p0301

高动态GPS接收机载波跟踪算法研究与实现 里面有各种参数的设计与选择 这篇文章讲的比较详细(High dynamic GPS receiver carrier tracking algorithm to achieve The design and selection of various parameters of this article talking about a more detailed)

实现了正交匹配追踪算法的matlab编程(To achieve the orthogonal matching pursuit algorithm matlab programming)

结构分析的有限元与matlab一书中的源代码第四部分，二维线弹性有限元(originnl programs in the book of finite element method in structure analyses and matlab programming part 4)

一种合并频率的方法对时频分析及其有用所以才上传(a fast combination)

说明：  RLS算法多麦克风语音降噪.rar 最小二乘自适应滤波 里面包括文档等(RLS算法多麦克风语音降噪.rar)

the file contains detection and estimation theroy codes

指纹识别,包括预处理，后处理，特征识别，特征匹配等的代码(zhiwenshibie)

说明：  观察高斯序列 的时域和幅频特性。 ①固定p=8，q分别等于2、4、8，观察它们的时域和幅频特性，了解当q取不同值时，对信号序列的时域和幅频特性的影响 ②固定q=8，p分别等于8、13、14，观察p变化对信号序列时域和幅频特性的影响，注意p等于多少时，会发生明显的泄露现象，混叠是否也随之出现？记录实验中观察到得现象，绘出相应的时域序列和幅频特性曲线(Observed Gaussian sequence in time domain and amplitude-frequency characteristics. ① fixed p = 8, q is equal to 2,4,8 respectively, to observe them in time domain and amplitude-frequency characteristics, to understand when the different values of q, the signal sequence in time domain and amplitude-frequency characteristics of ② fixed q = 8 , p is equal to 8,13,14 respectively, to observe the changes in the signal sequence p in time domain and amplitude-frequency characteristics, note that p is equal to the number when the apparent leakage phenomenon, aliasing is also gone? Records of the experiment was observed phenomenon, draw the corresponding time-domain sequence and amplitude-frequency characteristic curves)

MATLAB 主要函数指令表，为初学者提供参考。(The main function of MATLAB instruction sheet, provide a reference for beginners.)

PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model imposing stability restrictions using Gibbs sampling y = b0 + y(t-1) b1 + y(t-2) b2 +,...,y(t-k) bk + E, E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T), V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k)( PURPOSE: MCMC estimates Bayesian heteroscedastic AR(k) model imposing stability restrictions using Gibbs sampling y = b0+ y(t-1) b1+ y(t-2) b2+,...,y(t-k) bk+ E, E = N(0,sige*V), sige = gamma(nu,d0), b = N(c,T), V = diag(v1,v2,...vn), r/vi = ID chi(r)/r, r = Gamma(m,k))