短波多模馈电网络设计中的数据插值应用

馈电网络是短波多模多馈天线系统的重要组成部分,因此,馈电网络的设计是多模多馈天线系统设计的关键技术.互联网络子结构分析理论以网络参数数据化后形成的矩阵数据库为基础,利用矩阵数据库方法表示非均匀性子结构网络单元的网络参数.文中基于互联网络子结构分析方法,利用数据插值算法对馈电网络的传输线变压器S参数测量数据库进行了无缝化处理.插值结果表明,该算法能在满足一定的插值精度和插值效率的条件下对数据进行有效的插值处理.     

Image classification using correlation tensor analysis

Images, as high-dimensional data, usually embody large variabilities. To classify images for versatile applications, an effective algorithm is necessarily designed by systematically considering the data structure, similarity metric, discriminant subspace, and classifier. In this paper, we provide evidence that, besides the Fisher criterion, graph embedding, and tensorization used in many existing methods, the correlation-based similarity metric embodied in supervised multilinear discriminant subspace learning can additionally improve the classification performance. In particular, a novel discriminant subspace learning algorithm, called correlation tensor analysis (CTA), is designed to incorporate both graph-embedded correlational mapping and discriminant analysis in a Fisher type of learning manner. The correlation metric can estimate intrinsic angles and distances for the locally isometric embedding, which can deal with the case when Euclidean metric is incapable of capturing the intrinsic similarities between data points. CTA learns multiple interrelated subspaces to obtain a low-dimensional data representation reflecting both class label information and intrinsic geometric structure of the data distribution. Extensive comparisons with most popular subspace learning methods on face recognition evaluation demonstrate the effectiveness and superiority of CTA. Parameter analysis also reveals its robustness.     

Precise Extraction of Charge Carrier Mobility for Organic Transistors

Unreliable mobility values, and particularly greatly overestimated values and severely distorted temperature dependences, have recently hampered the development of the organic transistor field. Given that organic field‐effect transistors (OFETs) have been routinely used to evaluate mobility, precise parameter extraction using the electrical properties of OFETs is thus of primary importance. This review examines the origins of the various mobilities that must be determined for OFET applications, the relevant extraction methods, and the data selection limitations, which help in avoiding conceptual errors during mobility extraction. For increased precision, the review also discusses device fabrication considerations, calibration of both the specific gate‐dielectric capacitance and the threshold voltage, the contact effects, and the bias and temperature dependences, which must actually be handled with great care but have mostly been overlooked to date. This review serves as a systematic overview of the OFET mobility extraction process to ensure high precision and will also aid in improving future research.     

A Sign-Component-Based Framework for Chinese Sign Language Recognition Using Accelerometer and sEMG Data

Identification of constituent components of each sign gesture can be beneficial to the improved performance of sign language recognition (SLR), especially for large-vocabulary SLR systems. Aiming at developing such a system using portable accelerometer (ACC) and surface electromyographic (sEMG) sensors, we propose a framework for automatic Chinese SLR at the component level. In the proposed framework, data segmentation, as an important preprocessing operation, is performed to divide a continuous sign language sentence into subword segments. Based on the features extracted from ACC and sEMG data, three basic components of sign subwords, namely the hand shape, orientation, and movement, are further modeled and the corresponding component classifiers are learned. At the decision level, a sequence of subwords can be recognized by fusing the likelihoods at the component level. The overall classification accuracy of 96.5% for a vocabulary of 120 signs and 86.7% for 200 sentences demonstrate the feasibility of interpreting sign components from ACC and sEMG data and clearly show the superior recognition performance of the proposed method when compared with the previous SLR method at the subword level. The proposed method seems promising for implementing large-vocabulary portable SLR systems.     

