DFT_FFT_Times

图1为均衡带限信号所引起失真的横向或格型自适应均衡器(其中横向FIR系统长M=11), 系统输入是取值为±1的随机序列，其均值为零；参考信号；信道具有脉冲响应： 式中用来控制信道的幅度失真(W = 2～4, 如取W = 2.9,3.1,3.3,3.5等)，且信道受到均值为零、方差(相当于信噪比为30dB)的高斯白噪声的干扰。试比较基于下列几种算法的自适应均衡器在不同信道失真、不同噪声干扰下的收敛情况(对应于每一种情况,在同一坐标下画出其学习曲线): 1）横向/格-梯型结构LMS算法 2）横向/格-梯型结构RLS算法 并分析其结果。(Figure 1 is a band-limited signal caused balanced horizontal or lattice distortion adaptive equalizer (FIR system in which the lateral length M = 11), the system input is the value of a random sequence of ± 1, mean zero reference signal channel has impulse response: Wherein the amplitude of the distortion for the control channel (W = 2 ~ 4, and so as to take W = 2.9,3.1,3.3,3.5), and subjected to a channel with zero mean and variance (equivalent SNR is 30dB) Gaussian white noise. Compare the following types of algorithm-based adaptive equalizer in a different channel distortion, the convergence of different noise interference (corresponding to each situation, draw their learning curve in the same coordinate): 1) landscape/grid- LMS algorithm ladder structure 2) landscape/grid- RLS algorithm ladder structure And analyze the results.)

this source code is very good

matlab图像特征提取程序。。。图像特征统计及计算(matlab image feature extraction procedures. . . Statistics and computing image features)

converts a a number to fixed point qmn format using matlab

拉东正反变换，应用于地震勘探中，对信号进行操作。可用于去噪以及信号分析等。(Radon transform and inverse transform )

基于SVPWM的三电平逆变的matlab仿真，非常适合计算机视觉方面的研究使用,计算时间和二维直方图。( Based on SVPWM three-level inverter matlab simulation, Very suitable for the study using computer vision, Computing time and two-dimensional histogram.)

高斯噪声随机变量发生器，这些随机变量有一个零均值，方差D。(Gaussian random variable generator，These random variables have a zero mean and variance D.)

GPS演示程序，比较TYPE2, DAMP2 and DAMP3 车辆模型。程序中仿真1.5公里90kph速度。来自电子书全球定位系统和惯性导航集成。

此程序实现图像的算法处理等，其中包括处理中的各种经典图片，一定对大家有帮助。(This program implementing the image processing algorithms, including the handling of the various classic pictures, will certainly be helpful to everyone)

S变换的MATLAB实现及其实例 希望对大家的学习有帮助(S Trasform)