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Chemical mechanical planarization (CMP) is vital for ensuring chip fabrication uniformity at nanometer scales. The emergence of a series of phenomenological CMP process models (Stine et al., 1997; Gbondo-Tugbawa, 2002; Xie, 2007; Vasilev, 2011) suggests that the existing model upgrade approach is largely based on a change in phenomenological model assumptions, demanding deep insights into complex process mechanisms and protracted period for accuracy improvements. To tackle this issue, this paper proposes a decoupling regression strategy for model upgrades. This strategy employs a data-driven approach to enhance the coupling relationships within the model, facilitating continuous improvement of simulation accuracy based on the existing model. It is capable of achieving improvements in model accuracy even in scenarios where modelers lack insight into complex process mechanisms. We validate our method by upgrading the Density Step Height (DSH) model to the Extend-DSH model to address poor erosion predictions at the 28nm node. Comparing model predictions with silicon data reveals that the Extend-DSH model aligns better with the measured data, reducing the root mean square error from 159.31Å to 6.89Å and increasing the coefficient of determination from -0.83561 to 0.6058, showcasing the effectiveness of the proposed chip-level CMP model upgrade method grounded in the decoupling regression strategy. [ABSTRACT FROM AUTHOR]

Transistor random threshold voltage variations due to process fluctuations seriously affects the stability of Static Random Access Memory (SRAM). In this paper, a SRAM bit transistors threshold voltage $({Vth})$ deviation monitoring scheme and system is proposed. This scheme ingeniously achieves on-chip measurement of all transistors threshold voltages without altering compact SRAM bit array layout. Control signal strategies and Transistor ${Vth}$ Determination Circuit (TVDC) for different types of Devices Under Test (DUTs) have been proposed. The system is implemented using a 65 nm CMOS process with a core area of 0.01875mm2. Through Monte Carlo analysis, the Weighted Average (WA) difference of the proposed scheme and the direct measurement method is not more than 10mV, and the Root Mean Square Error (RMSE) difference is not more than 3mV. This system can also effectively detect the cell position of the transistor threshold voltage mismatch simulated by modifying the substrate voltage. For SRAM arrays of different scales, the method proposed in this paper has area efficiency and flexible reconfigurability. [ABSTRACT FROM AUTHOR]

In motion-control applications, noise and dynamic nonlinearities influence the performance of control systems and lead to unpredictable disturbances. The dc servo motors used in motion control applications should have precise control methods to achieve the desired responses. Therefore, predicting and compensating for the disturbance are essential for increasing system robustness and achieving high precision and fast reaction. This article introduces the polynomial predictive filtering (PPF) method to estimate the states of a system using polynomial extrapolation of consecutive and evenly spaced sensor data. Acceleration-/torque-based experiments are conducted to validate the effectiveness and viability of the proposed method. The difference between the real-time sensor data and the PPF-based predicted value shows a standard deviation of less than 0.15 and $1 \times 10^{-5}$ for the velocity and disturbance torque, respectively. [ABSTRACT FROM AUTHOR]

High accuracy, high adaptability and low complexity have always been the goals of on-board state of health (SoH) estimation in a well-designed battery management system for electric vehicles. In this article, we proposed a practical SoH estimation method for LiFePO4 batteries based on Gaussian mixture regression (GMR) and incremental capacity (IC) analysis. According to the close correlation between the SoH and the IC curve of LiFePO4 battery, the peak coordinates of the curve are extracted as capacity feature to train an aging model using GMR. The capacity degradation of batteries is well described by multiple Gaussian distributions, and the estimation accuracy of SoH is improved. Furthermore, the coordinates of three peaks in the curve are separately analyzed as capacity feature to enhance the adaptability of the aging model to different charging regions. Finally, the complexity of the proposed method is compared with that of other methods. The results show that taking the coordinates of any peak as the capacity feature, SoH can be accurately estimated based on GMR even for different tested batteries, and the mean absolute error and root mean square error are both less than 1%. [ABSTRACT FROM AUTHOR]

Exoskeleton assistance can reduce metabolic cost and increase preferred walking speed in unimpaired and impaired groups, but individual outcomes are highly variable. Assistance may influence step regulation, leading to individual modulation of gait variability, energetic cost, and balance control. In this study, we aimed to understand the effects of a powered ankle exoskeleton on step regulation and its relationship to self-selected walking speed, cost of transport, and gait variability. We asked 12 unimpaired young adults to walk at their comfortable walking speed on a self-paced treadmill in their regular shoes, with the exoskeleton tracking zero torque, and in two trials using proportional myoelectric control. We measured preferred walking speed, cost of transport (COT), mean and standard deviation of gait parameters, (step length, step time, and step width) and computed long-term correlations via detrended fluctuation analysis (DFA). In all exoskeleton trials, subjects walked significantly slower than in their shoes. However, the COT was equivalent between shoes and both proportional myoelectric control trials. Subjects also increased medio-lateral balance control by increasing their mean step width and reducing both short-term variability and long-term auto-correlation for this parameter. In the second powered trial subjects returned to the levels of control over step width exhibited during regular shoe walking. During the unpowered condition subjects showed a significant association between step width regulation, walking speed, and COT. However, these parameters were not significantly associated when the assistance was turned on. Together, these results demonstrate that the response to assistance is closely related to the stepping strategy, especially in the initial stages of learning. [ABSTRACT FROM AUTHOR]

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a non-invasive human-machine interface. This approach is usually subject-specific, so that the training on one individual does not generalise to different subjects. In this paper, we propose a method based on Bidirectional Encoder Representation from Transformers (BERT) structure to predict the movement of hands from the root mean square (RMS) feature of the sEMG signal following $\mu $ -law normalization. The method was tested for within-subject and cross-subject conditions. We trained the model with two hard sample mining methods, Gradient Harmonizing Mechanism (GHM) and Online Hard Sample Mining (OHEM). The proposed method was compared with classic approaches, including long short-term memory (LSTM) and Temporal Convolutional Network (TCN) as well as a recent method called Long Exposure Convolutional Memory Network (LE-ConvMN). Correlation coefficient (CC), normalized root mean square error (NRMSE) and time costs were used as performance metrics. Our method (sBERT-OHEM) achieved state-of-the-art performance in cross-subject evaluation as well as high performance in subject-specific tests on the Ninapro dataset. The above tests are based on the same randomly selected 10 subjects. Generally, in the cross-subject situation, with the increasing of the subjects’ number, it unavoidably leads to the decline of the performance. While the performance of our method on 38 subjects was significantly higher than the other methods on 10 subjects in cross-subject conditions, which further verified the advantage of our methods. [ABSTRACT FROM AUTHOR]

