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BEMD对图像进行分解，包络构造采用Delaunay三角剖分和三次插值，得到分解的结果。 输入：灰度bmp图像 输出：imf1 imf2 imf3 残差(BEMD image decomposition, the envelope structure using Delaunay triangulation and cubic interpolation, the result of decomposition. Input: greyscale bmp image output: imf1 imf2 imf3 residuals)

经典的基本PSO粒子群优化算法的测试函数，包括主程序和测试函数，非常经典(The classic PSO basic particle swarm optimization test functions, including the main program and test functions, very classic)

图像拼接的matlab代码，可以作为参考(Image mosaic matlab code, can be used as reference)

多模型交互跟踪多模型交互跟踪多模型交互跟踪多模型交互跟踪(Multi-track interaction model)

工业图像处理软件，但没有数据库，具体操作说明见(Industry image processing software, but there is no database, see the specific operation instructions)

gmsk的详细c语言编程过程，很适合初学者使用(the detailed c language gmsk programming process, it is suitable for beginners)

基于SVPWM的电机转速双闭环控制。波形完好，适合学习。(Double Closed Loop Control of Motor Speed Based on SVPWM. Waveform is good, suitable for learning.)

Visual Cryptography

SAP，ABAP编程，利用SMARTFORM生成PFD文档并通过EMAIL发送(SAP, ABAP programming, using SMARTFORM to generate PFD documents and sent by EMAIL)

An implementation of Unscented Kalman Filter for nonlinear state estimation. (Nonlinear state estimation is a challenge problem. The well-known Kalman Filter is only suitable for linear systems. The Extended Kalman Filter (EKF) has become a standarded formulation for nonlinear state estimation. However, it may cause significant error for highly nonlinear systems because of the propagation of uncertainty through the nonlinear system. The Unscented Kalman Filter (UKF) is a novel development in the field. The idea is to produce several sampling points (Sigma points) around the current state estimate based on its covariance. Then, propagating these points through the nonlinear map to get more accurate estimation of the mean and covariance of the mapping results. In this way, it avoids the need to calculate the Jacobian, hence incurs only the similar computation load as the EKF. For tutorial purpose, this code implements a simplified version of UKF formulation, where we assume both the process and measurement noises are additive to avoid augment of state and a)