Search

Between the Boxer Movement and the 1911 Revolution, missionaries in China confronted distinct social challenges compared to their counterparts in home countries. In response, American missionary Young John Allen and his Chinese assistant, Van Yi, co-translated James Wideman Lee's The Making of a Man into Chinese as Ren Xue. This translation aimed to counter Spencer's social evolutionism, introduced by native intellectuals like Yan Fu, offering a reinterpretation of the relationships among individuals, nations, and God. Additionally, it sought to remap philosophy, evolutionism, ethics, and religion tailored for late Qing China. In contrast to clergymen in America and China, Chinese native intellectuals developed their unique reinterpretations and reshaping of philosophy, science, evolutionism, ethics, and religion for China. Through a cross-comparison of works by Yan Fu, Huxley, Spencer, Lee and Allen, this study explored the diverse responses to Spencer's evolutionary theory and related issues among the advocates of evolutionism, Chinese intellectuals, and clergies in America and China. [ABSTRACT FROM AUTHOR]

The path tracking controller often cannot maintain the lateral stability of vehicles on large curvy roads with low adhesion road, which is easy to cause vehicle instability. In this paper, an adaptive active disturbance rejection control (ADRC) is designed to generate a front wheel angle and an additional yaw moment at the same time to guarantee the vehicle tracking accuracy and vehicle stability at the same time. The vehicle two-degree-of-freedom reference model and the path tracking error model are built to derive the error dynamics state equation. The multiinput multioutput ADRC decoupling control is studied. The BP neural network is designed to online adjust the four output parameters that are difficult to be determined in the nonlinear state feedback to form an adaptive ADRC. The yaw moment distribution scheme is studied next. By the joint simulation and hardware-in-the-loop experiment, the results show that the proposed adaptive ADRC can obtain good vehicle tracking accuracy and vehicle stability at the same time not only on both high and low adhesion roads but on the road with large curvatures. [ABSTRACT FROM AUTHOR]

Ultrafast laser processing has recently emerged as a new tool for processing fiber-reinforced polymer (FRP) composites. In the astronautic industry, the modified epoxy resin (named 4211) and the modified cyanate ester resin (known as BS-4) are two of the most widely used polymers for polymer-based composites. To study the removal mechanism and ablation process of different material components during the ultrafast laser processing of FRPs, we isolated the role of the two important polymers from their composites by studying their femtosecond UV laser (260 fs, 343 nm) ablation characteristics for controllable machining and understanding the related mechanisms. Intrinsic properties for the materials' transmission spectrum, the absorption coefficient and the optical bandgap (Eg), were measured, derived, and compared. Key parameters for controllable laser processing, including the ablation threshold (Fth), energy penetration depth (δeff), and absorbed energy density (Eabs) at the ablation threshold, as well as their respective "incubation" effect under multiple pulse excitations, were deduced analytically. The ablation thresholds for the two resins, derived from both the diameter-regression and depth-regression techniques, were compared between resins and between techniques. An optical bandgap of 3.1 eV and 2.8 eV for the 4211 and BS-4 resins, respectively, were obtained. A detectable but insignificant-to-ablation difference in intrinsic properties and ablation characteristics between the two resins was found. A systematic discrepancy, by a factor of 30~50%, between the two techniques for deriving ablation thresholds was shown and discussed. For the 4211 resin ablated by a single UV laser pulse, a Fth of 0.42 J/cm2, a δeff of 219 nm, and an Eabs of 18.4 kJ/cm3 was suggested, and they are 0.45 J/cm2, 183 nm, and 23.2 kJ/cm3, respectively, for the BS-4 resin. The study may shed light on the materials' UV laser processing, further the theoretical modeling of ultrafast laser ablation, and provide a reference for the femtosecond UV laser processing characteristics of FRPs for the future. [ABSTRACT FROM AUTHOR]

