Signal Waveform Research Articles

Establishing the case for a May 2010 low-yield, unannounced nuclear test in North Korea

AbstractNew data, analyses and modelling are presented concerning the unprecedented mid-May 2010 series of fission product detections in ground level air on and around the Korean Peninsula. For the first time Ba-140 is revealed at Ussuriysk, for which only La-140 had been reported. Thus aerosol particles containing the same parent-daughter pair Ba-140/La-140 were detected at both Ussuriysk and Okinawa, establishing beyond reasonable doubt that their physical, spatial and temporal origins are the same. Together with Ce-141 and Cs-137, all with short-lived xenon isotope parents, a supercritical fission excursion, which experienced a near prompt filtered vent, is the only viable scenario for their explanation. New modelling suggests that the vent occurred around 9 s after the excursion and that the CTBT-relevant xenon isotopes Xe-133 and Xe-135 were ‘quenched’ around 25 min later and released some 10–20 h afterwards. Published corroborating seismic and infrasound data of an event at the North Korean nuclear test site 8 min and 45 s past midnight on 12 May 2010 is subsequently reviewed. These papers adopted a conventional depth of the event although the data suggested a shallower one. Despite arguments in the seismic community about its exact nature, it is prudent to test how well the waveform signals marry the radionuclide detection pattern. Thus the location and time are input into a new atmospheric transport model. The advanced software suite MATCH was used in forward mode with prompt and delayed releases, revealing the presence of plumes at each detection site at the time of their first detection and extending over the observed timeframe. Thus a very consistent picture of a shallow low yield nuclear test is obtained.

Research on the prediction method of porcelain insulator pollution degree based on electric field monitoring

The online monitoring of insulator surface pollution states is crucial for predicting/preventing pollution flashover accidents. This paper conducts artificial pollution tests on porcelain insulators at five pollution levels to obtain the electric field signal during the flashover process. Subsequently, the time-frequency performance of the electric field signal waveform at various discharge stages is discussed in detail. Finally, a method for predicting pollution degrees by combining back propagation (BP) neural networks and non-contact electric field signal monitoring is proposed and validated. Research results indicate that during the pollution flashover process of the porcelain insulator, the peak value of the electric field signal shows an upward trend in sections. As the equivalent salt deposit density (ESDD) increases, the curves of electric field values rise. The development of dry-band arcing clearly distorts the electric field signal waveform at its crest and trough parts, increasing the total harmonic distortion (THD) rate. The THD can reach 100–130 % when closing to flashover, with the third harmonic acting as the main component. By taking kmax (the rising rate of the E-field signal amplitude), g (the ratio of Emax to Erms), K3 (the third harmonic coefficient), and THD as inputs to the BP neural network model, an ESDD prediction with a relative error lower than 10.63 % is realized. This research can offer novel technical support for predicting pollution degrees.

Resistance spot welding defect detection based on vectorized dynamic resistance signal and LightGBM classifier

The problem of real-time detection of welding defects is a difficult problem in resistance spot welding. It is found that the dynamic resistance has a strong connection with the growth of the nugget. The dynamic resistance signals with welding defects are significantly different from those of normal welding, and the dynamic resistance signals between different welding defects show different characteristics, and the dynamic resistance signals of the same kind of welding defects may also differ from each other. The most common practice today to realize the detection of resistive defects is by extracting the time-domain features of the signal waveforms. However, this approach is highly subjective, so this article proposes a double-size mesh division method to process the dynamic resistance signal. Experiments prove that the method can retain the characteristics of the signal curve well, and it is also improves the training speed and accuracy compared with the mesh division method. Finally, the processed signals are classified using the light gradient boosting machine classifier with an accuracy of 98.55%.