In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity contributes to the phase measured at each voxel. Therefore, estimating velocity from potentially wrapped phase measurements is the task of solving a system of noisy congruence equations. We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can greatly exceed twice the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Correlations are directly estimated from multi-coil data without requiring knowledge of coil sensitivity maps, dephasing factors, or the actual per-voxel signal-to-noise ratio. Derivation of the estimator yields explicit probabilities of unwrapping errors and the probability distribution for the velocity estimate; this, in turn, allows for optimized design of the phase-encoded acquisition. These probabilities are also incorporated into spatial post-processing to further mitigate wrapping errors. Simulation, phantom, and in vivo results for three-point PC-MRI acquisitions validate the benefits of reduced estimation error, increased recovered velocity range, optimized acquisition, and fast computation. A phantom study at 1.5T demonstrates 48.5% decrease in root mean squared error using PRoM with post-processing versus a conventional “dual-venc” technique. Simulation and 3T in vivo results likewise demonstrate the proposed benefits. [ABSTRACT FROM AUTHOR]

The evaluation of the state of transformer oil-immersed insulation based on the traditional methods becomes unreliable due to the nonuniform aging effect. To address this issue, a novel model for accessing the aging information of transformer oil-immersed insulation at the hotspot is proposed. In this article, frequency-domain spectroscopy (FDS) is selected as the carrier for characterizing the aging states of oil-immersed insulation. The multiobjective function for reversing the FDS of the hotspot is constructed based on the dielectric response equivalent circuit. Then, the nondominated sorting genetic algorithm with the multiconstraint scheme is proposed to solve the multiobjective function, by which the FDS data of the transformer hotspot region are acquired. The verification results indicate that the average standard deviation between the computed FDS of the hotspot region and the measured data is less than 0.26. The contribution of this article is in the exploration of the proposed model as a potential tool to extract the FDS data of the hotspot region. This will promote a more reliable aging evaluation of the transformer oil-immersed paper insulation at the hotspot. [ABSTRACT FROM AUTHOR]

The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate knowledge of these settings to be difficult to obtain in practice. This paper addresses the problem of determining inverter reactive power control settings from net load advanced metering infrastructure (AMI) data. The estimation is first cast as fitting parameterized control curves. We argue for an intuitive and practical approach to preprocess the AMI data, which exposes the setting to be extracted. We then develop a more general approach with a data-driven reactive power disaggregation algorithm, reframing the problem as a maximum likelihood estimation for the native load reactive power. These methods form the first approach for reconstructing reactive power control settings of solar PV inverters from net load data. The constrained curve fitting algorithm is tested on 701 loads with behind-the-meter (BTM) PV systems with identical control settings. The settings are accurately reconstructed with mean absolute percentage errors between 0.425% and 2.870%. The disaggregation-based approach is then tested on 451 loads with variable BTM PV control settings. Different configurations of this algorithm reconstruct the PV inverter reactive power timeseries with root mean squared errors between 0.173 and 0.198 kVAR. [ABSTRACT FROM AUTHOR]

This work proposes to develop and evaluate a deep learning framework that jointly optimizes k-t sampling patterns and reconstruction for head and neck dynamic contrast-enhanced (DCE) MRI aiming to reduce bias and uncertainty of pharmacokinetic (PK) parameter estimation. 2D Cartesian phase encoding k-space subsampling patterns for a 3D spoiled gradient recalled echo (SPGR) sequence along a time course of DCE MRI were jointly optimized in a deep learning-based dynamic MRI reconstruction network by a loss function concerning both reconstruction image quality and PK parameter estimation accuracy. During training, temporal k-space data sharing scheme was optimized as well. The proposed method was trained and tested by multi-coil complex digital reference objects of DCE images (mcDROs). The PK parameters estimated by the proposed method were compared with two published iterative DCE MRI reconstruction schemes using normalized root mean squared errors (NRMSEs) and Bland-Altman analysis at temporal resolutions of $\Delta \text{t}$ = 2s, 3s, 4s, and 5s, which correspond to undersampling rates of R = 50, 34, 25, and 20. The proposed method achieved low PK parameter NRMSEs at all four temporal resolutions compared with the benchmark methods on testing mcDROs. The Bland-Altman plots demonstrated that the proposed method reduced PK parameter estimation bias and uncertainty in tumor regions at temporal resolution of 2s. The proposed method also showed robustness to contrast arrival timing variations across patients. This work provides a potential way to increase PK parameter estimation accuracy and precision, and thus facilitate the clinical translation of DCE MRI. [ABSTRACT FROM AUTHOR]

An improved particle swarm optimization (PSO) algorithm based on Gaussian distribution model is proposed to realize the autotuning of weighting factors for the cost function design in the model predictive control method. First, the design principle of the weighting factors in model predictive torque control for permanent magnet synchronous motor system is analyzed. Then, using the root mean square of the current error in the two-phase rotating coordinate system and the system switching frequency as references, the objective function of the particles in the PSO is designed by considering the main control goals of reducing the torque ripple, the current total harmonic distortion, and the switching frequency. The Gaussian individual optimal distribution model is used to update the particle position on the structure of the conventional PSO algorithm. The experimental results show that the proposed method can solve the problem of weighting factors design as it reduces the switching frequency of the system while achieving excellent steady-state performance. [ABSTRACT FROM AUTHOR]

In this paper, a model fusion method (MFM) is proposed for online state of charge (SOC) and state of power (SOP) co-estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs). Firstly, a particle swarm optimization-genetic algorithm (PSO-GA) method is cooperated with a 2-RCCPE fractional-order model (FOM) to construct battery open-circuit voltage (OCV)-SOC curve, which only relies on a part of dynamic load profile without the prior knowledge of an initial SOC. Secondly, a dual extended Kalman filter (DEKF) algorithm based on a 1-RC model is employed to identify the model parameters and estimate battery SOC with the extracted OCV-SOC curve. Furthermore, battery polarization dynamics in a SOP prediction window is analyzed from two aspects: (1) self-recovery; and (2) current excitation. They are separately simulated using 2-RCCPE FOM and 1-RC model, and then integrated through a model fusion for online SOP estimation, which enables an analytical expression of battery peak charge/discharge current in a prediction window without weakening the nonlinear characteristic of FOM. Experimental results demonstrate the improved performance of the proposed MFM for online discharge SOP estimation, where the mean absolute error and root mean square error are only 0.288 W and 0.35 W, respectively, under the urban dynamometer driving schedule profile. [ABSTRACT FROM AUTHOR]

This paper presents an enabling comprehensive vehicle stability assessment system covering vehicle longitudinal, yaw and roll stability. First, the longitudinal, lateral and vertical tire forces are separately estimated using the strong tracking unscented Kalman filter and the conventional Kalman filter based on low-cost on-board sensors. Then, a comprehensive vehicle longitudinal, yaw and roll stability space is established by utilizing the normalized tire friction ellipse and the Load Transfer Ratio value. Finally, the vehicle stability is determined and predicted based on current driver's inputs. The hardware-in-loop experimental results show that the proposed vehicle stability assessment system can accurately estimate the longitudinal, lateral and vertical tire forces with the normalized root mean square errors of 1.87 $\%$ , 1.07 $\%$ and 1.43 $\%$ , and exhibits satisfying performance for vehicle longitudinal, yaw and roll stability evaluation and prediction. This bears significance for the efficient functioning of active control systems to improve vehicle safety under critical driving conditions. [ABSTRACT FROM AUTHOR]

