Lagrange_fft_bj

根据所给样本数据用三种不同的方法绘制该公路并估计其长度。 分别用线性插值法、最近邻域插值法和三次样条插值法计算公路长度，并用勾股定理估计公路的长度 (According to the sample data in three different ways, the road map and estimate its length. , Respectively, by linear interpolation, nearest neighbor interpolation domain and cubic spline interpolation method to calculate the length of highways and roads with an estimated length of Pythagorean Theorem)

计算DGGE图谱各泳道的多样性指数，输入参数x为图谱的泳道的亮度（灰度）矩阵(DGGE profiles calculated the diversity index of each lane, the input parameter x for the map of lane brightness (gray) matrix)

Quade test for non parametric two way ANalysis Of VAriance

线性神经网络 Matlab程序 附加多个案例 适合神经网络入门(Linear Neural Network)

Motion Tracking =============== This tarball contains all code required to run the tracking algorithm on a sequence of images. Run the file run_tracker.m in Matlab and follow the instructions. You will need to have a directory of sequentially numbered images available. After entering the path and file types the tracker will begin processing. Once the data window appears the algorithm begins building a background model and attempts to track objects. By clicking on any of the four subwindows you can investigate the background representation (a Mixture of Gaussians) of any pixel. The two windows that then appear display the mixture once as a two-dimensional scatter plot (ignoring the blue colour component), and once as a one-dimensional evolution of the red colour component only. These plots make the internal processing visible and should help determining suitable parameters to be set in mixture_parameters.m.

OFDM with PEAK TO AVERAGE POWER RATIO

Source code: M. A. Haque and M. K. Hasan, "Noise robust multichannel frequency-domain LMS-type algorithms for blind channel identification", IEEE Signal Processing Letters, pp.305-308, vol.15, 2008.

matlab 实现数据的短时傅里叶变换 自己测试通过(short time Fourier transform matlab achieve data test by)

说明：  用双音频输入重新仿真，实现频谱扩展功能，产生滤波后高斯过程功率谱密度和相关高斯序列时域值(Re-simulation with dual audio inputs, to achieve spread spectrum function, resulting in filtered Gaussian process Gaussian power spectral density and the associated value of time-domain sequence)

palm vein recognition matlab