angle_count

本软件小巧玲珑，使用简单。 当窗体移动到屏幕顶端时，会像 OICQ 那样自动缩进屏幕顶端。 同时也具用 Winamp 的窗体相吸的效果。(the Software 1000, the use of simple. When Form Mobile at the top of the screen, such as automatic indentation OICQ top screen. Also used with the Winamp Form PHASE results.)

topsis代码我看了没有什么问题如果有问题请与我联系(topsis code I saw no problem if you have questions please contact me)

详细介绍matlab模型预测控制工具箱的使用model predictive control toolbox(Details Matlab toolbox model predictive control using model predictive control toolbox)

基于CV模型的matlab程序，简单，易于入门(matlab code for kalman filter(based on CV module))

matlab 代码 k-means 算法 实现2－D数据的聚类(matlab code for k-means algorithm is 2-D data clustering)

使用对话框选择需要处理的avi文件，对avi格式的视频数据进行多高斯估计(The video data to avi format more gaussian estimate)

实现列主元高斯消去法的方程求解，主要针对压缩和非压缩矩阵(Equation solver to achieve out PCA Gaussian elimination, mainly for compressed and uncompressed matrix)

MUSIC算法，平面阵，用于信号测向定位，很好用。(MUSIC algorithm, planar array for measuring signal to locate, easy to use.)

该matlab代码实现了“综合多分支分裂式层次化聚类” (Matlab implementation of Omnibus Multi-Branching Divisive Hierarchical Clustering (OMBDHC) “A competitive omnibus performance criterion for divisive multi-branching hierarchical clustering” by Soeria-Atmadja et al. (submitted). Note that OMB-DHC package is implemented in Matlab code and therefore demands the Matlab core program, as well as the Statistics toolbox of Matlab. Divisive hierarchical clustering (DHC) has emerged as a promising alternative for the identification of sub-structures in multivariate data. Of particular interest is multi-branching DHC, which allows flexible numbers of subclusters at each hierarchical level. One poorly explored issue in multi-branching DHC concerns the actual importance of the two performance criteria (and their associated algorithms) to automatically create clusters and select number of clusters, respectively. Another interesting but hitherto unexplored issue is the possibility to employ a single omnibus performance criterion that guide)

基于子空间方法的运动分割技术研究，包GPCA with spectral clustering，RANSAC Local Subspace Affinity (LSA)，三种方法(Motion segmentation technique based on subspace method, including the GPCA with spectral clustering, RANSAC Local Subspace Affinity (LSA))