Although the guided image filter (GIF) is an excellent edge-preserving filter, it generally suffers from halo artifacts due to the local property and the fixed regularization parameter. To address the problem, a weighted guided image filter (WGIF) was proposed by incorporating an edge-aware weighting into the GIF. In the filtering process, WGIF employs an averaging strategy for edge-aware weighting. Although the averaging strategy is a highly efficient method, it is susceptible to extreme values and tends to obscure critical factors, so it often leads to inaccurate results. Consequently, the output results quality of the WGIF is often degraded. To remedy the deficiency, a weighted guided image filter with entropy evaluation weighting (EEW-WGIF) is proposed in this paper. EEW-WGIF employs an edge-aware weighting strategy based on entropy evaluation method to detect edges more accurately, and incorporates an explicit constraint based on the gradient variation to better preserve edges. To verify the filtering effectiveness of the EEW-WGIF, it was applied to edge-preserving smoothing filtering, exposure images fusion, single image detail enhancement, structure-transferring filtering and image denoising. Experimental results show that the proposed filter can achieve excellent performance in both visual quality and objective evaluation. [Display omitted] • An edge-aware weighting strategy based on entropy evaluation method is proposed, which is more reliable in calculating the importance of each edge-aware factor. • The proposed EEW-WGIF incorporates an explicit constraint based on gradient variation and hyperbolic function to handle the edges so that the edges can be better preserved. • The EEW-WGIF was applied to edge-preserving smoothing filtering, exposure images fusion, single image detail enhancement, structure-transferring filtering and image denoising. [ABSTRACT FROM AUTHOR]

A novel marching on in time and Poggio-Miller- Chang-Harrington-Wu-Tsai (PMCHWT)-based time domain integral equation solver for analyzing transient scattering from composite multiregion structures comprising homogeneous penetrable volumes and perfectly conducting surfaces is presented. To render the marching on in time procedure stable, observer and source integrals are approximated via multipoint collocation and evaluated in closed form. The proposed scheme avoids the costly evaluation of five dimensional integrals required in full space-time Galerkin schemes, and proves to be stable for all targets investigated, including coated perfect electrically conducting surfaces and multiregion penetrable volumes. [ABSTRACT FROM AUTHOR]

The adaptive block size processing method in different image areas makes block-matching and 3D-filtering (BM3D) have a very good image denoising effect. Based on these observation, in this paper, we improve BM3D in three aspects: adaptive noise variance estimation, domain transformation filtering and nonlinear filtering. First, we improve the noise-variance estimation method of principle component analysis using multilayer wavelet decomposition. Second, we propose compressive sensing based Gaussian sequence Hartley domain transform filtering to reduce noise. Finally, we perform edge-preserving smoothing on the preprocessed image using the guided filtering based on total variation. Experimental results show that the proposed denoising method can be competitive with many representative denoising methods on the evaluation criteria of PSNR. However, it is worth further research on the visual quality of denoised images. [ABSTRACT FROM AUTHOR]

We analyzed a three-dimensional thermal-electrical coupled model based on COMSOL to characterize the thermal damage response of a woven carbon fiber reinforced polymer (CFRP) composite with and without a lightning strike protection (LSP) system. Fuzzy fiber (FF) fabric serving as a carbon-based protection layer was attached to the outermost ply of the CFRP composite to fabricate a fuzzy fiber reinforced polymer (FFRP) composite. CFRP and FFRP composites with temperature-dependent properties were inspected to predict lightning-induced damage resulting from a 20- and 40-kA peak current for 100 μs. The predicted area of thermal damage and the appearance of the composite surface agreed fairly well with post-lightning damage observed from experiments, thus demonstrating the credibility of the numerical model. LSP characteristics were evaluated for a range of CFRP composite properties in the outermost layer by enhancing the functional conductivity of the top layer in the in-plane and out-of-plane directions. The irreversible thermal damage region in the in-plane and thickness directions was dramatically mitigated by enhanced electrical conductivity, whereas a slight matrix decomposition damage change was observed by varying the thermal conductivity. Due to the increased electrical conductivity and integration of the lightweight FF carbon-based protection layer into the uppermost composite layer, the depth and area of damage can be limited by decreasing the thermal damage penetration through the underlying composites. These results reveal that a highly conductive FF layer may serve as a lightweight and effective anti-lightning strike layer for protecting the underlying composite. [ABSTRACT FROM AUTHOR]

