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this document is about of splicing detection of image

利用各待评方案的指标体系，通过计算各待评价方案与理想解的接近程度，作为评价各方案的依 据的一种多目标决策评价方法(topsis sort)

Tests for KeyManagerFactory</code> class constructors and methods.

simulation solar mppt withb resistance

A matlab tutorial to design PMSM motor speed control systems

simulation matlab machine synchrone a aimant permanent

当空间调制（sm）系统中每个符号含有3bits信息(即3bit/s/Hz的频谱利用率)时我们选择：4根发送天线，BPSK调制方式和2根发送天线，4QAM调制方式进行比较。(When the spatial modulation (sm) system, each symbol comprising 3bits information (i.e. 3bit/s/Hz spectral efficiency) when we choose: four transmitting antennas, BPSK modulation method and two transmission antennas, 4QAM modulation for comparison.)

核概率密度估计算法，对于以往的有优化，可以得到概率密度及分布函数。(Nuclear probability density estimation algorithm optimized for the past there, you can get the probability density and distribution functions.)

利用LPC算法实现对于语音信号的特征提取，将一帧的语音信号用简化模型的参数来表示(The use of the LPC algorithm to extract to achieve For the the characteristics of of the voice signal, will the the a of the voice to represent the by the signal with a simplified the the the parameters of of the model)

MATLAB 牛顿下山法的一个小程序，原理就不详细说了。因牛顿迭代法受初值选取的限制，为防止迭代发散，对迭代过程再附加一项要求:|f(x(k+1))|<|f(x(k))|,将牛顿法迭代的结果:x(k+1) =x(k)-f(x(k))/f (x(k))和前一近似值x(k)适当加权平均做为新的改进值:x(k+1)=u*x(k+1) +(1-u)*x(k),其中u(0<> 迭代时u取1开始，逐次减半计算，直至附加要求符合为止。实例计算中x(k)=x(0)不变，只更新u和x(k+1)，直至:|f(x(k+1))|<|f(x(k))|（即|f(x(1))|<|f(x(0))|)，然后更新下山因子为u=1,继续以牛顿法迭代。(MATLAB Newton-down a small program, the principle is not explained in detail. Newton iteration by the initial value of the selected limit, in order to prevent additional iterative divergence, the iterative process a request: | f (x (k+1)) | < | f (x (k)) | Newton iteration Results: x (k+1) ' = X (k)-f (x (k))/f' (x (k)) and a previous approximation x (k), appropriately weighted average as a new improved value: x (k+1 ) = u* x (k+1) ' + (1-u)* x (k), where u (0 < u> iteration take successive calculated at 50 until the additional requirements found so far. examples of calculation of X ( k) = x (0) unchanged profile u and x (k+1, hungry) until: | f (x (k+1)) | < | f (x (k)) | (i.e. | f (x (1)) | < | f (x (0)) |), then profile downhill factor for u = 1, to continue to Newton iteration.)