Local normalized linear summation kernel for fast and robust recognition

Kernel-based methods are effective for object detection and recognition. However, the computational cost when using kernel functions is high, except when using linear kernels. To realize fast and robust recognition, we apply normalized linear kernels to local regions of a recognition target, and the kernel outputs are integrated by summation. This kernel is referred to as a local normalized linear summation kernel. Here, we show that kernel-based methods that employ local normalized linear summation kernels can be computed by a linear kernel of local normalized features. Thus, the computational cost of the kernel is nearly the same as that of a linear kernel and much lower than that of radial basis function (RBF) and polynomial kernels. The effectiveness of the proposed method is evaluated in face detection and recognition problems, and we confirm that our kernel provides higher accuracy with lower computational cost than RBF and polynomial kernels. In addition, our kernel is also robust to partial occlusion and shadows on faces since it is based on the summation of local kernels.     

Learning state machine-based string edit kernels

During the past few years, several works have been done to derive string kernels from probability distributions. For instance, the Fisher kernel uses a generative model M (e.g. a hidden Markov model) and compares two strings according to how they are generated by M. On the other hand, the marginalized kernels allow the computation of the joint similarity between two instances by summing conditional probabilities. In this paper, we adapt this approach to edit distance-based conditional distributions and we present a way to learn a new string edit kernel. We show that the practical computation of such a kernel between two strings x and x^′ built from an alphabet Σ requires (i) to learn edit probabilities in the form of the parameters of a stochastic state machine and (ii) to calculate an infinite sum over Σ^* by resorting to the intersection of probabilistic automata as done for rational kernels. We show on a handwritten character recognition task that our new kernel outperforms not only the state of the art string kernels and string edit kernels but also the standard edit distance used by a neighborhood-based classifier.     

基于S3C2440的嵌入式Linux内核设计

该文介绍了三星公司的基于ARM9内核S3C2440芯片嵌入式Linux系统设计。在确认已经移植好的U-boot的基础上,进行控制模块基于ARM9的Linux嵌入式系统设计方案,裁剪并移植Linux内核。     

基于Fisher块对角LNMF的彩色人脸识别

为提高对彩色人脸的识别率,提出一种基于Fisher块对角局部非负矩阵分解(LNMF)的识别算法。采用块对角矩阵编码彩色图像不同通道的颜色信息,在LNMF算法中增加块对角约束和Fisher判别约束,对不同通道的颜色信息同时进行计算并融入人脸的类别信息,用于提取人脸特征。在CVL和PIE彩色人脸数据库上的实验结果验证了该识别算法的有效性。     

基于自适应阈值设置的运动目标检测算法

提出一种基于自适应阈值设置的运动目标检测算法,该算法不同干传统的全局阈值设置方法,而是利用核密度对背景像素点进行密度估计,给出一种新的全局和局部阈值相结合的自适应阈值设置方法。该方法考虑不同位置的像素颜色分布复杂度不同,针对每个像素点自适应设置局部阈值,能克服全局阈值的不足,提高检测的精度。对多个标准视频进行实验,实验结果证明了该算法的有效性。     

基于KISOMAP-LDA-KNN算法TE过程故障诊断研究

针对化工连续生产过程的时序性及非线性等特征,文章提出了一种基于KISOMAP-LDA-KNN的非线性故障辨识方法。首先采用核等距映射（KISOMAP）算法在保持训练数据内在几何结构下进行非线性降维,然后使用线性判别（LDA）算法保持数据的最佳分类效果下进行降维,完成过程的特征提取,最后用K近邻（KNN）算法进行模式分类。将上述方法应用到TE过程,仿真结果验证了该故障诊断方法有较高的辨识能力。     

基于多核支持向量机的飞机重着陆诊断

将支持向量机应用于飞机重着陆的诊断研究。通过分析飞机着陆阶段的运动方程,确定造成飞机重着陆的主要影响因素,将传统的单一指标诊断扩展到多指标诊断。提出一种多核支持向量机,并在此基础上建立飞机重着陆诊断模型。与传统支持向量机和神经网络模型比较表明,该模型精度高,具有更强的泛化能力。     

基于Lua的Linux内核测试工具开发

为减小传统的通过编写大量代码来调用Linux内核及驱动接口进行测试的编程难度,缩短测试周期,采用将Lua脚本语言载入Linux内核并使用脚本语言进行内核及驱动测试的方法,设计并实现一种简易Linux内核驱动测试工具。实验结果表明,该工具所占用的Linux内核空间小,使用灵活方便,可对内核及驱动模块进行准确有效的测试。     

基于CK-CPU的Linux2.6实时性能优化

针对Linux2.6在实时中断应用上的局限性,提出一种基于CK-CPU的实时性能优化方案。设计用于实时中断的快速中断请求(FIQ)机制,在FIQ处理程序退出时增加实时进程调度策略,将FIQ服务程序的公共代码和堆栈保留在便笺式存储器中,减少FIQ服务程序的内存访问次数并加快其执行速度。实验结果证明,该方案可显著提高Linux2.6的实时性能。     

实时操作系统Toppers在ARM 7上移植的研究

为简化应用程序的编写,在嵌入式系统开发时,采用实时嵌入式操作系统开发平台,将源码开放的嵌入式实时操作系统Toppers/asp内核,移植到嵌入式微控制器ARM7(S3C44B0)。该方法能解决系统启动和加载过程中的特定工作状态等问题,明确Toppers/asp内核在移植过程中的重点和难点。经多任务运行结果表明,Toppers嵌入式实时操作系统稳定可靠。