uncoded

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.

Ansi To Ascii and Ascii to Ansi converter

metodos numericos interpolacion segundo grado

Code for calculating 3D Potential fiel for robot path plannning it needs som correction to become work correctly

此程序涉及仿射投影算法的一个函数表达，适用于simulink仿真。(This procedure involves a function of affine projection algorithm expression, suitable for simulink simulation.)

书籍《多传感器数据融合》P44页算法的具体实现，可用于目标跟踪、卫星定位的仿真程序。(Books, multi-sensor data fusion P44-page concrete realization of algorithm can be used for target tracking, satellite positioning the simulation program.)

说明：  有频带方差和谱熵法。自己实现加窗分帧。里面设计傅里叶变换还有矩阵的运算。(speech endpoint detection. frequence_diff and pushang.)

自己建的双馈式异步风力发电机模型，经过一系列封装后建成的整体模型。(Build the doubly fed induction generator model, and after a series of integrated model of the whole model.)

模拟噪声的程序，可以根据需要改写，主要功能是实现噪声模型 (Additive Noise Channel/Additive Guassian Noise Channel is the simplest of all channels. File usage details: op_channel = additivenoise(input_signal) mathematical Model: s(t) input signal to channel (=sig_ip) n(t)additive random noise process added by channel (=noise_mat) a(t) attenutation factor , here it is taken as random(=att_fact) out put signal r(t) = a .* s(t)+ n(t) an additional file named through_anc is there which shows 2 figures, 1. input signal 2. output signal )

采用harr对激光强度图像分解与重构 可选择不同的分解方法，分解层数(failed to translate)