Fea_Gabor_brodatz

Griewang and Michalewicz test bed for Genetic Algorithm Optimization Problems. This is a standard Test bed used by several authors to test the efficiency of their proposed algorithms.

MATLAB环境下实现单片空间后方交会—为课程实习内容(MATLAB environment to achieve the monolithic space resection- Curriculum internship content)

说明：  EEMD：总体经验模态分解，一种改进的EMD方法。(Total empirical mode decomposition is an Improved EMD method.)

计算机图形学生成基本曲线、圆、直线方面的程序，仅供参考(jisuanji tuxingxue shengcheng jibenquxian fangmian de cheng xu jingong cankao)

算术编码的Matlab实现，用于该编码的各种压缩、加密应用中，参考程序。(Arithmetic Coding program using Matlab.)

运用帧间差法检测，基于kalman滤波的目标跟踪。(Use of the inter-frame difference method detection, based kalman filtering target tracking.)

标幺化模型下的感应电机转矩——转速特征曲线的验证，标幺化的转速范围为[0-2]，如仿真步长不为1/8e3，仿真前请调整(Under the per-unit model induction motor torque- speed characteristic curve verification, per unit of speed range [0-2], such as simulation step 1/8e3 simulation before adjustment)

欢迎大家下载学习，包括最小二乘法、SVM、神经网络、1_k近邻法,基于chebyshev的水声信号分析，课程设计时编写的matlab程序代码，基于欧几里得距离的聚类分析，模式识别中的bayes判别分析算法，包括脚本文件和函数文件形式。( Welcome to download the study, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Based chebyshev underwater acoustic signal analysis, Course designed to prepare the matlab program code, Clustering analysis based on Euclidean distance, Pattern Recognition bayes discriminant analysis algorithm, Including script files and function files in the form.)

generate primary users in cognitive radio

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)