stbc11

说明：  ICI reduction可能程序有些复杂，见谅，望大家多多指正(ICI reduction可能程序有些复杂，见谅)

用Matlab写的基于多尺度小波模最大法的图像边界分割。用Matlab写的基于多尺度小波模最大法的图像边界分割。用Matlab写的基于多尺度小波模最大法的图像边界分割。(Written using Matlab image boundary segmentation based on multi-scale wavelet largest. Written using Matlab image boundary segmentation based on multi-scale wavelet largest. Written using Matlab image boundary segmentation based on multi-scale wavelet largest.)

Path manually writing code, is to allow the ns2 manual routing is used, this is our experimental multichannel implementation, hoping to share.

说明：  matlab using this edf file

完整的jpeg压缩解压程序，基于matlab环境，调试无误(Complete jpeg compression decompression program, based on matlab environment, debug and correct)

　提高电源的动态响应　　非线性和复杂的系统是一个转换器　　电力电子的有趣的挑战。摘要介绍了　　设计和实现数字PID控制器使用　　混沌粒子群优化(复)提振　　转换器,提出了自动调优转换器的方法　　最优启动响应。当系统启动时,处理器　　单元分析了开环启动响应转炉和　　在一些迭代使用复准备最佳反应　　算法。使用逻辑映射混沌映射序列　　增加它的收敛速度和精度。模拟　　和实现结果显示提出的效率　　方法(Improving dynamic response of DC-DC converters which are nonlinear and complicate systems is an interesting challenge in power electronics. This paper presents the design and implementation of digital PID controller using the chaotic particle swarm optimization (CPSO) for a Boost converter and proposes an approach for auto tuning converter optimum startup response. When the system starts, processor unit analyses the open loop startup response of converter and prepares optimum response during some iteration using CPSO algorithm. Chaotic mapping using logistic map sequences increases its convergence rate and resulting precision. Simulation and Implementation results show efficiency of proposed approach)

东南大学的《结构模态分析及其损伤诊断》，系统地介绍了结构模态信号处理的算法，是模态信号处理编程和学习的必备选择。(" Modal Analysis and Damage Diagnosis" Southeast University, introduced the algorithm modal signal processing, signal processing modal learning programming and necessary choice.)

说明：  有感知器神经网络实现1与0的识别，输出误差曲线(it is a good program!if you needed,be quick to load it down)

可用于解决整数规划方面的问题。用matlab编写的源代码。(integer programming)

自适应处理算法：先有采样快拍数据计算采样协方差矩阵，再来计算自适应权矢量。(adaptive processing algorithms prior sample snapshot data of the sampling covariance matrix, again computing adaptive weight vector.)