Search Results (143)

Forest canopy height (FCH) is a vital indicator for assessing forest health and ecosystem service capacity. Over the past two decades, full-waveform (FW) LiDAR has been widely employed for estimating forest biophysical variables due to its high precision in measuring vertical forest structures. However, the impact of terrain undulations on forest parameter estimation remains challenging. To address this issue, this study proposes a bisection approximation decomposition (BAD) method for processing GEDI L1B data and FCH estimation. The BAD method analyzes the energy composition of simplified echo signals and determines the fitting parameters by integrating overall signal energy, the differences in unresolved signals, and the similarity of inter-forest signal characteristics. FCH is subsequently estimated based on waveform peak positions. By dynamically adjusting segmentation points and Gaussian fitting parameters, the BAD method achieved precise separation of mixed canopy and ground signals, substantially enhancing the physical realism and applicability of decomposition results. The effectiveness and robustness of the BAD method for FCH estimation were evaluated using 2049 footprints across varying slope conditions in the Harvard Forest region of Petersham, Massachusetts. The results demonstrated that digital terrain models (DTMs) extracted using the GEDI data and the BAD method exhibited high consistency with the DTMs derived using airborne laser scanning (ALS) data (coefficient of determination R² > 0.99). Compared with traditional Gaussian decomposition (GD), wavelet decomposition (WD), and deconvolution decomposition (DD) methods, the BAD method showed significant advantages in FCH estimation, achieved the smallest relative root mean square error (rRMSE) of 17.19% and greatest mean estimation accuracy of 84.57%, and reduced the rRMSE by 10.74%, 21.49%, and 28.93% compared to GD, WD, and DD methods, respectively. Moreover, the BAD method exhibited a significantly stronger correlation with ALS-derived canopy height mode data than the relative height metrics from GEDI L2A products (r = 0.84, p < 0.01). The robustness and adaptability of the BAD method to complex terrain conditions provide great potential for forest parameters using GEDI data. Full article

On 5 September 2022, the moment magnitude (Mw) 6.7 Luding earthquake struck in the Xianshuihe Fault system on the eastern edge of the Tibet Plateau, illuminating the seismic gap in the Moxi segment. The fault system geometry and rupture process of this earthquake are relatively complex. To better understand the underlying driving mechanisms, this study first uses the Interferometric Synthetic Aperture Radar (InSAR) technique to obtain static surface displacements, which are then combined with Global Positioning System (GPS) data to invert the coseismic slip distribution. A machine learning approach is applied to extract a high-quality aftershock catalog from the original seismic waveform data, enabling the analysis of the spatiotemporal characteristics of aftershock activity. The catalog is subsequently used for fault fitting to determine a reliable fault geometry. The coseismic slip is dominated by left-lateral strike-slip motion, distributed within a depth range of 0–15 km, with a maximum fault slip > 2 m. The relocated catalog contains 15,571 events. Aftershock activity is divided into four main seismic clusters, with two smaller clusters located to the north and south and four interval zones in between. The geometry of the five faults is fitted, revealing the complexity of the Xianshuihe Fault system. Additionally, the Luding earthquake did not fully rupture the Moxi segment. The unruptured areas to the north of the mainshock, as well as regions to the south near the Anninghe Fault, pose a potential seismic hazard. Full article

Acupuncturing the ST36 acupoint can evoke a responding activity in the spinal dorsal root ganglia and generate spikes. In order to identify the responding mechanism of different acupuncture manipulations, in this paper the spike history of neurons is taken as the starting point and the coupling generalized linear model is adopted to encode the neuronal spiking activity evoked by different acupuncture manipulations. Then, maximum likelihood estimation is used to fit the model parameters and estimate the coupling parameters of stimulus, the self-coupling parameters of the neuron’s own spike history and the cross-coupling parameters of other neurons’ spike history. We use simulation data to test the estimation algorithm’s effectiveness and analyze the main factors that evoke neuronal responding activity. Finally, we use the coupling generalized linear model to encode neuronal spiking activity evoked by two acupuncture manipulations, and estimate the coupling parameters of stimulus, the self-coupling parameters and the cross-coupling parameters. The results show that in acupuncture experiments, acupuncture stimulus is the inducing factor of neuronal spiking activity, and the cross-coupling of other neurons’ spike history is the main factor of neuronal spiking activity. Additionally, the higher the amplitude of the neuronal spiking waveform, the greater the cross-coupling parameter. This lays a theoretical foundation for the scientific application of acupuncture therapy. Full article