In order to make the weighted guided image filter (WGIF) more fully utilize the edge direction of the image, a steering kernel weighted guided image filter (SKWGIF) is proposed by employing the steering kernel to learn the edge direction and incorporating the learning results into the WGIF. However, SKWGIF does not provide a good compromise between the two possibly contradictory objectives of edge-preserving and smoothing, where the image edges are inevitably smoothed. To overcome the drawback, a SKWGIF with gradient constraint (GC-SKWGIF) is proposed by introducing the gradient constraints into the SKWGIF. The gradient constraints allow the filter to take into account the gradient variation of the edges in the filtering process, and therefore the image edges can be better preserved. To verify the effectiveness of the proposed filter, the GC-SKWGIF is applied to edge-aware smoothing, tone mapping of high range dynamic images, image denoising and haze removal. Both theoretical analysis and experimental results show that the proposed filter can produce good resultant images. [Display omitted] • A new edge-aware weighting is defined by using local variance and local gradient magnitude of 3 × 3 windows of all pixels. • A steering kernel weighted guided image filter with gradient constraint (GC-SKWGIF) is proposed by considering the gradient variation at the edges and introducing the gradient constraint. • Experiments such as edge-aware smoothing, tone mapping of high dynamic images, image denoising and haze removal show that GC-SKWGIF has very outstanding performance. [ABSTRACT FROM AUTHOR]

The development of time-domain integral equation solvers with robust late-time stability properties has been a long-standing quest. Among the many methods that have been investigated, those leveraging quasi-exact integration techniques appear to be most successful. This article presents stable and accurate marching-on-in-time (MOT) solvers for time-domain electric, magnetic, and combined field integral equations (EFIE, MFIE, and CFIE) based on quasi-exact integration techniques. The novel MOT solvers exhibit excellent stability while yielding highly accurate results, as demonstrated by various numerical examples. In addition, the solvers’ excellent stability and accuracy properties are used to examine spurious modes encountered when time-domain integral equations are applied to closed surfaces. It is demonstrated that MOT solutions to the time domain EFIE and MFIE often are polluted by spurious modes at the cavity’s resonant frequencies whereas those of the CFIE solver are devoid of such contamination. [ABSTRACT FROM AUTHOR]

Clinical notes are comprehensive files containing explicit information about a patient's visit. However, accurately assigning medical codes from clinical documents can be a persistent challenge due to the complexity of clinical data and the vast range of medical codes. Moreover, the large volume of medical records, the noisy medical records, and the uneven quality of coders all negatively impact the quality of the final codes. Deep learning technology has recently been integrated into automatic International Classification of Diseases (ICD) coding tasks to improve accuracy. Nevertheless, the imbalanced class problem, the complexness of code associations, and the noise in lengthy records still restrict the advancement of ICD coding tasks in deep learning. Thus, we present the Note-code Interaction Denoising Network (NIDN) that employs the self-attention mechanism to pull critical semantic features in electronic medical records (EMRs). Our model utilizes the label attention mechanism for retaining code-specific text expression. We introduce Clinical Classifications Software coding for multitask learning, capturing the functional relationships of medical coding to oblige in model prediction. To minimize the impact of noise on model prediction and improve the label distribution imbalance, a denoising module is introduced to filter noise. Our practical consequences indicate that the model NIDN exceeds competitive models on a third version of Medical Information Mart for Intensive Care data set. [ABSTRACT FROM AUTHOR]

