siftDemoV4ofMatlab

The C Programming Language

matlab实现，主要实现wiener滤波、qrst复合波检测、RR间期检测(Matlab realization main Wiener filter, qrst composite wave detection, RR interval detection, etc.)

Solve 2d wave equation with Finite difference method有限差分法解二维波动方程(Solve 2d wave equation with Finite difference method)

在对能量收集传感网中的压缩感知技术的学习研究过程中，探讨了满足RIP下，所需的测量数m；允许MSE下，可实现的系统延迟；利用LDT，分析SNR不均匀性对k限制特征值的影响。(For energy harvesting sensor networks in the compressed sensing technology learning research, discusses meet the RIP, the required number of measurements m allow MSE, the delay can be implemented system the use of LDT, analysis SNR heterogeneity on k limit the impact of eigenvalues.)

MATLAB 中直方图均衡化和规定化处理函数格式如 下:(Histogram equalization in MATLAB and provisions to deal with functions of the following format:)

适当选择辅助变量，使之满足相应条件，参数估计值就可以是无偏一致。估计辅助变量法的计算量与最小二乘法相当，但辨识效果却比最小二乘法好的多。尤其当噪声是有色的，而噪声的模型结构又不好确定时，增广最小二乘法和广义最小二乘法一般都不好直接应用，因为他们需要选用特定的模型结构，而辅助变量法不需要确定噪声的模型结构，因此辅助变量法就显得更为灵活，但辅助变量法不能同时获得噪声模型的参数估计。(Choose appropriate secondary variables, meet the relevant conditions and parameter estimate can be unbiased consistent. Estimated auxiliary variable method calculation and least square method is quite, but the identification effect is much better than the least square method. Especially when the noise is colored, and noise model structure and not sure, augmented the least squares and the generalized least squares method is generally not used directly, because they need to choose specific model structure, and auxiliary variable method does not need to make sure that noise model structure, so the auxiliary variable method is more agile, but not at the same time auxiliary variable method for noise model parameter estimation.)

RSSI强度值采集例子，每隔一段距离采集一段RSSI值，从matlab中绘制折线图，并由最小二乘法分析你合成曲线(RSSI strength values ​ ​ collected examples, at some distance acquisition period of the RSSI value, the last drawing, analyze your synthetic curve by the least squares method)

不错的GM_EM代码。用于聚类分析等方面。( GM_EM- fit a Gaussian mixture model to N points located in n-dimensional space. Note: This function requires the Statistical Toolbox and, if you wish to plot (for k = 2), the function error_ellipse Elementary usage: GM_EM(X,k)- fit a GMM to X, where X is N x n and k is the number of clusters. Algorithm follows steps outlined in Bishop (2009) Pattern Recognition and Machine Learning , Chapter 9. Additional inputs: bn_noise- allow for uniform background noise term ( T or F , default T ). If T , relevant classification uses the (k+1)th cluster reps- number of repetitions with different initial conditions (default = 10). Note: only the best fit (in a likelihood sense) is returned. max_iters- maximum iteration number for EM algorithm (default = 100) tol- tolerance value (default = 0.01) Outputs idx- classification/labelling of data in X mu- GM centres)

本书介绍了小波变换在地震资料处理中的应用，并介绍了MATLAB工具箱(This book introduces the application of wavelet transform in seismic data processing, and introduces the MATLAB toolbox )

matlab 6.0 ,word格式，适用广大群众学习matlab.(matlab 6.0)