OFDMadaptiemodulation

信号处理中波束形成时各种加权矩阵，包括长球函数权值和Kaiser加权(Beamforming signal processing for the various weighting matrix, including the prolate spheroidal functions and Kaiser weighting function)

在通信建模与仿真的过程中，QPSK是其中的一个简单例子(the simulator of QPSK is very important)

vc和matlab混合编程，在vc中调用matlab的函数进行计算和处理，本程序中调用了matlab图像处理函数打开图像并进行灰度处理(vc and matlab mixed programming, matlab function calls are calculated and processed in the vc, the program calls the matlab image processing function to open and grayscale image processing)

MATLAB模型预测控制工具箱进行模型预测控制器设计的全过程(Model Predictive Control Toolbox for MATLAB model predictive controller design process)

用matlab实现图像锐化功能的程序代码(Image sharpening using matlab function code)

Matlab6.5符号运算及应用，分析了Matlab符号运算及绘图功能，并提供了大量的实例(Matalab 6.5 symbolic operation and the application of Matlab symbolic computation and graphics functions, and provide a large number of examples)

多载波码分多址（MCCDMA）在瑞利信道的仿真(Multi-carrier code division multiple access (MCCDMA) simulation in Rayleigh Channel)

经典的压缩感知重建算法，可以很方便的使用。(it good)

CBIR system using fuzzy logic in matlab

Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins (1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed each iteration, rather than just one.