Stationary_Series1_AR1

说明：  1对一段音频进行matlab双声道限时取样，并画图 2产生一段随机整数噪声，并加入到原样本中(1 on a two-channel audio matlab limited sampling, and drawing a random integer generated noise 2, and added to the original sample)

human robot simulation in matlab kinematic

　　车辆监测用测速雷达是依据多普勒效应原理实现的。由雷达发出一束微波，遇被测目标（车辆）时微波被反射回来，再由雷达接收反射波。如果目标相对于雷达有距离上的运动，反射波的频率将与发射频率发生差异，而这种差异的大小正比于目标与雷达的相对运动速度。于是，雷达通过检测反射波频率和发射波频率的差，就可以计算出被测车辆的移动速度。本次课程设计是利用离散付里叶变换进行频谱分析，设计数字滤波器组，并对滤波器组加窗降低副瓣，通过频域方法测量速度。(Vehicle monitoring using radar gun is based on the realization of the principle of the Doppler effect. By the radar emits a microwave, in case the measured target (vehicle) when the microwave is reflected back, and then by receiving the reflected radar waves. If the target relative to the radar on a distance of motion, the frequency of the reflected wave and the emission frequency of the difference, and this difference is proportional to the relative target and radar velocity. Thus, by detecting the reflected radar wave frequency and the frequency difference between the transmitted wave, we can calculate the movement speed of the vehicle under test. This course is designed to take advantage of the discrete Fourier transform spectral analysis, digital filter design group, and filter bank windowed lower sidelobes by frequency domain methods to measure speed.)

超宽带matlab仿真，用simulink搭建(UWB Matlab simulation, using Simulink structures)

power spectrum density for different digital communications schemes

kernel-ica package is a Matlab program that implements the Kernel ICA algorithm for independent component analysis (ICA). The Kernel ICA algorithm is based on the minimization of a contrast function based on kernel ideas.

说明：  噪声对消算法； 消除信号中得噪声； 多传感器处理方法；(Noise cancellation algorithm; Eliminate noise in the signal; Multi-sensor processing method;)

本程序为实值空间ESPRIT算法源程序，是基本ESPRIT算法在性能上的一种改进(This program is in real space ESPRIT algorithm source code, is a basic algorithm ESPRIT an improvement in performance)

支持向量机的工具包,可在Matlab65下使用,具有分类和回归功能.(Support Vector Machine tool kit can be used Matlab65 with classification and regression functions.)

fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!. Given the samples of a normal distribution, the PDF parameter is found fits data to the probability of the form: p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2)) with parameters: u,sig^2 format: result = fit_ML_normal( x,hAx ) input: x - vector, samples with normal distribution to be parameterized hAx - handle of an axis, on which the fitted distribution is plotted if h is given empty, a figure is created. output: result - structure with the fields sig^2,u - fitted parameters CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value RMS - RMS error of the estimation type - ML