Despite significant progress made in image semantic segmentation, most research has primarily focused on daytime scenes. Semantic segmentation of nighttime images is equally critical for autonomous driving; however, this task poses more challenges due to inadequate lighting and difficulties associated with obtaining accurate manual annotations. In this paper, we propose a novel method called Latent Domain Knowledge Distillation (LDKD) for accurate nighttime semantic segmentation. Within a teacher-student framework, our LDKD combines domain alignment and knowledge distillation in a mutually reinforcing manner. First, we introduce a bidirectional photometric alignment module capable of generating daytime-like and nighttime-like latent images in real-time. This module effectively bridges the appearance gap between the source domain (daytime) and the target domain (nighttime). Second, we enhance collaboration between the teacher and student networks. The student network unifies image-level and feature-level domain alignment, while the teacher network generates reliable pseudo-labels by distilling knowledge from the latent domain. Furthermore, to account for potential noise in pseudo-labels, we propose a noise-tolerant learning method aimed at mitigating the risks associated with overreliance on pseudo-labels during model training. Extensive experiments on Dark Zurich and ACDC datasets show that our LDKD achieves state-of-the-art performance, demonstrating the effectiveness of our method for nighttime semantic segmentation. [ABSTRACT FROM AUTHOR]

Objectives: The present study is designed to investigate the impact of coronary angiography-derived index of microcirculatory resistance (caIMR) on left ventricular performance recovery.Background: IMR has been established as a gold standard for coronary microvascular assessment and a predictor of left ventricular recovery after ST-segment elevation myocardial infarction (STEMI). CaIMR is a novel and accurate alternative of IMR.Methods: The present study retrospectively included 80 patients with STEMI who underwent primary percutaneous coronary intervention (PCI). We offline performed the post-PCI caIMR analysis of the culprit vessel. Echocardiography was performed within the first 24 hours and at 3 months after the index procedure. Left ventricular recovery was defined as the change in left ventricular ejection fraction (LVEF) more than zero.Results: The mean age of the patients was 58.0 years with 80.0% male. The average post-PCI caIMR was 43.2. Overall left ventricular recovery was seen in 41 patients. Post-PCI caIMR (OR: 0.948, 95% CI: 0.916-0.981, p = 0.002), left anterior descending as the culprit vessel (OR: 3.605, 95% CI: 1.23-10.567, p = 0.019), and male (OR: 0.254, 95% CI: 0.066-0.979, p = 0.047) were independent predictors of left ventricular recovery at 3 months follow-up. A predictive model was established with the best cutoff value for the prediction of left ventricular recovery 2.33 (sensitivity 0.610, specificity 0.897, and area under the curve 0.765). In patients with a predictive model score less than 2.33, the LVEF increased significantly at 3 months.Conclusions: The post-PCI caIMR can accurately predict left ventricular functional recovery at 3 months follow-up in patients with STEMI treated by primary PCI, supporting its use in clinical practice. [ABSTRACT FROM AUTHOR]

In order to improve the intelligent vehicle lane-changing performance, an active lane-changing control algorithm is proposed considering the changes of road curvature and vehicle speed. Firstly, the vehicle dynamics model considering vehicle speed variation and lane-changing safety distance is established, and the expected lane-changing trajectory model under the curved road is designed simultaneously. Then, taking the yaw rate and longitudinal speed as the control objectives of lateral and longitudinal motions, respectively, the sliding-mode variable structure control method based on Lyapunov stability condition is adopted, and the trajectory tracking controller is designed by combining the inverted method to track the desired lane-changing trajectory. Finally, the lane-changing trajectory model and trajectory tracking controller are verified in simulation platform of CarSim/Simulink and hardware-in-the-loop (HIL) test bench. The results show that the proposed trajectory tracking control method can perform the lane-changing behavior well under different road curvatures and vehicle speeds while maintaining high trajectory tracking control accuracy. [ABSTRACT FROM AUTHOR]

