demo_turbo

DSB的调制与解调MATLAB下的系统仿真(DSB modulation and demodulation system simulation under MATLAB)

基于matlab的极值算法滤波，滤除超声信号中的脉冲噪声(based on matlab-the filter using extreme algorithm to filter out impulse noise)

In this session, I will focus on the gridding method for fast image reconstruction from non- Cartesian data [1-5]. While gridding is not the only game in town, it is the reconstruction of choice when reconstruction time is an issue. Other reconstruction techniques include rBURS [6] and non-Cartesian iterative methods [7,8]. Non-Cartesian iterative methods generally use gridding and inverse gridding as their core operation, so even when gridding is not the right solution for an application, an understanding of gridding can be useful. I will cover the main steps of the gridding algorithm and then discuss some implementation tips that can signicantly reduce the computation time. Finally, I will present some illustrations and viewpoints that I have found to aid in an intuitive understanding of the gridding method, which can be helpful when designing the density compensation function and judging the appropriateness of gridding as a solution for a particular application.

program of matlab GUI 精通matlab GUI光盘代码(Proficient in matlab GUI CD code)

衰落信道下的盲估计研究MIMO-OFDM,mimo子空间仿真程序(ofdm)

matlab实现对伪随机序列PN码非均匀采样程序(PN code matlab realize the non-uniform sampling procedures)

带滤波器的PID控制，对于高频信号有着良好的滤波作用。(PID control with a filter for filtering high-frequency signal has a good effect.)

systemvue for FBMC BER

说明：  卷积码的MATLAB仿真及其性能测试，包括卷积码的编码和VITERBI译码程序(Convolutional codes of MATLAB simulation and performance testing, including the convolutional code encoding and decoding procedures VITERBI)

“Applied Econometrics using MATLAB”配套的计量经济Matlab包(MATLAB code for: 1. least-squares, simultaneous systems (2SLS,3SLS, SUR) 2. limited dependent variable (logit, probit, tobit) and Bayesian variants 3. time-series (VAR, BVAR, ECM) estimation and accompanying forecasting functions 4. ridge, Theil-goldberger, switching regimes, robust regression 5. regression diagnostics, e.g. Belsley, Kuh Welsch, Cook-Weisberg 6. cointegration testing 7. statistical distributions (CDF, PDF and random deviate generation) 8. Bayesian Gibbs sampling estimation and MCMC convergence diagnostics 9. maximum likelihood and Bayesian spatial econometrics functions 10. lots of other stuff, over 350 functions)