Radio

MATLAB5.X程序设计语言.pdf喜欢matlab的可以看一下(MATLAB5.X programming language. Pdf like matlab can look at)

语音端点检测算法，在matlab里面实现语音端点的准确检测。为读者开发噪声环境下的精确VAD提供思路。(voice endpoint detection algorithm in Matlab voice inside the precise endpoint detection. Readers development under noisy environments to provide accurate VAD ideas.)

信任函数理论，也称为D-S证据理论，是经典概率理论的扩展。特别适应于决策级信息融合。(Belief function theory, also known as DS evidence theory is an extension of classical probability theory. Especially adapted to Decision Level Information Fusion.)

支持向量机，实现2或多分类，基于matlab仿真，内有说明(ex6.m- Octave script for the rst half of the exercise ex6data1.mat- Example Dataset 1 ex6data2.mat- Example Dataset 2 ex6data3.mat- Example Dataset 3 svmTrain.m- SVM rraining function svmPredict.m- SVM prediction function plotData.m- Plot 2D data visualizeBoundaryLinear.m- Plot linear boundary visualizeBoundary.m- Plot non-linear boundary linearKernel.m- Linear kernel for SVM [?] gaussianKernel.m- Gaussian kernel for SVM [?] dataset3Params.m- Parameters to use for Dataset 3 ex6 spam.m- Octave script for the second half of the exercise spamTrain.mat- Spam training set 1 spamTest.mat- Spam test set emailSample1.txt- Sample email 1 emailSample2.txt- Sample email 2 spamSample1.txt- Sample spam 1 spamSample2.txt- Sample spam 2 vocab.txt- Vocabulary list getVocabList.m- Load vocabulary list porterStemmer.m- Stemming function readFile.m- Reads a le into a character string submit.m- Submission script that sends your solutions to our servers submitWeb.m- Alternative s)

计算小波熵程序，先计算出小波分解后的小波系数，对其求熵得出结果。(Wavelet entropy calculation program, first calculate the wavelet coefficients of the wavelet decomposition, the results of its request entropy.)

该文档系统的介绍了STK与Matlab的接口及其在复杂航天任务仿真分析中的应用，文档中附有matlab与STk连接的源码及其应用的程序(STK and Matlab)

语音信号的lpc分析，该程序实现了与语音预测器的阶数与预测残差能量的关系(The speech signal LPC analysis, the program with the order of the prediction residual energy of the speech predictor)

Laplacian Eigenmaps [10] uses spectral techniques to perform dimensionality reduction. This technique relies on the basic assumption that the data lies in a low dimensional manifold in a high dimensional space.[11] This algorithm cannot embed out of sample points, but techniques based on Reproducing kernel Hilbert space regularization exist for adding this capability.[12] Such techniques can be applied to other nonlinear dimensionality reduction algorithms as well.

自适应粒子群算法，自适应粒子群算法，在普通的粒子群算法里面加入了熵和平均粒距的概念，收敛速度大大提高，而且不容易陷入局部最优，能更有效的解决复杂问题。(Adaptive particle swarm algorithm, adaptive particle swarm optimization, in which ordinary PSO joined the entropy and the concept of average distance, speed up the convergence, but not easy to fall into local optimum, more effective solutions to complex problems.)

Crout Algorithm for Power Flow