In this article, a high-input power (>30 dBm) rectifier circuit for 2.45-GHz microwave wireless power transmission is presented. To overcome the shortcomings of the traditional Schottky rectifier circuit, the novel rectifier circuit is designed based on a GaAs field-effect transistor (FET) and a Schottky diode together as the rectification part. A system-level verification demonstrates that the novel rectifier circuit has a potential application value for microwave wireless power transmission system. Measurement results show that higher input power range and MW-dc power conversion efficiency are realized at the same time by this design scheme. The maximum MW-dc conversion efficiency of the novel rectifier circuit can reach 74.42% when the input power is 30 dBm, and it is 20% higher than the peak conversion efficiency of the traditional rectifier without FET. In addition, the proposed rectifier circuit can operate with over 50% efficiency when the input power varies from 16 to 31 dBm, and has a good performance with the value of load ranging from 70 to 730 Ω, it is mean that the novel rectifier circuit is able to provide power for some electronic devices with different impedance. [ABSTRACT FROM AUTHOR]

The interior resonance problem of time domain integral equations (TDIEs) formulated to analyze acoustic field interactions on penetrable objects is investigated. Two types of TDIEs are considered: The first equation, which is termed the time domain potential integral equation (TDPIE), suffers from the interior resonance problem, i.e., its solution is replete with spurious modes that are excited at the resonance frequencies of the acoustic cavity in the shape of the scatterer. Numerical experiments demonstrate that, unlike the frequency-domain integral equations, the amplitude of these modes in the time domain could be suppressed to a level that does not significantly affect the solution. This is achieved by increasing the numerical solution accuracy through the use of a higher-order discretization in space and the band limited approximate prolate spheroidal wave function with high interpolation accuracy as basis function in time. The second equation is obtained by linearly combining TDPIE with its normal derivative. The solution of this equation, which is termed the time domain combined potential integral equation (TDCPIE), does not involve any spurious interior resonance modes but it is not as accurate as the TDPIE solution at non-resonance frequencies. In addition, TDCPIE's discretization calls for treatment of hypersingular integrals. [ABSTRACT FROM AUTHOR]

Internal resonant modes are always observed in the marching-on-in-time (MOT) solution of the time domain electric field integral equation (EFIE), although “relaxed initial conditions,” which are enforced at the beginning of time marching, should in theory prevent these spurious modes from appearing. It has been conjectured that, numerical errors built up during time marching establish the necessary initial conditions and induce the internal resonant modes. However, this conjecture has never been proved by systematic numerical experiments. Our numerical results in this communication demonstrate that, the internal resonant modes' amplitudes are indeed dictated by the numerical errors. Additionally, it is shown that in a few cases, the internal resonant modes can be made “invisible” by significantly suppressing the numerical errors. These tests prove the conjecture that the internal resonant modes are induced by numerical errors when the time domain EFIE is solved by the MOT method. [ABSTRACT FROM PUBLISHER]

During the multi-attribute group decision-making (MAGDM) processing, the individuals often hold different opinions about the alternatives. It is necessary to aggregate the different individual opinions into a unified group opinion. In the real world, experts sometimes use linguistic expressions to evaluate attributes in uncertain environments. To address the problem of reducing the information loss of expert opinion aggregation in MAGDM, this paper proposes a MAGDM approach based on linguistic concept lattices in the context of uncertain linguistic expression. A linguistic concept lattice for multi-expert linguistic formal context is first constructed based on linguistic truth-valued lattice implication algebra, which can express both comparable and incomparable linguistic information in the decision-making process. Different expert opinions are aggregated via the extent of fuzzy linguistic concepts, which can reduce information loss in the aggregation process. Second, meet-irreducible elements in the linguistic concept lattice are introduced to reduce the computational complexity of obtaining all fuzzy linguistic concepts in the decision-making process. the distance between the intents of different fuzzy linguistic concepts is considered to enhance the rationality of linguistic decision results. In addition, the expert's decision-making process for each alternative is visualized via linguistic concept lattices. Finally, the case study and comparative analysis illustrate the validity and rationality of the proposed approach in MAGDM with linguistic information. • The fuzzy linguistic concepts are used to aggregate expert opinions. • The comparable and incomparable linguistic information is handled in MAGDM. • Visualize the decision-making process of experts by constructing concept lattices. • A novel linguistic concept lattice-based MAGDM approach is proposed. [ABSTRACT FROM AUTHOR]

