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这是一个很好的关于turbo乘积码的matlab代码(This is a very good turbo product codes on the Matlab code)

复合材料分层理论的程序，适合新手使用。只是简单运用了一阶理论(This program consists of two file: 1) Laminate.m 2) input.dat (input.dat) needs to be modified according to the laminate to be analysed. All the plies properties and loadings goes into this input file. The program produces an output file containing all the anaylsis results. TO run the program simply execute: Laminate( input.dat , output.out ) )

This program simulates the transmission performance of BPSK under Additive White Gausian Noise (AWGN) environment.

功率谱密度相关方法的MATLAB实现代码，包含基础讲解和算例代码(Power spectral density related methods of MATLAB code, including basic explanation and example code)

对阵列信号进行维数的压缩，并用music算法估计出原始信号的DOA。(Array signal compression dimension, and estimate the DOA music algorithm with the original signal.)

MATLAB软件的介绍，使用和操作方法以及实例分析。(MATLAB software introduction, use and operation methods, and case analysis. )

原档LCD程序，调用LCD文件可使用LCD方法处理信号(The original LCD program, calling LCD files can use LCD method to process signals.)

关于UKF的仿真程序，注释详细，还包含有两个演示程序，分别针对一维和二维的跟踪情况，与EKF作比较，充分说明了UKF在非线性情况下优于EKF(The simulation program on the UKF, comments in detail, also includes two demo program, respectively for the one-dimensional and two-dimensional tracking of cases, compared with the EKF, UKF speaks better than the EKF in nonlinear case)

DDS的matlab环境下function函数(The DDS function function in Matlab environment)

这是一个关于稀疏高斯过程的matlab源码，可以用于计算测试输入的高斯预测值。( spgp_pred computes the SPGP predictive distribution for a set of test inputs. You need to supply a set of pseudo-inputs or basis vectors for the approximation, and suitable hyperparameters for the covariance. You can use any method you like for finding the pseudo-inputs , with the simplest obviously being a random subset of the data. It is coded for Gaussian covariance function, but you could very easily alter this. It is also fine to use for high dimensional data sets. spgp_lik is the SPGP (negative) marginal likelihood and gradients with respect to pseudo-inputs and hyperparameters. So you can use this if you wish to try to optimize the positioning of pseudo-inputs and find good hyperparameters, before using spgp_pred . I would recommend initializing the pseudo-inputs on a random subset of the data, and initializing the hyperparameters sensibly. Its current limitations are that 1) it is slow and memory intensive for high dimensional data sets 2) it is heavi)