ModelingAccelSigProcess

随机图网络的matlab的程序代码，包括三种不同随机连接方式(random networks)

用matlab的9个编程例子附程序，可以更好的理解掌握MATLAB，供大家学习与交流。(9 programming example attached matlab program, you can better understand and grasp the MATLAB for everyone to learn and exchange.)

在matlab中利用Volterra非线形滤波器进行系统辨识，并带有一个应用示例。代码带有详细注释。(In the matlab filter using Volterra non-linear system identification, and with an application example. Code with detailed comments.)

最小化L1范数求解，通过L1-LS工具包。(L1 norm minimization solution, through the L1-LS kit.)

说明：   已知某控制系统结构图如下所示,要求设计校正环节G(s),使系统对于阶跃输入的稳态误差为零。使系统校正后的相角裕量γ≥45°，幅值裕量kg≥10db。(Known structure of a control system as shown below, links to design correction G (s), allowing the system to step input for the steady-state error to zero. The system after correction phase margin γ ≥ 45 °, the amplitude margin kg ≥ 10db.)

The library of linear classifier

基于Sage-Husa自适应卡尔曼滤波器的海浪磁场噪声抑制算法以及海浪磁场噪声的产生(Sage-Husa Adaptive kalman Filter applicated in the suppression of magnetic fields noise induced by ocean waves)

电子书：matlab基础基础及其应用。这是学习matlab的很好的书，另外一起打包了英文的matlab编程技巧(EBook: matlab based on the foundation and its application. This is a good learning matlab book, another package of the English matlab programming skills)

ASM是由Cootes和泰勒推出的多分辨率方法的一个例子。 基本思想： 在ASM模型训练，训练从手工绘制的图像轮廓。发现的ASM模型在训练使用主成分分析（PCA），使该模型自动识别数据的主要变化是，如果可能的轮廓/好的对象的轮廓。还包含了ASM模型的协方差矩阵描述行垂直纹理口岸时，在正确的位置。 (Description This is an example of the basic Active Shape Model (ASM) as introduced by Cootes and Taylor, with multi-resolution approach. Basic idea: The ASM model is trained from manually drawn contours in training images. The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour. Also the ASM models contains covariance matrices describing the texture of the lines perpendicular to the control points when in the correct positions. After creating the ASM model, an initial contour is deformed by finding the best texture match for the control points. This is an iterative process, in which the movement of the control points is limited by what the ASM model recognizes from the training data as a "normal" object contour. Literature: - Ginneken B. et al. "Active Shape Model Segmentation with Optimal Features", IEEE Transactions on Medical I)

说明：  脑电信号分析中的空间滤波器，分析节律信号非常有效。(EEG analysis of the spatial filter, analyze rhythm signal is very effective.)