mjd_and_date

介绍MATLAB处理语音信号的基本方法，如打开、保存、录音(MATLAB introduce the basic speech signal processing methods, such as open, save, sound recording)

A practical system for improved efficiency in frequency division multiplexed wireless networks

This m file is for reading multiple velocity files generated by PIV measurements or numerical simulaiton into matlab

说明：  通过求矩阵特征多项式的根来求其特征值 幂法求矩阵的主特征值及主特征向量 瑞利商加速幂法求对称矩阵的主特征值及主特征向量 收缩法求矩阵全部特征值 收缩法求矩阵全部特征值 位移逆幂法求矩阵离某个常数最近的特征值及其对应的特征向量 QR基本算法求矩阵全部特征值 (Characteristic polynomial by the root of a matrix to find the eigenvalues ​ ​ of a matrix power method and the main characteristics of the main features of the value of acceleration power vector Rayleigh quotient method Symmetric Matrix Eigenvalue and the main characteristics of the main vector of contraction method to shrink the value of all the characteristics of a matrix Law of the value of all the characteristics of a matrix inverse power law shift away from a constant of a matrix eigenvalue recently and its corresponding eigenvector matrix QR algorithm for getting all the characteristics of the basic values)

to read mps file in matlab

3 MATLAB functions for simulation of OFDM signal in Rayleigh fading channel. Including models for OFDM frame generating, cyclic prefix, pramble, as well as model for Rayleigh channel, QAM modulation function and a example of test process.

关于GPS/INS组合导航的matlab仿真程序 大家共同学习下(On GPS/INS Integrated Navigation System of the matlab simulation program to study under the common)

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.

image retrieval using L2 distance:euclidien distance between 2 histograms

ECMA Section: 12.6.3 The for..in Statement.