MMSE

一种目前最流行的人脸识别算法程序，利用的是特征脸识别算法，程序简单，每行都有注释(A face recognition is currently the most popular algorithm procedures, take advantage of the features of the face recognition algorithm, the procedure is simple, there are notes on each line)

通信系统防真　，用matlab编写，ＰＳＫＳＡ(communication system simulation by matlab.ＰＳＫＳＡ)

高光谱干涉测厚算法的MATLAB例程，通过傅立叶变换实现，解析干涉高光谱对应的样品厚度，分辨率可实现纳米级别(High spectral interference thickness measurement algorithm MATLAB routines, Fourier transform, resolve interference hyperspectral corresponding sample thickness, the resolution can achieve nanometer level)

矿井超宽带方面的一篇博士论文，里面包含大量信道建模方面知识，对于初学者深刻理解多径信道方 面的内容大有裨益。(tudy on channel models of the Ultra-wideband wireless communication in undergrand mines)

跳跃扩散模型结合条件异方差自回归模型的参数估计(Jump diffusion model combined with conditional heteroskedasticity self-parametric regression models estimate)

说明：  跳频通信中模式相加的原代码（配合Mmod.m，reMmod.m,U.m）一起使用。（附MATLAB库函数说明）(The sum of frequency-hopping mode of communication in the original code (with Mmod.m, reMmod.m, Um) use. (With MATLAB library function description))

ofdm simulation, cantains ber, snr. it s short code ofdm mimo, wimax wide.

说明：  磁带滚动轴半径的测量，R2008B以上版本运行(Measurement of the radius of the tape roll axis, R2008B or later to run)

产生LFM信号，纯LFM信号,雷达线性调频波，包括波形、频域分析等(LFM signal is generated, pure LFM signal)

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)