Pre- and post-fire forest canopy height mapping in Southeast Australia through the integration of multi-temporal GEDI data, satellite images, and Convolution Neural Network

ABSTRACT This study leveraged Convolutional Neural Network (CNN) models to estimate canopy height in Southeast Australian forests before and after the 2019–2020 bushfire event, using inputs from Sentinel-1, Sentinel-2, spectral indices, and terrain features and GEDI canopy height (rh99) as target variables. Our research consisted of three primary objectives: (1) an evaluation of GEDI rh99 canopy height in relation to an nDSM derived from airborne LiDAR, (2) the development of two CNN models for pre- and post-fire scenarios, and (3) utilizing the trained CNN models to generate pre- and post-fire canopy height maps and canopy height change maps across the continuous landscape. GEDI rh99 accuracy analysis revealed R 2 = 0.85, RMSE = 7.25 m and nRMSE = 0.23 for pre-fire GEDI and R 2 = 0.78, RMSE = 10.1 m and nRMSE = 0.3 for post-fire GEDI. The pre-fire CNN model achieved R 2 = 0.75, RMSE = 7.29 m and nRMSE = 0.22, and post-fire CNN model achieved R 2 = 0.62, RMSE = 11.67 m, and nRMSE = 0.34 when validated against nDSM. Bias analysis was conducted, revealing that GEDI rh99 underestimated nDSM across all canopy height ranges, and slope has no significant impact on GEDI accuracy. Notably, post-fire GEDI rh99 exhibited significant underestimation in high to extreme fire severity. The CNN models’ prediction displayed overestimation in short forests < 20 m and increasing underestimation in taller forests > 20 m and underestimation in high and extreme fire severity. Subsequent investigation indicated that canopy layer thinning in high fire severity conditions resulted in weak GEDI waveform signals and consequent bias. Additionally, the GEDI rh99 bias was propagated to the CNN models, together with insensitivity of spectral band values to canopy height in dense and burnt forests potentially contributed to models’ bias. Finally, we demonstrated CNN models’ ability to monitor forest recovery by generating canopy height maps spanning East Gippsland from 2019 to 2023.

Study on Radiation Damage of Silicon Solar Cell Electrical Parameters by Nanosecond Pulse Laser

This experimental study investigates the damage effects of nanosecond pulse laser irradiation on silicon solar cells. It encompasses the analysis of transient pulse signal waveform characteristics at the cells’ output and changes in electrical parameters, such as I–V curves before and after laser irradiation under varying laser fluence and background light intensities, and explores the underlying action mechanisms of laser irradiation. The study reveals that as the laser fluence increases up to 4.0 J/cm2, the peak value of the transient pulse signal increases by 47.5%, while the pulse width augments by 88.2% compared to the initial transient pulse signal. Furthermore, certain parameters, such as open-circuit voltage, short-circuit current, and peak power obtained, from the measured I–V curve indicate a threshold laser fluence for functional degradation of the solar cell at approximately 1.18 ± 0.42 J/cm2. Results obtained from laser irradiation under different background light intensities underscore the significant influence of background light on laser irradiation of silicon cells, with the most severe damage occurring in the absence of light. Moreover, findings from laser irradiation at multiple locations on the silicon cell demonstrate a linear decrease in the output voltage of the silicon cell with an increase in the number of irradiation points.

Federated Multiple Tensor-on-Tensor Regression (FedMTOT) for Multimodal Data Under Data-Sharing Constraints

In recent years, diversified measurements reflect the system dynamics from a more comprehensive perspective in system modeling and analysis, such as scalars, waveform signals, images, and structured point clouds. To handle such multimodal structured high-dimensional (SHD) data, combining a large amount of data from multiple sites is necessary (i) to reduce the inherent population bias from a single site and (ii) to increase the model accuracy. However, impeded by data management policies and storage costs, data could not be easily shared or directly exchanged among different sites. Instead of simplifying or facilitating the data query process, we propose a federated multiple tensor-on-tensor regression (FedMTOT) framework to train the individual system model locally using (i) its own data and (ii) data features (not data itself) from other sites. Specifically, federated computation is executed based on alternating direction method of multipliers (ADMM) to satisfy data-sharing requirements, while the individual model at each site can still benefit from feature knowledge from other sites to improve its own model accuracy. Finally, two simulations and two case studies validate the superiority of the proposed FedMTOT framework.

