ArrayIntervalMax

此代码为角点检测程序，可用在对摄像机的标定中获得图像中角点的像素坐标。用MATLAB编写(this code to corner detection procedures can be used in the calibration of the camera image obtained corner coordinates of the pixel. Using MATLAB)

在高斯与一径瑞利衰落信道下对DVB-T系统的同步算法进行仿真，计算正确符号同步的概率(in Gaussian with a Rayleigh fading channel for DVB-T system synchronization algorithm simulation, correct calculation of the probability Symbol Synchronization)

Matlab based Static Simulator, from Laiho s book for UMTS

说明：  this is good code in matlab

说明：  实现了AM的双边带调制解调功能！！！其中函数为自编(Achieved with bilateral AM modulation and demodulation functions! ! ! Which function as self-)

说明：  基于CRAFT的多阶段柔性设备布局优化算法 　　多阶段设备布局下的总成本包括设备拆装成本、物料搬运成本以及区域利用成本,问题的目标是最小化总成本。首先,在分析问题的基础上,建立基于多阶段设备布局优化目标的数学模型 其次,采用改进的CRAFT启发式算法优化单阶段设备布局 进一步,提出基于单阶段设备布局算法的多阶段启发式算法,优化多阶段设备布局 最后,数值实验表明算法优化后总成本降幅达28.57％. (CRAFT-based multi-stage flexible equipment layout optimization algorithm under the multi-stage equipment, the layout of the total cost including equipment disassembly cost, material handling costs as well as regional use of cost, the goal is to minimize the total cost. First of all, the analysis of the problem on the basis of the equipment based on the layout of multi-stage mathematical model of optimization objectives Secondly, the adoption of improved CRAFT heuristic algorithm to optimize the layout of single-stage device to further, based on equipment layout algorithm for single-stage multi-stage heuristic algorithm, optimize the layout of multi-stage equipment Finally, numerical experiments show that the optimized algorithm for the total cost of 28.57 percent decrease.)

说明：  通过Matlab程序实现了多目标粒子群智能算法，可用于解决多目标的优化求解问题(The multi-objective particle swarm intelligent algorithm is realized through the Matlab program, which can be used to solve the multi-objective optimization problem)

这是有关MATLAB学习的一些编程技巧和使用的一个程序示例(MATLAB STUDY)

will read images from a file imagelist1 and resize all of them to the size of 320 rows and 240 column.

To use precomputed kernel, you must include sample serial number as the first column of the training and testing data (assume your kernel matrix is K, # of instances is n):