FFT_Analysis

这是matlab用户手册，对于初学者很有益，里面涉及到各个方面，很有价值的参考资料。(This is a matlab user manual, very useful for beginners, which relate to various aspects valuable reference.)

Thresholding is used to segment an image by setting all pixels whose intensity values are above a threshold to a foreground value and all the remaining pixels to a background value. Whereas the conventional thresholding operator uses a global threshold for all pixels, adaptive thresholding changes the threshold dynamically over the image. This more sophisticated version of thresholding can accommodate changing lighting conditions in the image, e.g. those occurring as a result of a strong illumination gradient or shadows.ff

说明：  包含两种短时傅里叶变换程序跟反变换程序 STFT、stft2是两个短时傅里叶变换程序 istft是短时傅里叶变换逆变换程序(There are two kinds of short time Fourier transform program and inverse transform program STFT and stft2 are two short time Fourier transform programs ISTFT is a short time Fourier transform inverse program)

计算信噪比的程序，可以直接调用，方便而且简单。(SNR calculation procedure can be called directly, convenient and simple.)

说明：  通过MATLAB计算各种混沌系统的Lyapunov的值，同时还可添加自己的程序计算Lyapunov的值(MATLAB calculated through a variety of chaotic systems the value of Lyapunov, but can also add your own procedures for calculating the value of Lyapunov)

其中详细的介绍了matlab中的simulink仿真时ise联合仿真时所用到的system generator的用法，对做fpga很有帮助哦(Described in detail the usage of the system generator used in the joint simulation in matlab simulink simulation ise to do fpga helpful.)

应用独立分量分析方法对图片进行图像分离处理，可以将医学影像的图片进行分离，单独进行分析。(Independent component analysis of images for image separation processing, medical imaging picture can be separated, analyzed separately.)

遗传算法和二进制粒子群算法相结合，使遗传算法过早收敛的情况得到改善(The combination of genetic algorithm and binary particle swarm optimization algorithm, the premature convergence of genetic algorithm is improved.)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

一个基于matlab与vc混合编程小例子,对初学者非常有利(a Matlab-based program mixed with vc small examples, very beneficial for beginners)