lms

关于OFDM信道估计算法的仿真，比较详细(Simulation of channel estimation on OFDM)

应用共轭梯度法综合优化交叉耦合滤波器。参考文献<Synthesis of Cross-coupled’Resonator Filters Using an Analytical Gradient-Based Optimization Technique >Smain Amari(Synthesis of Cross-coupled’Resonator Filters Using an Analytical Gradient-Based Optimization Technique _Smain Amari-)

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)

this function is used for fitting data. This function is used with another function called funct_for_curve_fitting.m and data_for_fitting.txt

说明：  这个是多带谱语音检测，多带谱不仅具有谱熵的有点，还具有能量分析的有点，所以检测的效果很好！(This is a multi-band spectra of voice activity detection, multi-spectral bands not only has a bit of entropy, also has a bit of energy analysis, so the effect of a very good test!)

最近在学matlab，闲暇时间做了一个会动的小球(recent study in Matlab, leisure time doing the moves of a small ball)

雷达电子对抗中的无源干扰的重要形式之一——箔条干扰(chaff jamming)

Matlab圆弧插补程序(显示圆心)，完善上个不能无法显示圆弧圆心的缺陷，能够标记圆心，显示圆心坐标(Matlab circular interpolation procedure (display center), the perfect can not can not be displayed in the center of the arc defects, to mark the center of the circle, and display center coordinates)

超声回波声场数据处理显示（matlab 源程序）(ultrasound )

implementation of most algorithms in Simon haykin s book titled "Neural Network a comprehensive foundation."