bin-packing-matlab

Matlab code for computing different things

里面涉及很多的混沌数据处理的算法，可以提供给大家使用(There are many chaotic data processing algorithms involved.)

基于区域生长的阈值分割，Matlab环境下使用。(region growth)

Boost converter used in power electronics is used to boost the output voltage

matlab程序句柄绘图算法设计教程matlab program handles the drawing algorithm tutorial(matlab program handles the drawing algorithm tutorial matlab program handles the drawing algorithm tutorial)

使用案例讲述了MATLAB的数据预处理，有源代码说明( Data preprocessing)

this source code is good

统讲述MATLAB6.5 的集成工作平台，引导读者初会MATLAB 基本用法。 第二、三章系统叙述MATLAB6.1 的四大基本数据类型（数值数组、字符串数组、元胞数组、 构架数组），至于其它扩展数据类型（函数句柄、符号数据、内联函数、unit 数组、稀疏类） 则另辟章节专述。此后，本书用九个独立章分述MATLAB6.5 的数值计算、符号计算、函数 和数据可视、面向对象编程、GUI 交互操作界面设计、EXE 独立应用程序生成、实现不同 软件平台交互的API、M-book 数据图形文字环境集成等八大通用功能。本书用专门的一章 深入浅出地阐明SIMULINK 的分层建模、仿真功能、与MATLAB 交互的功能。(The the integration work platform on the system tells MATLAB6.5 the guide readers to the beginning of MATLAB basic usage. The second and third chapters system described the MATLAB6.1 four basic data types (numeric array, string array, cell array, the array architecture), as extended data types (function handle symbol data, inline functions, unit array , the sparse class) are covered in separate chapters specifically mentioned. Since then, with nine separate chapter book are as MATLAB6.5 the numerical computation, symbolic computation, functions, and data visualization, object-oriented programming, GUI interactive interface design, EXE standalone application generation, different software platform to interact The data graphics API, M-book text environment integration Eight generic function. Book special chapter layman' s language to clarify SIMULINK hierarchical modeling, simulation capabilities, and MATLAB interaction.)

这是第二能量熵的matlab代码，抑制载波型差分相位调制，一个师兄的毕设。( This is the second energy entropy matlab code, Suppressed carrier type differential phase modulation, A complete set of brothers.)

EMD matlab实现代码，可以直接用。经验模态分解（Empirical Mode Decomposition，EMD）是由 Huang等人于1998年提出的一种针对非线性、非平稳信号的自适应信号分解算法。(Empirical Mode Decomposition (EMD) is an adaptive signal decomposition algorithm for non-linear and non-stationary signals proposed by Huang et al. in 1998.)