用matlab编写的用于转子动力学分析的有限元程序，可实现转子临界转速和振型分析、稳态响应和瞬态响应分析等功能。...

mfcc coefficient means mel frequency cepstral coefficient

基于主成分分析的人脸识别，用主成分分析提前特征，用模版匹配进行分类(face recognition based on PCA)

程序可以提取点云数据的特征点，提取三维散乱点云的边界(Program point cloud data can be extracted feature points extracted boundary of D Scattered Clouds)

对灰度图像进行立体匹配，在图像的最大视差范围内进行搜索(To stereo matching of gray image, the image of the maximum parallax within the scope of the search)

Matlab code for simulation of LMS algorithm

说明：  基于字典学习的字典更新的算法，称作近似K-SVD算法(The algorithm of dictionary updating based on dictionary learning is called approximate K-SVD algorithm.)

2-band discrete wavelet transform (DWT) Dual-Tree Complex Wavelet Packet(The 2-band discrete wavelet transform (DWT) provides an octave-band analysis in the frequency domain, but this might not be ‘optimal’ for a given signal. The discrete wavelet packet transform (DWPT) provides a dictionary of bases over which one can search for an optimal representation (without constraining the analysis to an octave-band one) for the signal at hand. However, it is well known that both the DWT and the DWPT are shift-varying. Also, when these transforms are extended to 2-D and higher dimensions using tensor products, they do not provide a geometrically oriented analysis. The dual-tree complex wavelet transform (DT-CWT), introduced by Kingsbury, is approximately shift-invariant and provides directional analysis in 2-D and higher dimensions. In this paper, we propose a method to implement a dual-tree complex wavelet packet transform (DTCWPT), extending the DT-CWT as the DWPT extends the DWT. To find the best complex wavelet packet frame for a given signal, w)

将Matlab中现成的数学函数编译为动态链接库，供VC调用，可以省去工程应用中要编写各种C++数学算法的烦恼。这几个工程是我总结的几个案例，分别是VC6.0和VS2008下调用Matlab r2010a生成的dll的例子，还包括2个我自己做的矩阵求逆和数值积分的例子。(Will be ready in Matlab mathematical functions compiled to a dynamic link library for VC call, eliminating the need to write a variety of engineering applications in C++ mathematical algorithms trouble. These projects is a summary of a few cases, respectively, under VC6.0 and VS2008 dll call Matlab r2010a generated examples, including two of my own to do matrix inversion and numerical integration example.)

提出一种新的显着性检测方法，通过将区域级显着性估计和像素级显着性预测与CNN（表示为CRPSD）相结合。对于像素级显着性预测，通过修改VGGNet体系结构来执行完全卷积神经网络（称为像素级CNN）以执行多尺度特征学习，基于该学习进行图像到图像预测以完成像素级显着性检测。对于区域级显着性估计，首先设计基于自适应超像素的区域生成技术以将图像分割成区域，基于该区域通过使用CNN模型（称为区域级CNN）来估计区域级显着性。通过使用另一CNN（称为融合CNN）融合像素级和区域级显着性以形成nal显着图，并且联合学习像素级CNN和融合CNN。对四个公共基准数据集的大量定量和定性实验表明，所提出的方法大大优于最先进的显着性检测方法。(A new saliency detection method by significant regional level estimates and forecast significant pixel level and CNN (expressed as CRPSD) combined. For pixel-level significant prediction to perform a full convolution neural network by modifying VGGNet architecture (called pixel-level CNN) learning to perform multi-scale features, image to image prediction to complete the pixel level detection based on the significant learning . For regional levels significantly estimate, the first generation technology to design image is divided into regions based on adaptive super-pixel area, based on the model of the region through the use of CNN (CNN called regional level) to estimate regional levels significantly. By using another CNN (CNN called fusion) Fusion pixel level and regional level to form nal significant saliency map, and the Joint Learning pixel level fusion CNN and CNN. Four common reference data set of a large number of quantitative and qualitative experiments show that the proposed m)

wavelet_rec.m,