National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Radar & Synthetic aperture radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Topological Electronic Structure and Its Temperature Evolution in Antiferromagnetic Topological Insulator MnBi 2 Te 4

Abstract: The intrinsic magnetic topological insulator MnBi2Te4 exhibits rich topological effects such as quantum anomalous Hall effect and axion electrodynamics. Here, by combining the use of synchrotron and laser light sources, we carry out comprehensive and high-resolution angle-resolved photoemission spectroscopy studies on MnBi2Te4 and clearly identify its topological electronic structure. In contrast to theoretical predictions and previous studies, we observe topological surface states with diminished gap forming a characteristic Dirac cone. We argue that the topological surface states are mediated by multidomains of different magnetization orientations. In addition, the temperature evolution of the energy bands clearly reveals their interplay with the magnetic phase transition by showing interesting differences between the bulk and surface states, respectively. The investigation of the detailed electronic structure of MnBi2Te4 and its temperature evolution provides important insight into not only the exotic properties of MnBi2Te4, but also the generic understanding of the interplay between magnetism and topological electronic structure in magnetic topological quantum materials.

A Bilateral CFAR Algorithm for Ship Detection in SAR Images

Abstract: A bilateral constant false alarm rate (CFAR) algorithm for ship detection in synthetic aperture radar (SAR) images is proposed in this letter. Compared to the standard CFAR algorithm, the proposed algorithm can reduce the influence of SAR ambiguities and sea clutter, by means of a combination of the intensity distribution and the spatial distribution of SAR images. The spatial distribution plays an equally important role as the intensity distribution. It is estimated before ship detection by a new kernel density estimation algorithm proposed in this letter. The experimental results of typical SAR images show that the algorithm is effective.

Longitudinal wave band gaps in metamaterial-based elastic rods containing multi-degree-of-freedom resonators

Abstract: Wave propagation and vibration transmission in metamaterial-based elastic rods containing periodically attached multi-degree-of-freedom spring–mass resonators are investigated. A methodology based on a combination of the spectral element (SE) method and the Bloch theorem is developed, yielding an explicit formulation for the complex band structure calculation. The effects of resonator parameters on the band gap behavior are investigated by employing the attenuation constant surface plots, which display information on the location, the width and the attenuation performance of all band gaps. It is found that Bragg-type and resonance-type gaps co-exist in these systems. In some special situations, exact coupling between Bragg and resonance gaps occurs, giving rise to super-wide coupled gaps. The advantage of multi-degree-of-freedom resonators in achieving multiband and/or broadband gaps in metamaterial-based rods is demonstrated. Band gap formation mechanisms are further examined by analytical and physical models, providing explicit formulae to locate the band edge frequencies of all the band gaps.

A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification

Abstract: Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features.