matlab_programming_examples_of_source_analysis

一个水下无人自主航行器（AUV）的MATLAB/simulink仿真程序，写得很详细，有附带的s函数和m文件供参考学习。看资源里水下无人航行器的仿真程序比较少，特上传来与大家分享。(An underwater autonomous Vehicle (AUV) in the MATLAB/simulink simulation program, written very detailed, with the s functions and m files for reference study. To see resources in unmanned underwater vehicle simulation program relatively small, special upload to share with you.)

多径衰落信道matlab仿真程序，有说明(multi-path flat fading)

Convert a vector auto-regressive model of order k to state-space form

Fit time series Q(t) to a logistic function. Inputs (vectors of same size): t (time) & Q Outputs: Qpre (logistic model fit to data) and three independent parameters: thalf, Qinf, and a, describing the logistic Q(t) = Qinf/(1 + exp(-a*(t-thalf))) Qinf is value as t --> infinity thalf is time of symmetric inflection point a is time decay constant Written by James Conder, Southern Illinois University, Oct. 2010

实现了通信中交织和解交织过程，参数可自定义输入(Realize the communication intertwined intertwined reconciliation process, input parameters can be customized)

半监督LDS算法,是由Olivier Chapelle提出来的，他将半监督的流行假设和聚类假设结合起来。 (LDS for Semi-supervised learning，which is proposed by Olivier Chapelle,it combinations the monifold assumption and cluster assumption.)

3d reconstruction using mathematical morphology

针对信号分选的几种常见算法，包括pri变换，sdif（序列差直方图）算法，cdif（累积差直方图）算法，可以解决类似于固定重频信号，抖动重频信号，滑变重频信号和参差重频信号的分选等(For several common signal sorting algorithms, including pri transform, sdif (sequence difference histogram) algorithm, cdif (cumulative difference histogram) algorithm, can solve similar fixed repetition frequency signals, jitter signal repetition, repetition becomes slippery sorting and mixed signal repetition frequency signals, etc.)

matlab语言编写，学生体育锻炼分析系统—GUI版本(Physical Training of Students)

Title: "Modeling Lung Cancer Diagnosis Using Bayesian Network Inference" This demo illustrates a simple Bayesian Network example for exact probabilistic inference using Pearl s message-passing algorithm. Introduction: Bayesian networks (or belief networks) are probabilistic graphical models representing a set of variables and their dependencies. The graphical nature of Bayesian networks and the ability of describing uncertainty of complex relationships in a compact manner provide a method for modelling almost any type of data.