matlab

我想应该很不错吧，你们说呢是是方式三分？？？？(it is good )

信道建模 仿真程序收集和整理，供参考，MIMO，信道建模MATLAB程序(Channel modeling and simulation program to collect and organize, for reference, MIMO, channel modeling program MATLAB)

语音信号的TDOA算法 TCT双边变换 宽带doa算法 基于MUSIC(TDOA algorithm TCT bilateral transform speech signal)

A speech signal filterbank, using melscale frequency and framebanking.( 1. For speech signal can be represented as a discrete sequence of frames (or feature vectors) that can be used as the input to a speech recogniser. Important ideas and techniques that are used in the assignment are the design of a (Mel frequency scale) audio filterbank, , windowing of a continuous audio signal, spectrum analysis of the signal, filtering as multiplication in the frequency domain, the visual representation of a speech signal as a spectrogram, appreciation of the acoustic variability in real speech utterances. 2. To gain a deeper knowledge of the application of MATLAB programming to audio signal processing. 3. To gain practice in the art of writing a formal report: structure, content, style, use of diagrams, presentation etc. etc. )

本程序为与《多采样率系统》一书配套的（中文界面）MATLAB可视化程序(Multiraten systeme in matlab，easly Using )

amg_chirprate11080 八通道四抽取调频率估计程序 amg_chirprate110808a 八通道八抽取调频率估计程序 (Traditional channelized receiver, signal, channel eight extraction)

本程序能够实现分数阶傅里叶变换的功能，也就是Disfrft的编程。(Disfrft)

Image coding based on a morphological representation

一篇课程论文，介绍滚动轴承的故障诊断，有详细的matlab程序(A course papers, on the rolling bearing fault diagnosis, detailed matlab program)

试者工作特征曲线 （receiver operating characteristic curve，简称ROC曲线），又称为感受性曲线（sensitivity curve）。得此名的原因在于曲线上各点反映着相同的感受性，它们都是对同一信号刺激的反应，只不过是在几种不同的判定标准下所得的结果而已。接受者操作特性曲线就是以虚报概率为横轴，击中概率为纵轴所组成的坐标图，和被试在特定刺激条件下由于采用不同的判断标准得出的不同结果画出的曲线。(PLOTROC Plot receiver operating characteristic. Syntax plotroc(targets,outputs) plotroc(targets1,outputs1, name1 ,targets,outputs2, name2 , ...) Description PLOTROC(TARGETS,OUTPUTS) plots the receiver operating characteristic for each output class. The more each curve hugs the left and top edges of the plot, the better the classification. PLOTROC(TARGETS1,OUTPUTS2, name1 ,...) generates multiple plots. )