calc_3stage_FIR

说明：  BPSK,DBPSK的误码率仿真程序，以此证明BPSK与DBPSK的性能好坏。(BPSK, DBPSK BER simulation program, to prove the performance of BPSK and DBPSK good or bad.)

小波分析及其应用的matlab程序，都是示列，对于学习利用matlab进行小波分析很有用(Matlab wavelet analysis and its application procedure are shown out, for learning using wavelet analysis is useful matlab)

to generate rayleigh enveloppe

matlab仿真的STATCOM即svg滤波装置。(matlab simulation of the STATCOM that svg filter.)

电动机的转矩仿真模型。这是基于直接转矩控制的simulink仿真模型。(The motor torque simulation model. This is based on direct torque control simulink simulation model.)

阵列信号处理中DOA估计的MUSIC算法的matlab程序(The matlab code for MUSIC algorithm of DOA estimation of array signal processing)

HORN ANTENNA FDTD CODE IN MATLAB 2D STRUCTURE

说明：  本文研究了有限反馈下基于博弈论的联合功率分配与反馈速率控制优化问题。通过建立新的博弈模型，证明了其纳什均衡的存在性。同时，根据功率与反馈速率的关系，提出了一种迭代算法，可快速收敛到博弈不动点。仿真结果表明，联合优化算法的性能优于单单考虑反馈速率的算法。(In this paper, under the limited feedback joint power allocation based on game theory and feedback rate control optimization problems. Through the establishment of a new game model proved its existence of Nash equilibrium. Meanwhile, according to the relationship between power and the feedback rate proposed an iterative algorithm can quickly converge to a fixed point game. Simulation results show that the performance of joint optimization algorithm is superior to just consider the feedback rate of the algorithm.)

包括面积、周长、矩形度、伸长度，基于小波变换的数字水印算法matlab代码，小波包分析提取振动信号中的特征频率。( Including the area, perimeter, rectangular, elongation, Based on wavelet transform digital watermarking algorithm matlab code, Wavelet packet analysis to extract vibration signal characteristic frequency.)

半监督学习(Semi-Supervised Learning，SSL)是模式识别和机器学习领域研究的重点问题，是监督学习与无监督学习相结合的一种学习方法。半监督学习使用大量的未标记数据，以及同时使用标记数据，来进行模式识别工作。当使用半监督学习时，将会要求尽量少的人员来从事工作，同时，又能够带来比较高的准确性，因此，半监督学习目前正越来越受到人们的重视。(Semi-Supervised Learning (SSL) is a key issue in the field of pattern recognition and machine learning. It is a learning method combining supervised learning with unsupervised learning. Semi-supervised learning uses a large number of unlabeled data, as well as labeled data, for pattern recognition. When using semi-supervised learning, it will require as few people as possible to work, and at the same time, it can bring relatively high accuracy. Therefore, semi-supervised learning is receiving more and more attention.)