EKF

QPSK Modulation and demodulation Project

ر ی ا ض ی م ه ن د س ی س ر ی ف و ر ی ه Fourier series

KALMAN FILTER CODE for tracking

蒙特卡罗与matlab教程，是学习通信系统仿真的好助手(Monte Carlo and matlab tutorial, learn communication system simulation is a good assistant)

说明：  用matlab对仿真信号进行分解。包括fft变换、傅里叶变换、希尔伯特变换。可观察分解图形。(MATLAB is used to decompose the simulation signal. The FFT transform, the Fourier transform, the Hilbert transform. Observe the decomposition graph.)

说明：  现代通信系统MATLAB源码（刘树棠）教材配套，可以和书籍配套使用，效果很不错。(Modern communications systems MATLAB source code (刘树棠) supporting materials, and books can support the use of, the effect is pretty good.)

What is a data warehouse? Data warehouse architecture From data warehousing to data mining

统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines( This section should give the reader a quick overview of the methods implemented in STPRtool. • Analysis of linear discriminant function: Perceptron algorithm and multiclass modification. Kozinec’s algorithm. Fisher Linear Discriminant. A collection of known algorithms solving the Generalized Anderson’s Task. • Feature extraction: Linear Discriminant Analysis. Principal Component Analysis (PCA). Kernel PCA. Greedy Kernel PCA. Generalized Discriminant Analysis. • Probability distribution estimation and clustering: Gaussian Mixture Models. Expectation-Maximization algorithm. Minimax probability estimation. K-means clustering. • Support Vector and other Kernel Machines: Sequential Minimal Optimizer (SMO). Matlab Optimization toolbox based algorithms. Interface to the SVMlight software. Decomposition approaches to train the Multi-class SVM classifiers. Multi-class BSVM formulation trained by Kozinec’s algorithm, Mitchell- Demyanov-Molozenov algorithm )

这是saleh提出其高功率放大器模型的原文 很难找 给大家共享(This is saleh to present its high-power amplifier model of the original hard to find for everyone to share)

useful for nn beginner for the basic understanding