This article studies the mean standard deviation (mean-std) Canadian traveller problem (CTP). Different from the canonical CTP, which aims at minimizing the traveller's expected travel time, while considering edge breakdown probabilities, we introduce the reliability version of CTP, which tries to find a routing policy with the minimal linear combination of the travel time's mean and standard deviation. With the recent development of internet-of-things (IoT) technology, the transportation network's edges' travel-time statistics, i.e., mean and standard deviation, as well as the traversal probabilities, are available to the end users. With those information, we propose a dual dynamic programming (DDP) method, which simultaneously estimates the first-order and the second-order moments of a given decision-list (DL) policy, and thereby makes improvements towards to the optimal one through the generalized policy iteration (GPI) scheme. We construct an open source benchmark environment to evaluate the performance of different mean-std CTP solutions, and show that the DDP method outperforms state of the arts in a range of transportation networks. [ABSTRACT FROM AUTHOR]

A memristor is a passive circuit element that has numerous applications ranging from storage to processing. The attributes of memristor, such as low power, fast switching speed, and non-volatility, make it a promising candidate for computing applications. To deploy the memristors at the circuit level, a versatile, computationally efficient, and generic model is required to analyze the circuit performance. This article presents a computationally inexpensive, generic, and accurate phenomenological model of the memristor. The proposed model predicts an accurate current–voltage ($I$ – $V$) relationship based on the current conduction mechanism. The presented modeling technique can be incorporated into any practical memristor behavior by optimizing the fitting parameters. The model has the capability to optimize its accuracy and computational efficiency by calibrating the model parameters. The results are compared with the characterization data of numerous memristors and noteworthy models of memristors to validate the proposed approach. The results depict that the proposed model is more flexible, computationally efficient, versatile, and generic. It improves the simulation run time up to 24.47% with the relative root mean squared error of 0.4142%. The model exhibits remarkable results when compared with titanium dioxide-based devices. The proposed model can be deployed to various applications, such as logic design, memory design, and neuromorphic computing. [ABSTRACT FROM AUTHOR]

Estimating physiological parameters - such as blood pressure (BP) - from raw sensor data captured by noninvasive, wearable devices rely on either burdensome manual feature extraction designed by domain experts to identify key waveform characteristics and phases, or deep learning (DL) models that require extensive data collection. We propose the Data-Driven Guided Attention (DDGA) framework to optimize DL models to learn features supported by the underlying physiology and physics of the captured waveforms, with minimal expert annotation. With only a single template waveform cardiac cycle and its labelled fiducial points, we leverage dynamic time warping (DTW) to annotate all other training samples. DL models are trained to first identify them before estimating BP to inform them which regions of the input represent key phases of the cardiac cycle, yet we still grant the flexibility for DL to determine the optimal feature set from them. In this study, we evaluate DDGA's improvements to a BP estimation task for three prominent DL-based architectures with two datasets: 1) the MIMIC-III waveform dataset with ample training data and 2) a bio-impedance (Bio-Z) dataset with less than abundant training data. Experiments show that DDGA improves personalized BP estimation models by an average 8.14% in root mean square error (RMSE) when there is an imbalanced distribution of target values in a training set and improves model generalizability by an average 4.92% in RMSE when testing estimation of BP value ranges not previously seen in training. [ABSTRACT FROM AUTHOR]

In the past few decades, gas sensitive nanomaterials are usually deposited on specific area of the wafer by drop-casting or inkjet printing for application in gas sensor fabrication. However, wafer-level patterning of sensing materials with satisfactory reproducibility still encounters challenge. In this letter, we propose a facile ‘top-down’ strategy to manufacture wafer-scale gas sensing chips with high-throughput by photolithography using a well-mixed photoresist-nanomaterial suspension, followed by calcination. The fabricated gas sensors based on the proposed approach yield excellent reproducibility and uniformity of sensing response to ethanol detection with a relative standard deviation (RSD) <4.5 %, suggesting promising application for high-volume production of MEMS compatible gas sensors. [ABSTRACT FROM AUTHOR]

A framework that could achieve submeter-level unmanned aerial vehicle (UAV) horizontal navigation in multipath-free environments with cellular carrier phase measurements is developed. This framework exploits the "loose" synchronization between cellular base transceiver station (BTS) clocks. It is shown through extensive experimental data that the beat frequency stability of cellular BTSs approaches that of atomic standards and that the clock deviations can be realized as a stable autoregressive moving average model. This BTS clock model is referred to as loose network synchronization. A rule-of-thumb is established for clustering the clock deviations to minimize the position estimation error, while significantly reducing the computational complexity. The presented models allow the UAV to achieve sustained carrier phase-based meter- to submeter-accurate navigation. To demonstrate the efficacy of the developed framework, this article presents three UAV flight experiments in Southern California, USA, utilizing signals from different cellular providers transmitting at different frequencies. The three experiments took place in open, semiurban environments with nearly multipath-free, line-of-sight (LOS) conditions, in which the UAV traveled 1.72, 3.07, and 0.61 km, achieving a horizontal position root mean squared error of 36.61, 88.58, and 89.33 cm, respectively, with respect to the UAV's on-board navigation system. [ABSTRACT FROM AUTHOR]

Objective: Energy Expenditure (EE) estimation plays an important role in objectively evaluating physical activity and its impact on human health. EE during activity can be affected by many factors, including activity intensity, individual physical and physiological characteristics, environment, etc. However, current studies only use very limited information, such as heart rate and step count, to estimate EE, which leads to a low estimation accuracy. Methods: In this study, we proposed a deep multi-branch two-stage regression network (DMTRN) to effectively fuse a variety of related information including motion information, physiological characteristics, and human physical information, which significantly improved the EE estimation accuracy. The proposed DMTRN consists of two main modules: a multi-branch convolutional neural network module which is used to extract multi-scale context features from electrocardiogram (ECG) and inertial measurement unit (IMU) data, and a two-stage regression module which aggregated the extracted multi-scale context features containing the physiological and motion information and the anthropometric features to accurately estimate EE. Results: Experiments performed on 33 participants show that our proposed method is more accurate and the average root mean square error (RMSE) is reduced by 22.8% compared with previous works. Conclusion: The EE estimation accuracy was improved by the proposed DMTRN model with a well-designed network structure and new input signal ECG. Significance: This study verified that ECG was much more effective than HR for EE estimation and cast light on EE estimation using the deep learning method. [ABSTRACT FROM AUTHOR]

