imrotate

MATLAB源码MIMO-OFDM（simulinkANDmatlab）MIMO-OFDM不同信道估计的对比(MATLAB source MIMO-OFDM simulinkANDmatla MIMO-OFDM channel estimation with different contrast)

利用matlab的simulink工具进行最优化的设计，谢谢(Presentation and demo files for MathWorks webinar "Introduction to Simulink Design Optimization)

地磁场计算,大回源线圈产生的地磁场,不同电导率的地磁场计算(Earth' s magnetic field analysis, the source coil back to the geomagnetic field generated by the different conductivity of the Earth' s magnetic field calculation of)

This Matlab code implements Cauchy s steepest descent method using constant stepsize rule.

对花卉的数据集分别通过“五折法”、随机产生训练样本、欧式平方距离、绝对值距离、契比雪夫距离和马氏距离进行数据集的识别。(Data sets, respectively, for flowers through the " half of Law" , randomly generated training samples, European squared distance, absolute distance, Chebyshev distance and Mahalanobis distance for data set identification.)

说明：  利用分布傅里叶方法进行数值仿真，包含损耗、色散、SPM效应。 可以观察脉冲展宽与压缩，光孤子演化。(The distributed Fourier method is used for numerical simulation, including loss, dispersion and SPM effect. We can observe the pulse broadening and compression, and the evolution of optical solitons.)

MATLAB软件的回归分析教程和MATLAB的具体程序说明(Regression Analysis MATLAB software tutorials and MATLAB description of the specific procedures)

现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。(existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.)

Jacobi Polynomial Coefficient

Throughput uation in cognitive networks