基于嵌入式Linux系统的键盘驱动设计

摘要：为了适应嵌入式设备外设的多样性,本文以特殊矩阵键盘为例,设计了一套完整的驱动控制模块。硬件电路设计采用外扩3片SN74HCl64芯片的方式,节省了GPIO引脚的使用,大大提高了利用效率。同时,在此基础上引出了Linux内核中input子系统的特性和工作机制,呈现了较为完整的输入事件由内核空间传递到用户空间进程的过程。实验结果表明,设计的驱动模块具有良好的实时性和准确性。     

基于X86平台的简单多任务内核的分析与实现

描述了一个简单多任务内核的设计和实现方法。分析了该简单内核的基本结构和加载运行的基本原理,然后描述了其被加载进机器RAM中以及两个任务进行切换的运行方法。     

基于完全虚拟化的安全监控技术探析

近几年来随着计算机虚拟化技术的迅速发展,基于完全虚拟化技术的安全监控应运而生,它不仅能确保监控工具的有效性、牢固性,而且监控技术易于实现.本文将从安全监控架构中内核、内核可加戢模块两个方面存在的问题对基于完全虚拟化的安全监控技术进行探析.     

Key-dependent 3D model hashing for authentication using heat kernel signature

Multimedia-based hashing is considered an important technique for achieving authentication and copy detection in digital contents. However, 3D model hashing has not been as widely used as image or video hashing. In this study, we develop a robust 3D mesh-model hashing scheme based on a heat kernel signature (HKS) that can describe a multi-scale shape curve and is robust against isometric modifications. We further discuss the robustness, uniqueness, security, and spaciousness of the method for 3D model hashing. In the proposed hashing scheme, we calculate the local and global HKS coefficients of vertices through time scales and 2D cell coefficients by clustering HKS coefficients with variable bin sizes based on an estimated L² risk function, and generate the binary hash through binarization of the intermediate hash values by combining the cell values and the random values. In addition, we use two parameters, bin center points and cell amplitudes, which are obtained through an iterative refinement process, to improve the robustness, uniqueness, security, and spaciousness further, and combine them in a hash with a key. By evaluating the robustness, uniqueness, and spaciousness experimentally, and through a security analysis based on the differential entropy, we verify that our hashing scheme outperforms conventional hashing schemes.     

Interior-point methods for symmetric optimization based on a class of non-coercive kernel functions

In this paper, we generalize polynomial-time primal–dual interior-point methods for symmetric optimization based on a class of kernel functions, which is not coercive. The corresponding barrier functions have a finite value at the boundary of the feasible region. They are not exponentially convex and also not strongly convex like many usual barrier functions. Moreover, we analyse the accuracy of the algorithm for this class of functions and we obtain an upper bound for the accuracy which depends on a parameter of the class.     

Sparse kernel logistic regression based on L 1/2 regularization

The sparsity driven classification technologies have attracted much attention in recent years, due to their capability of providing more compressive representations and clear interpretation. Two most popular classification approaches are support vector machines (SVMs) and kernel logistic regression (KLR), each having its own advantages. The sparsification of SVM has been well studied, and many sparse versions of 2-norm SVM, such as 1-norm SVM (1-SVM), have been developed. But, the sparsification of KLR has been less studied. The existing sparsification of KLR is mainly based on L ₁ norm and L ₂ norm penalties, which leads to the sparse versions that yield solutions not so sparse as it should be. A very recent study on L _1/2 regularization theory in compressive sensing shows that L _1/2 sparse modeling can yield solutions more sparse than those of 1 norm and 2 norm, and, furthermore, the model can be efficiently solved by a simple iterative thresholding procedure. The objective function dealt with in L _1/2 regularization theory is, however, of square form, the gradient of which is linear in its variables (such an objective function is the so-called linear gradient function). In this paper, through extending the linear gradient function of L _1/2 regularization framework to the logistic function, we propose a novel sparse version of KLR, the 1/2 quasi-norm kernel logistic regression (1/2-KLR). The version integrates advantages of KLR and L _1/2 regularization, and defines an efficient implementation scheme of sparse KLR. We suggest a fast iterative thresholding algorithm for 1/2-KLR and prove its convergence. We provide a series of simulations to demonstrate that 1/2-KLR can often obtain more sparse solutions than the existing sparsity driven versions of KLR, at the same or better accuracy level. The conclusion is also true even in comparison with sparse SVMs (1-SVM and 2-SVM). We show an exclusive advantage of 1/2-KLR that the regularization parameter in the algorithm can be adaptively set whenever the sparsity (correspondingly, the number of support vectors) is given, which suggests a methodology of comparing sparsity promotion capability of different sparsity driven classifiers. As an illustration of benefits of 1/2-KLR, we give two applications of 1/2-KLR in semi-supervised learning, showing that 1/2-KLR can be successfully applied to the classification tasks in which only a few data are labeled.     

自适应模糊支持向量机算法研究

一个有效的核方法通常取决于选择一个合适的核函数。目前研究核方法的热点是从数据中自动地进行核学习。提出基于最优分类标准的核学习方法,这个标准类似于线性鉴别分析和核Fisher判别式。并把此算法应用于模糊支持向量机多类分类器设计上,在ORL人脸数据集和Iris数据集上的实验验证了该算法的可行性。     

传感器网络多密钥空间q复合密钥管理方案

无线传感器网络密钥管理研究,目前大都着眼于平面拓扑结构。但随着无线传感器网络规模日渐扩大,层次拓扑结构正成为主导。为此,提出了一种适用于分层无线传感器网络的多密钥空间q复合密钥预分配方案。通过安全连通性和节点抗捕获性分析表明该方案可在保证节点间安全连通概率的基础上,提高网络的安全阈值,提升网络的抗攻击能力,增加网络安全性。     

基于卷积树核的无指导中文实体关系抽取研究

该文提出了一种基于卷积树核的无指导中文实体关系抽取方法。该方法以最短路径包含树作为关系实例的结构化表示形式,以卷积树核函数作为树相似度计算方法,并采用分层聚类方法进行无指导中文实体关系抽取。在ACE RDC 2005中文基准语料库上的无指导关系抽取实验表明,采用该方法的F值最高可达到60.1,这说明基于卷积树核的无指导中文实体关系抽取是行之有效的。     

一种综合多特征的句子相似度计算方法

提出了一种综合多特征的句子相似度计算方法,该方法分别从句子的句法、词汇语义、词形三个方面来度量句子的相似度,最后将这三个方面加权整合计算得到句子的相似度。本方法综合考虑了句子的深层和表层信息,并对句子进行了词汇扩展,从而使句子相似度计算更加准确。