小程序

说明：  有关BP算法权值修正的过程 用matlab编程 还可以用于神经预测(The BP algorithm weight correction process with the MATLAB programming can also be used for neural prediction )

ixneighbors returns the indices of neighbor cells in a n*m matrix A. ic and icd are column vectors of same length where ic are the indices of cells in A and icd are the indices of the neighbor cells.

这是调速器模块，参数已经调好，可直接用于柴油机模型(this is very good thank you very much)

fft 快速傅立叶变换，可以进行有效的变换，用于频谱和功率普的分析。(fft)

这是一个关于BPSK系统的仿真程序，并画出在瑞利信道和高斯信道下的误码率图形(It is a BPSK system simulation program, and draw in the Rayleigh and Gaussian channels channel bit error rate under the graphics)

说明：  802.11a的方针，有维特比编译码，较之于结交志(the Matlab/Simulink system, matched filtering, carrier recovery, rehabilitation Timing important module, the better modulation and demodulation system 16Qam understanding)

EM算法用于混合高斯模型的参数估计，并附有一个例子进行说明，程序解释(EM algorithm is used for of the gaussian mixture model parameter estimation, and with an example specification, process explanation)

(2,1,2)卷积码链路仿真,采用viterbi译码算法,采用m语言编写,并非直接调用matlab内部函数,有助于对viterbi译码算法的理解((2,1,2) convolutional code link simulation using viterbi decoding algorithm using m language, not directly call matlab internal function, contribute to the understanding of viterbi decoding algorithm)

bootstrap matlab 程序,内含4个文件(bootstrap matlab programe，there are four files)

Gabor小波变换技术对医学CT图像进行纹理特征分类时，由于图像拍摄角度的变化会造成分类的误差。针对以上问题，在Gabor小波变换的基础上提出一种用于分析旋转不变医学图像的方法。该方法采用旋转规范化，即特征元素的循环移位使规范化后所有的图像都具有相同的主方向。实验结果表明，加入旋转规范化循环算子的Gabor小波变换在医学CT图像纹理特征分类时能够达到较好的精确度。(Gabor wavelet transform lacks in its ability to classify the medical CT image if it’s rotation invariant image. Aiming at the problem, an approach is presented for rotation invariant medical texture classification based on Gabor wavelet transform. Rotation normalization is achieved by circular shift of the feature elements, so that all images have the same dominant direction. Experimental result shows that Gabor wavelet transform with circular operator of rotation normalization has well precision to classify the medical CT image.)