bch_MATLAB

此程序写了，任意给定两个数，互相作为底数和指数，再求和。如果不满足条件，报错。(This program was made, any given two numbers to each other as the base and index, and then sum. If does not meet the conditions, an error.)

mitmatlab工具箱，有各种机器学习的算法(mitmatlab toolbox, there are a variety of machine learning algorithm)

半盲估计算法利用很少的导频符号，以LMMSE准则来估计、初始化信道系数，运用的两种算法进行对比，牺牲极小的带宽而尽可能准确的传输有用信息(Half blind algorithm using little pilot.finally symbols, with LMMSE criteria to estimate, initialization, the use of the channel coefficient two algorithm is compared, and sacrifice of tiny bandwidth and accurately as possible transmission useful information )

svm 的参数优化，利用交叉验证法选择最优参数c g，最终提高训练集的分类准确率,更好的提高分类器性能(Svm parameter optimization, the use of cross-validation method to the optimal parameter c g, and ultimately improve the training set classification accuracy，better improve the classifier performance)

用来显示3D图片的一个matlab程序,包括灰度范围(a matlab code to display 3d voxel)

kalman filter with state constraints lecture and example

基于小波基的树结构的贝叶斯方法，采用MCMC推断方法。(Tree structure of wavelet-based Bayesian methods using MCMC inference method.)

Radar Deception Jamming

摘要地层控制分析与设计问题 无人机（UAV）群系统实现时间。 研究了不同的地层。达到预定时间- 无人机的编队、编队协议各不相同。 首先是群系统，无人机的速度可能不同 当到达编队时。然后，基于共识的方法 用于处理时变信息的形成控制 无人机群系统的问题。必要和充分 无人机群系统实现时变的条件 提出了构造方案。(Abstract-formation control analysis and design problems for unmanned aerial vehicle (UAV) swarm systems to achieve time. varying formations are investigated. To achieve predefined time- varying formations, formation protocols are presented for UAV swarm systems first, where the velocities of UAVS can be different when achieving formations. Then, consensus-based approaches are applied to deal with the time-varyin formation control problems for UAV swarm systems. Necessary and sufficient conditions for UAV swarm systems to achieve time-varying formations are proposed.)

This Matlab code implements an edge based geometric active contour model without reinitialization. MATLAB Release: R13SP1 This Matlab code implements the level set method in Chunming Li et al s paper "Level Set Evolution without Re-initialization: A New Variational Formulation", in IEEE Proc. of CVPR 2005. EVOLUTION.m: core function that performs level set evolution. Demo1.m: a demo for a real cell images Demo2.m: a demo for a synthetic noisy image Demo1_ManualBinaryInitial: demo for the same image in Demo1, allows manual initialization by the user Demo2_ManualBinaryInitial: demo for the same image in Demo2, allows manual initialization by the user