s256

说明：  用MATLAB模拟leach算法，可以实现在网络中的leach算法的功能(Leach method using MATLAB simulation can be realized in the network function of leach method)

sh73a0 processor support - INTC hardware block.

最新SVDD资料，比旧版增加文件夹matlab中的2个文件(new SVDD，implements two new files than old version)

正弦函数的离散傅里叶变换，输出傅里叶变换系数(Discrete Fourier sine transform, Fourier transform coefficients output)

一种MPEG 压缩域上的快速场景分割算法的介绍及相关知识的详细解释(MPEG compressed domain of a quick scene segmentation algorithm on a presentation and a detailed explanation of knowledge)

PSO算法简单、易实现且参数较少，现已被应用于函数优化、神经网络训练、模糊系统控制以及其它遗传算法的应用领域(PSO algorithm is simple, easy to implement and less parameters, have been applied to function optimization, neural network training, fuzzy system control and other applications of genetic algorithms)

lms算法 数字信号处理中用到的 希望大家用到(lms digital signal processing algorithms were used in the hope that we used)

结合物理光学、几何光学原理，通过matlab对光学中基本的光学现象进行了模拟(Physical optics, geometrical optics principle matlab optics basic optical phenomena are simulated)

MATLAB 与C++混合编程。 MATLAB5.3的提供了C/C++数学库，其中的C++数学库功能很强，使用它可以用类似MATLAB 的语法编写C++程序，十分方便。(MATLAB and C++ mixed programming. MATLAB5.3 to provide a C/C++ math library, which C++ Math Library function strong, use it to write syntax similar to MATLAB C++ program, is very convenient.)

denoise In BayesShrink[5] we determine the threshold for each subband assuming a Generalized Gaussian Distribution(GGD) . The GGD is given by GG¾ X ¯ (x) = C(¾ X ¯ )exp¡ [® (¾ X ¯ )jxj]¯ (6) ¡ 1 < x < 1 ¯ > 0, where ® (¾ X ¯ ) = ¾ ¡ 1 X [ ¡ (3=¯ ) ¡ (1=¯ ) ]1=2 and C(¾ X ¯ ) = ¯ ¢ ® (¾ X ¯ ) 2¡ ( 1 ¯ ) and ¡ (t) = R1 0 e¡ uut¡ 1du. The parameter ¾ X is the standard deviation and ¯ is the shape parameter It has been observed[5] that with a shape parameter ¯ ranging from 0.5 to 1, we can describe the the distribution of coefficients in a subband for a large set of natural images.Assuming such a distribution for the wavelet coefficients, we empirically estimate ¯ and ¾ X for each subband and try to find the threshold T which minimizes the Bayesian Risk, i.e, the expected value of the mean square error.