Deep Neural Networks Based Denoising of Regional Seismic Waveforms and Impact on Analysis of North Korean Nuclear Tests

AbstractWe test a deep learning based denoising autoencoder algorithm on regional and teleseismic seismological and hydroacoustic datasets, which we compile from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on stations which can be relevant to investigate North Korean nuclear tests. Denoising of waveform records using autoencoder techniques potentially enables improved signal detection and processing due to lowered signal-to-noise ratios. We train and compare the performance of several different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if they can still be reliably used in downstream analysis for further inferences on the seismic source type, i.e. seismic moment tensor analysis. The declared North Korean nuclear tests are a suitable benchmark test set, as they have extensively been researched and their source type and location might be assumed known. Verification of the source type is of particular interest for potential nuclear tests under international law. We find that care needs to be taken using the denoised waveform data, as a slight bias is introduced in the seismic moment tensor analysis. However we also find promising results hinting at possible future use of the technique for standard analyses, as it improves the investigation of smaller events. Autoencoder based denoising techniques could be employed in future routine frameworks to increase earthquake catalog completeness and possibly aid in detecting smaller potential treaty relevant events.

Pulse Signal Generator for Simulated Gamma-ray Spectrometer Detection

This paper introduces an integrated nuclear pulse generator aimed at simulating authentic radiation detection signals, allowing for the convenient calibration and testing of nuclear instruments without the need for radioactive sources. An innovative method is adopted, generating energy spectrum distributions via Monte Carlo simulation and deriving nuclear pulse waveforms using difference equations. This approach enables real-time computation of particle transport processes on a hardware platform, and generates pulse sequences that closely resemble the statistical characteristics of actual nuclear events. Compared to traditional nuclear signal generators, this new type of generator significantly enhances the flexibility and real-time capabilities of the equipment, as it no longer relies on pre-stored empirical data or simulation software to generate energy spectrum data. As a case for gamma-ray spectrometer detection, the signal waveforms and energy spectrum distributions produced closely match those obtained using a NaI scintillation detector in a lead chamber to detect a Cs-137 source.

Free-breathing motion corrected magnetic resonance elastography of the abdomen.

Magnetic resonance elastography (MRE) is a non-invasive method to measure the viscoelastic properties of tissue and has been applied in multiple abdominal organs. However, abdominal MRE suffers from detrimental breathing motion causing misalignment of structures between repeated acquisitions for different MRE dimensions (e.g., motion encoding directions and wave phase offsets). This study investigated motion correction strategies to resolve all breathing motion on sagittal free-breathing MRE acquisitions in a phantom, in healthy volunteers and showed feasibility in patients. First, in silico experiments were performed on a static phantom dataset with simulated motion. Second, eight healthy volunteers underwent two sagittal MRE acquisitions in the pancreas and right kidney. The multi-frequency free-breathing spin-echo echo-planar-imaging (SE-EPI) MRE consisted of four frequencies (30, 40, 50, 60 Hz), eight wave-phase offsets, with 3 mm3 isotropic voxel size. Following data re-sorting in different number of motion states (4 till 12) based on respiratory waveform signal, three intensity-based registration methods (monomodal, multimodal, and phase correlation) and non-rigid local registration were compared. A ranking method was used to determine the best registration method, based on seven signal-to-noise and image quality measures. Repeatability was assessed for no motion correction (Original) and the best performing method (Best) using Bland-Altman analysis. Lastly, the best motion correction method was compared to no motion correction on patient MRE data [pancreatic ductal adenocarcinoma (PDAC, n=5) and metabolic dysfunction-associated steatotic liver disease (MASLD) (n=1)]. In silico experiments showed a deviation of shear wave speed (SWS) with simulated motion to the ground truth, which was (partially) resolved using motion correction. In healthy volunteers ranking resulted in the best motion correction method of monomodal registration using nine motion states, while no motion correction was ranked last. Limits of agreement were (-0.18, 0.14), and (-0.25, 0.18) m/s for Best and Original, respectively. Using motion correction in patients resulted in a significant increase in SWS in the pancreas (Original: 1.39±0.10 and Best: 1.50±0.17 m/s). After motion correction PDAC had a mean SWS of 1.56±0.27 m/s (Original: 1.42±0.25 m/s). The fibrotic liver mean SWS was 2.07±0.20 m/s (Original: 2.12±0.18 m/s). Motion correction in sagittal free-breathing abdominal MRE results in improved data quality, inversion precision, repeatability, and is feasible in patients.