Long stroke nanopositioning stages with high natural frequency and compact size are demanded in precision engineering. However, in previous researches, the natural frequency of the long stroke nanopositioning stage is normally below 50 Hz because the actuated force, moving mass, stiffness, motion range, and damping ratio are not considered simultaneously and optimized thereafter. To solve this problem, a long stroke nanopositioning stage driven by the self-damping moving magnet actuator (SMMA) and supported by the flexure guide is integrated and designed in this article. The SMMA consists of two identical tooth-slot structure stators and one moving magnet which is embedded inside the flexure guide. Copper plates that functioned as eddy-current dampers are attached on the tooth surfaces to suppress the resonance peak at the natural frequency. An integrated dynamics model that consists of force, damping coefficient, and stiffness is established. The dimensional parameters of the proposed positioning stage are optimized to improve the system performances based on the developed model. Experimental results show that the natural frequency of the positioning stage reaches up to 71 Hz. The stage can achieve 20 nm positioning resolution within a motion range of $\pm$ 5 mm, and the root mean square tracking errors for 1 Hz sinusoidal and triangular commands are below 0.1% of the trajectory amplitudes. [ABSTRACT FROM AUTHOR]

In this article, we present a novel approach for forecasting significant wave heights in oceanic waters. We propose an algorithm based on the WaveWatch III, differencing, and a transformer neural network (Transformer). The data becomes stationary after first-order differencing, performed with the observed significant wave height and the wave height forecasts obtained from WaveWatch III. We perform a case study on a group of 92 buoys using WaveWatch III hindcasts. The Transformer model then provides the statistical forecasts of the residuals. The Transformer-based proposed framework obtains the root mean square error of 0.231 m for two days ahead forecasting. Our proposed method outperforms existing state-of-the-art machine learning and numerical approaches for significant wave heights prediction. Our results suggest that combining numerical and machine learning approaches gives better performance than using either alone. [ABSTRACT FROM AUTHOR]

In offline data-driven multiobjective optimization, no new data are available during the optimization process. Approximation models, also known as surrogates, are built using the provided offline data. A multiobjective evolutionary algorithm can be utilized to find solutions by using these surrogates. The accuracy of the approximated solutions depends on the surrogates and approximations typically involve uncertainties. In this article, we propose probabilistic selection approaches that utilize the uncertainty information of the Kriging models (as surrogates) to improve the solution process in offline data-driven multiobjective optimization. These approaches are designed for decomposition-based multiobjective evolutionary algorithms and can, thus, handle a large number of objectives. The proposed approaches were tested on distance-based visualizable test problems and the DTLZ suite. The proposed approaches produced solutions with a greater hypervolume, and a lower root mean squared error compared to generic approaches and a transfer learning approach that do not use uncertainty information. [ABSTRACT FROM AUTHOR]

The surface albedo of Arctic sea ice is a critical variable in the Earth’s energy budget, and various Arctic sea ice albedo datasets have been derived from remote sensing data. However, the influences of reflectance anisotropy and melt ponds were not well considered in previous studies. To improve the estimation accuracy and calculation efficiency, we developed a method for estimating the surface albedo of Arctic sea ice using an ensemble back-propagation neural network (EBPNN) model. A bidirectional reflectance distribution function (BRDF)/albedo dataset of snow, white ice, and melt ponds was constructed using the asymptotic analytical radiative transfer (AART) model, and the nonlinear relationships between the surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) and surface albedo were established using the EBPNN model (prediction correlation coefficient $(R)= 0.996$ and root mean square error (RMSE) = 0.023). Our results are consistent with the in situ measurements and the Medium Resolution Imaging Spectrometer (MERIS) product ($R= 0.916$ , RMSE = 0.085, and bias = 0.001), in which the reflectance anisotropy of the snow/ice and the influence of melt ponds were fully considered, and the calculation efficiency was significantly improved by using the EBPNN model. The results of this study provide new insights for estimating the Arctic sea ice albedo from satellite observations, and the proposed EBPNN method can be used to generate a long-term Arctic sea ice albedo dataset with superior estimation accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

The current regional-scale process-based photosynthesis models use biome-specified values of maximum carboxylation rate at 25 °C ($V_{m25}$) in simulating ecosystem gross primary productivity (GPP). These models ignore the variations in $V_{m25}$ over time and space, resulting in substantial errors in regional estimates of cropland GPP. Thus, to resolve this problem, we used the ensemble Kalman filter (EnKF) to assimilate tower-based GPP from five maize flux sites into a process-based mode to obtain the “apparent” value of $V_{m25}$ and then modeled this parameter using machine learning (ML) algorithms. The results showed that $V_{m25}$ increased during the early growing season and then decreased after reaching a peak value in the middle of the growing season. The coefficient of determination ($R^{2}$) root mean square error (RMSE) for satellite-driven coupled photosynthesis and evapotranspiration simulator (SCOPES)-Crop with EnKF-derived varied $V_{m25}$ in simulating daily GPP across all site-days increased (decreased) by 0.17 (5.63 $\mu \text {mol}\,\text {m}^{-2}\,\text {s}^{-1}$) on average compared to that for the model with fixed $V_{m25}$. We used four ML algorithms, namely artificial neural network, random forest, extreme gradient enhancement, and convolutional neural network (CNN), to model the $V_{m25}$ of maize. The CNN algorithm yielded the best results. The average of the $R^{2}$ (RMSE) values of simulated GPP using CNN-based $V_{m25}$ over the three flux sites is 0.93 (1.95 $\mu \text {mol}\,\text {m}^{-2}\,\text {s}^{-1}$), higher (smaller) than that using fixed $V_{m25}$. This study implies that representing the seasonal variations in $V_{m25}$ can facilitate improved estimates of GPP and the ML methods are useful tools for modeling the variation in $V_{m25}$. [ABSTRACT FROM AUTHOR]

Currently, an increasing number of technical systems are equipped with multiple cameras. Limited by cost and size, they are often restricted to a single hardware encoder. The combination of all views into a single superframe allows for streaming all camera views at the same time, but it prevents individual rate/quality adaptations on those camera views. We propose a preprocessing filter concept that allows for individual rate/quality adaptation while using a single encoder. Additionally, we create a preprocessor model that estimates the required preprocessing filter parameters from the specified encoding parameters. This means our approach can be used with any existing multi-view adaptation scheme designed for controlling multiple encoders. We design both an analytical and a Machine Learning-based bitrate model. Because both models perform equally well, we suggest using either one as the core part of our preprocessor model. Both models are specifically designed for estimating the influence of the quantization parameter, frame rate, frame size, group of pictures length, and a Gaussian low-pass filter on the video bitrate. Furthermore, the rate models outperform state-of-the-art bitrate models by at least 22% regarding the overall root mean square error. Our bitrate models are the first of their kind to consider the influence of a Gaussian low-pass filter. We evaluate the preprocessing approach by streaming six camera views in a teledriving scenario with a single encoder and compare it to using six individual encoders. The experimental results demonstrate that the preprocessing approach achieves bitrates similar to the individual encoders for all views. While achieving a comparable rate and quality for the most important views, our approach requires a total bitrate that is 50% smaller than when using a single encoder approach without preprocessing. [ABSTRACT FROM AUTHOR]

