power_twelvepulses

Linear Spatial Pyramid Matching Using Sparse Coding 图像分类实现代码(Linear Spatial Pyramid Matching Using Sparse Coding image classification implementation code)

关于图像的二值化三种算法程序，实现图像的二值化(With regard to the image binarization process three kinds of algorithms to achieve the image binarization)

wsn小组的Open-ZB，实现其MAC层802.15.4协议源码(wsn group‘s Open-ZB，implement of 802.15.4 MAC protocol)

说明：  在学习过程中自行学习编写的《优化阵列信号处理》例题程序，有需要的下。(This is the "optimized array signal processing" example program that I learned to write in the learning process, download if necessary.)

不用多说了，和Hogh搭配使用检测直线的程序 直线Hough变换利用图像空间和Hough参数空间的点－线对偶性，把图像空间中的检测问题转换到参数空间。通过在参数空间里进行简单的累加统计，然后在Hough参数空间寻找累加器峰值的方法检测直线。(Needless to say, Hogh mix and the use of linear detection procedures Hough transform space and the use of images Hough parameter space The point-line duality, the image space detection switch to the parameter space. Through the parameter space for a simple cumulative statistics, then Hough parameter space for accumulator peak detected straight.)

说明：  兰伯特投影变换，可将大地坐标与兰伯特坐标系左边相互转换(Lambert projection transformation)

In this paper, an attractive approach for teaching genetic algorithm (GA) is pre-sented. This approach is based primarily on using MATLAB in implementing the genetic operators: crossover, mutation and selection. A detailed illustrative example is presented to demonstrate that GA is capable of finding global or near-global optimum solutions of multi-modal functions. An application of GA in designing a robust controller for uncertain control systems is also given to show its potential in designing engineering intelligent systems

说明：  径向基函数神经网络的优点：逼近能力，分类能力和学习速度等方面都优于BP神经网络，结构简单、训练简洁、学习收敛速度快、能够逼近任意非线性函数，克服局部极小值问题。原因在于其参数初始化具有一定的方法，并非随机初始化。代码为RBF的matlab程序，可以使用但是需要自己配置数据。(Radial Basis Function Neural Network (RBF-NN) has the advantages of better approximation ability, classification ability and learning speed than BP Neural Network (BP Neural Network). It has simple structure, simple training, fast learning convergence, and can approximate any non-linear function to overcome the local minimum problem. The reason is that its parameter initialization has a certain method, not random initialization.)

svd feature extraction

系统辨识中LMS，NLMS的比较,输入信号为高斯白噪声(compare LMS with NLMS in the system identification)