WiMAX

应用MATLAB编写的二维时域有限差分程序。是初学FDTD的好帮手。建议初学者参考。(Application of MATLAB to prepare two-dimensional finite difference time domain procedure. FDTD are learning a good helper. Recommended reference for beginners.)

GS算法的matlab例子，本例子仿真了一个圆形的光环结构，其它的参数见matlab中的注释(GS algorithm matlab example, the example of simulation of a circular ring structure, and other parameters, see the Notes matlab)

基于Matlab的时域卷积的动态演示的实现，对进行DSP工作的通知可能有一定帮助。欢迎下载(Matlab-based time-domain convolution of the realization of the dynamic presentation of the work carried out to inform the DSP may have some help. Welcome to download)

Code for design of derivative fillters

在自动控制系统的过程中，白噪声的产生方法，MIT 稳定方程 持续时间 稳定时间(The automatic control system of the process, the white noise generation method, MIT stabilization stabilization time duration equation)

说明：  这个M代码是从一位学者的Python修改而来的 卡尔曼滤波算法对室内温度的估计。 下面是Python版本的注释：卡尔曼过滤器的Python示例演示(This M code is modified from Andrew D. Straw's Python implementation of Kalman filter algorithm. Below is the Python version's comments: Kalman filter example demo in Python)

Channel des with Q function and CDf function and more

用BP神经网络优化模糊控制器，进行学习，系统有两个输入，输出误差曲线和期望曲线，BP学习误差曲线(BP neural network to optimize the fuzzy controller learning system has two inputs, the output error curve and expectations of the curve, the BP learning error curve)

说明：  完整的curvelet变换(2.0)实现(complete curvelet transform (2.0) Implementation)

现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。(existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.)