The dispersion of structural waves, where wave speed varies with frequency, introduces significant challenges in accurately localizing occupants in a building based on vibrations caused by their movements. This study presents a novel multi-sensor vibro-localization technique that accounts for dispersion effects, enhancing the accuracy and robustness of occupant localization. The proposed method utilizes a model-based approach to parameterize key propagation phenomena, including wave dispersion and attenuation, which are fitted to observed waveforms. The localization is achieved by maximizing the joint likelihood of the occupant’s location based on sensor measurements. The effectiveness of the proposed technique is validated using two experimental datasets: one from a controlled environment involving an aluminum plate and the other from a building-scale experiment conducted at Goodwin Hall, Virginia Tech. Results for the proposed algorithm demonstrates a significant improvement in localization accuracy compared to benchmark algorithms. Specifically, in the aluminum plate experiments, the proposed technique reduced the average localization precision from 7.77 cm to 1.97 cm, representing a ∼74% improvement. Similarly, in the Goodwin Hall experiments, the average localization error decreased from 0.67 m to 0.3 m, with a ∼55% enhancement in accuracy. These findings indicate that the proposed approach outperforms existing methods in accurately determining occupant locations, even in the presence of dispersive wave propagation. Full article

Accurate modeling of the switching transients of SiC MOSFETs is essential for overvoltage evaluation, EMI prediction, and other critical applications. Due to the fast switching speed, the switching transients of SiC MOSFETs are highly sensitive to parasitic parameters and nonlinear components, making precise modeling challenging. This paper proposes a hybrid model for SiC MOSFET, in which the analytical model is treated as the basis to provide the fundamental waveforms (knowledge-driven), while the neural network (NN) is utilized to fit the high-order and nonlinear features (data-driven). An NN training method with augmented data is proposed to minimize the training datasets. Verification results show that, even though the NN is trained with the data from a single operating condition, the model can accurately predict switching transients of other operating conditions. The proposed methodology has the potential to co-work with the “black-box” or “grey-box” models to enhance the model accuracy. Full article

The ICESat-2 Land and Vegetation Height (ATL08) product is a new control point dataset for large-scale topographic mapping and geodetic surveying. However, its elevation accuracy is typically affected by multiple factors. The study aims to propose a new approach to classify ATL08 terrain estimates into different accuracy levels and extract reliable ground control points (GCPs) from ICESat-2 ATL08. Specifically, the methodology is divided into three stages. First, the ATL08 terrain estimates are matched with the raw ATL03 photon cloud data, and the ATL08 terrain estimates are used to fit a continuous terrain curve. Then, using the fitted continuous terrain curve and raw ATL03 photon cloud data, a pseudo-waveform is generated for grading the ATL08 terrain estimates. Finally, all the ATL08 terrain estimates are graded based on the peak characteristics of the generated pseudo-waveform. To validate the feasibility of the proposed method, four study areas from the National Ecological Observatory Network (NEON), characterized by various terrain features and forest types were selected. High-accuracy airborne lidar data were used to evaluate the accuracy of graded ICESat-2 terrain estimates. The results demonstrate that the method effectively classified all ATL08 terrain estimates into different accuracy levels and successfully extracted high-accuracy GCPs. The root mean square errors (RMSEs) of the first accuracy level in the four selected study areas were 0.99 m, 0.51 m, 1.88 m, and 0.65 m, representing accuracy improvement of 51.7%, 58.2%, 83.1%, and 68.8%, respectively, compared to the original ATL08 terrain estimates before classifying. Additionally, a comparison with the conventional threshold-based GCP extraction method demonstrated the superior performance of our proposed approach. This study introduces a new approach to extract high-quality elevation control points from ICESat-2 ATL08 data, particularly in forested areas. Full article

