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matlab编程基础，介绍matlab编程语言的ppt，方便matlab学习(matlab Programming on the matlab programming language ppt, easy to learn matlab)

MATLAB 81404614dsp-usingmatlab(81404614dsp-usingmatlab)

STM32制作的MP3的全部资料，用谱方法计算流体力学一些流动现象的整体稳定性,是路径规划的实用方法。( STM32 all the information produced by the MP3, Spectral methods of computational fluid dynamics flow of some of the overall stability of the phenomenon, Is a practical method of path planning.)

说明：  对于给定的信号数据，用emd进行信号分解得到固有模态函数imf(implement emd to obtain imfs)

有关分数阶离散傅里叶变换的程序以及文章，文章详细介绍了离散傅里叶变换，有助于读者的学习(Fractional Fourier transform on the procedures and articles, the article introduces the discrete Fourier transform, to help readers learn)

说明：  MATLAB 数值计算 入门 源码 &sup2 introduction to MATLAB &sup2 linear equations &sup2 interpolation &sup2 zero &macr nding &sup2 least squares &sup2 quadrature &sup2 ordinary di&reg erential equations &sup2 random numbers &sup2 Fourier analysis &sup2 eigenvalues and singular values &sup2 partial di&reg erential equations(Numerical Computing with MATLAB)

Alternate CPU1 startup vector for BMIPS4350.

Matlab中实现轮对分析的程序以及相关的程序包,可以实现火车轮轨接触关系的分析(Matlab achieving round of analysis of the procedures and the relevant package, can be achieved train wheel-rail contact between the analysis)

能使桌面上不停的换色.请先生成EXE再运行不然会出错。(Enable non-stop for the desktop color. Please re-run the EXE generated will not go wrong.)

包括kf，ekf，pf，upf可以自己定制模型参数，完成滤波(ReBEL currently contains most of the following functional units which can be used for state-, parameter- and joint-estimation: Kalman filter Extended Kalman filter Sigma-Point Kalman filters (SPKF) Unscented Kalman filter (UKF) Central difference Kalman filter (CDKF) Square-root SPKFs Gaussian mixture SPKFs Iterated SPKF SPKF smoothers Particle filters Generic SIR particle filter Gaussian sum particle filter Sigma-point particle filter Gaussian mixture sigma-point particle filter Rao-Blackwellized particle filters The italicized algorithms above are not fully functional yet (or included in the current release), but will be in the next or future releases. The code is designed to be as general, modular and extensible as possible, while at the same time trying to be as computationally efficient as possible. It has been tested with Matlab 7.2 (R2006a). )