Automatic Estimation of the Interference Subspace Dimension Threshold in the Subspace Projection Algorithms of Magnetoencephalography Based on Evoked State Data.

A class of algorithms based on subspace projection is widely used in the denoising of magnetoencephalography (MEG) signals. Setting the dimension of the interference (external) subspace matrix of these algorithms is the key to balancing the denoising effect and the degree of signal distortion. However, most current methods for estimating the dimension threshold rely on experience, such as observing the signal waveforms and spectrum, which may render the results too subjective and lacking in quantitative accuracy. Therefore, this study proposes a method to automatically estimate a suitable threshold. Time-frequency transformations are performed on the evoked state data to obtain the neural signal of interest and the noise signal in a specific time-frequency band, which are then used to construct the objective function describing the degree of noise suppression and signal distortion. The optimal value of the threshold in the selected range is obtained using the weighted-sum method. Our method was tested on two classical subspace projection algorithms using simulation and two sensory stimulation experiments. The thresholds estimated by the proposed method enabled the algorithms to achieve the best waveform recovery and source location error. Therefore, the threshold selected in this method enables subspace projection algorithms to achieve the best balance between noise removal and neural signal preservation in subsequent MEG analyses.

A Low-Cost Wideband Digital Array Antenna Based on Stretch Processing Technique

Wideband digital phased array radar offers the advantages of high range resolution, which improves the recognition ability for multiple targets, group targets, and high-speed targets. Traditional wideband phased arrays use true time delay to compensate for aperture fill time; however, the cost increases significantly. In this paper, a wideband elemental digital array architecture based on the stretch processing method is proposed. By utilizing the time-domain and frequency-domain translation equivalence of the LFM (Linear Frequency Modulation) signal waveform, the equivalent aperture fill time is compensated for through frequency shift and phase shift after stretch processing. Compared to traditional wideband digital arrays, this method can dramatically reduce the required sampling rate and lower the requirements on antenna hardware, thereby reducing the manufacturing cost of system. A comprehensive analysis of the signal processing process and stretch processing method is provided. And an antenna array prototype is developed to verify the T/R channel compensation and wideband beamforming. Measured results show that the antenna is capable of ±60° scanning in azimuth plane and ±40° scanning in elevation plane, with a bandwidth of 500 MHz in S-band. The results demonstrate excellent wideband beam performance and accurate lobe scanning, which confirms the validity of the proposed wideband architecture for stretch processing, frequency shift, and phase shift. This method can be widely applied to the low-cost design and wideband performance improvement of wideband digital array radar.

APC: Contactless healthy sitting posture monitoring with microphone array

The prevalence of poor sitting posture in daily work has become a growing concern among office workers and students due to the associated health problems. To address this issue, we design an acoustic sitting posture care system (termed, APC) based on a circular microphone array. Compared with classic posture recognition technologies such as visual perception and sensors, acoustic sensing naturally possesses advantages such as privacy protection and contactless capabilities. Concretely, our system leverages a customized and inaudible sound signal sent from a speaker to a user’s body, and an echo signal preprocessing method to sense the body posture. Our system comprises three modules: signal generation and collection, signal preprocessing, and posture classification. The signal generation and collection module is designed to create an appropriate signal waveform for transmitting the sound signal. We also develop a unique alignment method for received signals to implement background interference cancellation. In the signal preprocessing module, we propose a body profile extraction method based on the phase difference between received signals. In the posture classification module, we design an attention mechanism based classification network that can map the output of the previous module to different sitting posture categories. The experimental results show that our proposed method achieves an average accuracy of 98.4% for five common sitting postures. Furthermore, case studies conducted under different practical conditions have validated the robustness of our system.

