ksrlin

digital image decimation-interpolation

threshold is a key technique for the segmentation and edge detection in an image for image processing. Here in this file the threshold is on the based of the maximum entropy of an image using shannon entropy method.

Control laboratory. Lab Course

基于KPCA_SVM的柴油机状态识别方法的研究 把KPCA与SVM共同用到柴油机状态识别中(KPCA_SVM based on the state of the diesel engine study to identify methods of KPCA and SVM to identify the common use of diesel engine state)

人工鱼群算法，好的算法借鉴，希望对大家有帮助(Artificial fish school algorithm)

说明：  小波变换的matlab程序，包括离散小波变换，多尺度变换，小波包分析，小波用于图像压缩、水印、去噪(Matlab wavelet transform procedures, including the discrete wavelet transform, multi-scale transform, wavelet packet analysis, wavelet image compression, watermarking, denoising)

It is a three dimensional interpolation with tricubic convolution interpolation,which is the method used in Matlab.But, it is more useful and faster the the code implement by Matlab.

matlab学习教程，详细介绍matlab相关知识，提供给需要学习了解matlab的朋友，熟练掌握matlab作为仿真工具的使用！(matlab tutorials, detailed knowledge of matlab, matlab available to those who need to learn about a friend, as a simulation tool to master the use of matlab!)

Median filtering with threshold written in Matlab

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.