user

现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。(existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.)

In this paper, we introduce a new machine-learning-based data classification algorithm that is applied to network intrusion detection. The basic task is to classify network activities (in the network log as connection records) as normal or abnormal while minimizing misclassification. Although different classification models have been developed for network intrusion detection, each of them has its strengths and weaknesses, including the most commonly applied Support Vector Machine (SVM) method and the Clustering based on Self-Organized Ant Colony Network (CSOACN). Our new approach combines the SVM method with CSOACNs to take the advantages of both while avoiding their weaknesses. Our algorithm is implemented and evaluated using a standard benchmark KDD99 data set. Experiments show that CSVAC (Combining Support Vectors with Ant Colony) outperforms SVM alone or CSOACN alone in terms of both classification rate and run-time efficiency.

RANSAC image registration software

这是一个简单的模糊逼近(matlab)源代码，可供参考(This is a simple fuzzy approximation (matlab) source code is available for reference)

this is used to find the peak count in a signal

Matlab的MPEG程序. 它是MPEG心理声学模型的MatLab实现。(Matlab Program of MPEG. This is an implementation of the MPEG psychoacoustic model 1, layer I, for MATLAB.)

基于MATLAB的语音信号端点检测，短时能量分析，短时过零率，和GUI界面实现(aaaaa)

这个是一个image processing 的实验，说的是如何利用matlab中的函数做基本的image处理(this is an image processing lab based on matlab, so it says how to use basic matlab functions to do image processing problems)

说明：  这是本人用Matlab实现音频噪声滤除的试验，要求分析并选用适当滤波器(using Matlab audio noise filtering tests, requirements analysis and the choice of appropriate filter)

data for EEG Epileptic