With the development of altimeters’ retracking algorithms, the signal-to-noise ratio of altimeter data in coastal regions has been improved significantly in recent years. However, due to the complexity of coastal waves, the traditional satellite-buoy/satellite-satellite collocation method using a spatial-temporal window, which is widely used in the evaluation of significant wave height (SWH) measurements of altimeters in the open ocean, is not reliable because coastal waves can vary significantly even in a small window. This makes it difficult to quantitatively evaluate the errors of altimeter coastal SWH observations. This study proposes an automated dynamic collocation method to solve this problem. The method combines SWH from coastal buoy observations and numerical wave model (NWM) outputs to generate an SWH reference field. The coastal altimeter observations can then be compared with this reference dataset. To test the effectiveness of the method, the SWH data from Sentinel-3A (S3A) synthetic aperture radar (SAR) mode in the coastal region of southwest England were evaluated. The results indicate that the coastal SWHs from the S3A SAR mode is good quality with an overall root mean square error of about 0.3 m when the offshore distance is more than 10 km. The results also show that the presented method works better than previous methods for the evaluation of coastal altimeter SWH observations. This method can be applied in future studies to evaluate and improve the performance of other coastal remotely sensed SWH observations. [ABSTRACT FROM AUTHOR]

Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way into the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep neural network-based unmixing model with transformers. A transformer network captures nonlocal feature dependencies by interactions between image patches, which are not employed in convolutional neural network (CNN) models, and hereby has the ability to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, that is, Samson, Apex, and Washington DC Mall, and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at https://github.com/preetam22n/DeepTrans-HSU. [ABSTRACT FROM AUTHOR]

Accurate and high-frequency sea-level monitoring is of great importance in ocean environments and global climatic studies, but traditional techniques have their respective limitations. In the last decades, the application of Global Navigation Satellite System Multipath Reflectometry (GNSS-MR) in sea-level monitoring has developed rapidly. Recently, more available GNSS signals are expected to bring new opportunities to improve its performance and achieve high spatial–temporal resolution. In this article, a new algorithm is developed to optimize the method of multi-GNSS multipath reflectometry and improve the precision and sampling rate for GNSS-MR sea-level monitoring. In order to make full use of the short-term multipath oscillation information, a sliding window is used to collect the signal-to-noise ratio (SNR) sequences. A weighted iterative least-square method is introduced to combine the selected SNR observations of GPS, GLONASS, Galileo, and BDS systems and retrieve sea level at 10-min intervals at BRST station for one year. A novel index called local kurtosis (LK) is proposed, which can be used to evaluate the quality of the Lomb–Scargle periodogram (LSP) and design the weight matrix in the least-square combinatorial process. Compared to using individual signals, the optimized combination algorithm decreased the root mean square error (RMSE) by 78%, from 0.610 to 0.134 m, and increased the correlation coefficient $R^{2}$ from 0.851 to 0.992. In addition, the tidal constituents monitored by multi-GNSS-MR and tide gauge are highly consistent, demonstrating that the multi-GNSS-MR can accurately retrieve daily and subdaily tidal constituents of periods longer than 10 min. [ABSTRACT FROM AUTHOR]

Lake ice thickness (LIT), a thematic product of lakes as an essential climate variable (ECV), is sensitive to changes in air temperature and on-ice snow mass. Here, a novel and efficient analytic method (retracking approach) is presented for the estimation of LIT from Ku-band (13.6 GHz) radar altimetry data. The new retracker, referred to as ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ , is based on the physical modeling of the conventional radar echoes (also called low-resolution mode or LRM) over ice-covered lakes that show a characteristic step-like feature in their leading edge attributed to the reflection of radar waves at the snow–ice and ice–water interfaces. The method is applied to Jason-2 and Jason-3 data acquired over Great Slave Lake, Canada, over three ice seasons (2013–2016). As expected, the agreement between the Jason-2 and Jason-3 LIT estimates over their overlapping period (2016 ice season) is excellent with a mean bias error of 0.013 m and root mean square error (RMSE) of 0.024 m. LIT estimates from ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ are in good agreement with simulations from a thermodynamic lake ice model and in situ measurements with RMSE values of the order of a few centimeters for the three winter seasons. The retracker also provides a robust way to assess the accuracy of LIT estimates which is in the order of 0.10 m when the ice cover is well established and prior to melt onset. In addition, ${{\mathtt {LRM}}\_{}{\mathtt {LIT}}}$ captures the seasonal transitions during the freeze-up and breakup periods and ice growth over different winter seasons, making it a promising method for monitoring inter-annual variability and trends in LIT from past and current conventional radar altimetry missions. [ABSTRACT FROM AUTHOR]

The accurate assessment of lightning-induced over-voltages is essential for proper insulation coordination studies. In this paper, a novel machine learning based Gaussian process regression model is developed for the prediction of lightning-induced over-voltages considering wide range of ground resistivity and permittivity. The lightning-induced over-voltages have been computed using two-dimensional finite-difference time-domain technique associated with Agrawal field to line coupling model. To account for the input space variability, seven predictor variables namely: resistivity, permittivity, return stroke velocity, distance from flashpoint, return stroke peak current, front-time, and height of an overhead line are considered to determine the response variable, that is, lightning-induced over-voltages. Accordingly, the training of the Gaussian process regression model is carried out using lightning-induced over-voltages obtained from two-dimensional finite-difference time-domain technique for both first and the subsequent strokes. The estimation accuracy of model is appraised using root mean squared error indicating that the exponential kernel function has the least error than other kernel types for describing the covariance. The predictive performance is evaluated by corroborating the predictions from the model against the random test cases generated from two-dimensional finite-difference time-domain technique. The predicted results showed a good agreement with those obtained from finite-difference time-domain technique. [ABSTRACT FROM AUTHOR]

A generalized, reduced-size machine learning architecture for single-snapshot amplitude-only direction finding (AODF) is proposed for uniform circular arrays. A method for reusing angle of arrival (AoA) estimation models that are accurate over narrower fields of view is described. The efficacy of the proposed method is demonstrated using an ultrawideband circular array of miniaturized transverse electromagnetic (TEM) horns covering 1.5–5.5 GHz. Reasonable azimuth estimations performed on this retrofitted system are obtained over 2.6:1 bandwidth (1.5–4.0 GHz). Antenna performance features that impact the accuracy of AODF are also recognized. Root mean square error less than 5° is achieved in simulation above 15 dB signal-to-noise ratio (SNR) and above 20 dB SNR in measurement. The improved accuracy over the conventional correlation method of 52%–85% is demonstrated in an SNR domain of 10–40 dB. This performance improvement is obtained while maintaining a footprint reduction of 80%–95%, and an AoA estimation time speed-up of at least 85%. [ABSTRACT FROM AUTHOR]

