一种实用的多数据库量子信息检索协议

针对目前量子私有信息检索不能适用与云存储的多数据库问题,基于现在成熟的量子密钥分发方法,提出了一种适合在多数据库环境下,实用的量子私有信息检索协议。对于不同大小的数据库,协议可通过调节参数θ和k,在保证数据库安全及用户隐私的情况下,完成信息的检索。性能分析结果表明,协议的通信复杂度低,检索成功率高、易于实施。     

嵌入式盲文单手输入系统设计与实现

计算机录入编辑盲文是信息处理的特殊应用领域，是特殊教育中的重要研究课题。文中将盲文制作为特殊符号，通过制作字库，编写个性化码表，然后嵌入到主流输入法，从而实现盲文与汉字混排以及实现单手盲文输入。该系统具有易学易记性、盲文编码多样性、嵌入性强等优点，并通过实验证明输入盲文效率能提高5~6倍，在盲文出版、盲文印刷、盲文教学等领域有重要的应用价值。但盲文字符在不同平台(如智能手机)与不同操作系统兼容性问题还有待进一步研究开发。     

一款紧凑增强型可变阻抗电磁带隙结构

基于传统AI-EBG结构,提出了一种小尺寸的增强型电磁带隙结构,实现了从0.5~9.4 GHz的宽频带-40 dB噪声抑制深度,且下截止频率减少到数百MHz,可有效抑制多层PCB板间地弹噪声。文中同时研究了EBG结构在高速电路应用时的信号完整性问题,使用差分信号方案可改善信号完整性。     

继电保护中多路模拟量采集系统的设计

介绍了一种基于高性能浮点DSP芯片TMS320C32、CPLD芯片XC95288和A/D采样芯片AD976组成的多路采集系统的工作原理以及设计方法。通过对第一路施加特殊的电压量,在CCS开发环境下读取采样缓冲区的值,并利用Matlab对采样数据进行了全波傅氏变换。此外,该系统已在继电保护中得到广泛应用,实践表明,该系统能较好地解决多路模拟量的采集,并确保了采样数据的安全可靠性。     

高强不锈钢绞线网与ECC黏结-滑移关系模型

高强不锈钢绞线网与ECC的黏结是二者协同工作的基础，且黏结 滑移关系模型是其黏结性能的综合反映，故通过对17组51个高强不锈钢绞网增强ECC薄板试件进行单边拉拔试验，研究横向钢绞线间距、纵向钢绞线直径和相对锚固长度等因素对钢绞线网在ECC中黏结性能的影响规律。试验结果表明，横向钢绞线的设置可使黏结破坏由脆性破坏转变为延性破坏；高强不锈钢绞线网与ECC的黏结滑移曲线可分为5个阶段，分别为上升段、微降段、延性强化段、下降段和残余段。基于试验结果，对钢绞线网在ECC中的黏结破坏特征和黏结 滑移机理进行分析，在相关黏结-滑移关系模型的基础上，提出钢绞线网与ECC的黏结 滑移关系模型，并进行模型参数分析。所提模型及模型参数计算公式与试验结果吻合良好，能较好地反映钢绞线网与ECC的界面黏结滑移特征。     

A computer aided design method for car form and its application based on shape parameters

In the early design stage, automotive modeling should both meet the requirements of aesthetics and engineering. Therefore, a vehicle CAD (computer aided design) model that can be easily adjusted by feedbacks is necessary. Based on CE-Bézier surface, this paper presents a set of algorithms for parametric segmentation and fairing surface generation in a car model. This model is defined by a simplified automotive template and relevant control points, shape parameters and segmentation parameters, which can be modified to alter the car form efficiently. With this model and the corresponding adjustment method, more than fifty various vehicle models are established in this research according to different parameters. And two methods for calculating similarity index between car models are constructed, which are suitable for brand design trend analysis and modelling design decisionmaking.     

Large increase of Curie temperature in (110)-oriented La0.7Sr0.3MnO3 films

From the perspectives of scientific researches and practical applications, it is desirable to explore high operating temperature ferromagnetic films. The effect of biaxial strain on magnetic properties of (110)-oriented La_0.7Sr_0.3MnO₃ films was studied. High quality La_0.7Sr_0.3MnO₃ films were grown on (110)-oriented perovskite single crystal substrates using pulsed laser deposition, varying substrate-induced misfit strains from ??2.27–0.75%. A remarkable enhancement of Curie temperature has been achieved for (110)-oriented La_0.7Sr_0.3MnO₃ films clamped with small misfit strains (i.e., grown on LAST (110)). The enhanced Curie temperature of (110)-oriented La_0.7Sr_0.3MnO₃ films could be attributed to the misfit strain between the films and the underlying substrates and may have technological implication for applications at high temperature environments.     

Preparation of nanocrystalline nickel oxide from nickel hydroxide using spark plasma sintering and inverse Hall-Petch related densification

Nanocrystalline nickel oxide (NiO) was prepared from nickel hydroxide by Spark plasma sintering (SPS) and the mechanisms involved in the densification of NiO were studied. Reverse precipitated nickel hydroxide powders were SPS processed at 400, 600 and 700?°C with 70?MPa pressure. Pure NiO with 12?nm crystallite size formed after 400?°C sintering process. However NiO grains had grown to 18 and 38?nm after 600 and 700?°C sintering respectively. NiO pellets prepared using 600 and 700?°C SPS sintering schedules had relative densities of 83% and 94% respectively. Two displacement rate regimes were observed during densification of NiO in both 600 and 700?°C sintering processes. Decomposition of nickel hydroxide and particle sliding of NiO led to first displacement rate maximum while inverse Hall-Petch based plastic deformation facilitated densification during the constant second displacement rate regime. No densification occurred during sintering holding times indicating the limited role that diffusion played during densification.     

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

We present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.