FaceRecProOV_Src

国际通用标准循环工况，并附有车速转化程序，可用于matlab车速仿真数据(International standard standard cycle conditions, with a speed conversion program, can be used for matlab vehicle speed simulation data)

gps卫星信号捕获程序。。。。。。。。。。。。(buhuo)

matlab的随机过程仿真实现，包含泊松过程，指数分布，高斯分布等(matlab random process simulation to achieve, including the Poisson process, exponential distribution, Gaussian distribution)

说明：  侦察无人机搭载SAR设备对运动目标进行跟踪侦察的路径规划。(The path planning of tracking and reconnoitering the moving target with SAR equipment.)

干涉测向仪通过测量入射波到达天线阵列中单元天线之间的相位差,决定入射波的入射角(Interferometer by measuring the incident wave direction finder antenna array element antenna reaches the phase difference between the incident angle of incidence of the decision)

用matlab来实现IIR滤波器的设计实验，提供程序代码。(Matlab to achieve IIR filter design experiments to provide the program code.)

matlab教程，详细的介绍了matlab的使用方法，操作步骤基流程(matlab tutorial matlab detailed introduction to use, step-based process operations)

用MATLAB实现的心形动画，有助于帮助MATLAB函数的理解(Heart-shaped with a MATLAB implementation of the animation, helping to help the understanding of MATLAB Functions)

a matlab program to find phase of an input signal

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.