ofdm(1)

performs connected component labeling and removes noises by using size filter.

基于Rao算法的光子计数检测及相关评价,详细介绍了光子的技术方法(Rao algorithm based on photon counting detection and related evaluation of the technical details of the method of photon)

经验模态分解，依据数据自身的时间尺度特征来进行信号分解，给出本征函数。(Empirical mode decomposition, based on the data itself characteristic time scale for signal decomposition, given the intrinsic function.)

说明：  求反求参数（无约束非线性优化方法）的matlab实现 无约束非线性优化算法-Powell法优化子程序 matlab广义最小二乘算法程序(Reverse order parameter (unconstrained nonlinear optimization method) to achieve the matlab unconstrained nonlinear optimization algorithm-Powell method to optimize the generalized least-squares algorithm matlab subroutine procedure)

本软件是伯南克提出时变的因子扩展VAR模型，能够考察变量间的冲击效应，及其结构变化特征，克服通常VAR的变量少等缺陷。(This software is the factor Bernanke proposed changing the extended VAR model, to study impact effect among the variables, and structural changes, less often overcome VAR variable defects.)

THIS EXAMPLES CODE FOR C++

直流电机调速的MATLAB仿真（开环闭环加矫正不加矫正）simulink模型，空载以及有负载的仿真实例。MATLAB simulation of DC motor speed control (open-loop closed-loop plus correction without correction) simulink model, simulation examples, and there are loads of load.(MATLAB simulation of DC motor speed control (open-loop closed-loop plus correction without correction) simulink model, simulation examples, and there are loads of load.)

MUNK剖面声线结构仿真,加载数据可以验证可行性，希望对您有帮助。(MUNK voice sectional structural simulation, load the data to verify the feasibility of, I hope for your help.)

说明：  BP_Adaboost模型即把BP神经网络作为弱分类器，反复训练BP神经网络预测样本输出，通过Adaboost算法得到多个BP神经网络弱分类器组成的强分类器，利用此方法设计出强分类器。(The BP_Adaboost model uses the BP neural network as a weak classifier, repeatedly trains the BP neural network to predict the sample output, and uses the Adaboost algorithm to obtain a strong classifier composed of multiple BP neural network weak classifiers. This method is used to design a strong classifier.)

Modified LLL (MLLL) Lattice Reduction Algorithm Original Paper: A Customized Lattice Reduction Multiprocessor for MIMO Detection by Shahriar Shahabuddin et. al.