum Hall channels, an electron injected into one channel splits into two fractional charges behaving as abelian anyons. Their unconventional statistics is revealed by negative cross-correlations between dilute quasiparticle beams. Similarly to fractional quantum Hall observations, we show that the negative signal stems from a time-domain braiding process, here involving the incident fractional quasiparticles and spontaneously generated electron-hole pairs. Beyond the dilute limit, a theoretical understanding is achieved via the edge magnetoplasmon description of interacting integer quantum Hall channels. Our findings establish that, counter-intuitively, the integer quantum Hall regime provides a platform of choice for exploring and manipulating quasiparticles with fractional quantum statistics.

Transient cavity-cavity strong coupling at terahertz frequency on LiNbO3 chips.

Terahertz (THz) microcavities have garnered considerable attention for their ability to localize and confine THz waves, allowing for strong coupling to remarkably enhance the light-matter interaction. These properties hold great promise for advancing THz science and technology, particularly for high-speed integrated THz chips where transient interaction between THz waves and matter is critical. However, experimental study of these transient time-domain processes requires high temporal and spatial resolution since these processes, such as THz strong coupling, occur in several picoseconds and microns. Thus, most literature studies rarely cover temporal and spatial processes at the same time. In this work, we thoroughly investigate the transient cavity-cavity strong-coupling phenomena at THz frequency and find a Rabi-like oscillation in the microcavities, manifested by direct observation of a periodic energy exchange process via a phase-contrast time-resolved imaging system. Our explanation, based on the Jaynes-Cummings model, provides theoretical insight into this transient strong-coupling process. This work provides an opportunity to deeply understand the transient strong-coupling process between THz microcavities, which sheds light on the potential of THz microcavities for high-speed THz sensor and THz chip design.

Deep Learning for Electromyographic Lower-Limb Motion Signal Classification Using Residual Learning.

Electromyographic (EMG) signals have gained popularity for controlling prostheses and exoskeletons, particularly in the field of upper limbs for stroke patients. However, there is a lack of research in the lower limb area, and standardized open-source datasets of lower limb EMG signals, especially recording data of Asian race features, are scarce. Additionally, deep learning algorithms are rarely used for human motion intention recognition based on EMG, especially in the lower limb area. In response to these gaps, we present an open-source benchmark dataset of lower limb EMG with Asian race characteristics and large data volume, the JJ dataset, which includes approximately 13,350 clean EMG segments of 10 gait phases from 15 people. This is the first dataset of its kind to include the nine main muscles of human gait when walking. We used the processed time-domain signal as input and adjusted ResNet-18 as the classification tool. Our research explores and compares multiple key issues in this area, including the comparison of sliding time window method and other preprocessing methods, comparison of time-domain and frequency-domain signal processing effects, cross-subject motion recognition accuracy, and the possibility of using thigh and calf muscles in amputees. Our experiments demonstrate that the adjusted ResNet can achieve significant classification accuracy, with an average accuracy rate of 95.34% for human gait phases. Our research provides a valuable resource for future studies in this area and demonstrates the potential for ResNet as a robust and effective method for lower limb human motion intention pattern recognition.

Weather Radar Parameter Estimation Based on Frequency Domain Processing: Technical Details and Performance Evaluation

Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter.

Distributed Anti-Eavesdropping Fusion Estimation Under Energy Constraints

This paper studies the distributed fusion estimation problem with energy-constrained sensors in the presence of eavesdroppers, where smart sensors send their local estimates to a remote fusion center (FC). To enhance privacy level, a novel encryption strategy is proposed by establishing an optimization problem, where the optimization objective is constructed by maximizing a combination of the fusion terminal estimation error covariance and the cost of encryption process in finite time domain. Meanwhile, the established problem is decomposed into several independent sub-optimization problems under a relaxation condition, and a sufficient condition that depends on the system parameters is derived such that the sub-optimization problems have optimal solutions. In this case, an optimal encryption strategy is designed with an analytical solution. Finally, a simulation example is given to show the effectiveness of the proposed methods.

