Mitsubishi-PLC

完备图像稀疏表示是一种最新的图像表示模型，采用过完备字典中原子的线性组合形式实现图像的稀疏表示．传统 的过完备图像稀疏表示模型采用重建误差的平方和作为保真项．该保真项没有充分考虑到人眼对图像的感知特性，无法度量图 像中边缘、轮廓、纹理等局部几何结构的变化．本文基于过完备稀疏表示理论思想，建立了新的稀疏性正则化的图像稀疏表示模 型．模型中的正则项约束图像表示系数的稀疏性，保真项采用更符合视觉感知的结构相似性度量．基于正交匹配追踪算法，提出 了基于结构相似度的正交匹配追踪算法．实验结果表示，新的模型能够更好地重构图像的结构信息，获得更好的重建视觉效果． (Image Sparse Representation Model bed StmctIlral SiIIIilarity )

自己编写的高压直流输电中平波电抗器的计算程序，在实际系统中做过验证，满足实际工程需要，只需要输入所需要的参数，自动计算平波电抗器的电感，并进行了优化处理(I have written HVDC Zhongping wave reactor calculation procedure done in the actual system validation, meet the actual project needs, only need to enter the required parameters, automatic calculation of smoothing reactor inductance and optimization of processing)

本程序运用bing yang的PAST算法和PASTD算法跟踪信号子空间，并利用MUSIC算法估计信号的波达方向(DOA)(This procedure utilizes the PAST algorithm and the PASTD algorithm (put forward by Bing Yang)track signal subspace, and uses MUSIC algorithm to estimate signal prameter Direction Of Arrival(DOA) )

LMS滤波算法，分别对锯齿波、方波、正弦波进行了滤波，并将滤波前后的信号进行了对比，很好的恢复出了真实信号。(LMS filter algorithm. The sawtooth wave, square wave, sine wave are filtered, and the signals are contrasted before and after filtering. The real signals are all well recoveried by this algorithm.)

模式识别中的模糊聚类，采用隐马尔科夫链，考虑了先验概率，进行图像识别。(Pattern recognition, fuzzy clustering, using hidden Markov chain, taking into account a priori probability for image recognition.)

anti skid control system for front wheeled drive vehicle. using carsim and matlab simulink

基于HIO算法的破解POCS（基于约束集投影）加密系统的MATLAB程序。(HIO algorithm based on crack POCS (projection based on a set of constraints) MATLAB program encryption system.)

isothermal solution of reynolds equation

A DC/DC Boost Converter was developed using the simulink tool box

粒子滤波算法，通过此程序，可以成功的仿真出粒子滤波。在滤波过程中粒子滤波可以处理任意形式的概率，而不像Kalman滤波只能处理高斯分布的概率问题。他的一大优势也在于此。 (Particle filter, through this program, you can emulate the success of the particle filter. In the process of filtering particle filter can handle any form of probabilities, rather than the Kalman filter can only handle a Gaussian probability distribution. He s a big advantage this.)