We developed an untethered robotic tuna featuring tensegrity joints for the purposes of simplifying the design procedure, reserving enough internal space, reducing the frictional loss of structures and generating a relatively smooth fish body wave. To achieve these objectives, a novel shell-like tensegrity joint was introduced, paired with a single-motor multiple-joint driving mechanism. The morphology matching design method of the tensegrity joint was proposed to fit the streamlined fish body, where the deflection angles of each joint were predetermined to generate the specific body waveform. Stiffness analysis shows that the tensegrity joint could function equivalently to a traditional rotational joint, given certain geometric conditions. Based on the fabricated robotic tuna prototype, extensive free-swimming experiments were performed to optimize its swimming performance by varying key parameters, including the caudal fin‘s shape, flexibility and rotational stiffness and joint deflection angles. The results reveal that the robotic tuna achieved the highest swimming speed of 1.31 body lengths per second (BL/s) at a driving frequency of 2.4 Hz, and the maximum stride length increased to 0.81 BL/cycle at 1 Hz, demonstrating the effectiveness of the proposed design scheme. This study provides valuable insight for developing high-performance bio-inspired autonomous underwater vehicles. Full article

The measurement of human breathing is crucial for assessing the condition of the body. It opens up possibilities for various intelligent applications, like advanced medical monitoring and sleep analysis. Conventional approaches relying on wearable devices tend to be expensive and inconvenient for users. Recent research has shown that inexpensive WiFi devices commonly available in the market can be utilized effectively for non-contact breathing monitoring. WiFi-based breathing monitoring is highly sensitive to motion during the breathing process. This sensitivity arises because current methods primarily rely on extracting breathing signals from the amplitude and phase variations of WiFi Channel State Information (CSI) during breathing. However, these variations can be masked by body movements, leading to inaccurate counting of breathing cycles. To address this issue, we propose a method for extracting breathing signals based on the trajectories of two-chain CSI ratios on the I/Q plane. This method accurately monitors breathing by tracking and identifying the inflection points of the CSI ratio samples’ trajectories on the I/Q plane throughout the breathing cycle. We propose a dispersion model to label and filter out CSI ratio samples representing significant motion interference, thereby enhancing the robustness of the breathing monitoring system. Furthermore, to obtain accurate breathing waveforms, we propose a method for fitting the trajectory curve of the CSI ratio samples. Based on the fitted curve, a breathing segment extraction algorithm is introduced, enabling precise breathing monitoring. Our experimental results demonstrate that this approach achieves minimal error and significantly enhances the accuracy of WiFi-based breathing monitoring. Full article

The GF-7 satellite, China’s inaugural sub-meter-level stereoscopic mapping satellite, has been deployed for a wide range of applications, including natural resource investigation, environmental monitoring, fundamental surveying, and the development of global geospatial information resources. The satellite’s stable platform and reliable imaging systems are crucial for achieving high-quality imaging and precise attitude measurements. However, the satellite’s operation is affected by both internal and external factors, which induce vibrations in the satellite platform, thereby affecting image quality and mapping accuracy. To address this challenge, this paper proposes a novel method for constructing a satellite platform vibration model based on geographic location information. The model is developed by integrating composite data from star sensors and gyroscopes (gyro) with subsatellite point location data. The experimental methodology involves the composite processing of gyro data and star sensor optical axis angles, integration of the processed data through time-matching and normalization, and denoising of the integrated data, followed by trigonometric fitting to capture the periodic characteristics of platform vibrations. The positions of the satellite substellar points are determined from the satellite orbit data. A rigorous geometric imaging model is then used to construct a vibration model with geographic location correlation in combination with the satellite subsatellite point positions. The experimental results demonstrate the following: (1) Over the same temporal range, there is a significant convergence in the waveform similarities between the gyro data and the star sensor optical axis angles, indicating a strong correlation in the jitter information; (2) The platform vibration exhibits a robust correlation with the satellite’s geographic location along its orbit. Specifically, the model reveals that the GF-7 satellite experiences the maximum vibration amplitude between 5° S and 20° S latitude during its ascending phase, and the minimum vibration amplitude between 5° N and 20° N latitude during the descending phase. The model established in this study offers theoretical support for optimizing satellite attitude and mitigating platform vibrations. Full article

