planseperator

说明：  关于UMHexagonS方式的一些论文，使用CAJviewer阅读(UMHexagonS)

基于2阶消失矩的Daubechies尺度函数的TE波2维MRTD仿真，吸收边界条件为FDTD/PML连接边界。(2D MRTD TE mode baseed on 2nd-vanishing moments of Daubechies scaling function simulation, the absorbing boundary condition is FDTD/PML connection boundary.)

progarm for ur use

security in cloud based applications papers

《统计与自适应信号处理》是国外最新出版的一本统计信号处理教材。其主要特点是对统计与自适应信号处理的理论方法、实现及应用给出了较完整的论述和介绍。作者根据其多年的教学经验，并针对现代信号处理的理论及应用方面的重要性，选定谱估计、信号建模、自适应滤波与阵列信号处理等几个关键论题进行了详细的讨论。《统计与自适应信号处理》强调基本概念和理论方法，目的是为读者以后深入研究一些信号处理的新课题打下良好的基础。为了便于掌握，书中的数学描述和推导仅限于高年级本科生、研究生和工程技术人员能够理解的水平，因此《统计与自适应信号处理》具有较强的实用性。 《统计与自适应信号处理》适用于大学高年级本科生和研究生作为选修现代信号处理课程的教学参考书，也可作为工程技术人员自修现代信号处理理论及应用的参考书("Statistical and Adaptive Signal Processing" is the latest edition of a foreign statistical signal processing materials. Its main characteristic is the statistical theory and adaptive signal processing methods, and applications are given to achieve a more complete discussion and description. According to their years of teaching experience, and for modern signal processing theory and application of the importance of the selected spectral estimation, signal modeling, adaptive filtering and array signal processing, and several other key topics were discussed in detail. "Statistical and Adaptive Signal Processing," emphasizing basic concepts and theoretical methods, the reader is intended to signal processing after the in-depth study some new issues and lay a good foundation. In order to facilitate control, the mathematical description and derivation of the book is limited to advanced undergraduates, graduate students and engineers to understand the levels, "Statistical and Adaptive Signal)

根据时域信号计算三分之一倍频程下的加速度有效值及功率谱(calculate rms in 1/3 oct freq domain)

关于模式识别中运用到的模糊分类方法及分类器设计，是很好的参考程序。(About ISODATA algorithm Matlab language)

matlab 图像处理教程及方法 matlab 图像处理教程及方法 (Matlab image processing course and method Matlab image processing course and method)

Simulated Annealing for solving TSP

说明：  二进制基本遗传算法例程序，运用matlab M文件编写。(Binary basic genetic algorithm example program, the use of Matlab M documentation.)