Background: Clinical notes are documents that contain detailed information about the health status of patients. Medical codes generally accompany them. However, the manual diagnosis is costly and error-prone. Moreover, large datasets in clinical diagnosis are susceptible to noise labels because of erroneous manual annotation. Therefore, machine learning has been utilized to perform automatic diagnoses. Previous state-of-the-art (SOTA) models used convolutional neural networks to build document representations for predicting medical codes. However, the clinical notes are usually long-tailed. Moreover, most models fail to deal with the noise during code allocation. Therefore, denoising mechanism and long-tailed classification are the keys to automated coding at scale. Results: In this paper, a new joint learning model is proposed to extend our attention model for predicting medical codes from clinical notes. On the MIMIC-III-50 dataset, our model outperforms all the baselines and SOTA models in all quantitative metrics. On the MIMIC-III-full dataset, our model outperforms in the macro-F1, micro-F1, macro-AUC, and precision at eight compared to the most advanced models. In addition, after introducing the denoising mechanism, the convergence speed of the model becomes faster, and the loss of the model is reduced overall. Conclusions: The innovations of our model are threefold: firstly, the code-specific representation can be identified by adopted the self-attention mechanism and the label attention mechanism. Secondly, the performance of the long-tailed distributions can be boosted by introducing the joint learning mechanism. Thirdly, the denoising mechanism is suitable for reducing the noise effects in medical code prediction. Finally, we evaluate the effectiveness of our model on the widely-used MIMIC-III datasets and achieve new SOTA results. [ABSTRACT FROM AUTHOR]

This paper designs and experimentally demonstrates an electronic hardware architecture to measure impulse signals without resorting to high-speed analog-to-digital converters (ADCs). The proposed architecture is based on a frequency-domain approach. It consists of multiple channels to down-convert the input signal, each of which obtains a small portion of the input's spectrum. Overlapping bands are designated in between every two adjacent channels, so that multiple narrow-band outcomes can be accurately merged to construct the entire spectrum through postprocessing. The resultant complex-valued spectrum is equivalent to that where the impulses are directly measured in the time domain. The architecture in this paper resembles traditional superheterodyne receivers, and hence, its circuit implementation is simple and robust and has low cost. In our circuit implementation, all the components involved (like oscillators and mixers) are commercially available and have low costs. The circuit is tested by two types of input impulses, both having spectra centered at 2.1 GHz. The input signals' spectra (including both magnitude and phase) obtained by our circuit show excellent agreement with the data measured by a 40-GHz oscilloscope. When the input signal-to-noise ratio is as low as 2 dB, the output of our frequency-domain approach is equivalent to that of a 4-b high-speed ADC. [ABSTRACT FROM AUTHOR]

This article presents a low-cost circuit that generates narrow pulses with programmable waveform, amplitude, and position based on the spectrum stitching method. Specifically in this article, narrow pulses are generated through stitching the spectra of five subsignals, and the subsignals are generated by three regular arbitrary waveform generators with a bandwidth of 200 MHz. The experimental results of pulse generation with various waveforms, amplitude profiles, and position profiles are demonstrated. The pulses demonstrated in this article have a pulse width between 2 and 4 ns, with spectral coverage of about 500 MHz, which is well beyond the capacity of each of the three arbitrary waveform generators used in this work. The experimental results of this article indicate that the spectrum stitching technique has excellent potential to increase the effective bandwidth of arbitrary waveform generators using low-cost circuits. [ABSTRACT FROM AUTHOR]

