simulink_SpreadSpectrum1

research papers for m tech

bw Image Segmentation

提供了平均分布和正态分布的功能，并显示出来(Provide evenly distributed and the normal distribution function)

MIMO-OFDM WIRELESS COMMUNICATIONS WITH MATLAB，这本书主要是围绕MIMO+OFDM的，每一章分析不同的系统参数，并且附带相应的Matlab程序。(MIMO-OFDM WIRELESS COMMUNICATIONS WITH MATLAB, this book focuses mainly on MIMO+ OFDM, and each chapter analysis of various system parameters, and with the corresponding Matlab program.)

有关基于外辐射源信号雷达成像算法的运动补偿(For outside the emitter signal radar imaging algorithm-based motion compensation)

北京航空航天大学出版的matlab从零到进阶第二章的示例程序(Beijing University of Aeronautics and Astronautics matlab published the first chapter from zero to advanced sample program)

34 节点配电网潮流计算，这是其中一部分，另外两部分为ieee34mod2和ieee34linecode(34 node distribution power flow calculation, which is part of it, and the other two into ieee34mod2 ieee34linecode)

一个产生PFM的simulink模型，可以改变占空比与频率(generating repeating sequence)

Discrimination Between Internal Faults and Other Disturbances in Transformer Using the Support Vector Machine-Based Protection Scheme

说明：  遗传算法（Genetic Algorithm，GA）最早是由美国的 John holland于20世纪70年代提出,该算法是根据大自然中生物体进化规律而设计提出的。是模拟达尔文生物进化论的自然选择和遗传学机理的生物进化过程的计算模型，是一种通过模拟自然进化过程搜索最优解的方法。该算法通过数学的方式,利用计算机仿真运算,将问题的求解过程转换成类似生物进化中的染色体基因的交叉、变异等过程。在求解较为复杂的组合优化问题时,相对一些常规的优化算法,通常能够较快地获得较好的优化结果。遗传算法已被人们广泛地应用于组合优化、机器学习、信号处理、自适应控制和人工生命等领域。(Genetic algorithm (GA) was first proposed by John Holland of the United States in the 1970s. It is designed and proposed according to the evolution law of organisms in nature. It is a computational model of biological evolution process simulating natural selection and genetic mechanism of Darwin's biological evolution theory. It is a method to search the optimal solution by simulating natural evolution process. The algorithm transforms the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematical method and computer simulation. When solving complex combinatorial optimization problems, compared with some conventional optimization algorithms, it usually can get better optimization results quickly. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life.)