matlab_vlfeat

matalb微分方程的解法，对于数理方程的学习和数学建模有参考价值。(matalb Differential Equation, for the study of mathematical equations and mathematical modeling of a reference value.)

基于子空间算法的ofdm系统半盲估计，希望对信道估计的同仁有帮助！(subspace algorithm based on the semi-blind system ofdm estimate, and I hope to channel estimation colleagues help!)

三次样条插值法，MATLAB程序，计算方法作业，内有说明(Cubic spline interpolation method, MATLAB procedures, calculation methods work, there are instructions)

使用高阶累积量对MPSK信号进行调制识别，包括随机梯度算法，相对梯度算法，包括最小二乘法、SVM、神经网络、1_k近邻法。( Using high-order cumulants of MPSK signal modulation recognition, Including stochastic gradient algorithm, the relative gradient algorithm, Including the least squares method, the SVM, neural networks, 1 _k neighbor method.)

matlab资料大全，本人多年积累。内含有18513MC混合编程源代码！(matlab Sourcebook, I accumulated over the years. Contains 18513MC hybrid programming source code!)

matlab仿真出光栅函数的菲涅尔衍射，其中有四副图，原图，二维衍射图样，函数图象和三维衍射图样(matlab simulation of the Fresnel diffraction grating function, including four plans, artwork, two-dimensional diffraction patterns, functions, images and three-dimensional diffraction pattern)

thesis program of the inverter

自适应波束形成 mvdr算法与基于GSC结构的smi算法的比较(Adaptive beamforming algorithm based smi mvdr GSC structure comparison algorithm)

matlabd的乱七八糟的没有用 不要下载(matlabd)

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.