T2F

说明：  用MATLAB做的地面动目标检测，通过杂波抑制，dcpa等等相位中心天线技术，来检测出地面的动目标。(this code is the result of the code ,in fact ,it is the result of the code ,it is using the radar to detect the moveing targets in the ground ,that is it.)

The ULIB is a collection of efficient building blocks for developing high-performance and scalable systems in C and C++. These building blocks mainly cover, multithreading, parallel computing, efficient data indexing, numerical approximation, and cryptography.

This matlab code can be used to generate a parity check matrix for the LDPC code and further encoded and decoded in a complete communication system.

说明：  有用的动态仿真技术，希望对学习控制理论和虚拟现实技术有用。(useful dynamic simulation technology, and I hope to learn from control theory and virtual reality technology useful.)

智能天线小程序，计算电流分布，方向图等等对初学者有帮助(smart antenna， easy for junier learner)

使用蚁族算法，用MATLAB编程。实现优化编程，实用。(study MATLAB to use good idear.)

一种用于人体姿态估计的强大算法,可以检测并且定位人体的四肢及躯干和头部的位置，用于更高层次的分析。(Pose estimation)

是信号处理的基础，基于分段非线性权重值的Pso算法，通过反复训练模板能有较高的识别率，matlab编写的元胞自动机，用于图像处理的独立分量分析，利用贝叶斯原理估计混合logit模型的参数，可以实现模式识别领域的数据的分类及回归，信号处理中的旋转不变子空间法。( Is the basis of the signal processing, Based on piecewise nonlinear weight value Pso algorithm, Through repeated training KCajOMWlate have higher recognition rate, matlab prepared cellular automata, Independent component analysis for image processing, Bayesian parameter estimation principle mixed logit model, You can achieve data classification and regression pattern recognition, Signal Processing ESPRIT method.)

隐马尔科夫模型是关于时序的概率模型，描述由一个隐藏的马尔科夫链随机生成不可观测的状态随机序列，再由各个状态生成一个观测而产生观测序列的过程。隐藏的马尔科夫链随机生成的状态的序列，称为状态序列；每个状态生成一个观测，而由此产生的观测的随机序列，称为观测序列。马尔科夫链由初始概率分布、状态转移概率分布以及观测概率分布确定(The hidden Markov model is a probabilistic model for time series. It describes the process of randomly generating unobservable state random sequences from a hidden Markov chain, and then generating an observation by each state to produce an observation sequence. A sequence of randomly generated states of hidden Markov chains, called a sequence of states; each state produces an observation, and the resulting random sequence of observations is called an observation sequence. Markov chain is determined by initial probability distribution, state transition probability distribution and observation probability distribution)

说明：  蒙特卡罗马尔科夫链学习相关知识，了解马尔科夫和蒙特卡罗常用方法。(Markov chain Monte Carlo to learn relevant knowledge, understanding common Markov and Monte Carlo methods.)