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Data usually resides on a manifold, and the minimal dimension of such a manifold is called its intrinsic dimension. This fundamental data property is not considered in the generative adversarial network (GAN) model along with its its variants; such that original data and generated data often hold different intrinsic dimensions. The different intrinsic dimensions of both generated and original data may cause generated data distribution to not match original data distribution completely, and it certainly will hurt the quality of generated data. In this study, we first show that GAN is often unable to generate simulation data, holding the same intrinsic dimension as the original data with both theoretical analysis and experimental illustration. Next, we propose a new model, called Hausdorff GAN, which removes the issue of different intrinsic dimensions and introduces the Hausdorff metric into GAN training to generate higher quality data. This provides new insights into the success of Hausdorff GAN. Specifically, we utilize a mapping function to map both original and generated data into the same manifold. We then calculate the Hausdorff distance to measure the difference between the mapped original data and the mapped generated data, toward pushing generated data to the side of original data. Finally, we conduct extensive experiments (using MNIST, CIFAR10, and CelebA datasets) to demonstrate the significant performance improvement of the Hausdorff GAN in achieving the largest Inception Score and the smallest Frechet inception distance (FID) score as well as producing diverse generated data at different resolutions. [ABSTRACT FROM AUTHOR]

To keep safety of the antenna in wireless capsule endoscopy (WCE) to human body and its use range in different groups of people, seven human abdomen models are established where age, tissue thickness, and organ shape have been considered. On the basis of plane wave propagating theory in lossy multilayered media, each model is composed of simulated skin, subcutaneous fat, muscle, visceral fat, and gastrointestinal organ. Different dielectric constants for the biological tissue component are used. A bendable dual-band antenna is designed for the WCE system, conformed to the capsule shell. An L-shaped branch is used to get better impendence matching. Both the ISM band adopted for data transmission and the WMTS band for midfield wireless power transmission are studied. Fresh pork abdominal tissue samples are used as experiment materials, and it is properly kept consistent with the real human abdominal as far as possible. The experimental results show the performance of antenna stable in the ISM (433 MHz) band. The specific absorption ratio value is checked in different models. The results indicate that dielectric parameters varying with age cannot be ignored, and the antenna must be aimed at middle-aged and elderly population with high incidence of gastrointestinal diseases. [ABSTRACT FROM AUTHOR]

A feasible solution for mitigating radio blackout has been proposed using the magnetic field component of low-frequency (LF) electromagnetic waves (referred to as the LF magnetic field) to transmit critical low-rate data because of the low attenuation of the LF magnetic field in the electrically small plasma. Previous works have modeled the hypersonic plasma flow field as spherical or cylindrical steady-state plasma, ignoring the time-varying properties of the plasma. This article presents the dynamic propagation of the LF magnetic field in a hypersonic plasma flow field generated by a shock tube, introducing a colored-Gaussian time-varying electron density model. Analytical expressions for the propagation characteristics of the LF magnetic field in cylindrical time-varying plasma are derived using an equivalent circuit. Meanwhile, a frequency-division-multiplex experimental method for a shock tube is presented to quantitatively measure the propagation characteristics of a multifrequency LF magnetic field. The experimental results are in good agreement with the theoretical results. As the fluctuation intensity of the electron density increases, the fluctuation intensities of the time-varying envelope and phase shift increase, and the power spectral density broadens. As the frequency of the LF magnetic field increases, the effects of the time-varying plasma on the propagation characteristics strengthen. These results are important to the design of LF communication systems and modulation algorithms in the scenario of shock-tube-generated plasma. [ABSTRACT FROM AUTHOR]

3-D digital city vividly presents a real-world city and has been widely needed for many application domains. Numerous pole-like objects (PLOs), including trees, street lamps, and traffic signs, are an indispensable part of 3-D digital city. The point cloud data of mobile laser scanning (MLS) systems can capture both the geometric shape and geospatial coordinates of the PLOs while moving along the roads. This article is motivated to accurately extract and efficiently model PLOs from the point cloud data. The main contributions of this article are as follows: 1) a divergence-incorporated clustering algorithm is proposed to extract trunks accurately from the pole-like 3-D distribution perspective of point cloud; 2) an adaptive growing strategy of alternately extending and updating 3-D neighbors is proposed to get the complete canopy points of various shapes and density; and 3) the part-based modeling is proposed to synthesize the point cloud of PLOs with meaningful 3-D shapes, providing a way to model objects for the 3-D digital city vividly and efficiently. The proposed method is tested on three data sets with different interference, shape of the canopy, and point density. Experimental results demonstrate that the proposed method can extract and model the PLOs effectively and efficiently for 3-D digital city. The precision of trunk extraction is 98.45%, 98.08%, and 92.39%, the completeness of canopy extraction is 80.54%, 89.84%, and 89.29%, and the modeling time for a PLO is 0.011, 0.038, and 0.063 s in three data sets. [ABSTRACT FROM AUTHOR]

