OFDM_matlab

说明：  ＣＤＭＡ的信道估计Ｓ函数　，希望对大家有用(CDMA channel estimation of the S function, in the hope that useful)

说明：  华中科技大学matlab课件，共五章，第一章 计算机辅助设计与仿真技术概述，第二章 matlab语言基础，第三章 控制系统的数学描述与建模，第四章 控制系统的分析方法，第五章 SIMULINK仿真基础(华中科技大学的matlab课件，一共五章，华中科技大学电气与电子工程学院应用电子工程系)

matlab应用实例，对于一个电路图，应用matlab仿真解题，求出电路某支路电流及波形(matlab application example, a circuit diagram for the application of problem-solving matlab simulation, find a branch circuit current and waveform)

已知自变量t，因变量y，y和t满足一下函数式： dy/dt=0.001414/(1/(4.5727*10^4*k1*(1-0.02119*y)^k2)+1/k3/45727)；（需要指出的是，该公式中参数可进一步合并，在本帖中并未作合并处理） 已知 t=；y=；拟合出参数k1，k2，k3。(Known independent variable t, the dependent variable y, y, and t satisfy my functional: dy/dt = 0.001414/(1/(4.5727* 10 ^ 4* k1* (1-0.02119* y) ^ k2)+1/k3/45727) (It should be noted that the equation parameters can be further consolidated in this post are not treated as a merger) is known t = y = fitting parameters k1, k2, k3.)

Matlab code for adaptive filter this folder contains two functions and main programs

This code is for ECG waveform generator.

说明：  TD-SCMDA的训练序列Midamble code的仿真，并对信道进行估计。(TD-SCMDA training sequence Midamble code simulation, and channel estimation.)

基于MATLAB的例程 实现HUFFMAN编码算法 不定长编码 基于不同符号的概率分布(Variable-length coding based on the probability distribution of different symbols based on MATLAB routines to achieve HUFFMAN coding algorithm)

关于希尔伯特黄变换例程，对于初学者具有参考意义。(Wavelet analysis book source code has certain reference value, very good.)

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.