CTSP_SA

数据资料是数学模型与实际问题接轨的重要途径和手段，有时还可以直接从数据资料中发现规 律组建模型。数据分析和处理是统计学研究的主要内容，也是计算数学的工作领域，近年来随 着计算机科学的发展，对“海量”数据的分析形成了一门新的交叉学科“数据挖掘”。这一章仅 介绍用统计学的最小二乘法构造拟合数据模型，以及用计算数学的函数插值方法处理数据解决 实际问题。(Is a mathematical model of data integration an important practical problem with the ways and means, and sometimes directly from the data can also be found in the law of the formation model. Statistical data analysis and processing is the main research, but also work areas of computational mathematics, computer science in recent years, along with the development of the " mass" analysis of the data formed a new cross-disciplinary " data mining." This chapter describes only the structure with a statistical method of least squares fit data models, and computational mathematics and data processing function interpolation methods to solve practical problems.)

运用matlab语言编写的c均值算法，分为两个聚类(Use matlab language c-means algorithm is divided into two clusters)

基于Simulink和PSIM的光伏阵列的联合仿真(PSIM and Simulink co-simulation-based photovoltaic arrays)

LMS自适应滤波器算法(algorithm about LMS(least-Mean-squrt) adaptive signal processing)

说明：  在matlab环境下生成一个正方形中心嵌套一个圆，并利用curvelet变换显示出其各个子层的系数图，良好的体现了curvelet变换的各向异性的性质。(In the matlab environment to generate a nested square center of a circle, and the use of curvelet transform shows that its coefficient of sublayer maps, a good reflection of the curvelet transform the nature of the anisotropy.)

ssfm for soliton propagation

求序列向量xn的傅里叶变换和离散傅里叶变换并画出它们的幅频特性和相频特性曲线，验证离散傅里叶变换是傅里叶变换的等间隔采样，验证IDFT和DFT的唯一性(Find the Fourier transform of the sequence xn vector and the discrete Fourier transform and draw their amplitude-frequency characteristic and phase-frequency characteristic curve, verify the discrete Fourier transform is the Fourier transform of equally spaced sampling, verification of IDFT and DFT Uniqueness)

这是对广义高斯分布中两个参数alpha,beta的估计方法，对自然图像的大量统计特征就符合这一分布。(function [f, g] = dggbeta(beta, absx) DGGBETA Derivative of generalized Gaussian pdf with respective to beta [F, G] = DGGBETA(BETA, ABSX) Return partial derivative and second order partial derivative This is an utility function for GGMLE ABSX is the distance of the data from the mean Function is undefined for negative beta if beta <= 0 f = NaN g = NaN )

In this work, we are interested in the problem of the coding channel with impulsive noise, whose goal is to correct the errors due to the channel at the time of a numeric transmission. In particular, the convolutional turbocodes constitutes the last progress in this domain and it is very close to the theoretical limit predicted by Shannon in 1948. The objective of this work is to study and to test this coding with the modulation BPSK, to improve the bit error rate (BER) in presence a additive white impulsive GGD noise using the MAP algorithm.

对正弦波进行FFT（快速傅立叶变换）得到幅频谱图，均方根谱图，功率谱图，对数谱图，并进行IFFT恢复出正弦波。(FFT and IFFT)