In hyperspectral images (HSI), most feature extraction and data classification methods rely on corrected dataset, in which the noisy and water absorption bands are removed. This can result in not only extra working burden but also information loss from removed bands. To tackle these issues, in this article, we propose a novel spatial–spectral feature extraction framework, multiscale 2-D singular spectrum analysis (2-D-SSA) with principal component analysis (PCA) (2-D-MSSP), for noise-robust feature extraction and data classification of HSI. First, multiscale 2-D-SSA is applied to exploit the multiscale spatial features in each spectral band of HSI via extracting the varying trends within defined windows. Taking the extracted trend signals at each scale level as the input features, the PCA is employed to the spectral domain for dimensionality reduction and spatial–spectral feature extraction. The derived spatial–spectral features in each scale are separately classified and then fused at decision-level for efficacy. As our 2-D-MSSP method can extract features and simultaneously remove noise in both spatial and spectral domains, which ensures it to be noise-robust for classification of HSI, even the uncorrected dataset. Experiments on three publicly available datasets have fully validated the efficacy and robustness of the proposed approach, when benchmarked with 10 state-of-the-art classifiers, including six spatial–spectral methods and four deep learning classifiers. In addition, both quantitative and qualitative assessment has validated the efficacy of our approach in noise-robust classification of HSI even with limited training samples, especially in classifying uncorrected data without filtering noisy bands. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude (alpha), mean (mu), and standard deviation (sigma) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma, alpha and mu values. The paper concludes with a number of open questions and outlines future research directions. [ABSTRACT FROM AUTHOR]

Firing accuracy is a critical quantity of interest in the performance analysis of electromagnetic railgun (EMRG) exterior ballistics. Since the simulation technique is always used to simulate the complex physical system at a low cost, it is employed to evaluate the firing accuracy of railgun projectile with uncertainty in this paper. An exterior ballistics simulation model of the railgun, including 3-DOF flight mechanical model, relative kinematic model, and guidance law model, is developed to simulate the ballistics behavior in the missile target intercept mission. The firing accuracy is studied in this paper to compare the capability of EMRG with different ranges and muzzle velocities. In order to explore the firing accuracy of the guided projectile, the guidance precision analysis and ballistics uncertainty quantification are studied. In the guidance precision analysis, the shot range and efficiency are used to make a comparison. In addition, the sensitivity analysis and firing accuracy analysis are also performed in terms of uncertainty quantification. The simulation results illustrate that the simulation of the guided projectile can give some practical guide to the projectile design of EMRG. [ABSTRACT FROM AUTHOR]

Energy efficiency is a critical quantity of interest in the performance of electromagnetic railgun (EMRG). To improve the energy efficiency of EMRG with the presence of uncertainty, a hybrid robust optimization method is proposed. EMRG simulation model is developed to simulate the behavior of exterior ballistics and the corresponding robust optimization issue is formulated. With the help of the impact factor analysis, several significant factors of energy efficiency are selected as the design variables while the insignificant ones are not taken into account. In the robust optimization, the polynomial chaos expansion approach coupled with Latin hypercube design is proposed to propagate the uncertainty for alleviating the computational burden of fitness function (energy efficiency) evaluation. Then, the genetic algorithm is employed to solve the EMRG energy efficiency optimization problem with the constraint of mean miss distance. The optimization results illustrate that the hybrid robust optimization method is an efficient approach to improve the energy efficiency, which can be applied in the practical application of EMRG projectile design. [ABSTRACT FROM AUTHOR]

Electromagnetic railgun (EMRG) is a promising candidate weapon system for the future surface-fire support mission characterized by quick response, hypersonic velocity, and high damage efficiency. Since the huge power consumption during launching, priority should be given to the precise strike. Firing accuracy of EMRG, which refers to the closeness degree of the projectiles’ impact points to the target, is an important performance metric in the EMRG exterior ballistic performance assessment. To provide a systematic solution for the measure of firing accuracy, the assessment factors are formulated in detail. Then, a general framework, including trajectory simulation, data preprocessing, parameters estimation, and hit probability calculation, is proposed to evaluate EMRG firing accuracy. Considering the trajectory simulation experiments may be time consuming, a new multicriteria optimal Latin hypercube design method is presented to maintain the balance between experiment time and sampling performance. Finally, firing accuracy assessment of 16-kg projectiles is performed using the designed framework and proposed experimental design method. The result reveals that the circular error probability is about 5 km under current defined scenarios, which means the initial disturbances may have great impact on the firing accuracy of projectiles without guidance and control units. [ABSTRACT FROM PUBLISHER]

