writing sfunctions

一个matlab的图像边缘源码，打开后带参运行，图像处理(bianyuanlianjieyuanma)

MIMO-OFDM,是汪裕民那本书的。大家餐卡一下！(MIMO-OFDM, is Wang Yumin that book. Meal card you look!)

模拟实现非线性的时候 ，机器人定位与构图的情况。(SLAm the code)

人工神经网络hopfield网络的一个演示，采用matlab编写(ANN hopfield network of a demonstration, prepared using Matlab)

关于欧洲标准的cost207的信道模型，以matlab函数形式给出。(on European standards cost207 the channel model, presented in the form of Matlab function.)

利用matlab的simulink，如代码中所示的图形(Using matlab to simulink, as shown in the graphic code)

optics算法 [RD,CD,order]=optics(x,k)； Aim: Ordering objects of a data set to obtain the clustering structure Input: x - data set (m,n) m-objects 对象数, n-variables 变量数 k - number of objects in a neighborhood of the selected object(OPTICS Ordering Points To Identify the Clustering Structure)

This contains all the types of ALOHA

Matlab中的研华1710驱动文件，非专业人士勿用。Matlab中的研华1710驱动文件，非专业人士勿用。(In the Matlab driver file Advantech 1710, non professionals do not use.)

CurveLab is a collection of Matlab and C++ programs for the Fast Discrete Curvelet Transform in two and three dimensions. For the 2d curvelet transform, the software package includes two distinct implementations: the wrapping-based transform and the transform using unequally-spaced fast Fourier trans- form (USFFT). Both variants are based on the Curvelet transform as described in ‘New Tight Frames of Curvelets and Optimal Representations of Objects with Piecewise C2Sin- gularities’, Comm. Pure Appl. Math. 57 (2004) 219-266. The implementation is also discussed in detail in ‘Fast Discrete Curvelet Transforms’, Multiscale Model. Simul. 5 (2006), no. 3, 861-899. We advise users to become familiar with these references. The two implementations diff er by the choice of spatial grid used to translate curvelets at each scale and angle.(CurveLab is a collection of Matlab and C++ programs for the Fast Discrete Curvelet Transform in two and three dimensions. For the 2d curvelet transform, the software package includes two distinct implementations: the wrapping-based transform and the transform using unequally-spaced fast Fourier trans- form (USFFT). Both variants are based on the Curvelet transform as described in ' New Tight Frames of Curvelets and Optimal Representations of Objects with Piecewise C2Sin-gularities' , Comm. Pure Appl. Math. 57 (2004) 219-266. The implementation is also discussed in detail in ' Fast Discrete Curvelet Transforms' , Multiscale Model. Simul. 5 (2006), no. 3, 861-899. We advise users to become familiar with these references. The two implementations differ by the choice of spatial grid used to translate curvelets at each scale and angle.)