date-calculate

matlab code of wireless comunication

此书是关于数字信号处理的一些算法收集，很详细，全面。(This book is about the digital signal processing algorithms to collect some very detailed and comprehensive.)

说明：  粒子群加连续潮流法，能优化变压器分接头，进行多变量优化，使系统非常稳定(PSO plus continuous power flow, transformer taps can be optimized, multi-variable optimization, making the system very stable)

电力系统故障计算，能进行横向故障，纵向故障，同时能计算出各支路的短路电流。(Power system fault calculations, can be transverse faults, vertical fault, and can calculate the short-circuit current of each branch)

matlab code for psk mulation very usefull

watershed transform coding

2003年Park关于超分辨图像重建的综述性文献，文中回顾了时域与频域的各种重建方法，并进行了比较。比较全面(2003 Park on the super-resolution image reconstruction is summarized in literature, the paper recalled the time domain and frequency domain of various reconstruction methods and compared. More comprehensive)

说明：  ：针对MP3 编码方式和帧结构进行分析，利用帧中的比例因子提出了一种压缩域水印算法。算法通过引入帧变化分析步 骤，很好地实现了水印的自适应嵌入。对于不同的系统要求，还可以通过修改门限信噪比的值SNRlim按需进行水印嵌入，使算法 具有更大灵活性。同时在牺牲一定的嵌入空间前提下，实现了水印信息的自同步。实验表明，对于不同风格的音乐，该水印算法 具有很好的不可听性。(In this paper，the encoding method and the frame structure of MP3 are expounded in detail，a watermark algorithm is proposed based on the scale factor in MP3 frame.By introducing the step of frame altering analysis，the algorithm can realize that watermarks is embedded adaptively.By modifying threshold SNRlim（the ratio of signal to noise），the algorithm can meet different requirements of some system，that is what enable the algorithm to have a bigger flexibility.Based on expense of some embedding positions，the watermark can be embedded with self-synchronization.The experiment results show this algorithm can get a good audio effect with various types of audio.)

说明：  用MUSIC方法实现谱估计的程序，可以用于频谱分析，(Spectrum estimation using MUSIC method implementation procedures can be used for spectrum analysis,)

PCA:Principal Component Analysis Usage: [eigvector, eigvalue] = PCA(data, options) [eigvector, eigvalue] = PCA(data)(PCA Principal Component Analysis Usage: [eigvector, eigvalue] = PCA(data, options) [eigvector, eigvalue] = PCA(data))