Digital-Signal-Processing-Sanjit-K

OFDM的仿真程序，经过瑞利多径的仿真。其中子载波数为4.(OFDM simulation procedure, after the simulation of multipath Rayleigh. Subcarriers of which the number of 4.)

H.S.Park, D-S.Kim, and W.H.Kwon "A Scheduling Method for Network-based Control Systems", IEEE Transaction on Control System Technology, Vol.10, No.3, pp. 318-330, May, 2002 (Simulation Example 1- Matlab Code) (HSPark, DS.Kim, and WHKwon A Scheduling Method for Network-based Control Systems , IEEE Transaction on Control System Technology, Vol.10, No.3, pp. 318-330, May, 2002 (Simulation Example 1- Matlab Code))

基于Matlab的GPS卫星信号的采集仿真程序(Matlab GPS satellite signal acquisition-based simulation program)

基于matpower的配电网潮流计算程序，33节点与69节点(Calculation program, 33 nodes and 69 node distribution network based on the flow matpower)

PID极点配置控制（二阶系统，对象参数已知）(PID pole placement control (second-order system, object parameters are known))

Base-band signal generation usin matlab

converter is suppressed according to CISPR 22 standard limits. The fundamental frequency component with higher order harmonics of 2 MHz resonant converter after the application of filter is at 33.18 dBµ V. The designed filter has attenuated the common mode conducted noise by 140 dBµ V.

自然边界元数学理论，于德浩教授编著。对于椭圆编制问题规划给出了较为详细数学理论解释，对于工程和理学专业的程序的编写有较好的辅助作用。(Natural Boundary Element mathematical theory, Professor Yu Dehao ed. For the preparation of the problem oval plan gives a more detailed explanation of the mathematical theory, for the preparation of professional engineering and Science program has a good supporting role.)

说明：  LDPC码编解码程序，LDPC，Matlab，VMSK(LDPC encoding and decoding procedures)

此为一篇介绍mcmc方法的外文文献 非常适合刚刚接触mcmc方法的初学者(This paper introduces the readers of the Proceedings to an important class of computer based simulation techniques known as Markov chain Monte Carlo (MCMC) methods. General properties characterizing these methods will be discussed, but the main emphasis will be placed on one MCMC method known as the Gibbs sampler. The Gibbs sampler permits one to simulate realizations from complicated stochastic models in high dimensions by making use of the model’s associated full conditional distributions, which will generally have a much simpler and more manageable form. In its most extreme version, the Gibbs sampler reduces the analysis of a complicated multivariate stochastic model to the consideration of that model’s associated univariate full conditional distributions.)