PEM-based random vibration analysis under service-braking conditions on an irregular track

PurposeAccording to relevant research, non-uniform speed has a significant impact on the vehicle-track systems. Up to now, research work on it is still very limited. In this paper, the PEM is adopted to further transform it into a deterministic process to solve the vehicle’s problem of running at a non-uniform speed.Design/methodology/approachThe multi-body vehicle model has 10 degrees of freedom and the track is regarded as a finite long beam supported by lumped sleepers and ballast blocks. They are connected via linear Hertz springs. The vertical track irregularity is a Gaussian stationary process in the space domain. It is transformed into a uniformly modulated nonstationary random process in the time domain with respect to the non-uniform vehicle speed. By solving the equation of motion of the coupled vehicle-track system with the pseudo-excitation method, the pseudo-response and consequently the power spectral density and the standard deviation of the structural response can be obtained.FindingsTwo kinds of vehicle braking programs are taken in the numerical example and some beneficial conclusions are drawn.Originality/valueThe pseudo-excitation method (PEM) was used to perform the random vibration analysis of a coupled non-uniform speed vehicle-track system. Transforming the track irregularity into a uniformly modulated nonstationary random process in time domain with respect to the non-uniform vehicle speed was undertaken. The pseudo-response of the coupled system is solved by applying the Newmark algorithm with constant space integral steps. The random vibration transfer mechanism of the coupled system is fully discussed.

An empirical model to predict elongation of polyamide mooring lines

Polyamide mooring lines are being proposed for station-keeping of floating wind turbines in shallow water. However, their mechanical behaviour is quite different to that of the polyester fibre ropes used for deep water offshore oil & gas platform moorings. This paper presents an empirical model which can predict the specific strain response of a polyamide 6 mooring line under stochastic loading. Particular attention is given to the non-linear load-strain behaviour, hysteresis, and the influence of loading history. The identification of the model, using 12 stochastic tests performed on wet 3-strand polyamide sub-rope samples, is described. This model reproduces the specific features of the response of fully wetted PA ropes well, with reasonable accuracy and, for the extreme (maximum and minimum) values it provides a much better prediction than the linear “dynamic stiffness” model. This new model can be integrated in the time domain processing of a suitable mooring analysis software.

Storage in the deep aquifer: Stochastic process in time and frequency domain using groundwater level response to seismic activation of the Tohoku earthquake, Japan, 2011

AbstractThis study delves into the interplay of Rayleigh wave‐groundwater‐aquifer dynamics, utilizing seismic data from designated stations, particularly the magnitude 9 event on March 11, 2011 (02:46:23 PM epicenter), Tohoku earthquake, Japan. Auto spectra uncover significant peaks, linking groundwater level with the vertical motion of Rayleigh waves. High coherence between groundwater levels and seismogram vertical displacements confirms this connection. Coherence analysis validates it at the significant frequency bands. Groundwater storage coefficient estimation aligns with typical values of confined aquifers, ranging from 1.0E‐05 to 1.0E‐03. This consistency applies across different methods and other earthquake events, highlighting seismic impact. The findings enhance understanding of aquifer behavior during seismic events, aiding groundwater management and earthquake readiness in susceptible regions. Further research should expand datasets for coefficient accuracy. Insights advance aquifer behavior comprehension during seismic events, guiding groundwater management and earthquake readiness in prone areas.

A 1984-Pixels, 1.26 nW/Pixel Retinal Prosthesis Chip With Time-Domain In-Pixel Image Processing and Bipolar Stimulating Electrode Sharing

This article presents a 1984-pixel retinal prosthesis (RP) chip with time-domain in-pixel image processing. The proposed time-domain image processing (TDIP) algorithm efficiently generates the edge-extracted stimulation and significantly reduces the stimulus power consumption. For the area-and power-efficient time-domain processing, a leakage current-based pulsewidth comparator (PWC) and its control scheme are also proposed. The proposed RP chip adopts the bipolar stimulation-based local return that minimizes image dispersion and a sequence control scheme that produces 1984 stimulation points with 1024 electrodes. The RP chip was implemented in a 180 nm CMOS process and achieved a power consumption of 1.26 nW/pixel, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 44.7 better than the previous state-of-the-art image processing RP chip.

