remote_accell

1）已知2N点实数序列 N=64。用一个64点的复数FFT程序，一次算出 ，并绘出频谱 。 （2）已知某序列 在单位圆上的N=64等分样点的Z变换 。用N点IFFT程序计算 ，绘出图像 。 (1) known 2N-point sequence of real numbers N = 64. With a 64-point complex FFT process, a calculated and mapped the spectrum. (2) known to a certain sequence in the unit circle on the N = 64 sub-samples of the Z transform. N-point IFFT procedures used, the mapped image.)

采用带宽为3，中心频率为3的复morlet小波对示例信号进行了分析，并绘出时频图。(Using bandwidth of 3, center frequency is 3 complex morlet wavelet to sample signal is analyzed, and draw the time-frequency diagrams.)

This function lets users digitize a published graph that is saved as a image e.g. jpeg

ICP图像配准算法的matlab程序。Robust Registration of 2D and 3D Point Sets-Andrew Fitzgibbon University of Oxford(Robust Registration of 2D and 3D Point Sets-Andrew Fitzgibbon University of Oxford)

偏微分方程的matlab解法，详细讲解了使用该工具求解偏微分方程的方法和途径，是一本很有价值的参考书(Solution of partial differential equations matlab, detailed account of the use of the tools for solving partial differential equations of the ways and means, are a valuable reference book)

世界经典教材《现代控制系统》第八版MATLAB仿真源码，好书好程序，是国内很多书籍的范本！(The world' s classic textbook " Modern control systems," eighth-source version of MATLAB simulation, better procedures for books, books are a model for many!)

说明：  粒子滤波的5个示例程序，对初学者非常有用(5examples for particle filter ,useful for new users)

Exit Chart with advance plot in matlab

matlab 程序例子(利用matlab 求位平面)(Matlab procedures example (using Matlab for bit-plane))

Signal subspace identification is a crucial first step in many hyperspectral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction, yielding gains in algorithm performance and complexity and in data storage. This paper introduces a new minimum mean square error-based approach to infer the signal subspace in hyperspectral imagery. The method, which is termed hyperspectral signal identification by minimum error, is eigen decomposition based, unsupervised, and fully automatic (i.e., it does not depend on any tuning parameters). It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. State-of-the-art performance of the proposed method is illustrated by using simulated and real hyperspectral images.