Solutions_exercice9

MUSIC算法是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。MUSIC算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(MUSIC algorithm is a matrix decomposition method based on feature space. From the geometric point of view, the observed spatial signal processing can be decomposed into signal subspace and noise subspace, it is clear that the two spaces are orthogonal. The signal received by the array to the subspace of the covariance matrix of the signal component corresponding eigenvectors, the noise subspace of the covariance matrix of all by the smallest eigenvalue (noise variance) eigenvectors corresponding to the composition. MUSIC algorithm is the use of orthogonal properties between these two complementary space to estimate the spatial orientation of the signal. Noise subspace of all vectors are used to construct the spectrum, all the spatial orientation of the spectrum corresponding to the peak position of the signal wave direction. MUSIC algorithm greatly improves the resolution measurements, while the antenna array adapted to any shape, but the prototype MUSIC algorithm requires to wave sign)

【实例简介】这是一个和Giacomo Livan、Marcel Novas和Pierpaolo Vivo的《随机矩阵导论》一书相关的Matlab文件集合。

Error Vector Magnitude Calculation

本程序详细区别了matlab中pca、svd、eig三个函数的区别和联系。对于学习pca有极大帮助(This program detailing the differences between the differences and connections in matlab pca, svd, eig three functions. Pca great help for learning)

matlab中遗传算法的源码，简单易懂，学习用很不错(M code of genetic algorithm in matlab)

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.)

关于随机分布控制理论中最基础的思想，主要是基于平方根B样条建模。(About randomly distributed control theory the most basic idea is mainly based on the square root of the B-spline modeling.)

periode lambda bragg grating

These files contain all the code necessary to run the example in the Webinar "Signal Processing for Machine Learning in MATLAB". They also include code to automate the download and preparation of the dataset used. In that webinar we presented an example of a classification system able to identify the physical activity that a human subject is engaged in, solely based on the accelerometer signals generated by his or her smartphone. We used consolidated signal processing methods to extract a fairly small number of highly-descriptive features, and finally trained a small Neural Network to map the feature vectors into the 6 different activity classes of a pre-recorded dataset. The topics discussed include: * Signal manipulation and visualisation * Design and application of digital filters * Frequency-domain analysis * Automatic peak detection * Feature extraction from signals * Train and test of simple Neural Networks

基于PPP随机几何方法以及蒙特卡洛方法对蜂窝通信通信系统进行仿真(The simulation of cellular communication system is based on PPP stochastic geometry method and Monte Carlo method)