Robotic-Sound-Source-Separation-using-Independent

本算法在Matlab2008b 环境下实现了立体视觉中摄像机的基础矩阵计算。包括main, sevenPoint、eightpoint 和eightpoint_norm三个函数。 main.m 是程序的入口，包括生成和读入实验数据，分别调用sevenPoint、eightpoint 和eightpoint_norm 三个函数求基础矩阵，最后绘制出最后的结果。sevenPoint.m 是用7 点法求解基础矩阵。eightpoint.m 是用8 点法求解基础矩阵，eightpoint_norm.m 是数据规格化后的8 点法求解基础矩阵。(The algorithm is implemented in Matlab2008b environment based stereo vision camera matrix calculations. Including the main, sevenPoint, eightpoint and eightpoint_norm three functions. main.m is the entry, including the generation and reading test data were calling sevenPoint, eightpoint and eightpoint_norm three fundamental matrix function evaluation, and finally draw the final result. sevenPoint.m 7 method is based on matrix. eightpoint.m 8-point method is based on matrix, eightpoint_norm.m the data after the 8-bit normalized fundamental matrix method. )

mov background subtraction

图像模板匹配计算，逐象素移动计算当前模板数据，提取图像特征点(image template matching, pixel-by-mobile current template data, image feature extraction point)

图像秘密共享方法，应用shamir门限方案（2,3）方案进行试验，本文有认证，防篡改的功能(Image secret sharing method, application shamir threshold scheme (2,3) test program, the paper has certified, tamper-resistant feature)

关于凸优化资料，目前最新版本的matlab图优化工具加载方法和学习算例。里面讲述了多目标优化、内点法等一些凸优化具体算例，希望对大家有用。(About convex optimization data, the latest version of the loading method matlab figure optimization tools and learning examples. Which describes a multi-objective optimization, interior point method and some convex optimization numerical example, we want to be useful.)

trisurf画一个三维球，并进行着色。(To figure a colored sphere in matlab function trisurf)

先进PID控制MATLAB仿真（第3版）/刘金琨 中的第一章仿真程序(Advanced PID control MATLAB simulation (3)/Liu Jinkun Chapter emulator)

经典的利用matlab做的卡尔曼滤波。代码可以运行。读者可以和PF对比；(Classic Kalman filter using matlab to do. Code can run. Readers can compare and PF )

This code implements the direct kinematics of a 3 revolute robot. It also includes a simple graphic model of this operation.

基于fft的截断奇异值分解 （TSVD）正则化方法(TSVD: Truncated Singular Value Decomposition: TSVD)