ASK

两点调制信号源的MATLAB生成，调制方式为CP4FSK，输出两路幅度可调的两点调制型号源(Two modulation signals to generate MATLAB sources, modulation for CP4FSK, the output rate of two-way adjustable two-point modulation type source)

The file generate kasammi codes

Generate lorenz data. You can control the parameters and it may be useful for nonlinear analysis.

此程序为MATLAB环境下编写的用于对图像的平均梯度进行计算 从而对图像的清晰度 图像的质量进行评价的代码。(This program is written in MATLAB environment is used to calculate the average gradient image sharpness of the image and thus to evaluate the image quality of the code.)

通过matlab 平台，自己编写的Matlab程序。是关于滤波器的(the program of matlab is wrriten by myself that about sifting )

目录 1.图像反转 2 2.灰度线性变换 2 3.非线性变换 4 4.直方图均衡化 5 5. 线性平滑滤波器 6 6.中值滤波器 7 7.用Sobel算子和拉普拉斯对图像锐化： 8 8.梯度算子检测边缘 9 9.LOG算子检测边缘 11 10.Canny算子检测边 缘 12 11.边界跟踪 （bwtraceboundary函数） 13 12.Hough变换 14 13.直方图阈值法 16 14. 自动阈值法：Otsu法 18 15.膨胀操作 19 16.腐蚀操作 20 17.开启和闭合操作 21 18.开启和闭合组合操作 22 19.形态学边界提取 24 20.形态学骨架提取 25 21.直接提取四个顶点坐标 26 22.文件打开窗口 27 (Contents 1 image reversal 22 23 gray linear transformation. Nonlinear transformation 44. Histogram equalization 55 linear smoothing filters 66. Median filter 77. Sobel operator and Rapp Las Image Sharpening: 88. gradient edge detection operator 9 9.LOG edge detection operator 11 10.Canny count of edge detection 1211. the border tracking (bwtraceboundary function) 1413 13 12.Hough transform histogram. threshold Law 1614 automatic thresholding method: Otsu 1815 expansion operation 1916. corrosion operation 2017. opening and closing operation of 2118. combination of opening and closing operation 2219. morphology boundary extraction 24 20 morphological skeleton extraction 2521 direct extraction of four vertex coordinates 2622 File Open window 27)

数据同化的经典算法，集合卡尔曼滤波算法，用Matlab写的，里面附有一些参考文献，初学者适用(Classical algorithm of data assimilation, ensemble Kalman filter algorithm, written using Matlab, which with some references, for beginners)

this is very good iris recognition code iris segmentation

可以对离散点进行波面泽尼克拟合，程序很好用(Wave surface Zernike fitting can be used for discrete points)

A. Fusion at the Feature Extraction Level The data obtained from each sensor is used to compute a feature vector. As the features extracted from one biometric trait are independent of those extracted from the other, it is reasonable to concatenate the two vectors into a single new vector. The primary benefit of feature level fusion is the detection of correlated feature values generated by different feature extraction algorithms and, in the process, identifying a salient set of features that can improve recognition accuracy [14]. The new vector has a higher dimension and represents the identity of the person in a different hyperspace. Eliciting this feature set typically requires the use of dimensionality reduction/selection methods and, therefore, feature level fusion assumes the availability of a large number of training data.