googleearth-r36

clip&Filter,PTS和SLM三种降低PAPR的方法以及其优化算法，代码简单，注释丰富，绝对可以运行，有很大的参考价值。(clip )

神经计算的MATLAB教程，包含常用算法实现及其原理，BP/HEBBIAN/SMO等等，神经计算专用(The MATLAB neural computing tutorials, including the commonly used algorithm, BP/HEBBIAN/SMO, etc., etc., dedicated neural computing)

列文伯格—马奎特算法的matlab实现程序(The Matlab program of the Levenberg-Marquardt algorithm)

一个完整的PSO程序，可以实现优化，并且由很好的可视化见面，且可用来调节PID参数(A complete PSO program, you can optimize, and met by a good visualization, and can be used to adjust the PID parameters)

包括优化算法源程序及算法原理介绍。自适应协方差矩阵进化策略，一种2000年左右发展起来的新优化算法，简称CMA-ES，最近十几年得到了学术界的广泛关注，非常有应用前景。这是一种黑箱子优化算法，可以再程序末自己定义优化函数，也可以调用外部程序（如有限元等）进行计算，然后提取优化函数的解。该优化算法在各种变量维度下的表现优于蚁群算法、粒子群算法，有较好的收敛性，这归功于其严谨的数学表达。(Covariance matrix adaptation evolution strategy, about one kind of new optimization algorithms developed in 2000, referred to as CMA-ES, the last ten years has been widespread concern in academic circles, very prospect. This is a black box optimization algorithm, you can then define their own optimization program at the end of the function, you can also call an external program (such as finite element, etc.) are calculated, and then extract the optimal solution function. The optimization algorithm in a variety of variable dimensions outperformed ant colony algorithm, particle swarm optimization, better convergence, thanks to its rigorous mathematical expression.)

Symbol synchronization

高斯坐标，大地坐标，空间直角坐标之间的相互转换，参照控制测量下册公式编写。(Gauss coordinates, geodetic coordinates, space conversion between rectangular coordinates, the formula reference book prepared under the control measure.)

系统地阐述了低密度校验码基于图模型的编译码思想，介绍了密度进化理论，对影响低密度校验码纠错性能的两个主要因素——度序列设计和围长设计进行了深入分析，详细介绍并设计了LDPC的编解码(Systematic exposition of the LDPC graph model based codecs idea, introduces the density evolution theory, the two main factors that affect the performance of LDPC error correction- the degree of sequence design and girth design depth analysis, detailed and designed LDPC codec)

用周期图谱法以及其改进算法进行的经典谱估计(Mapping method using the cycle as well as its improved algorithm for the classical spectral estimation)

说明：  改进的gmm（高斯混合模型）算法，是单一高斯几率密度函数的衍生(Improved gmm (GMM) algorithm, a single Gaussian probability density function of the derivative)