Large-scale model sizes and expensive computing costs cause the challenge of deploying and applying large pre-trained models. Hence, this paper presents a novel Triple Concepts Attention Mechanism and a lightweight TCAMixer model for edge devices to classify texts. Furthermore, the TCAMixer abstracts textual concepts in a human way, which is unmatched by other counterparts such as pNLP-Mixer (a projection-based MLP-Mixer model for Nature Language Processing) and HyperMixer (a hyper network using dynamic token-mixing layers). Experimental results on several public datasets demonstrate that the TCAMixer outperforms the counterparts by a significant margin, for example, achieving 3% higher accuracy with a smaller model size of 0.177M. Additionally, the TCAMixer achieves a performance of 85% to 98.7% compared to that of large pre-trained models but only occupies 1/3000 to 1/2000 of their size on most test datasets. [ABSTRACT FROM AUTHOR]

Electromagnetic scattering from electrically large objects with multiscale features is an increasingly important problem in computational electromagnetics. A conventional approach is to use an integral equation-based solver that is then augmented with an accelerator, a popular choice being a parallel multilevel fast multipole algorithm (MLFMA). One consequence of multiscale features is locally dense discretization, which leads to low-frequency breakdown and requires nonuniform trees. To the authors’ knowledge, the literature on parallel MLFMA for such multiscale distributions capable of arbitrary accuracy is sparse; this paper aims to fill this niche. We prescribe an algorithm that overcomes this bottleneck. We demonstrate the accuracy (with respect to analytical data) and performance of the algorithm for both PEC scatterers and point clouds as large as $755{\lambda }$ with several hundred million unknowns and nonuniform trees as deep as 16 levels. [ABSTRACT FROM AUTHOR]

Coronary calcified lesions can exert serious effects on stent expansion. A calcium scoring system, based on optical coherence tomography (OCT), has been previously developed to identify relatively mild calcified lesions that would benefit from plaque modification procedures. Therefore, the present study aimed to establish a novel OCT-based scoring system to predict the stent expansion of moderate and severe calcified lesions. A total of 33 patients who underwent percutaneous coronary intervention (PCI; 34 calcified lesions were observed using coronary angiography) were retrospectively included in the present study. Coronary angiography and OCT images were subsequently reviewed and analyzed. Furthermore, a calcium scoring system was developed based on the results of multivariate analysis before the optimal threshold for the prediction of stent underexpansion in patients with moderate and severe calcified lesions was determined. The mean age of the patients was 67±10 years. The present analysis demonstrated that the final post-PCI median stent expansion was 70.74%, where stent underexpansion (defined as stent expansion <80%) was observed in 23 lesions. The mean maximum calcium arc, length and thickness, which were assessed using OCT, were found to be 230˚, 25.10 mm and 1.18 mm, respectively. A multivariate logistic regression model demonstrated that age and the maximum calcium arc were independent predictors of stent underexpansion. A novel calcium scoring system was thereafter established using the following formula: (0.16 x age) + (0.03 x maximum calcium arc) according to the β-coefficients in the multivariate analysis, with the optimal cut-off value for the prediction of stent underexpansion being 16.87. Receiver operating characteristic curve analysis demonstrated that this novel scoring system yielded a larger area under the curve value compared with that from a previous study's scoring system. Therefore, in conclusion, since the calcium scoring system of the present study based on age and the maximum calcium arc obtained from OCT was specifically developed in the subjects with moderate and severe calcified lesions, it may be more accurate in predicting the risk of stent underexpansion in these patients. [ABSTRACT FROM AUTHOR]