Silent Brain Infarction (SBI) is increasingly recognized in patients with cardiac conditions, particularly Atrial Fibrillation (AF) in elderly patients and those undergoing Transcatheter Aortic Valve Implantation (TAVI). While these infarcts often go unnoticed due to a lack of acute symptoms, they are associated with a threefold increase in stroke risk and are considered a precursor to ischemic stroke. Moreover, accumulating evidence suggests that SBI may contribute to the development of dementia, depression, and cognitive decline, particularly in the elderly population. The burden of SBI is substantial, with studies showing that up to 11 million Americans may experience a silent stroke annually. In AF patients, silent brain infarcts are common and can lead to progressive brain damage, even in those receiving anticoagulation therapy. The use of cerebral embolic protection devices (CEPDs) during TAVI has been explored to mitigate the risk of stroke; however, their efficacy remains under debate. Despite advancements in TAVI technology, cerebrovascular events, including silent brain lesions, continue to pose significant challenges, underscoring the need for improved preventive strategies and therapeutic approaches. We propose a device consisting of a strut structure placed at the base of the treated artery to model the potential risk of cerebral embolisms caused by atrial fibrillation, thromboembolism, or dislodged debris of varying potential TAVI patients. The study has been carried out in two stages. Both are based on computational fluid dynamics (CFD) coupled with the Lagrangian particle tracking method. The first stage of the work evaluates a variety of strut thicknesses and inter-strut spacings, contrasting with the device-free baseline geometry. The analysis is carried out by imposing flow rate waveforms characteristic of healthy and AF patients. Boundary conditions are calibrated to reproduce physiological flow rates and pressures in a patient’s aortic arch. In the second stage, the optimal geometric design from the first stage was employed, with the addition of lateral struts to prevent the filtration of particles and electronegatively charged strut surfaces, studying the effect of electrical forces on the clots if they are considered charged. Flowrate boundary conditions were used to emulate both healthy and AF conditions. Results from numerical simulations coming from the first stage indicate that the device blocks particles of sizes larger than the inter-strut spacing. It was found that lateral strut space had the highest impact on efficacy. Based on the results of the second stage, deploying the electronegatively charged device in all three aortic arch arteries, the number of particles entering these arteries was reduced on average by 62.6% and 51.2%, for the healthy and diseased models respectively, matching or surpassing current oral anticoagulant efficacy. In conclusion, the device demonstrated a two-fold mechanism for filtering emboli: (1) while the smallest particles are deflected by electrostatic repulsion, avoiding micro embolisms, which could lead to cognitive impairment, the largest ones are mechanically filtered since they cannot fit in between the struts, effectively blocking the full range of particle sizes analyzed in this study. The device presented in this manuscript offers an anticoagulant-free method to prevent stroke and SBIs, imperative given the growing population of AF and elderly patients. Full article

During the voltage regulation of on-load tap changers (OLTCs), the movement of the contacts can easily cause arcing, which may lead to erosion or malfunction. To reduce the energy and probability of arcing, we focus on designing an optimal range for the sliding speed and contact pressure of the contacts to minimize arc energy. Initially, our research introduces a novel OLTC arc testing platform to simulate the motion of static and dynamic contacts, exploring the relationship between different sliding speeds, contact pressures, and factors like arc voltage waveform, arcing rate, arc resistance, and arc energy. Subsequently, by employing multiple nonlinear regression methods, we establish functional relationships between sliding speed and arc energy, as well as contact pressure and arc energy, evaluating the fit using correlation coefficients. Finally, through analyzing their nonlinear behaviors, we determine the ideal sliding speed and contact pressure. The results indicate that when the OLTC contacts slide at an optimal speed between 89 and 103 mm/s and optimal contact pressure between 1.5 and 1.7 N, the arc energy can be minimized, thereby enhancing the performance and lifespan of the on-load tap changer. This study offers feasible insights for the design and operation of OLTCs, aiding in the improvement of power system regulation. Full article

Steel beams are widely used load-bearing components in bridge construction. They are prone to internal stress concentration under low-frequency vibrations caused by natural disasters and adverse loads, leading to microcracks and fractures, thereby accelerating the instability of steel components. Therefore, dynamic stress monitoring of steel beams under low-frequency vibrations is crucial to ensure structural safety. This study proposed an external stress sensor based on PMN-PT material. The sensor has the advantages of high sensitivity, comprehensive frequency response, and fast response speed. To verify the accuracy and feasibility of the sensor in actual engineering, the LETRY universal testing machine and drop hammer impact system were used to carry out stress monitoring tests and finite element simulations on scaled I-shaped steel beams with PMN-PT sensors attached. The results show that: (1) The PMN-PT sensor has exceptionally high sensitivity, maintained at 1.716~1.726 V/MPa in the frequency range of 0~1000 Hz. The sensor performance is much higher than that of PVDF sensors with the same adhesive layer thickness. (2) Under low-frequency random vibration, the sensor’s time domain and frequency domain output voltages are always consistent with the waveform of the applied load, which can reflect the changes in the structural stress state in real time. (3) Under the impact of a drop hammer, the sensor signal response delay is only 0.001 s, and the sensitivity linear fitting degree is above 0.9. (4) The simulation and experimental results are highly consistent, confirming the superior performance of the PMN-PT sensor, which can be effectively used for stress monitoring of steel structures in low-frequency vibration environments. Full article

