GPS信号的产生

借用MFO算法实现对于火电机组调度问题的优化，在处理约束变量时使用了stochastic ranking的方法(The MFO algorithm is adopted to optimize the scheduling problems of thermal power units. When dealing with constraint variables, the stochastic ranking method is adopted)

ADXL345数字加速度传感器STM32代码(stm32 adxl345)

ABAQUS复合材料损伤起始与扩展的子程序，拉伸采用hashin, 压缩采用PUCK.适用于显示分析(ABAQUS Composite Damage Initiation and Extension Subprogram, Hashin for Tensile and PUCK for Compression. Suitable for Display Analysis)

实现大小写互换，即输入大写子母程序会自动转成小写子母(Realization case swap)

用于配电网重构的粒子群算法，可以给大家参考(Particle swarm optimization for distribution network reconfiguration)

DCT控制原理，离合器模型，入门了解的好帮手(DCT control principle, clutch model, access to a good understanding of the helper)

说明：  ay emulator5 code in c language

常用的盲分离算法有二阶统计量方法、高阶累积量方法、信息最大化（ Infomax ）以及独 立成分分析（ ICA ）等。这些方法取得最佳性能的条件总是与源信号的概率密度函数假设有关， 一旦假设的概率密度与实际信号的密度函数相差甚远，分离性能将大大降低。本文提出采用 核函数密度估计的方法进行任意信号源的盲分离，并通过典型算例与几种盲分离算法进行了 性能比较，验证了方法的可行性。(The commonly used blind separation algorithms include two order statistics, higher order cumulants, and maximization of information Infomax) and independent component analysis (ICA). The conditions for obtaining the best performance of these methods are always related to the hypothesis of the probability density function of the source signal. The separation performance will be greatly reduced when the assumed probability density is very different from the density function of the actual signal. In this paper, a method of kernel function density estimation for blind separation of arbitrary signal sources is proposed. The performance of several blind separation algorithms is compared with some typical examples, and the feasibility of the method is verified.)

这是浙大鲍虎军教授演讲的纹理映射技术，内容十分详细。(This is Professor of Hu-Jun Bao Zheda speech texture mapping techniques, very detailed.)

particle_filter_demo