ZSep

说明：  自己做的一些资料，不大好，但可以看看，一起交流(we can communicate each other)

matlab的二次拟合程序源码与大家分享！直接是M文件！(matlab secondary source fitting procedures to share with you! M file directly!)

软件程序按照发射端所掌握的各用户信道状态信息的程度共分为两部分：即完整信道状态信息(CSIT)和部分信道状态信息(CSIP)。其中，每一部分都包括预编码(precoding)和用户调度(scheduling) 在CSIT中，precoding又按照各用户的数据流数分为单数据流和多数据流两种情况。在每种情况下，首先考察了不同预编码算法的性能表现，包括两种ZF、MMSE、SINR、SLNR。之后又考察了功率分配算法的性能表现（文件名中含有PD表明其含有功率分配的过程）。按照不同指标进行功率分配的，在文件名中进行了区分，如PD_CN代表以信道范数为参考指标进行功率分配。Scheduling部分首先观察了RoundRobin、MaxH和MMSLNR三种算法的性能对比。之后在Kc和Round部分分别观察了不同预选用户数和不同最大替换轮数下MMSLNR算法的表现。 在CSIP中，只对各用户单数据流的情况进行了仿真。采用的预编码算法主要有DSLNR（即直接运用CSIT下的预编码算法）、ESLNR（即对SLNR进行均值计算的，在CSIP中，引入均值计算的与SLNR有关的算法，其文件名中都有modified以示区别）、EMMSE（即陈明老师那边的那篇文章中的预编码算法）。Scheduling中也只是简单的观察了RoundRobin、MaxH、DMMSLNR和EMMSLNR（前者没有均值计算，后者有）的性能对比。(The degree of channel state information for each user software program in accordance with the master transmitter consists of two parts : the complete channel state information (CSIT) and partial channel state information (CSIP). Wherein , each of which comprises a pre- coding (precoding) and the user scheduling (scheduling) In CSIT in , precoding and each user according to the number of data streams into a single data stream and multiple data streams in both cases . In each case , first examine the performance of different pre- coding algorithm, including two ZF, MMSE, SINR, SLNR. After they inspected the power allocation algorithm performance ( file name contains PD showed that it contained power allocation process ) . According to different indicators for power distribution , make a distinction in the file name , such as PD_CN representative channel norm-referenced index for power distribution . Scheduling section first observed the performance comparison RoundRobin, MaxH and MMSL)

说明：  一个论坛上的程序，自己运行调试后通过。包括了Lozi混沌系统的映射图、分岔图、最大Lyapunov指数计算和混沌同步。

一些MATLAB应用程序，MATLAB计算机模拟(Some MATLAB application, MATLAB simulation)

smart antenna how they works good for arab peaple

matlabGUI设计实例，可以参看一下(matlabGUI design examples, you can see what)

DIFFERENT ARTICLES , USEFUL FOR ELECTRICAL ENGINEERING , ECONOMIC DISPATCH

) Compression using huffman code -with a number of bits k per code word -provide huffman table Huffman coding is optimal for a symbol-by-symbol coding with a known input probability distribution.This technique uses a variable-length code table for encoding a source symbol. The table is derived in a particular way based on the estimated probability of occurrence for each possible value of the source symbol .Huffman coding uses a specific method for representing each symbol, resulting in a prefix code that expresses the most common characters using shorter strings of bits than those used for less common source symbols.The Huffman coding is a procedure to generate a binary code tree. () Compression using huffman code -with a number of bits k per code word -provide huffman table Huffman coding is optimal for a symbol-by-symbol coding with a known input probability distribution.This technique uses a variable-length code table for encoding a source symbol. The table is derived in a particular way based on the estimated probability of occurrence for each possible value of the source symbol .Huffman coding uses a specific method for representing each symbol, resulting in a prefix code that expresses the most common characters using shorter strings of bits than those used for less common source symbols.The Huffman coding is a procedure to generate a binary code tree. )

illumination-robust face recognition via sparse representation