Linearly time-varying channel estimation and training power allocation for OFDM/MIMO systems using superimposed training

We address the problem of estimating the linearly time-varying (LTV) channel of orthogonal frequency division multiplexing (OFDM)/multiple-input multiple-output (MIMO) systems using superimposed training (ST).The LTV channel is modeled by truncated discrete Fourier bases.Based on this model,a two-step approach is adopted to estimate the LTV channel over multiple OFDM symbols.We also present performance analysis of the channel estimation and derive a closed-form expression for the channel estimation variance...     

《C语言程序设计》教学策略的探索与实践

本文从对《C语言程序设计》公共基础课教学现状的调查与分析入手,将改善和提高《C语言程序设计》公共基础课教学质量作为一个系统工程,从教学目标、教学内容、教学方法与结构以及教学评价四方面提出相应的教学策略与实践方法,以期达到提高学习效果,完成课程目标的最终目的。     

MAX7219在单片机系统显示电路中的应用

介绍8位串行LED显示驱动器MAX7219的特性,并给出了单片机系统中MAX7219与MCS-51的硬件接口设计,以及相应的软件流程图和编程实现。     

云平台信息安全整体保护技术研究

目前云计算发展迅速,云平台的安全问题也应引起足够重视。本文根据云平台的结构特点,结合信息系统整体保护要求,提出了一种针对云平台的信息安全整体保护技术方案。该方案将云平台分为云计算环境,云区域边界,云通信网络以及云安全管理中心四个部分,分别对这四部分做了安全性分析,并提出了每一部分的安全保障技术设计要求。     

基于网络检测平台应对网络灾害探讨

网络灾害作为互联网给人类社会带来的各种潜在风险和负面效应之一,正在影响着社会各个行业。除了在网络安全技术、网络道德和相关法律等方面开展研究外,网络检测平台通过检测网络拓扑结构和其他网络动态属性,构建网络地图,可以为网络灾害的预防和控制提供相关依据。     

Transformation of a thin-walled solid model into a surface model via solid deflation

A simplified geometric model with lower dimensionality, such as a mid-surface model, is often preferred over a detailed solid model for the analysis process, if the analysis results are not seriously impacted. In order to derive a mid-surface model from a thin-walled solid model, in this paper, we propose a novel approach called the solid deflation method. In this method, a solid model is assumed to be created by using air to inflate a shell that comprises the surface of the solid model. First, the model is simplified by the removal of any detailed features whose absence would not alter its overall shape. Next, the solid model itself can be converted into a degenerate solid model with zero thickness. Finally, a surface model is generated by splitting large faces paired in the thinned solid model, selecting one face per pair for creating a sheet model, and sewing the selected faces. Using this method, a more practical and usable mid-surface model can be very efficiently generated from a solid model because it can circumvent not only the tedious trimming and extension processes of the medial axis transformation method but also the time-consuming patch joining process of the mid-surface abstraction approach.     

基于BSC-AHP的绩效评估模型研究与实现

为了解决企业战略目标执行难的问题,提升绩效评估合理性。笔者在深入研究现存理论和方法的基础上,采用平衡记分卡理论从企业战略目标中提取关键评估指标,并提出了用分解矩阵的方法将评估指标分解到各评估对象。最后引用层次分析法确定各指标权重。建立了引导、鼓励员工工作向有利于企业战略的执行与控制的绩效评估指标体系和评价模型。开发的系统在某印刷企业得到应用并取得良好效果,有效证明了模型的可行性和科学性。     

基于Linux平台PCI设备驱动程序设计

首先从FA卡的基本概念及其特点入手,并分析了PCI总线的基本工作原理,最后完成了一个以FA卡为对象设备的简单驱动程序的编写和调试工作。     

基于J2EE的新闻发布系统的分析与设计

本文针对J2EE技术进行深入研究,对Struts框架进行深入剖析,研究Struts框架的内部结构、运作流程,以及对MVC设计模式的分析研究和对MYSQL数据库进行深入了解,并提出了一个以J2EE为平台,使用Struts框架,引入MVC设计模式的基于J2EE的新闻发布系统。本系统提高了用户获取新闻信息的及时性,使用户能更...     

Speed up kernel discriminant analysis

Linear discriminant analysis (LDA) has been a popular method for dimensionality reduction, which preserves class separability. The projection vectors are commonly obtained by maximizing the between-class covariance and simultaneously minimizing the within-class covariance. LDA can be performed either in the original input space or in the reproducing kernel Hilbert space (RKHS) into which data points are mapped, which leads to kernel discriminant analysis (KDA). When the data are highly nonlinear distributed, KDA can achieve better performance than LDA. However, computing the projective functions in KDA involves eigen-decomposition of kernel matrix, which is very expensive when a large number of training samples exist. In this paper, we present a new algorithm for kernel discriminant analysis, called Spectral Regression Kernel Discriminant Analysis (SRKDA). By using spectral graph analysis, SRKDA casts discriminant analysis into a regression framework, which facilitates both efficient computation and the use of regularization techniques. Specifically, SRKDA only needs to solve a set of regularized regression problems, and there is no eigenvector computation involved, which is a huge save of computational cost. The new formulation makes it very easy to develop incremental version of the algorithm, which can fully utilize the computational results of the existing training samples. Moreover, it is easy to produce sparse projections (Sparse KDA) with a L ₁-norm regularizer. Extensive experiments on spoken letter, handwritten digit image and face image data demonstrate the effectiveness and efficiency of the proposed algorithm.