Distribution systems are becoming more active and provide more power support for transmission systems via their connection points at the transmission-distribution (T-D) interfaces. This paper proposes a chance constrained optimization model to estimate the flexibility area at a T-D interface. In this model, network flexibility is for the first time involved and facilitated by introducing power flow routers (PFRs) into the distribution system. The chance-constrained flexibility area (CCFA) given by the proposed model provides a description of the aggregated power dispatchability of distribution system with the guarantee of low risks of voltage violations under renewable uncertainties. We exploit the mechanism of network flexibility and reveal that it significantly reduces the voltage standard deviations, which cannot be achieved by node power flexibility. To evaluate the CCFA, an efficient two-layer iteration algorithm is designed. The inner layer finds the boundary point of CCFA along a given direction for active and reactive power. The outer layer determines those key directions to be included in the estimation as the corresponding points capture the features of the CCFA boundary. Case studies show that PFRs significantly enlarge the CCFA, which reveals that network flexibility is an important supplement to node power flexibility in T-D interaction. [ABSTRACT FROM AUTHOR]

Studies have shown that there is a tight connection between cognition skills and brain morphology during infancy. Nonetheless, it is still a great challenge to predict individual cognitive scores using their brain morphological features, considering issues like the excessive feature dimension, small sample size and missing data. Due to the limited data, a compact but expressive feature set is desirable as it can reduce the dimension and avoid the potential overfitting issue. Therefore, we pioneer the path signature method to further explore the essential hidden dynamic patterns of longitudinal cortical features. To form a hierarchical and more informative temporal representation, in this work, a novel cortical feature based path signature neural network (CF-PSNet) is proposed with stacked differentiable temporal path signature layers for prediction of individual cognitive scores. By introducing the existence embedding in path generation, we can improve the robustness against the missing data. Benefiting from the global temporal receptive field of CF-PSNet, characteristics consisted in the existing data can be fully leveraged. Further, as there is no need for the whole brain to work for a certain cognitive ability, a top ${K}$ selection module is used to select the most influential brain regions, decreasing the model size and the risk of overfitting. Extensive experiments are conducted on an in-house longitudinal infant dataset within 9 time points. By comparing with several recent algorithms, we illustrate the state-of-the-art performance of our CF-PSNet (i.e., root mean square error of 0.027 with the time latency of 518 milliseconds for each sample). [ABSTRACT FROM AUTHOR]

In this article, a temperature-dependent threshold voltage extraction method based on noise measurement is proposed. This method induces a mathematical solution for acquiring channel temperatures ($T$) of transistors in the saturation region to extract threshold voltages. In order to gain accurate channel temperature, a small-signal equivalent circuit model was established to determine transconductance ($g_{m}$). Then, the extracted transconductance and measured noise power spectral density (NPSD) were used to obtain channel temperatures. On this basis, the temperature-dependent threshold voltage ($V_{\mathrm {th}}$) was derived. To validate the reliability of this method, a four-Fin six-finger N-FinFET and a four-Fin eight-finger N-FinFET were fabricated using the 14-nm bulk FinFET technology. In this study, the scattering parameters were obtained from 0.2 to 40.2 GHz. Also, the NPSDs were measured from 1 to 500 Hz, while gate voltage and drain voltage were set as 0.3, 0.49, and 0.75 V. Besides, the transistor $I_{\mathrm {ds}}$ – $V_{\mathrm {ds}}$ characteristics was also investigated when drain–source bias voltage changed from 0.05 to 0.9 V. Compared with the measurement results, the calculation results of drain–source current model using temperature-dependent threshold voltage achieved higher accuracy. Also, the root mean square errors (RMSEs) were less than 0.60% for four-Fin six-finger N-FinFET and less than 0.51% for four-Fin eight-finger N-FinFET. Therefore, the proposed extraction method can be applied to accurately predict transistor temperature-dependent threshold voltage. [ABSTRACT FROM AUTHOR]

Objective: Assessment of motor severity in cerebellar ataxia is critical for monitoring disease progression and evaluating the effectiveness of therapeutic interventions. Though wearable sensors have been used to monitor gait tasks in order to enable frequent assessment, existing solutions only estimate gait performance severity rather than comprehensive motor severity. In this study, we propose a new approach that analyzes sub-second movement profiles of the lower-limbs during gait to estimate overall motor severity in cerebellar ataxia. Methods: A total of 37 ataxia subjects and 12 healthy subjects performed a 5 m walk-and-turn task with two ankle-worn inertial sensors. Lower-limb movements were decomposed into one-dimensional sub-movements, namely movement elements. Supervised regression models trained on data features of movement elements estimated the Brief Ataxia Rating Scale (BARS) and its sub-scores evaluated by clinicians. The proposed models were also compared to models trained on widely-accepted spatiotemporal gait features. Results: Estimated total BARS showed strong agreement with clinician-evaluated scores with $r^2$ = 0.72 and a root mean square error of 2.6 BARS points. Movement element-based models significantly outperformed conventional, spatiotemporal gait feature-based models. Conclusion: The proposed algorithm accurately assessed overall motor severity in cerebellar ataxia using inertial data collected from bilaterally-placed ankle sensors during a simple walk-and-turn task. Significance: Our work could support fine-grained monitoring of disease progression and patients’ responses to medical/clinical interventions. [ABSTRACT FROM AUTHOR]

Objective: Using a musculoskeletal modelling framework, we aimed to (1) estimate knee joint loading using static optimization (SO); (2) explore different calibration functions in electromyogram (EMG)-informed models used in estimating knee load; and (3) determine, when using an EMG-informed stochastic method, if the measured joint loadings are solutions to the muscle redundancy problem when investigating only the uncertainty in muscle forces. Methods: Musculoskeletal models for three individuals with instrumented knee replacements were generated. Muscle forces were calculated using SO, EMG-informed, and EMG-informed stochastic methods. Measured knee joint loads from the prostheses were compared to the SO and EMG-informed solutions. Root mean square error (RMSE) in joint load estimation was calculated, and the muscle force ranges were compared. Results: The RMSE ranged between 192-674 N, 152-487 N, and 7-108 N for the SO, the calibrated EMG-informed solution, and the best fit stochastic result, respectively. The stochastic method produced solution spaces encompassing the measured joint loading up to 98% of stance. Conclusion: Uncertainty in muscle forces can account for total knee loading and it is recommended that, where possible, EMG measurements should be included to estimate knee joint loading. Significance: This work shows that the inclusion of EMG-informed modelling allows for better estimation of knee joint loading when compared to SO. [ABSTRACT FROM AUTHOR]