Correction of scanning laser Doppler vibrometer measurements when subjected to six degree-of-freedom base motion

Scanning laser Doppler vibrometer (SLDV) measurements are affected by sensor head vibrations as though they are vibrations of the target surface itself. Previous work has established a fully general theoretical analysis which shows that the only measurement required for measurement correction is of the vibration velocity at the incident point on the final steering mirror in the direction of the outgoing laser beam. Two practical—but not quite perfect—options for measurement correction were presented (one more suitable to manufacturer implementation, one more applicable to the vibration engineer end user). In both cases, placement of the correction transducer is critical with correction working for totally arbitrary instrument vibration and scan angle. Experimental validation, employing frequency-domain based processing, has been completed for one degree-of-freedom, on-axis vibration. Simultaneously, advancements in the data processing approach have realised improved correction in practice, especially for lower frequencies and for transient, as opposed to statistically stationary, vibration. In this paper, extension of the experimental validation to six degree-of-freedom instrument vibration is presented for the first time. In combination with the latest data processing approaches, reductions in the measurement error of 29.4 and 28.2 dB for the frequency- and time-domain processing techniques, respectively, are realised.

All-Optical Data Processing with Photon-Avalanching Nanocrystalline Photonic Synapse.

Data processing and storage in electronic devices are typically performed as a sequence of elementary binary operations. Alternative approaches, such as neuromorphic or reservoir computing, are rapidly gaining interest where data processing is relatively slow, but can be performed in a more comprehensive way or massively in parallel, like in neuronal circuits. Here, time-domain all-optical information processing capabilities of photon-avalanching (PA) nanoparticles at room temperature are discovered. Demonstrated functionality resembles properties found in neuronal synapses, such as: paired-pulse facilitation and short-term internal memory, in situ plasticity, multiple inputs processing, and all-or-nothing threshold response. The PA-memory-like behavior shows capability of machine-learning-algorithm-free feature extraction and further recognition of 2D patterns with simple 2 input artificial neural network. Additionally, high nonlinearity of luminescence intensity in response to photoexcitation mimics and enhances spike-timing-dependent plasticity that is coherent in nature with the way a sound source is localized in animal neuronal circuits. Not only are yet unexplored fundamental properties of photon-avalanche luminescence kinetics studied, but this approach, combined with recent achievements in photonics, light confinement and guiding, promises all-optical data processing, control, adaptive responsivity, and storage on photonic chips.

Automatized localization of induced geothermal seismicity using robust time-domain array processing

The surveillance of geothermal seismicity is typically conducted using seismic networks, deployed around the power plants and subject to noise conditions in often highly urbanized areas. In contrast, seismic arrays can be situated at greater distances and allow monitoring of different power plants from one central location, less affected by noise interference. However, the effectiveness of arrays to monitor geothermal reservoirs is not well investigated and the increased distance to the source coincides with a decreased accuracy of the earthquake localizations. It is therefore essential to establish robust data processing and to obtain precise estimates of the location uncertainties. Here, we use time-domain array data processing and solve for the full 3-D slowness vector using robust linear regression. The approach implements a Biweight M-estimator, which yields stable parameter estimates and is well suited for real-time applications. We compare its performance to conventional least squares regression and frequency wavenumber analysis. Additionally, we implement a statistical approach based on changepoint analysis to automatically identify P- and S-wave arrivals within the recorded waveforms. The method can be seen as a simplification of autoregressive prediction. The estimated onsets facilitate reliable calculations of epicentral distances. We assess the performance of our methodology by comparison to network localizations for 77 induced earthquakes from the Landau and Insheim deep-geothermal reservoirs, situated in Rhineland-Palatinate, Germany. Our results demonstrate that we can differentiate earthquakes originating from both reservoirs and successfully localize the majority of events within the magnitude range of ML -0.2 to ML 1.3. The discrepancy between the two localization methods is mostly less than 1 km, which falls within the statistical errors. However, a few localizations deviate significantly, which can be attributed to poor observations during the winter of 2021/2022.

