Metallic Graphene Nanoribbons

Isolated graphene nanoribbons(GNRs)usually have energy gaps,which scale with their widths,owing to the lateral quantum confinement effect of GNRs.The absence of metallic GNRs limits their applications in device interconnects or being one-dimensional physics platform to research amazing properties based on metallicity.A recent study published in Science provided a novel method to produce metallic GNRs by inserting a symmetric superlattice into other semiconductive GNRs.This finding will broader the applications of GNRs both in nanoelectronics and fundamental science.     

煤与PVC共热解固体产物在燃烧中氯的释放特性

通过实验研究了煤与PVC共热解固体产物燃烧时氯的释放特性,结果表明,氯的释放量（率）与燃烧温度、煤与PVC共热解的温度以及共热解时PVC的加入量有关．燃烧温度越高,氯的释放率越大,900℃燃烧时,氯的释放率都在94％以上;热解温度越高。在同一温度燃烧时,氯的释放率越低．400℃时焦中氯的最高释放率达99．86％,而1000℃时焦中氯的最高释放率为94．35％．     

直流牵引电动机选用电刷的方法

煤矿行业中的常用的电机车属于直流传动系统,为适应机车的特殊需要,保证机车拉得多,跑得快,在设计时一般设计输出功率往往要比实际运行时的输出功率大许多,文章就如何选用好牵引电动机在运行中的电刷,提高牵引电动机的使用寿命做些研究和探讨。     

面向神经形态感知的人工脉冲神经元的研究进展

近年来,随着人工智能技术和脉冲神经网络(SNN)的迅猛发展,人工脉冲神经元的研究逐渐兴起。人工脉冲神经元的研究对于开发具有人类智能水平的机器人、实现自主学习和自适应控制等领域具有重要的应用前景。传统的电子器件由于缺乏神经元的非线性特性,需要复杂的电路结构和大量的器件才能模拟简单的生物神经元功能,同时功耗也较高。因此,最近研究者们借鉴生物神经元的工作机制,提出了多种基于忆阻器等新型器件的人工脉冲神经元方案。这些方案具有功耗低、结构简单、制备工艺成熟等优点,并且在模拟生物神经元的多种功能等方面取得了显著进展。文章将从人工脉冲神经元的基本原理出发,综述和分析目前已有的各种实现方案。具体来说,将分别介绍基于传统电子器件和基于新型器件的人工脉冲神经元的实现方案,并对其优缺点进行比较。此外,还将介绍不同类型的人工脉冲神经元在实现触觉、视觉、嗅觉、味觉、听觉和温度等神经形态感知方面的应用,并对未来的发展进行展望。希望能够为人工脉冲神经元的研究和应用提供有益的参考和启示。     

面向地理信息服务链的工作流技术应用

与地理信息服务需求不断扩大相适应,地理信息服务从单一的服务模式走向服务链的集合.本文分析了地理信息服务链在结构和流程上的需求,探讨工作流技术在地理信息服务链中的应用.     

LF精炼炉三相电极的建模及仿真

针对LF钢包精炼炉三相电极调节器存在的问题,采用机理法推导得出了三相电极间的影响系数,并进行了仿真研究。本课题为“九·五”攻关课题。     

高速高精度ADC系统研究

由于单个ADC模拟输入带宽的限制,混合滤波器组ADC系统不能够直接对高达数GHz的模拟信号进行采样.本文提出了基于混频器和低通滤波器的抽取器数学模型,并且从时域和频域进行详细的分析和证明.最后在混合滤波器组ADC系统的基础上,提出了基于混频器和低通滤波器的高速高精度混合滤波器组ADC系统,它完全满足软件无线电和雷达等多个领城的高速高精度要求.     

FPGA芯片的多路模拟信号源设计

基于国产FPGA芯片FMK50设计了一种多路高精度模拟信号源。设计由FPGA控制14位高精度DAC完成D/A转换,将产生的信号通过高速模拟开关及采样保持电路实现96路输出;采用内部ADC配合上位机设计自检模块对96路输出信号进行回采与标定。测试结果表明,系统在100%国产化的基础上稳定输出可配置的0～5 V电压信号且精度优于0.2%FS,自检模块设计大大提高了系统的输出精度及自动化程度。     

Constructing Facial Identity Surfaces for Recognition

We present a novel approach to face recognition by constructing facial identity structures across views and over time, referred to as identity surfaces, in a Kernel Discriminant Analysis (KDA) feature space. This approach is aimed at addressing three challenging problems in face recognition: modelling faces across multiple views, extracting non-linear discriminatory features, and recognising faces over time. First, a multi-view face model is designed which can be automatically fitted to face images and sequences to extract the normalised facial texture patterns. This model is capable of dealing with faces with large pose variation. Second, KDA is developed to compute the most significant non-linear basis vectors with the intention of maximising the between-class variance and minimising the within-class variance. We applied KDA to the problem of multi-view face recognition, and a significant improvement has been achieved in reliability and accuracy. Third, identity surfaces are constructed in a pose-parameterised discriminatory feature space. Dynamic face recognition is then performed by matching the object trajectory computed from a video input and model trajectories constructed on the identity surfaces. These two types of trajectories encode the spatio-temporal dynamics of moving faces.     

Image coding quality assessment using fuzzy integrals with a three-component image model

Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.