Gravitational waves from nonradial oscillations of stochastically accreting neutron stars

Abstract Oscillating neutron stars are sources of continuous gravitational waves. We study analytically the excitation of stellar oscillations by the mechanical impact on the stellar surface of ‘clumps’ of stochastically accreted matter. We calculate the waveform and spectrum of the gravitational wave signal emitted by the accretion-driven pulsations. Results are generated for an idealised model of a nonrotating, unmagnetised, one-component star with uniform polytropic index npoly assuming Newtonian gravity and the Cowling approximation. We find that the excited mode amplitudes grow with increasing npoly and mode order n. The gravitational wave signal forms a sequence of amplitude-modulated packets for npoly = 1, lasting ∼10−3s after each impact. The gravitational wave strain increases with increasing npoly, but decreases with increasing n and increasing multipole order l for npoly = 1. In the observing band of current long-baseline interferometers, g-modes emit higher, narrower peaks in the amplitude spectral density than f- and p-modes, with the highest peaks reaching ∼10−26Hz−1/2 for modes with damping time τnl ∼ 108yr. The root-mean-square strain hrms, calculated by summing over modes with 2 ≤ l ≤ 4 and τnl ≤ 108yr, spans the range 10−33 ≤ hrms ≤ 10−32 for 1 ≤ npoly ≤ 2.

Implementation of an epicardial implantable MEMS sensor for continuous and real-time postoperative assessment of left ventricular activity in adult minipigs over a short- and long-term period.

The sensing of left ventricular (LV) activity is fundamental in the diagnosis and monitoring of cardiovascular health in high-risk patients after cardiac surgery to achieve better short- and long-term outcome. Conventional approaches rely on noninvasive measurements even if, in the latest years, invasive microelectromechanical systems (MEMS) sensors have emerged as a valuable approach for precise and continuous monitoring of cardiac activity. The main challenges in designing cardiac MEMS sensors are represented by miniaturization, biocompatibility, and long-term stability. Here, we present a MEMS piezoresistive cardiac sensor capable of continuous monitoring of LV activity over time following epicardial implantation with a pericardial patch graft in adult minipigs. In acute and chronic scenarios, the sensor was able to compute heart rate with a root mean square error lower than 2 BPM. Early after up to 1 month of implantation, the device was able to record the heart activity during the most important phases of the cardiac cycle (systole and diastole peaks). The sensor signal waveform, in addition, closely reflected the typical waveforms of pressure signal obtained via intraventricular catheters, offering a safer alternative to heart catheterization. Furthermore, histological analysis of the LV implantation site following sensor retrieval revealed no evidence of myocardial fibrosis. Our results suggest that the epicardial LV implantation of an MEMS sensor is a suitable and reliable approach for direct continuous monitoring of cardiac activity. This work envisions the use of this sensor as a cardiac sensing device in closed-loop applications for patients undergoing heart surgery.

Reconstruction method considering the electrostatic signal waveform of velocity distribution in flame based on EST

The velocity distribution of flame is an important combustion dynamic parameter that is used to analyze the stability, heat transfer and propagation of combustion. Digital particle image velocimetry and laser Doppler velocimetry are commonly used to measure the velocity distribution of flame. However, these methods work by adding tracer particles into the flame. A dual-section electrostatic tomography (EST) system is designed to detect the induction signal of the charged particles generated by the ionization process of combustion. The velocity distribution of flame is reconstructed through the electrostatic direct velocity tomography (EDVT) method. However, the different distance between the movement trajectory of the charged particles and a pair of electrodes for the cross-correlation calculation causes the difference in the induced signal waveform. It leads to an error in the cross-correlation velocity calculation. By analyzing the influence of the single charge induction signal waveform on the calculation of cross-correlation velocity, the reason for this error is clarified. Therefore, an electrostatic signal-waveform-based direct velocity tomography (ESDVT) method is proposed. The experimental results from a belt-type electrostatic device show that the average value of the L2-norm error of the ESDVT method is reduced by 40.9% compared with the EDVT method. Finally, using the method proposed in this paper, the velocity distribution at different sections of the Meker flame is reconstructed without adding tracer particles.

VCH: A Velocity Measurement Method Combining HFM Signals

During sonar detection, the fuzzy function of hyperbolic frequency modulation (HFM) in the waveform of active signal is in the shape of a “oblique blade,” which leads to the coupling characteristics of its fuzzy function in the time dimension and frequency dimension. Doppler causes a frequency shift in the frequency dimension and a time delay in the time dimension, which makes it impossible for a single HFM signal to measure speed accurately. However, the time delay caused by the same moving target to HFM signals with different frequency bands and different pulse widths is also different. A velocity measurement method combining HFM signals (VCH) is proposed, which employs the time delay correlation between the combined HFM signals to achieve the target distance and speed. In the VCH method, the echo signal is no longer matched and filtered as a whole but is divided into two channels and matched and filtered separately. And then, the combined HFM signals are used to obtain the distance and speed of the target. Extensive simulation results show that the proposed method can estimate the distance and speed of moving targets accurately, and it has reference value for engineering application.

