432soft

对摄像机进行标定。学习之用。学写代码的话比看OPENCV的代码要好，使用的话OPENCV或者MATLAB都足够了(Calibration of the camera. Learning. Write code, then look OPENCV than the code is better, the use of words or MATLAB are OPENCV enough)

说明：  本文给出了用温熵图证明卡诺定理的更直观更简单的方法 并通过实例证明采用温熵图处理某些热力学问题得出结论的过程也比使用P - V 图或常用的热力学方法更为直观、方便。(In this paper, with a temperature entropy diagram of Carnot theorem proved simpler and more intuitive way and through examples demonstrate some of the thermodynamic temperature and entropy map processing a conclusion of the process than using the P- V diagram or thermodynamic method commonly used more intuitive and convenient.)

DC motor selection via torque calculation

wavelet小波处理算法及其应用。包含了小波处理的基本应用，图像的去噪等等处理(wavelet implementation)

this file implement stochastic method in pattern recognition

说明：  分数阶动态系统的数值算法Numerical Algorithm for Fractional Dynamic Systems(Numerical Algorithm for Fractional Dynamic Systems)

lms算法的matlab实现程序，比较经典，非常适合作为初学者的学习参考(lms algorithm matlab implementation process, more classic, ideal for beginners to learn as a reference)

经典的信息论准则估计信号源个数。 使用空间谱估计理论，模拟发射信号由天线阵列接受采样。经过AIC和MDL两种算法，估计出采样序列中信号的个数，所加噪声是白噪声。(Classical information theory for sources number estimation. It s spatial spectrum estimation theory. it analogs transmission signal from the antenna array . It uses two algorithms“ AIC and MDL ”to estimate the number of sampled signal sequence. the added noise is white noise.)

H_K（Ho-Kashyap）算法的线性分类器 可用于分类简单情况 适合初学者(H_K (Ho-Kashyap) algorithm for the linear classifier can be used for classification are quite simple for beginners)

稳态kalman滤波算法仿真通式 本程序考虑线性离散时不变随机系统。系统模型为x(t+1)=fai*x(t)+gama*w(t) y(t)=H(t)*x(t)+v(t)。有6个参数：状态转移阵fai，输入噪声系数gama，观测阵H，输入 噪声方差Q，观测噪声方差R，观测y( Steady-state kalman filtering algorithm simulation program to consider the general form linear discrete time-invariant stochastic system. System model x (t+1) = fai* x (t)+ gama* w (t) y (t) = H (t)* x (t)+ v (t). There are six parameters: state transition matrix fai, input noise figure gama, observation matrix H, enter noise variance Q, observation noise variance R, observation y)