Nowadays, the dual closed loop proportional-integral-lead (PI-Lead) controller is widely used, especially for the servo systems where high performance is required for motion control, like lithography machines and vehicle-used Lidars. Considering complex industrial applications and working conditions, more comprehensive and systematic research about the structure and potential problem of the PI-Lead controller is necessary for enhancing its performance and robustness. In this article, the failure mechanism of the classic antiwindup methods in the PI-Lead controller is analyzed. By adjusting the location of control blocks, a novel reversed structure based PI-Lead controller is proposed as a complement to the traditional antiwindup methods for effective operation and to make the system stable under severe impact disturbances. Then, a self-commissioning strategy is designed based on a fast root mean square error (FRMSE) index and without additional manual tuning factors. Compared with classic indicators, the proposed FRMSE index can achieve faster instability detection and accelerate the tuning process to protect the equipment. [ABSTRACT FROM AUTHOR]

With the development of speech synthesis technology, the existing synthetic speech detection (SSD) methods cannot generalize well for unknown synthesis algorithms. And thus, this kind of speech forensics task meets a great challenge and has attracted great enthusiasm. We observe that the process of speech synthesis always includes the resampling and pooling/smoothing operations, which will change the speech’s local autoregressive (AR) and statistic distribution. In this paper, based on AR modeling and standard deviation statistics, we propose novel front-end speech features, i.e., ARS in short form, as the input of an SSD classifier. In addition, a new back-end classifier is constructed based on the dense convolution and short connection, and we name it scDenseNet. Experimental results on the ASVspoof2019 logical access (LA) dataset demonstrate that the ARS has a strong representation and sensitivity to spoofing attacks, and achieves promising performance on SSD. The proposed scDenseNet outperforms the previous version DenseNet on both EER and t-DCF scores, and achieves the best performance when compared with other state-of-the-art classifiers studied in this paper. Furthermore, based on the proposed scDenseNet, incorporating ARS with popular features such as the linear frequency cepstral coefficients (LFCC) significantly enhances the fusion performance and yields an EER score of 0.98%. [ABSTRACT FROM AUTHOR]

Aiming to provide feasible solutions for the realization of the robust and natural myoelectric control systems, a novel myoelectric control scheme supporting gesture recognition and muscle force estimation is proposed in this study. Eleven grasping gestures abstracted from daily life are selected as the target gesture set. The high-density surface electromyography (HD-sEMG) of the forearm flexor and the grasping force signal are collected simultaneously. The synchronous prediction of gesture category and instantaneous force is realized by the multi-task learning (MTL) technique. Especially, a post-processing algorithm based on threshold method is conducted to overcome the influence of force variation on the accuracy of gesture recognition. The experimental results show that the proposed post-processing method can decrease the classification error significantly. Specifically, the overall gesture classification error is reduced by 27 ~ 30 percent compared with not using the post-processing method; and 16 ~ 24 percent compared with using classical post-processing methods. The whole scheme can realize the synchronous gesture recognition and force estimation with 9.35 ± 11.48% gesture classification error and 0.1479 ± 0.0436 root-mean-square deviation force estimation accuracy. Meanwhile, it is feasible in different number of electrodes and well meets the real-time requirement of the EMG control system in response time delay (about 28.22 ~ 113.16ms on average). The proposed framework provides the possibility for myoelectric control supporting synchronous gesture recognition and force estimation, which can be extended and applied in the fields of myoelectric prosthesis and exoskeleton devices. [ABSTRACT FROM AUTHOR]

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifiable respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respiratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respiratory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low-frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings. [ABSTRACT FROM AUTHOR]

This work investigates a multibeam system for joint sensing and communication (JSC) based on multiple-input multiple-output (MIMO) 5G new radio (NR) waveforms. In particular, we consider a base station (BS) acting as a monostatic sensor that estimates the range, speed, and direction of arrival (DoA) of multiple targets via beam scanning using a fraction of the transmitted power. The target position is then obtained via range and DoA estimation. We derive the sensing performance in terms of probability of detection and root mean squared error (RMSE) of position and velocity estimation of a target under line-of-sight (LOS) conditions. Furthermore, we evaluate the system performance when multiple targets are present, using the optimal sub-pattern assignment (OSPA) metric. Finally, we provide an in-depth investigation of the dominant factors that affect performance, including the fraction of power reserved for sensing. [ABSTRACT FROM AUTHOR]

In this work, we study the combination of adaptive filters in colored noise environments. First, a combination framework using delayed weights is introduced to tackle the colored noise. Based on this, delayed convex and affine combinations of two LMS filters are developed, resulting in the so-called Dcvx-LMS and Daff-LMS algorithms. Then, the convergence behaviors of the two algorithms are investigated using standard mean-square deviation analysis. In addition, to speed up the convergence and reduce the computational complexity, we propose delayed combination with periodic feedback, delayed combined-step-size and block implementation methods. Finally, simulation results demonstrate the superiority of our algorithms over previously reported techniques in the presence of colored noise. [ABSTRACT FROM AUTHOR]

Limited by manufacturing technology, in moving-base gravity gradiometry, accelerometer mounting errors and mismatch cause a rotating accelerometer gravity gradiometer (RAGG) to be susceptible to its own motion. In this study, we comprehensively consider accelerometer mounting errors, accelerometer linear scale factors imbalances, accelerometer second-order error coefficients, RAGG internal movement, and construct three RAGG models—namely, a numerical model, an 54 parameters analytical model, and a 25 parameters simplified analytical model. Moreover, by setting the gravitational gradient excitation of RAGG to zero, the RAGG motion error model is derived. The analytical model and motion error model are used to interpret the error propagation mechanism and develop RAGG technical solutions, such as motion error compensation, fault diagnosis, etc. The numerical model is used to facilitate the verification of the RAGG analytical model and RAGG technical solutions. We performed multifrequency gravitational gradient simulation experiment and dynamic sweep frequency experiments to verify the three RAGG models, and evaluate the dynamic noise floor of the analytical models. In dynamic experiments, standard deviation of RAGG vertical specific force grows from 10 to 40 mg (1 g = 9.8 m/s2); that of the lateral specific force grows from 5 to 20 mg; the noise density of the analytical model remains stable within 2.5 E/✓ Hz, and that of the simplified analytical model is in the range of 2.64–41.27 E/✓Hz. [ABSTRACT FROM AUTHOR]

Arcing faults are a major problem in the electrical networks of aircraft. Installing a protection system, activated when a fault is detected, is one of the most effective solutions for avoiding the fires and accidents that they cause. Thanks to recent advances in artificial intelligence (AI), we can now envisage the development of reliable detectors that meet aeronautical specifications. However, in order to train an AI unit, it is necessary to create a large database of arc signals. This article proposes a method for generating thousands of arc signals, using the state-space representation formalism, according to Andrea’s arc model. We will show how it is possible to simulate arc-fault signals by using the studies of two cases of arc faults. We will compare the results of the simulations with experimental measurements in order to demonstrate that the arc signals obtained are identical to those of the experiment with root-mean-square deviation errors of 0.048 V and 0.005 A. [ABSTRACT FROM AUTHOR]