高精度双光束干涉指零仪的研制

在激光陀螺动态测角过程中,瞄准并建立一个可靠的零点是比较关键的问题。利用双光束干涉原理设计了指零仪的光路,描述了其瞄准原理和电路组成,分析了研制过程中应该注意的问题。利用高精度速率偏频激光陀螺的输出脉冲对指零仪进行了稳定性和重复性的实验,在转速为144(°)/s时指零仪的指零精度优于±0.1″,指零重复精度优于±0.05″。结果证明,所研制的指零仪具有很高的指零精度和良好的动态响应能力,验证了设计的合理性和可行性,为高精度激光陀螺测角仪的研制奠定了良好的基础。     

基于计算全息的双随机相位图像加密技术

基于计算全息,结合4f系统双随机相位图像加密技术,提出了一种图像加密以及传输的新方法。分析了计算全息(CGH)记录图像独具的扩频效应及特有的解密密钥,并实现了针对全息图不同的频谱单元及组合,采用原相位板、共轭板以及两者的有序组合作为解密密钥的计算机模拟,研究了其抗噪性能,新方法提高了图像加密的安全性。     

物联传感网噪声过滤的自适应线性权值算法

针对物联传感网络由于数据类型不一致造成散粒噪声.提出一种物联传感网络散粒噪声过滤的自适应线性权值算法.提取物联传感网络散粒噪声特征参数,对上述参数进行线性变换,获取噪声关联特征参数自适应线性权值,完成物联传感网络散粒噪声过滤.实验表明,该算法能够有效过滤物联传感网络中的散粒噪声,取得了令人满意的结果.     

A New Strategy to Effectively Suppress the Initial Capacity Fading of Iron Oxides by Reacting with LiBH4

In this work, a new facile and scalable strategy to effectively suppress the initial capacity fading of iron oxides is demonstrated by reacting with lithium borohydride (LiBH₄) to form a B‐containing nanocomposite. Multielement, multiphase B‐containing iron oxide nanocomposites are successfully prepared by ball‐milling Fe₂O₃ with LiBH₄, followed by a thermochemical reaction at 25–350 °C. The resulting products exhibit a remarkably superior electrochemical performance as anode materials for Li‐ion batteries (LIBs), including a high reversible capacity, good rate capability, and long cycling durability. When cycling is conducted at 100 mA g^?1, the sample prepared from Fe₂O₃–0.2LiBH₄ delivers an initial discharge capacity of 1387 mAh g^?1. After 200 cycles, the reversible capacity remains at 1148 mAh g^?1, which is significantly higher than that of pristine Fe₂O₃ (525 mAh g^?1) and Fe₃O₄ (552 mAh g^?1). At 2000 mA g^?1, a reversible capacity as high as 660 mAh g^?1 is obtained for the B‐containing nanocomposite. The remarkably improved electrochemical lithium storage performance can mainly be attributed to the enhanced surface reactivity, increased Li⁺ ion diffusivity, stabilized solid‐electrolyte interphase (SEI) film, and depressed particle pulverization and fracture, as measured by a series of compositional, structural, and electrochemical techniques.     

General Synthesis of Dual Carbon‐Confined Metal Sulfides Quantum Dots Toward High‐Performance Anodes for Sodium‐Ion Batteries

Sponge‐like composites assembled by cobalt sulfides quantum dots (Co₉S₈ QD), mesoporous hollow carbon polyhedral (HCP) matrix, and a reduced graphene oxide (rGO) wrapping sheets are synthesized by a simultaneous thermal reduction, carbonization, and sulfidation of zeolitic imidazolate frameworks@GO precursors. Specifically, Co₉S₈ QD with size less than 4 nm are homogenously embedded within HCP matrix, which is encapsulated in macroporous rGO, thereby leading to the double carbon‐confined hierarchical composites with strong coupling effect. Experimental data combined with density functional theory calculations reveal that the presence of coupled rGO not only prevents the aggregation and excessive growth of particles, but also expands the lattice parameters of Co₉S₈ crystals, enhancing the reactivity for sodium storage. Benefiting from the hierarchical porosity, conductive network, structural integrity, and a synergistic effect of the components, the sponge‐like composites used as binder‐free anodes manifest outstanding sodium‐storage performance in terms of excellent stable capacity (628 mAh g^?1 after 500 cycles at 300 mA g^?1) and exceptional rate capability (529, 448, and 330 mAh g^?1 at 1600, 3200, and 6400 mA g^?1). More importantly, the synthetic method is very versatile and can be easily extended to fabricate other transition‐metal‐sulfides‐based sponge‐like composites with excellent electrochemical performances.