matlab

backstepping applied to tracking problem of a marine vehicle

快速ICA算法原理及MATLAB程序实现(Fast ICA algorithm and MATLAB program)

基于matlab的一个，计算机虚拟程序，有一个完整的算例。(Matlab a computer virtual program, a complete example.)

说明：  无线中继协作下的AF、DF性能传输分析，信道采用的瑞利信道。(AF and DF performance transmission analysis under the wireless relay cooperation, Rayleigh channel adopted by the channel.)

频谱检测中能量检测ROC仿真程序 频谱检测中双门限检测ROC仿真程序(Spectrum detection, energy detection ROC emulator spectrum detection, double threshold detection ROC emulator)

关于小波包奇异值的程序，很好用的，而且配上了关于小波包奇异性的相关知识！(The program about the singular value of the wavelet packet is very useful, and it is matched with the related knowledge about the singularity of the wavelet packet.)

ica工具包，包含基于峭度的ica算法，基于最大信噪比的ica算法等(failed to translate)

说明：  整个系统分为数据采集和数据分析两大部分，以友好的图形界面与用户进行交互沟通。 声音数据采集部分实现数据采集功能,根据用户选择的采样频率和预设的采样时间来从声卡获得用户需要的数据。并且将采集到的数据及其频谱图以图形方式很直观的呈现于用户面前。 图像采集分析部分，读入图像后，根据选择，对其进行Linear Conformal、Affine、Projective、Polynomial、Piecewise Linear、Sinusoidal、Barrel、Pin Cushion变换(The whole system is divided into data collection and data analysis of two parts, with a friendly graphical interface to communicate with the user interaction. Sound data collection part of the data collection function, according to user-selected sampling frequency and sampling time to the default user from the sound card to obtain the data needed. And the collected data and the spectrum is very intuitive graphically presented in front of the user. Image acquisition and analysis section, read the image, based on the choice of its Linear Conformal, Affine, Projective, Polynomial, Piecewise Linear, Sinusoidal, Barrel, Pin Cushion Transformation )

这是一个经典的形变模型实施,在一个单一的文件用简单的可以理解的代码。 功能包括两部分一个简单的弱分类器和一个促进部分: 弱分类器试图找到最佳阈值的数据维数对数据进行分离成两个阶级1和1 要求的进一步提高分类器部分迭代,每一步是变化分类权重miss-classified例子。这造成了一连串的“弱分类器”,表现得像一个“强大分类器” (This a classic AdaBoost implementation, in one single file with easy understandable code. The function consist of two parts a simple weak classifier and a boosting part: The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes-1 and 1 The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier" . )

MIMO遍历信道模型容量仿真，对初学MIMO 的人来说非常好啊( MIMO Traverse channel model capacity simulation)