VB_MATLAB_COM

mathematica5简明教程 关于mathematica的教程，值得看一看咯！！！(mathematica5 concise tutorial mathematica about the tutorial, it is worth a look pyronaridine! ! !)

地震数字处理的一个挺好的软件包,内附有实际例子(A very good package of Seismic digital processing)

Simple Scheduling using ICA

产生一个随机信号和两个不同频率但频率间隔很小的正弦信号，要求对两信号之和进行如下分析： （1） 求该随机信号的自相关性系数、自相关函数，画出对应的图形； （2） 利用不同的参数建模方法求出两个随机信号的功率谱； （3） 利用极大似然估计、递推最小二乘法等常用的参数估计方法估计所建模型，包括AR模型、MA模型和ARMA模型的的参数，阶次自定；并与Matlab工具箱里的一些建模函数的运算结果进行比较； （4） 利用陷波滤波和MUSIC滤波方法对该信号的频谱进行估计； （5） 利用Wiener滤波、LMS滤波对该含噪声的正弦信号进行去噪声处理； （6） 假设该信号是一个飞行器的某个方向的线位移信号，可否利用Kalman滤波对该信号进行滤波？ （7） 利用高阶谱理论对该信号进行谱估计和相应的AR模型、MA模型和ARMA模型估计； （8） 利用小波变换（变换函数可以直接用Matlab里的函数）对该信号进行去噪声处理，并和前面的去噪声方法进行比较。 (Produce a random signal and two different frequency but frequency interval small sine signals, required to make the following analysis of two signals: (1) for the random signal from the correlation coefficients, the autocorrelation function and draw the corresponding graphics (2) make use of different parameters modeling method for out two random signal power spectrum (3) using maximum likelihood estimation, recursive least square method and common parameters estimation method estimates that the model, including AR model, MA model and the parameters of the ARMA model we, order time decided oneself With Matlab toolbox and some of the modeling function is used compared the (4) use trapped wave filter and MUSIC of the signal filter method of spectrum to estimate (5) use Wiener filtering, LMS filtering on the sinusoidal signal with noise to noise treatment (6) assumes the signal is a vehicle of a certain direction of displacement signal line, can you use Kalman filtering on the)

本程序是利用全有全局搜索能力的遗传算法来优化bp神经网络的，优化神经网络的权值与阈值(This procedure is the use of all-global search capability of genetic algorithm to optimize bp neural network optimization neural network weights and thresholds)

用C++实现窗函数的设计，采用的是海明窗为例子(Using C++ to achieve the design of window function, Hamming window is used as an example)

this code extract the ellipse from noisy image by morphology method

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.)

信号在信道进行传输时，经常会出现串扰，采用基于最大似然序列估计（MLSE）的电子色散均衡器的方法，克服光纤通信中的由各种色散引起的码间干扰。 研究基于最大似然序列估计（MLSE）的均衡器，采用维特比算法实现，用matlab仿真出采用MLSE后的性能指标（眼图和误码率）的提升。 (Signal transmission channel often occur crosstalk, based on the maximum likelihood sequence estimation (MLSE) electronic dispersion equalizer of the ways to overcome optical fiber communications in a variety of dispersion caused by the inter-symbol interference. Research based on the maximum likelihood sequence estimation (MLSE) of the equalizer, the Viterbi algorithm, a simulation using matlab after using MLSE performance indicators (eye diagram and bit error rate) of the upgrade.)

GUI可视化界面设计，一个实例搞定，简单方便，容易操作(GUI visualization interface design, an instance to get simple and convenient, easy to operate)