Exterior ballistics performance evaluation plays a critical role in the design and development of the electromagnetic railgun system. The ballistics performance evaluation can be regarded as a multicriteria decision-making problem, for the reason that various characteristics, also termed criteria, have effects on the overall performance. A sophisticated simulation model, including six-DOF mechanics, railgun configuration, environment, and aerodynamics models are established to compute the exterior ballistics profile. Then a comprehensive framework based on cloud model and analytic hierarchy process method are proposed to evaluate ballistics performance. Eight criteria including ballistics velocity, energy efficiency, flight time, flight range, overload, attack angle, and sideslip angle stability are selected to determine exterior ballistics performance. The proposed approach yields the certainty degree of each criterion and integrates multicriteria into the whole performance. Two scenarios including 32-MJ launch energy and 200-km strike range are taken to generate alternatives for assessing ballistics performance, which demonstrate the proposed approach is feasible. Results show that in the same launch energy 32 MJ, the performance levels of 12- and 16-kg projectiles are I (excellent) and III (medium), respectively. Besides, gaining the projectile’s mass can improve performance in the same strike range 200 km. [ABSTRACT FROM PUBLISHER]

A novel auxiliary difference equation (ADE) finite-difference time-domain (FDTD) method with Crank–Nicolson (CN) scheme is proposed for electromagnetic simulation of dispersive materials in this letter. The proposed method introduces an ADE technique that establishes the relationship between the electric displacement vector and electric field intensity in dispersive media with a difference equation. Hence, the CN scheme is only applied to one of the Maxwell curl equations, resulting in reduced memory usage and computing time. To validate the advantages of the proposed approach, an example with plane wave propagation in a dispersive media is presented. Compared with the conventional ADE-CN-FDTD method, the proposed method consumes much less computing time while preserves excellent simulation accuracy. [ABSTRACT FROM AUTHOR]

Muzzle velocity is a key performance metric of the electromagnetic railgun. To obtain the optimal value of the muzzle velocity in terms of the parameters affecting it, an approach combining the orthogonal design method (ODM) and the harmony search (HS) algorithm is presented. There are up to 11 factors that affect the muzzle velocity, including eight trigger delay times of pulsed power supply (PPS), the operating voltage of PPS, the length of rail, and the mass of the projectile. The optimal values of these factors are computed and the degree of significance of these factors is ordered by the use of ODM analysis. Based on the significance order, the varying ranges of these factors are decided and a hierarchical optimization model is established. The optimization model is solved by the HS algorithm with the penalty function method, which is used to satisfy the process constraints. For example, the peak of current is bounded under 420 kA. Finally, the optimization result using ODM analysis along with the HS algorithm is compared with that using the HS algorithm alone. It is noted that by using ODM analysis along with the HS algorithm, the average of initial muzzle velocity is increased by 43.53%, and the average of best final muzzle velocity is increased by 1.34%. [ABSTRACT FROM PUBLISHER]

Aiming to resolve the manifold ambiguities for sparse arrays, a method based on the multiple signal classification (MUSIC) algorithm using planar substrate is proposed. The main reason for spurious MUSIC spectrum is that the steering vector of spurious direction of arrival (DOA) is a linear combination of the steering vectors of true DOAs. The main idea of the proposed method is to add some planar substrates at the front-ends of the elements of an array. The refractive indices and thicknesses of the substrates can be different from each other. This treatment breaks down the linearity which induces the ambiguity of a sparse array. Because the equivalent positions of the elements change for different DOAs after adding the substrates on the array, the spurious peaks will disappear or not overlap on the old peaks. Nevertheless the peaks of the true directions will remain. The spurious peaks can be discriminated according to this feature. Simulation results demonstrate the effectiveness and the feasibility of the method. Meanwhile the simulation results also show that the proposed method can partially resolve the trivial ambiguities even if the true DOA and the spurious DOA are confined to [0^\circ, 180^\circ). [ABSTRACT FROM PUBLISHER]

A novel design of a compact and integrated transverse-magnetic-pass optical waveguide polarizer in InGaAsP–InP for use with a telecom wavelength window is presented. The polarizer is realized by integrating an air-hole-type photonic crystal waveguide in a honeycomb lattice geometry. For 20-\mu\m-long polarizer devices with two different lattice constants a =350 and 590 nm, the flat bandwidth and the measured polarization extinction ratio are \sim80 nm, 16 dB and \sim50 nm, 16 dB, respectively. [ABSTRACT FROM PUBLISHER]