structure-in-matlab

说明：  ldpc码的基于联合校验—变量处理修正译码算法基础上的改进(the improvement of LDPC code decoding algorithm )

软件无线电SDR的仿真，使用matlab仿真。(Software Radio SDR simulation using Matlab simulation.)

通信原理实验代码，AM信号，单边带信号，双边带信号的产生以及傅里叶变换(Communication Theory test code, AM signal, single-sideband signal, as well as double-sideband signal generation Fourier transform)

compute mel spectrum speech recongintio in matlab program 1m

MIMO system simulation.

ARMA 模型最大似然估计MATLAB例程(ARMA model maximum likelihood estimation)

可以将matlab中的mat文件转换成txt文件，方便其他的应用程序调用。(Mat files can be converted into a txt file and facilitate the application calls.)

说明：   基于matlab 的叠加高斯白噪声的正弦信号频谱分析，给定正弦函数x=sin(2πf0n/N+π/3)+sin(2πf1n/N+π/4)，设定f0.f1的值，加了噪声之后做快速傅里叶变换，在matlab中分析其频谱，验证是否与f0,f1的值符合。再变换N值，看随着N值的变化对应点是否符合及有何变化。(Matlab based on the superposition of Gaussian white noise sinusoidal signal spectrum analysis, given the sine function x = sin (2蟺f0n/N+蟺/3)+sin (2蟺f1n/N+蟺/4) set value of f0.f1, do a quick noise Fourier transform to analyze the spectrum in matlab, verify that with f0, in line with the value of F1. Transform the value of N, see change as the value of N corresponding points of compliance and what changes.)

为了有效地应用遗传算法解决 鲁棒控制系统设计问题，将遗传算法与局部优化方法相结合，提出了基于降维扫描方法的自适应多目标遗传算法（DRSA-MOGA）。通过引入适应度函数标准化方法、基于最优Pareto解集搜索的降维扫描方法和适应度函数自适应调整方法，提高了算法的全局优化性能和局部搜索能力。仿真结果表明，DRSA-MOGA算法在不损失解的均匀度的情况下可以达到很高的逼近度(For effective application of genetic algorithms to solve robust control system design problems, genetic algorithms and local optimization method, based on reduced-order adaptive scanning method multi-objective genetic algorithm (DRSA-MOGA). Fitness function through the introduction of standardized methods, based on the Pareto optimal solution set of search methods and dimensionality reduction scan fitness function adaptive adjustment method, the algorithm improve the performance of global optimization and local search capabilities. The simulation results show that, DRSA-MOGA algorithm solution without loss of uniformity can be achieved under a high degree of approximation)

说明：  Adaptive Watermarking