Prior theoretical studies and experience confirm that the stability of marching-on-in-time (MOT) solvers pertinent to the analysis of scattering from free-standing three-dimensional perfect electrically conducting surfaces hinges on the accurate evaluation of MOT matrix elements resulting from a Galerkin discretization of the underlying time domain integral equation (TDIE). Unfortunately, the accurate evaluation of the four-dimensional spatial integrals involved in the expressions for these matrix elements is prohibitively expensive when performed by computational means. Here, a method that permits the quasi-exact evaluation of MOT matrix elements is presented. Specifically, the proposed method permits the analytical evaluation of three out of the four spatial integrations, leaving only one integral to be evaluated numerically. Since the latter has finite range and a piecewise smooth integrand, it can be evaluated to very high accuracy using standard quadrature rules. As a result, the proposed method permits the fast evaluation of MOT matrix elements with arbitrary (user-specified) accuracy. Extensive numerical experiments show that an MOT solver for the electric field TDIE that uses the proposed quasi-exact method is stable for a very wide range of time step sizes and yields solutions that decay exponentially after the excitation vanishes. [ABSTRACT FROM AUTHOR]

A quasi-planar conical antenna with broad bandwidth (more than 10:1) and omnidirectional radiation pattern has been designed. In order to achieve mechanical stability, conical radiator is realized through thin metallic coating over a cavity etched into a dielectric slab. Due to dielectric loading, the conical antenna in this paper is quasi-planar, low-cost, light, mechanically robust, and easy to fabricate, integrate, and re-configure. A simulation tool based on the Method of Moments is developed to analyze the quasi-planar antenna. With simulation results as the guideline, the dielectric loading's material and geometries are optimized to attain wideband input impedance and omnidirectional radiation pattern. Three antenna prototypes are fabricated by using high density polyurethane foam as the dielectric loading material. Simulation and measurement data show excellent agreements. Input impedance bandwidths of all the three prototypes are greater than 10:1; and all of them show omnidirectional patterns in the azimuth plane. These quasi-planar conical antennas can be readily applied to ultrawideband communication and radar applications. In this paper, an ultrawideband radar sensor network testbed is constructed using one of the three prototype designs. Target localization is successfully demonstrated by five radar nodes with the aid of grid based location estimation algorithm. [ABSTRACT FROM PUBLISHER]

In this paper, a compact and low-cost electronic circuit system is designed for time-reversal of ultra-wideband short impulses (with nanosecond and sub-nanosecond temporal durations). A frequency-domain approach is adopted to avoid high sampling rate in time. Specifically, the proposed system obtains the discrete spectra of input impulses first; then realizes time-reversal in frequency domain; and finally synthesizes the time-reversed impulses using discrete continuous wave elements. This system is composed of common and commercially available circuits, and hence, can embody a system-on-chip implementation. Its performance is verified by circuit-electromagnetic co-simulations using impulses with 3-10-GHz frequency band coverage. Advanced Design System and two full-wave Maxwell's equations solvers are used for circuit and electromagnetic simulations, respectively, and their results are coupled and integrated. In the circuit part, most of nonidealities of realistic circuits are taken into account. It is shown by the simulation results that, although realistic circuits unavoidably introduce errors to time-reversal, such errors do not affect the "focusing" phenomena in the context of electromagnetic wave propagation. As a conclusion, the proposed system can be deployed in practical time-reversal communication and radar applications. [ABSTRACT FROM AUTHOR]

Predicting crowd flow is of great importance to public safety and traffic management. The crowd flow is difficult to predict accurately and timely due to the uncertainty of the future positions. In this paper, we propose a novel Active Offset Network (AONet), in which ActiveGRU (Active Gate Recurrent Unit) is designed to predict the variation of pedestrians' positions in the crowd flow. Its inner location-variant recurrent structure is implemented by utilizing convolution operation on low dimensional spatio-temporal sequences to obtain fractional offset locations. Afterwards, the sampling locations are determined by bilinear interpolation on fractional offset locations. Moreover, a probabilistic sparse strategy is introduced to reduce the links between sampling locations during supervised training. Finally, the experiments over popular benchmarks demonstrate that our method can actively characterize the future positions of pedestrians. Meanwhile, the performance of the proposed AONet is superior over state-of-art baselines with regard to both accuracy and computational savings. [ABSTRACT FROM AUTHOR]