FIRfilters

说明：  采用频率调制 为1000Ｈｚ的载波，在解调前把加性白高斯噪声加入到调制信号。当噪声功率和调制信号功率比为０.００１，０.０１，０.０５，０.１和０.３是进行解调并绘出调制信号(The use of frequency modulation for the 1000Hz carrier, in the demodulation before AWGN added to the modulation signal. When the noise power and modulation signal power ratio of 0.3 is 0.001,0.01,0.05,0.1 and demodulation and modulation signals mapped)

数模题目代码，一个简易的matlabUI动画演示程序(Modulus subject code, a simple animation demo program matlabUI)

本代码的功能是使用BP神经网络对蝴蝶花进行分类(The functionality of the code is to use the BP neural network to classify the butterfly flower)

Facial expression in real time code..this is good for all face both Natural and IR

窄带波束形成，包括常规波束形成以及MVDR自适应波束形成，在单目标以及多目标情况下。(Narrowband beamforming, including conventional beamforming and MVDR adaptive beamforming, in the case of single-objective and multi-objective)

1. this function is for image (width=176, hight=144) only. but you can modify the related value to fit yours. 2. it is used to evaluate the motion vector between the Nth image and N-1th image. 3. it use Three Steps search algorithm. 4. this algorithm finds the closest pixel near the original pixel in three times.(1. This function is for image (width = 176, hight = 144) only. But you can modify the related value to fit yours.2. It is used to evaluate the motion vector between the Nth image and N-1th image.3 . it use Three Steps search algorithm.4. this algorithm finds the closest pixel near the original pixel in three times.)

主要是感知器程序集，用于模式分类，欢迎您的访问，谢谢您的支持和下载 (Sensor assembly is mainly used for pattern classification, welcome your visit, thank you for your support and download)

基于matlab来描述静电场的分布，基于matlab描述电偶极子的分布。(Matlab-based to describe the distribution of electrostatic field, matlab-based description of the distribution of electric dipole.)

a full asics of the matlab very easy to learn and identify

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 )