基于PCI总线的扫描电镜图像采集系统

图像采集系统是扫描电镜的一个重要组成部分，用于控制扫描电镜的扫描电源，视频图像采集，数字化与显示，电镜主控计算机与底层微机板的通讯和电镜照相，本图像采集系统基于PCI总线接口设计，硬件主要设计采用FPGA技术，实现了数字电路的模块化设计，集成度和灵活性高，目前，电镜图像卡已达到所要求的设计水平。     

新形势下电气工程及其自动化专业人才培养模式与知识体系框架

在分析了当前高等教育培养基本模式的基础上，根据科学技术和社会经济发展的要求，提出了人才培养的模式和要求。特别针对电气工程及其自动化领域的发展趋势以及对人才的需求，提出了该专业人才的知识框架体系。     

Flash器件中氧化硅/氮化硅/氧化硅的特性

对普遍采用的氧化硅/氮化硅/氧化硅( ONO)三层复合结构介质层的制备工艺及特性进行了研究分析,研究了ONO的漏电特性以及顶氧( top oxide)和底氧( bottom oxide)的厚度对ONO层漏电的影响.结果表明,采用较薄的底氧和较厚的顶氧,既能保证较高的临界电场强度,又能获得较薄的等效氧化层厚度,提高耦合率,降低编程电压.     

多服务组的流媒体安全通信机制

提出了一种基于多服务组的流媒体安全通信机制.现有的方案一般是针对用户只参加一个多播组的通信,很少涉及到一个用户同时参加多个多播组的通信,而且没有提供有效的发送方身份验证机制.本文提出了一种具有发送方身份验证功能的一个用户同时可以参加多个组通信的方案,大大地降低了用户和服务器的存储开销,与传统的密钥树方案相比,用户的存储开销降低为原来的l/lgn(n为组中的用户数目),有良好的可扩展性.另外,本机制实现了可靠的发送方身份验证功能.     

爆破震动作用下新浇基础混凝土的安全震动速度研究

针对新浇筑混凝土的爆破震动控制,设法利用爆破地震波(Rayleigh波)作用下的新浇筑基础混凝土受力状态的理论分析和动力有限元计算,确定的新浇筑混凝土中的震动速度及动拉应变分布,并结合不同龄期下新浇筑混凝土的允许极限拉应变,从理论上确定新浇筑基础混凝土的安全震动速度及主要影响因素.理论分析结果和计算实例表明,新浇筑基础混凝土的允许震动速度值随基岩对新浇筑混凝土的基础约束程度的提高和随混凝土强度的降低而降低,我国现行采用的新浇筑大体积混凝土基础面上的质点安全震动速度值具有较大的安全储备.     

Derivation of a second-order switching surface in the boundary control of buck converters

A second-order switching surface in the boundary control of buck converters is derived in this letter. The formulated switching surface can make the overall converter exhibit better steady-state and transient behaviors than the one with a first-order switching surface. The switching surface is derived by estimating the state trajectory movement after a switching action, resulting in a high state trajectory velocity along the switching surface. This phenomenon accelerates the trajectory moving toward the target operating point. The proposed control scheme has been successfully applied to a 120-W buck converter. The large-signal performance and a comparison with the first-order switching surface have been studied.     

On the capacities of bipartite Hamiltonians and unitary gates

We consider interactions as bidirectional channels. We investigate the capacities for interaction Hamiltonians and nonlocal unitary gates to generate entanglement and transmit classical information. We give analytic expressions for the entanglement generating capacity and entanglement-assisted one-way classical communication capacity of interactions, and show that these quantities are additive, so that the asymptotic capacities equal the corresponding 1-shot capacities. We give general bounds on other capacities, discuss some examples, and conclude with some open questions.     

基于TMS320VC5410的最小系统设计

TMS320VC5410是TI公司生产的一款定点数字信号处理(DSP)芯片。基于DSP的最小系统是以其简洁的硬件结构来实现DSP的一般功能,广泛应用于工业生产、教学、科研、电子设计等领域。主要介绍了基于TMS320VC5410的最小系统硬件电路设计方案,阐述了重要电路部分的抗干扰设计和软件调试过程中应注意的若干问题。最后提出了简单易行的软件调试方法以及部分程序代码。     

Classification of audio radar signals using radial basis function neural networks

Radial basis function (RBF) neural networks are used to classify real-life audio radar signals that are collected by a ground surveillance radar mounted on a tank. Currently, a human operator is required to operate the radar system to discern among signals bouncing off tanks, vehicles, planes, and so on. The objective of this project is to investigate the possibility of using a neural network to perform this target recognition task, with the aim of reducing the number of personnel required in a tank. Different signal classification methods in the neural net literature are considered. The first method employs a linear autoregressive (AR) model to extract linear features of the audio data, and then perform classification on these features, i.e, the AR coefficients. AR coefficient estimations based on least squares and higher order statistics are considered in this study. The second approach uses nonlinear predictors to model the audio data and then classifies the signals according to the prediction errors. The real-life audio radar data set used here was collected by an AN/PPS-15 ground surveillance radar and consists of 13 different target classes, which include men marching, a man walking, airplanes, a man crawling, and boats, etc. It is found that each classification method has some classes which are difficult to classify. Overall, the AR feature extraction approach is most effective and has a correct classification rate of 88% for the training data and 67% for data not used for training.