Huffman

对于无源雷达定位系统，采用测向方法对目标定位，并对定位精度进行了分析。(For passive radar location system, using the direction finding method of orientation, and the positioning precision is analyzed.)

线性预测在MATLAB中的实现，非常具有参考价值的文档(Linear prediction in the MATLAB implementation of the document is a reference value)

一堆沙子的横截面，可以使用 Margolus 型邻居仿真，但运动规则不同。 规则： 元胞有 2 个状态。状态= 0 是空的，状态= 1 代表沙子。 在任何时间步长，一个粒子，可以在2x2 块中向着底部运动。可能运动如下 所示。墙壁和底部将阻止粒子继续运动。 为了让该运动略有随机性，我亦补充说一项规则，有时处于下落状态的两个元胞还旋转，直到所有的动作都完成。(Cross-section of a pile of sand, you can type using the Margolus neighborhood simulation, but the movement rules are different. Rules: There are two cellular states. State = 0 is empty, state = 1 for sand. At any time step, a particle can be in the 2x2 block toward the bottom of the movement. May exercise the following. Walls and bottom to prevent the particles continue to campaign. To make the campaign a little random, I also added a rule, sometimes in the falling state of the two cell also rotate until all actions are completed.)

粒子滤波的matlab程序，下的非常详细，能够直接运行，初学者的福音(particle filter ，can run directly)

这个程序是基于MATLAB实现的偏最小二乘算法(MATLAB implementation is based on partial least squares algorithm)

FX滤波，matlab程序，用于地震资料处理(FX filtering, matlab program for seismic data processing )

基于MATLAB的BP神经网络人脸朝向识别(Towards the identification of BP neural network based on MATLAB Face)

说明：  Stewart平台是典型的并联机器人的代表，相比于串联机器人来说，其多链式闭环的结构让并联机器人的运动学求解变得复杂。该程序内有Stewart平台研究的一整套程序，包括：运动学逆解、正解、工作空间和动力学的matlab程序，可以减少你的工作量。(Stewart platform is a typical representative of parallel robot. Compared with serial robot, its multi chain closed-loop structure makes the kinematics solution of parallel robot more complex. The program contains a complete set of Stewart platform research procedures, including: inverse kinematics, forward kinematics, workspace and dynamics matlab program, can reduce your workload.)

Is-95 反向业务信道端到端simulink仿真(Is-95 reverse traffic channel end simulink simulation)

M/M/C模型的呼叫过程阻塞概率的仿真 Simulation M / M / C call blocking probability model(Simulation M/M/C call blocking probability model)