t_tide_v1.3beta

EEG数据下载和运动想象分类,可作为一个很好用的工具箱来分析生物医学信号。(EEG data,including motor image classfication be very useful for BCI.It can be a toolbox to analyse ECG and EEG data,includeing filtering.)

Viewpoint Adjustment Using Seam Carving Imitate Parallax with Seam Carving: Foreground objects or other objects of interest are tend to be more “interesting” than the background. Seam carving algorithm can automatically distinguish the two. Removing seams from one side of the object while adding seams to the other side will move the object in one direction while keeping the background relatively the same.

To apply spatial filtering for an image

此代码将leach和deec协议进行比较，对毕设要坐这方面的同学很有帮助，大家有兴趣可以看看。(This code will leach and deec protocol comparison, the complete set up to take the students very helpful in this regard, we are interested can look at.)

利用程序通过数值计算得到超奈奎斯特信号处理下的信道容量(Through numerical calculation, the channel capacity of the Faster-than-Nyquist signaling is obtained by numerical calculation)

基于matlab，针对调幅AM、调频FM和调相PM信号，分析在不同的噪声环境，包括高斯白噪声、色噪声、非高斯噪声及正弦干扰中，各种信号的二阶循环谱。可根据需要改变程序中信号或噪声环境，分析结果。(Based on matlab, AM for AM, FM FM and PM phase modulation signals, analysis of noise in different environments, including Gaussian white noise, colored noise, non-Gaussian noise and sinusoidal interference, the various second-order cyclic spectrum signals. May need to change the process in accordance with the signal or noise environment, the results of the analysis.)

说明：  用粒子滤波算法进行跟踪的matlab代码。(Using particle filter matlab code for tracking.)

water marking image code

Atmosphere model for 200km

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.