VoiceRecognitionProject

展示David G. Lowe的SIFT算法。有很多解释说明。通俗易懂，适合初学者。(David G. Lowe displayed the SIFT algorithm. There are many explanations. User-friendly, suitable for beginners.)

basics of matlab in ppt

This is demo code is ued for matlab demo

说明：  有脉冲干扰 下的柔性机械臂的模糊控制方法。采用神经网络训练出来的T—S模糊控制器，控制效果不错。只有仿真模块，没有文字说明。(pulse interference with the interference of fuzzy manipulator control methods. Using neural network training, the T-S fuzzy controller, control effectiveness. Only Simulation Module, there is no written statement.)

high pass windowed sync filter / plot/develop input output filter response

matlab/simulink环境中关于DFIG原理介绍和建模与仿真(matlab/simulink environment on the introduction of the principle of DFIG and Modeling and Simulation)

在四点星座图中，通过运用聚类的方式，实现对由于信道、噪声或其他原因出现随机偏移星座点的识别与归类。main.m为入口程序(The four-point constellation, the constellation points of the random offset due to channel noise or other reasons, through the use of clustering Identification and classification.)

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.)

Eigenfaces is used to representing a face by projecting original images onto a face space defined by eigenvectors, and then a new face is compared to known faces by calculating the distance between their projections onto face space. (Eigenfaces is used to representing a face b y original projecting images onto a face space d efined by eigenvectors. and then a new face is compared to known faces by c alculating the distance between their project ions onto face space.)

用matlab生成xilinx FIR参数，对其FIR 核进行配置(Matlab generate xilinx FIR parameters to configure their FIR)