tunning-PID-by-PSO

If the used buffer size is smaller than the actual buffer size, regenerate the lookup table and write the new table to the file.

MATLAB仿真技术与应用一书的配套光盘，内有m文件源码(MATLAB Simulation Technology and Application of a book on CD-ROM package, which has m source documents)

基于DS-CDMA的 MATLAB仿真M文件(DS-CDMA based on the MATLAB simulation of M documents)

matlab界面编程，适合于界面编程的初学入门。(The introduction to the programmer of matlab.)

matlab实现的多维矩阵的加法，可以实现矩阵的某几维的加法，用于一般性的函数(matlab implementation of the multi-dimensional matrix addition, can be achieved certain dimensional matrix addition, for general functions)

说明：  Bpsk在瑞利衰落信道下的仿真模型。simulink实现。可以通过仿真看到瑞利衰落信道对于bpsk性能的影响。并且可以比较rayleigh信道和AWGN信道下bpsk的性能。(Bpsk in Rayleigh fading channel simulation model. Simulink achieve. Simulation can see Rayleigh fading channels for BPSK performance impact. And to be compared Rayleigh Channel and AWGN under BPSK performance.)

it′s a program to make a PCM of digital comunnications in matlab with noise Gaussian

用matlab编程工具，编写具有操作界面的应用程序，通过菜单、对话框，选项框等界面控制，对算法进行演示。需要在应用系统中实现的算法及功能包括： 1、图像增强算法 （1）灰度线形变换：亮度及对比度可以调节（通过控制参数的改变，能够实时预览变化结果）； （2）直方图均衡：可在界面上对比均衡前后的效果。 2、图像变换算法 （3）对一幅彩色图像进行DCT变换和反变换，对比结果和原图； （4）仅保留左上角16X16的DCT系数，进行反变换，计算结果图的信噪比SNR。 3、图像分割算法 （5）采用最优阈值算法对灰度图像进行分割； （6）用Canny算法对灰度图像进行边缘检测； （7）用hough变换，检测边缘图像中的直线，并用不同颜色将检测出的直线叠加显示到原图像上。 (Preparation of a user interface with Matlab programming tools, applications, menus, dialog boxes, option boxes and other interface control, the algorithm demo. Algorithms and functions that need to be achieved in the application system including: 1, the image enhancement algorithm (1) gray linear transformation: the brightness and contrast can be adjusted (by controlling the change of the parameters, it is possible to real-time preview changes result) (2) Histogram equalization : contrast before and after equalization effect in the interface. 2, an image transformation algorithm (3) for a color image DCT transform and inverse transform, comparing the results and the original (4) to retain only the upper left corner of 16x16 DCT coefficient, the inverse transform, the signal-to-noise ratio SNR calculation results of FIG. . 3, image segmentation algorithm (5) The optimal threshold algorithm to gray-scale image segmentation (6) Canny algorithm for edge detection of gray-scale images ()

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)

Abstract— In the area of image processing, several methods of edge detection have been proposed till now like Sobel, Robert, Prewitt, Canny, Marr-Hildreth etc. All these methods are having their own advantages and disadvantages. In this paper a new method has been proposed for edge detection using Cellular Automata. Different rules have been applied for edge detection. The outputs are better in terms of clarity and computation time. Finally, the edge detection has been applied for detecting cancerous cells in brain. Using Cellular Automata rules helps in determining the exact location and size of tumor as edges detected using Cellular Automata are fine, continuous and clear. The results