MATLAB-music-method

本文利用MATLAB的可视化工具箱simulnk建立了直扩通信系统的仿真模型，为扩频信号源自检程序设计提供参考。(In this paper, the visualization MATLAB toolbox simulnk DSSS communication system established a simulation model for the spread spectrum signal self-test program designed to provide information.)

Included are the files wav1.m, wav2.m, wavecoef.mat and readme. wav2 function implements the tree structured wavelet transform of the input matrix, up to the given level of decomposition. Wav2 uses another function called wav1, which takes the well known wavelet transform of the given matrix. Daubechies wavelet coefficients are used for wavelet transform operation wahich is saved in wavcoeff.mat. (Included are the files wav1.m, wav2.m. wavecoef.mat and readme. wav2 function biennium max the tree structured wavelet transform of t he input matrix. given up to the level of decomposition. Wav2 use s wav1 another function called, which takes the well known wavelet transform of the given matrix. Daubechies wavelet coeffici separations are used for wavelet transform operation w ahich is saved in wavcoeff.mat.)

一个选择器特效大全的例子：KNMultiItemSelector，IOS编程学习源码，很好的参考资料。(A selector effects Guinness examples: KNMultiItemSelector, learning IOS programming source code, a good reference.)

应用于MATLAB上的聚类方法，简单，易学。(Applies to MATLAB on the clustering method, simple, easy to learn.)

testocx.m － 用于测试ActiveX控件ActVoice的MATLAB录音程序

在普通室内环境下录制‘我到北京去’，采样频率为8k用matlab分析这句话的倒谱(Recorded in the general indoor environment ' I went to Beijing' , the sampling frequency for the 8k cepstrum analysis of this sentence, with matlab)

Kolda Tensor Factorization toolbox. Useful for tensor signal processing

structure to store a complex number (consisting of real and complex part) and features addition, multiplication, and outs of complex numbers.

principal_chaleur a routine for implementing the EF P1 Lagrange for the heat equation in the following stationary condition with Nonhomogeneous Dirichlet

说明：  两种不同的假设： H1 ： 0 xn A fn wn ( ) cos(2 ) ( ) = ++ π θ n=1,2,…,N，f0 为规一化频率 H0 ： xn wn () () = n=1,2,…,N 其中 w[n]是均值为 0，方差为 2 σ n 的高斯白噪声，A 已知，样本间相互 独立，信号与噪声相互独立; 相位θ 是随机变量，它服从均匀分布 1 0 2 ( ) 20 p θ π θ π ?? ≤ ≤ = ??? 其它 1）改变输入信噪比（改变 A 或噪声方差均可），给定虚警概率，画出 输入信噪比与检测概率之间的理论曲线。(注意：理论检测曲线与样本 数有关) 2）改变样本数，用 Monte-Carlo 实验方法得出 PF＝0.001 时输入信噪比 与检测概率之间关系曲线（至少三条），并得出结论。 3）改变 M-C 实验次数，样本数不变，用 Monte-Carlo 实验方法得出 PF ＝0.001 时输入信噪比与检测概率之间关系曲线（至少三条），并得出 结论。(Draw the theoretical curve between input SNR and detection probability)