MATLAB-wavelet-analyse

说明：  medical image database breast cancer

说明：  经验模态分析（EMD）的MATLAB计算算法，经验模态分析是机械振动分析领域的重要方法(Empirical mode decomposition (EMD) of the MATLAB computing method, empirical mode analysis is an important field of mechanical vibration analysis method)

面阵阵列天线二维方向图仿真，代码简单易懂(The two-dimensional surface bursts columns antenna pattern simulation, the code is easy to understand)

Tabu search implementation in matlab

利用连续潮流法计算的电压裕度程序，自己编的，大家可以下载交流使用(Continuous flow method using the voltage margin procedures, their own series, you can download exchange use)

永磁同步电动机的开环控制的matlab/simulink仿真。供大家学习。(Permanent magnet synchronous motor open-loop control matlab/simulink simulation. For everyone to learn.)

语音识别是近年来发展非常迅速的一项计算机智能技术，广泛应用在语音控制、身份识别等多个领域。本次项目主要研究语音识别的预处理过程和特征参数的提取环节。通过对原始语音信号进行预加重和分帧、加窗，滤除低频干扰，提升对语音识别有用的部分，消除了部分噪音和失真。预处理之后进行信号的特征提取，主要选取了短时平均过零率和MFCC两个特征参数，应用matlab软件绘制波形图并提取特征参数矩阵，为之后的语音信号的识别打下了基础。(Speech recognition is a computer intelligent technology which has developed rapidly in recent years. It is widely used in speech control, identity recognition and other fields. This project mainly studies the pretreatment of speech recognition and the extraction of feature parameters. Through pre-emphasis, framing and windowing of the original speech signal, the low frequency interference is filtered out, the useful part of speech recognition is enhanced, and some noise and distortion are eliminated. After pretreatment, feature extraction of signal is carried out. Two feature parameters, short-term average zero-crossing rate and MFCC, are selected. Waveform diagram is drawn by MATLAB software and feature parameter matrix is extracted, which lays a foundation for speech signal recognition.)

This is sofe input and soft output programme you can use. Matlab is the similation tool.

Neural Network C language BP neural network solution differences or problems canny source HMM C language isodata K-MEANS License Plate Recognition System Vector Quantization C Language

DHMM，离线隐马尔科夫模型用于模式分类的好例子，特征采用Kmeans进行离散化。(DHMM, off-line Hidden Markov Model good example for pattern classification, feature discretization using Kmeans.)