The theoretical temporal resolution limit of silicon (Si) image sensors is 11.1 ps. The super temporal resolution (STR) is defined as a frame interval less than this limit. The temporal resolution of burst image sensors is significantly affected by “mixing” of signal electrons traveling from different starting positions. For example, the penetration depths of photons incident to a surface of a photodiode (PD) disperse exponentially, resulting in mixing of the photoelectrons from different depths at the other end, which causes a distribution of the arrival times. The temporal resolution is proportional to the standard deviation of the arrival time. Toward STR imaging, this article proposes measures to suppress various mixing phenomena during the travel of the signal electrons: 1) a germanium (Ge) PD for visible light to practically eliminate the effect of vertical mixing caused by the distribution of the penetration depth; 2) an inverted pyramid PD to efficiently suppress the horizontal mixing, keeping a 100% fill factor; 3) a standard column PD with negative potential on the vertical walls to squeeze the trajectory bundle of electrons falling on the guide gate on the front side; and 4) a resistive guide gate to linearize the potential profile to maximize the horizontal drift velocity to minimize the mixing together with the squeezed electron bundle. [ABSTRACT FROM AUTHOR]

State-of-the-art exoskeletons are typically limited by the low control bandwidth and small-range stiffness of actuators, which are based on high gear ratios and elastic components (e.g., series elastic actuators). Furthermore, most exoskeletons are based on discrete gait phase detection and/or discrete stiffness control, resulting in discontinuous torque profiles. To fill these two gaps, we developed a portable, lightweight knee exoskeleton using quasi-direct-drive (QDD) actuation that provides 14 N·m torque (36.8% biological joint moment for overground walking). This article presents 1) stiffness modeling of torque-controlled QDD exoskeletons and 2) stiffness-based continuous torque controller that estimates knee joint moment in real-time. Experimental tests found that the exoskeleton had a high bandwidth of stiffness control (16 Hz under 100 N·m/rad) and high torque tracking accuracy with 0.34 N·m root mean square error (6.22%) across 0–350 N·m/rad large-range stiffness. The continuous controller was able to estimate knee moments accurately and smoothly for three walking speeds and their transitions. Experimental results with eight able-bodied subjects demonstrated that our exoskeleton was able to reduce the muscle activities of all eight measured knee and ankle muscles by 8.60%–15.22% relative to the unpowered condition and two knee flexors and one ankle plantar flexor by 1.92%–10.24% relative to the baseline (no exoskeleton) condition. [ABSTRACT FROM AUTHOR]

A spectral decomposition method has been implemented to identify and quantify isotopic source terms in high-resolution gamma-ray spectroscopy in static geometry and shielding scenarios. Monte Carlo simulations were used to build the response matrix of a shielded high-purity germanium detector monitoring an effluent stream with a Marinelli configuration. The decomposition technique was applied to a series of calibration spectra taken with the detector using a multi-nuclide standard. These results are compared with decay-corrected values from the calibration certificate. For most nuclei in the standard (241Am, 109Cd, 137Cs, and 60Co), the deviations from the certificate values were generally no more than 6% with a few outliers as high as 10%. For 57Co, the radionuclide with the lowest activity, the deviations from the standard reached as high as 25%, driven by the meager statistics in the calibration spectra. In addition, a complete treatment of error propagation for the technique is presented. [ABSTRACT FROM AUTHOR]

To introduce the interference aggregation effect into the emission-limit-setting model, aggregate interference power arising from randomly distributed disturbance sources is studied considering the near- to far-field propagation of radiated disturbance. It is assumed that point sources are uniformly distributed in a two-dimensional model and that the distance to the victim receiver ranges from the near-field region to the far-field region. A piecewise approximation is applied to the distance attenuation curve with a corner distance that indicates the border between the near and far fields. The mean value, standard deviation, and probability density function (PDF) of the decibel-scaled aggregate interference power are theoretically and numerically analyzed at various source densities. The results show that the shape of the PDF markedly changes as the source density increases because of the mixed contribution of disturbing sources both from the near and far fields. Accordingly, the variations in the mean value and standard deviation of the aggregate interference power are strongly dependent on the distance attenuation at which major interfering sources are found. When introducing the interference aggregation effect into the emission requirements, both the distance attenuation characteristics and the estimated source density in a real environment should be carefully considered. [ABSTRACT FROM AUTHOR]

Simultaneous towed-streamer marine acquisition has the advantages of reducing the total time requirements and costs of surveys. However, the seismic records obtained are blended seismic data, so the successful deblending of such data is the key to this method. In this article, we propose an effective deblending method with a new thresholding operator based on the shaping regularization framework in the contourlet domain. The new thresholding operator consists of an adaptive Bayesian threshold and a new thresholding function. Because of its multiresolution, locality, and directionality properties, the contourlet transform can effectively capture geometrical structures, which are the main features in natural images. To make the traditional Bayesian threshold adaptive in the contourlet domain, we propose a scale adjustment factor, a direction adjustment factor, and an attenuation factor to modify the threshold, and we also adopt local adaptive elliptic windows to estimate the standard deviations of useful signals; eventually, we obtain an adaptive Bayesian threshold. Furthermore, the new thresholding function can overcome the shortcomings of the existing soft and hard thresholding functions. Experimental results demonstrate that our method can effectively separate blended data. [ABSTRACT FROM AUTHOR]

Highly accuracy classification and mapping of seafloor hydrothermal fields provides an important addition to research into the geological background and exploration of seafloor massive sulfides (SMS). Currently, acoustic remote sensing using multibeam sounding systems (MBES) is the primary means of achieving large-scale seafloor classification. However, the characteristics of complex topography, complex seafloor distribution, and deep water depth of the hydrothermal fields make it difficult to obtain accurate high-resolution seafloor classification maps using shipboard MBES surveys. Here, a seafloor classification strategy combining shipboard MBES and autonomous underwater vehicle (AUV) sidescan sonar data is proposed. First, a downscaling model is established, which downscales the MBES backscatter mosaic (12-kHz and 10-m resolution) to a resolution of 2 m. The second step performs feature extraction of the downscaled MBES backscatter mosaic (12-kHz and 2-m resolution), the AUV sidescan sonar backscatter mosaic (150-kHz and 2-m resolution), and a seafloor digital elevation model (2-m resolution). Finally, a deep neural network model was built for training and classification. To evaluate the classification performance of the model, the method was applied to the survey of the Duanqiao hydrothermal field. Results were verified using field data (deep-tow video). The overall root mean square error and coefficient of determination ($R^{2}$) for the classification were 0.032 and 0.840, respectively. The experimental results show that the method can effectively meet the challenges to seafloor classification presented by complex seafloor distribution, and can obtain an accurate high-resolution seafloor classification map. [ABSTRACT FROM AUTHOR]