On the efficient simulation of pass-by noise signals from railway wheels

The article presents an approach for calculating pass-by sound pressure radiated from railway wheels in the time domain using moving Green’s functions. The Green’s functions are obtained by using Finite Element (FE) and Boundary Element (BE) methods in the frequency domain, subsequent inverse Fourier transform, followed by convolution with a time series of rolling contact forces to obtain the pass-by time signals. However, traditional BE methods are computationally expensive due to the low structural damping of the wheel, necessitating a high frequency resolution.To overcome this issue, a modal approach is introduced in which the pass-by sound radiated by each wheel mode is calculated separately. This approach incorporates the dynamic response of the wheel in the time-domain processing and thus reduces the cost of the BE solution. A modal source signal is introduced to describe the excitation of each mode at each time step. The sound field radiated by unit modal amplitudes is calculated in BE and subsequently approximated by spherical harmonic (SH) equivalent sources, which allows for efficiently calculating acoustic transfer functions for varying relative positions of the wheel and a stationary receiver. Convolution of the source signal with the moving acoustic transfer function produces the pass-by pressure signal.The article investigates the directivity of the radiation from each mode and finds that most modes, including those with dominant radial deflection, radiate in mostly axial direction at high frequencies. Modes that dominate the pass-by pressure level are identified, both in frequency bands and with respect to the relative positioning of the wheel to the receiver. Finally, it is found that an SH expansion order of approximately 30 is required to satisfy the employed error measures, although lower orders may suffice for an auralisation of the signal.

Empirical Correlations for Predicting Flow Rates Using Distributed Acoustic Sensor Measurements, Validated with Wellbore and Flow Loop Data Sets

Summary Distributed acoustic sensing (DAS) is an emerging surveillance technology that is becoming increasingly popular in the oil and gas industry for real-time flow monitoring. However, there are limited studies that rigorously quantify flow rates using DAS. This work expands the existing literature by presenting a detailed workflow for accurately estimating fluid flow rates from DAS data using time- and frequency-domain signal processing. Three simple empirical correlation functions (linear, exponential, and cubic) are developed and tested to predict flow rates from DAS. The proposed correlations are demonstrated for flow rates ranging from 50 to 300 gallons per minute (GPM) in a vertical 5,163-ft-deep wellbore and from 12 to 36 GPM in a horizontal surface flow loop. Tests were performed using a single-phase flow of water as well as using synthetic oil-based drilling mud. Time-domain DAS processing using root-mean-square (RMS) value and frequency-domain processing using frequency band energy (FBE) is evaluated, followed by a statistical approach to minimize the influence of outliers. The RMS and FBE approaches are individually compared for flow prediction, and the performance of the correlations is rigorously evaluated on a blind data set that was not originally used for developing the correlations. For both the wellbore and flow loop data sets, a coefficient of determination (or R2) greater than 0.95 with an average flow rate prediction error of less than 10% was achieved for the best-performing correlation for the blind test data. The analysis procedure and workflow presented in this study can be adopted and extended to different operating conditions for quantitative flow rate prediction using DAS.

The absolute nodal coordinate formulation in the analysis of offshore floating operations Part I: Theory and modeling

The analysis of flexible structures is essential to the design and safety in various offshore floating operations. It is challenging to take full considerations of the flexibility and large motion generated by the coupled floating systems under dynamic loads. In this paper series, the absolute nodal coordinate formulation (ANCF) is introduced to model and study the flexible cable in various types of floating operations. ANCF employs coordinate slope instead of angle to enable arbitrary large deformation in flexible cable without considering centrifugal and Coriolis effects. Linear expression of inertial force is derived and the hydrodynamic-structure and soil–structure interactions are also fully considered in dynamic analysis. The theory and modeling of dynamic flexible cable, multibody hydrodynamics and dynamics of floating systems, as well as the numerical calculation process in time domain are derived. The in-house code MiNos dedicated for flexible cable dynamics and program KraKen for multibody floating operation analysis were developed. Dynamic analysis of flexible cable in several floating operation scenarios by ANCF is performed and the in-house code MiNos is validated. The results show the advantage of ANCF for solving flexible structures with fewer elements and good accuracy as well as the robustness, which makes this method feasible and suitable for dynamic cable problems in most situations of floating offshore operations. Part I of this paper series contains the detailed theory and modeling of ANCF and a short review on methodology of hydrodynamics and dynamics of floating systems. Part II focus on the in-house code validation and analysis of flexible cable and mooring line in actual floating operation scenarios.