To increase the detection accuracy of soil structure and moisture content in reconstituted soils under complex conditions, this study utilizes a 400 MHz ground-penetrating radar (GPR) to examine a study area consisting of loess, sandy loam, red clay, and mixed soil. The research involves analyzing the single-channel waveforms and two-dimensional images of GPR, preprocessing the data, obtaining envelope information via amplitude envelope detection, and performing a Hilbert transformation. This study employs a least squares fitting approach to the instantaneous phase envelope to ascertain the thickness of various soil layers. By utilizing the average envelope amplitude (AEA) method, a correlation between the radar’s early signal amplitude envelope and the soil’s shallow dielectric constant is established to invert the moisture content of the soil. The analysis integrates soil structure and moisture distribution data to investigate soil structure characteristics and moisture content performance under diverse soil properties and depths. The findings indicate that the envelope detection method effectively identifies stratification boundaries across different soil types; the AEA method is particularly efficacious in inverting the moisture content of reconstituted soils up to 3 m deep, with an average relative error ranging from 2.81% to 7.41%. Notably, moisture content variations in stratified reconstituted soils are more pronounced than those in mixed soil areas, displaying a marked stepwise increase with depth. The moisture content trends in the vertical direction of the same soil profile are generally consistent. This research offers a novel approach to studying reconstituted soils under complex conditions, confirming the viability of the envelope detection and AEA methods for intricate soil investigations and broadening the application spectrum of GPR in soil studies. Full article

Developing new features for time-series characterization is a current challenge in data science and machine learning. In this paper, we propose a new metric based on a simple and efficient algorithm, namely, the return map. The return map analysis is well established in the field of non-linear dynamics, in particular, for fitting the parameters of a chaotic system from a waveform, or to attack a chaotic communication channel. We show that our metrics work well for both non-linear dynamics and time-series feature extraction problems in the field of machine learning. In an experiment aiming to classify vibration signals of normal and damaged bearings, we compare our method with two other methods that reported to have excellent accuracy, based on entropy and statistical feature distribution, respectively. We show that our method achieves higher accuracy with almost the lowest time costs, which was confirmed in experiments with two different datasets containing three main classes of bearings: normal, with inner race faults, and with outer race faults, having different damage origins and recorded in various conditions. In particular, for the dataset supplied by Case Western Reserve University, our method reached an accuracy of 100% at signals of 5000 sample points length, with a total time of 0.4 s required for feature estimation, while the entropy-based method reached an accuracy of 95% with a time of 100 s, and a statistical feature distribution method reached an accuracy of 93% with a total time of 1.9 s. Results show that the developed method is better suited to real-time bearing condition monitoring applications than most of the methods reported to date. Full article

Three-dimensional echocardiography (3D ECHO) and magnetic resonance (MR) imaging are frequently used in patients and animals to evaluate heart functions. Inverse finite element (FE) modeling is increasingly applied to MR images to quantify left ventricular (LV) function and estimate myocardial contractility and other cardiac biomarkers. It remains unclear, however, as to whether myocardial contractility derived from the inverse FE model based on 3D ECHO images is comparable to that derived from MR images. To address this issue, we developed a subject-specific inverse FE model based on 3D ECHO and MR images acquired from seven healthy swine models to investigate if there are differences in myocardial contractility and LV geometrical features derived using these two imaging modalities. We showed that end-systolic and end-diastolic volumes derived from 3D ECHO images are comparable to those derived from MR images (R2=0.805 and 0.969, respectively). As a result, ejection fraction from 3D ECHO and MR images are linearly correlated (R2=0.977) with the limit of agreement (LOA) ranging from −17.95% to 45.89%. Using an inverse FE modeling to fit pressure and volume waveforms in subject-specific LV geometry reconstructed from 3D ECHO and MR images, we found that myocardial contractility derived from these two imaging modalities are linearly correlated with an R2 value of 0.989, a gradient of 0.895, and LOA ranging from −6.11% to 36.66%. This finding supports using 3D ECHO images in image-based inverse FE modeling to estimate myocardial contractility. Full article