Accurate TDOA estimation using matching cumulative estimator for LFM signal with small samples

This paper introduces two matching cumulative estimators designed for achieving gridless time difference of arrival (TDOA) estimation for linear frequency modulated (LFM) signals with limited samples. A designed matching basis function, customized to the source signal waveform, is employed to calculate with the received data. Time delay matching is then utilized to formulate a monotonic objective function related to the estimated TDOA, enabling gridless estimation. To enhance robustness, especially in scenarios characterized by small samples and low signal to noise ratios (SNRs), a nonlinear cumulative transformation, comprised of a series of strongly convex operations, is incorporated to mitigate the impact of noise on the peak calculation of the objective function. Simulation results validate the efficacy of the two proposed TDOA estimators, particularly under challenging conditions involving small samples and low SNRs.

Performance of a novel charge sensor on the ion detection for the development of a high-pressure avalancheless ion TPC

Within the project of building a time projection chamber using 100 kg of high-pressure 86SeF6 gas to search for the neutrinoless double-beta decay in the NvDEx collaboration, we are developing a CMOS charge sensor, named Topmetal-S, which is tailored for the experiment to detect the ions without gas amplification. In this work, the performance of the sensor is presented. The equivalent noise charge of the sensor is measured to be about 120 to 140 e- depending on the operating point, with the charge injection capacitance calibrated against external capacitors. The signal waveforms are investigated with various chip parameters and experimental settings. In addition to electrons, both negatively and positively charged ions could be detected, and their waveforms are studied using air and SF6 gases. Using the sensor, the mobility of negative ions in ambient air in the atmospheric pressure is measured to be 1.555 ± 0.038 cm2 · V-1 · s-1. Our study demonstrates that the Topmetal-S chip could be used as the ion detection charge sensor for the experiment. Further work is ongoing to reduce the noise of the sensor and to develop a small readout plane with tens of the sensors.

Method for lightning flashover types identification on transmission lines utilizing OPGW optical polarization state signal characteristics

The Optical Fiber Composite Overhead Ground Wire (OPGW) has undergone rapid development and has been extensively applied in the realm of intelligent power system status monitoring. This article conducts research on lightning signal waveform in optical fibers within the context of lightning fault location using the OPGW optical polarization state method. A transmission line model is simulated employing ATP-EMTP to capture electrical characteristics when lightning strikes the OPGW. Subsequently, by employing the Faraday magneto-optical effect principle, electrical signals are transformed into optical polarization states within the internal fibers of OPGW. Using such data, the optical signal propagation characteristics when lightning strikes OPGW are deduced. Finally, a method for discerning between induced lightning and direct lightning strikes through the analysis of time-frequency domain characteristics in OPGW. This method addresses the limitation of traditional OPGW lightning monitoring, which is unable to deduce electrical signals, thus achieving precise identification of lightning-induced transient faults in transmission lines.

Damage Analysis of Urban Comprehensive Pipe Gallery Caused by Internal Gas Explosion Based on HHT

In this paper, the method of embedding piezoelectric ceramic sensors is used to test the damage of the materials and models of the urban comprehensive pipe gallery. The monitoring signal is processed by HHT method, and the frequency and energy changes of the piezoelectric signal before and after the explosion were analyzed, thus the damage characteristics of the urban comprehensive pipe gallery under the internal gas explosion are determined. The results show that in the gas explosion test inside the pipe gallery model, the waveform and peak value of the piezoelectric signal before and after the explosion did not change significantly, indicating that there was no obvious damage to the side wall of the integrated pipe gallery. However, after further time-frequency analysis of the piezoelectric signal by HHT, it is found that the dominant frequency of the piezoelectric signal after explosion has a downward trend, so it is judged that there is a small damage in the pipe gallery after explosion.