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MATLAB模糊逻辑工具箱的分析与应用，很实用的，超星格式(MATLAB fuzzy logic toolbox of analysis and application, very useful, Superstar format)

A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. DCTs are important to numerous applications in science and engineering, lossy compression of audio (e.g. MP3) and images (e.g. JPEG) (where small high-frequency components can be discarded), to spectral methods for the numerical solution of partial differential equations. The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions.

光纤相干通讯数字相干接收机，算法和子系统。一篇很精简全面的总结。(Digital coherent optical receiver algorithms and subsystems in fiber coherent optical communication. A very good brief summary.)

这个是一个好好用的完美挂请大家用用好不好用大家说说(PostScript(R) Printer Description (a.k.a. PPD) files are text files containing information about special features available on PostScript printers such as paper sizes, screen values and fonts available on your printer. These files are in a very specific format that can be read by software applications. The information is used when printing your document by the printer driver for decision making purposes or to invoke particular features of the printer. )

This is useful document for femtocell network

Fuel cells 燃料电池 用以研究 分散型电源并接(Fuel cells)

MATLAB编程可计算吸收率电导率等公式，对石墨烯计算和拟合分析很有帮助(The fitting of computable absorptivity in MATLAB programming)

相控阵雷达的MIMO的仿真3,对于雷达中的仿真3(Phased array radar MIMO simulation for radar simulation)

主程序为mybss.m和fastica1.m。经过调试能够成功运行，和lmd一起使用效果不错，可以实现各频率的分离。(The main program is mybss.m and fastica1.m. After debugging can be run successfully, and lmd use with good results, the separation can be achieved for each frequency.)

说明：  流形学习（Manifold Learning）的一种算法：拉普拉斯特征映射（LE）(An algorithm of Manifold Learning: Laplacian Feature Mapping (LE))