Review of Broadband Metamaterial Absorbers: From Principles,Design Strategies,and Tunable Properties to Functional Applications

Metamaterial absorbers have been widely studied and continuously concerned owing to their excellent resonance features of ultra-thin thickness, light-weight, and high absorbance. Their applications, however, are typically restricted by the intrinsic dispersion of materials and strong resonant features of patterned arrays (mainly referring to narrow absorption bandwidth). It is, therefore essential to reassert the principles of building broadband metamaterial absorbers (BMAs). Herein, the research progress of BMAs from principles, design strategies, tunable properties to functional applications are comprehensively and deeply summarized. Physical principles behind broadband absorption are briefly discussed, typical design strategies in realizing broadband absorption are further emphasized, such as top-down lithography, bottom-up self-assembly, and emerging 3D printing technology. Diversified active components choices, including optical response, temperature response, electrical response, magnetic response, mechanical response, and multi-parameter responses, are reviewed in achieving dynamically tuned broadband absorption. Following this, the achievements of various interdisciplinary applications for BMAs in energy-harvesting, photodetectors, radar-IR dual stealth, bolometers, noise absorbing, imaging, and fabric wearable are summarized. Finally, the challenges and perspectives for future development of BMAs are discussed.     

Small Changes,Big Impact: Tungsten Bronzes with Extremely Large Second Harmonic Generation Achieved by the Transition Metal Doping on the B-Site

Two novel transition metal-doped tungsten bronze oxides, Pb_2.15Li_0.85Nb_4.85Ti_0.15O₁₅ (PLNT) and Pb_2.15Li_0.55Nb_4.85W_0.15O₁₅ (PLNW), are synthesized by high-temperature solid-state reactions. The Rietveld method using the high-resolution synchrotron radiation indicates that PLNT and PLNW crystallize in the orthorhombic polar noncentrosymmetric space group, Pmn2₁ (no. 31). As a class of tungsten bronze oxide, PLNT and PLNW retain a unique rigid framework composed of d⁰ transition metal cation (Ti⁴⁺ or W⁶⁺)-doped highly distorted NbO₆ octahedra along with the subsequently generated Pb/LiO₁₂ and PbO₁₅ polyhedra. Interestingly, the d⁰ transition metal-doped tungsten bronzes, PLNT and PLNW, exhibit extremely large second-harmonic generation (SHG) responses of 56 and 67 × KH₂PO₄, respectively. The observed immeasurably strong SHG is mainly attributed to a net polarization originating from the alignment of highly distorted NbO₆ octahedra with doped transition metals in the frameworks. It is believed that doping transition metal cations at the B-site of the tungsten bronze structures should be an innovative strategy to develop novel high-performance nonlinear optical materials.     

Traffic sign detection based on improved faster R-CNN for autonomous driving

The timely and accurate identification of traffic signs plays a significant role in realizing the autonomous driving of vehicles. However, the size of traffic signs accounts for a low proportion of the input picture, which increases the difficulty of detection. This paper proposes an improved faster R-CNN traffic sign detection method. ResNet50-D feature extractor, attention-guided context feature pyramid network (ACFPN), and AutoAugment technology are designed for the faster R-CNN model. ResNet50-D is selected as the backbone network to obtain more characteristic information. ACFPN is performed to decrease the loss of contextual information. And data augmentation and transfer learning are adopted to make model training more convenient and time-saving. To prove the availability of the proposed method, we compare it with mainstream approaches (SSD, YOLOv3, RetinaNet, cascade R-CNN, FCOS, and CornerNet-Squeeze) and state-of-the-art methods. Experimental results on the CCTSDB dataset show that the improved faster R-CNN achieves the frames per second of 29.8 and the mean average precision of 99.5%, which is superior to the state-of-the-art methods and more suitable for traffic sign detection. Moreover, the proposed model is extended to the Tsinghua-Tencent 100 K (TT100K) dataset and also achieves a competitive detection result.

     

A Cost‐Efficient Bifunctional Ultrathin Nanosheets Array for Electrochemical Overall Water Splitting

The design of cost‐efficient earth‐abundant catalysts with superior performance for the electrochemical water splitting is highly desirable. Herein, a general strategy for fabricating superior bifunctional water splitting electrodes is reported, where cost‐efficient earth‐abundant ultrathin Ni‐based nanosheets arrays are directly grown on nickel foam (NF). The newly created Ni‐based nanosheets@NF exhibit unique features of ultrathin building block, 3D hierarchical structure, and alloy effect with the optimized Ni₅Fe layered double hydroxide@NF (Ni₅Fe LDH@NF) exhibiting low overpotentials of 210 and 133 mV toward both oxygen evolution reaction and hydrogen evolution reaction at 10 mA cm^?2 in alkaline condition, respectively. More significantly, when applying as the bifunctional overall water splitting electrocatalyst, the Ni₅Fe LDH@NF shows an appealing potential of 1.59 V at 10 mA cm^?2 and also superior durability at the very high current density of 50 mA cm^?2.     

Automatic computation of pebble roundness using digital imagery and discrete geometry

The shape of sedimentary particles is an important property, from which geographical hypotheses related to abrasion, distance of transport, river behavior, etc. can be formulated. In this paper, we use digital image analysis, especially discrete geometry, to automatically compute some shape parameters such as roundness, i.e. a measure of how much the corners and edges of a particle have been worn away.In contrast to previous work in which traditional digital images analysis techniques, such as Fourier transform, are used, we opted for a discrete geometry approach that allowed us to implement Wadell's original index, which is known to be more accurate, but more time consuming to implement in the field.Our implementation of Wadell's original index is highly correlated (92%) with the roundness classes of Krumbein's chart, used as a ground-truth. In addition, we show that other geometrical parameters, which are easier to compute, can be used to provide good approximations of roundness.We also used our shape parameters to study a set of pebbles digital images taken from the Progo basin river network (Indonesia). The results we obtained are in agreement with previous work and open new possibilities for geomorphologists thanks to automatic computation.     

支持非第一范式的DBMS中DD的设计和实现

NFMDB数据库管理系统是在 RDBMS的基础上扩充而成的 ,它支持非第一范式 NF2 (Nonfirst normal form)及多媒体数据类型。首先 ,分析了集中式和分离式字典系统的特点 ,并阐述了主动 DD和被动 DD的区别 ;其次 ,讨论了 NFMDB系统为处理 NF2问题而引入的关系字典、属性字典等字典的构造 ,并给出了部分结构描述 ;然后 ,用 create语句描述了一个关于 NF2 存储模式实例 ,同时以 select语句为例讨论了用关系字典和属性字典为基础转换多维 NF2 问题为平滑关系的算法 ,所有这些操作均在关系字典和属性字典中给出了相应的存储描述 ;最后 ,探讨了以关系方式存储 DD的优点以及 DD与 DBMS的关系。由此得出以扩充 DD系统为基础处理 NF2 问题是可行的。     

C8051F040的CAN模块应用研究

以嵌入式单片机C8051F040内部嵌入的CAN模块为核心,介绍其CAN控制器的应用。在CAN通信模块的应用硬件电路基础上,设计了其具体的应用程序,并通过程序阐述了对其CAN模块相关控制器的读、写和控制原理,指出在使用时应注意的问题。     

基于神经网络的时间序列鲁棒预测

为了实现神经网络预测模型的便棒预测,提出一种基于非线性偏自相关的一般化预测模型辨识方法。该方法通过考察待预测时间序列的当前序列值对各阶历史序列的不可约自依赖,确定神经网络这类非线性自回归模型的自回归阶数。实现表明,该方法可有效地提高神经网络预测模型的鲁棒预测能力。