files

解决线性规划优化问题，在lingo界面中得到广泛的运用(solve linear programming optimization problems, the lingo interface widely used)

经典谱估计的几个经典算法，包括BT法，周期图法，改进周期图法等。。。MATLAB7.3上顺利编译通过(Classical spectral estimation of several classic algorithms, including the BT method, periodogram, modified periodogram method. . . MATLAB7.3 compile smoothly)

这是一个用MATLAB编的免疫克隆算法优化函数的程序，免疫克隆算法是一种比较新的有效的优化算法，近两年对他的研究很多。(Artificial Immune)

一个计算均匀圆阵角度估计模糊集的程序。通过计算阵列流形的弧长参数，求得阵列的模糊生成集。(A calculation of the angle estimation with uniform circular arrays ambiguity set procedures. Array manifold by calculating the arc length parameter, obtained an array of ambiguity generating set.)

this file represent normalization in binary normalization, bipolar normalization and lda using matlab

C++中使用fftw库，根据correlation filters的相关知识编写实现，可以是实现图片的相关滤波的功能，在一副图片中找到对应patch在图片中的位置，从而进行detection和tracking的相关改进(C++ fftw library use, according to the knowledge of the preparation to achieve correlation filters, which can be filtered so as to achieve a picture related to the correlation detection and tracking of improvements)

在AR人脸库上进行DCT变换，使用DCT变换后的图像进行 kernel fisher discriminant analysis,其中kernel 函数可以自己选择(In the AR face database on the DCT transform, using the DCT transformed image kernel fisher discriminant analysis, which can choose the kernel function)

一个蒙特卡罗算法程序，可以自己扩编改编次程序(a Monte Carlo algorithm procedures, the expansion can be adapted their meeting procedures)

PAPR Reduction based on a Modified PTS with Interleaving and Pulse Shaping method for STBC MIMO-OFDM System. It is with GUI. Using GUI we can see comparison with number of sub carrier, sub-block and over sampling factor as well BER plot comparison of normal and with PTS technique. You need to run MIMO_OFDM_with_PTS.m Note: Do not delete or rename amy file and place all the files in one single folder because some are functions which are called in GUI code.

说明：  二进制粒子群优化算法主代码， 其中bpso_project函数为特征子集评价函数， 对接自己的数据集与评价函数即可使用。(The main code of binary particle swarm optimization algorithm, in which the bpso_project function is the evaluation function of the characteristic subset, can be used by connecting its own data set and evaluation function.)