Wavelet based Denial-of-Service detection

Network Denial-of-Service (DoS) attacks that disable network services by flooding them with spurious packets are on the rise. Criminals with large networks (botnets) of compromised nodes (zombies) use the threat of DoS attacks to extort legitimate companies. To fight these threats and ensure network reliability, early detection of these attacks is critical. Many methods have been developed with limited success to date. This paper presents an approach that identifies change points in the time series of network packet arrival rates. The proposed process has two stages: (i) statistical analysis that finds the rate of increase of network traffic, and (ii) wavelet analysis of the network statistics that quickly detects the sudden increases in packet arrival rates characteristic of botnet attacks.Most intrusion detections are tested using data sets from special security testing configurations, which leads to unacceptable false positive rates being found when they are used in the real world. We test our approach using data from both network simulations and a large operational network. The true and false positive detection rates are determined for both data sets, and receiver operating curves use these rates to find optimal parameters for our approach. Evaluation using operational data proves the effectiveness of our approach.     

A composite numerical scheme for the numerical simulation of coupled Burgers’ equation

In this work, a composite numerical scheme based on finite difference and Haar wavelets is proposed to solve time dependent coupled Burgers’ equation with appropriate initial and boundary conditions. Time derivative is discretized by forward difference and then quasilinearization technique is used to linearize the coupled Burgers’ equation. Space derivatives discretization with Haar wavelets leads to a system of linear equations and is solved using Matlab7.0. Convergence analysis of proposed scheme exhibits that the error bound is inversely proportional to the resolution level of the Haar wavelet. Finally, the adaptability of proposed scheme is demonstrated by numerical experiments and shows that the present composite scheme offers better accuracy in comparison with other existing numerical methods.     

基于组合方法的人脸识别算法研究

该文试探一种加权融合流形学习的方法进行人脸识别,该算法通过Haar小波和局部线性嵌入（LLE）加权融合的方式来进行人脸识别。首先通过Haar小波变换对人脸图像进行一级分解,得到四个子图;然后利用LLE算法对四个子图降维处理,并加权融合;最后利用支持向量机（SVM）进行分类判决。通过在ORL库中进行实验,结果表明,该文提出的Haar＋LLE识别效率比单独使用主成分分析（PCA）和LLE更高效。     

基于Haar型LBP纹理特征的人体姿态估计

基于人体部件的树形模型表达直观且计算高效,被广泛应用在人体姿态估计中。然而模型本身在部件特征表达上的不足限制了姿态估计结果的准确度,为此,提出一种基于图结构模型和新型纹理特征的人体姿态估计算法。采用改进后的外观模型,从训练集中获得部件位置的先验知识,联系相邻部件之间的关系,并将其应用于测试图像的外观模型建模阶段。应用Haar型局部二值模式( HLBP )纹理特征,提取部件的纹理信息,对图像进行分块处理,并为每一块赋予不同的权重。实验结果表明,带权重的HLBP特征能更有效地提取部件的纹理特征,与HLBP特征、归一化HLBP特征和颜色特征相比能获得更高的准确度。     

一种提高模糊人脸图片识别率的方法

介绍了一个通过人脸检测程序对模糊人脸图片进行识别的方法：先对图片修改其灰度阈值,以使Haar能够对模糊人脸信息进行快速分辨,再使用0pencv人脸检测程序对模糊人脸图片进行检测。实验结果表明,该方法能够提高对模糊人脸的识别率。     

量子Haar小波变换算法设计及应用

设计了基于通用量子语言Q_language的量子Haar小波变换算法,该算法对于小波变换的应用和量子算法的完善具有重要的意义.分析了3个量子位Haar小波变换过程,给出了量子Haar小波变换算法的Q_language语言描述,并分析得出其时间和空间复杂度,然后给出了实现量子Haar小渡变换应用方法.     

基于级联Adaboost的目标检测融合算法

单一特征的模型对于颜色纹理变化较大的目标的检测往往存在检测率不高或检测速度慢的缺点. 本文提出了一种基于级联Adaboost的``级联--加和'融合算法. 融合模型由两个独立训练得到的级联Adaboost分类器组成, 分别利用边界片段特征和矩形类Haar小波特征描述整个目标以及目标的一个稳定部件. 级联--加和的融合决策以样本在两个分类器中被拒绝或通过的级数信息为依据. 在多个数据库上的实验证明这种融合检测算法不仅综合了Haar小波特征检测速度快和边界片段特征鲁棒性好的优点, 而且与单一特征的分类器相比, 检测性能也有所提高.     

A multi-matcher system based on knuckle-based features

We describe a new multi-matcher biometric approach, using knuckle-based features extracted from the middle finger and from the ring finger, with fusion applied at the matching-score level. The features extraction is performed by Radon transform and by Haar wavelet, then these features are transformed by non-linear Fisher transform. Finally, the matching process is based on Parzen window classifiers. Moreover, we study a method based on tokenised pseudo-random numbers and user specific knuckle features. The experimental results show the effectiveness of the system in terms of equal error rate (EER) (near zero equal error rate).

基于小波分解和鉴别共同矢量的人耳识别

针对高维、小样本的情况下使用Fisher线形鉴别分析进行特征提取存在的病态奇异问题,提出一种新的特征提取方法,即先对人耳样本图像进行二维离散小波分解,再利用DCV算法对小波分解后的低频信息分量作进一步的降维处理。不仅克服了小样本问题,也解决了直接使用DCV算法对人耳图像降维所引起的计算量大和计算速度过慢的问题。实验证明,该方法具有较好的识别率,是一种有效的特征提取算法。     

基于巡逻小车的人脸遮挡异常事件实时检测

近年来,随着城市化进程的加快,银行、政府、学校等场所的安全越来越成为人们关心的问题,智能监控已成为目前的一个研究热点问题. 本文主要研究室内巡逻小车监控下,面部异常遮挡问题. 本文首先对监控视频进行前景提取;接着,基于提取的前景,进行肩部定位、头部区域椭圆拟合;然后通过肤色检测判断人脸区域;最后,通过Haar检测器检测人脸区域的眼睛和嘴巴,以此来判断是否存在异常遮挡行为. 实验结果表明,本文提出的算法能够实时、有效的检测出面部异常遮挡问题.