plot2svg_20120915

pdf格式的,由张志涌编写.我在网上找了很久才找着,特推荐给大家下载.(pdf format, prepared by Zhang Zhiyong. I am looking for a long time before the Internet find, especially recommended to everyone to download.)

This is an implementation of the algorithm for calculating the Structural SIMilarity (SSIM) index between two images. Please refer to the following paper: Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity" IEEE Transactios on Image Processing, vol. 13, no. 4, pp.600-612, Apr. 2004.

机组组合问题的粒子群算例，不错的算例，大家一起学习(Unit Commitment particle swarm example, a good example, we will study together)

说明：  《Hyperspectral Remote Sensing》是一本关于高光谱遥感图像处理与应用的英文经典书籍，对于学习高光谱遥感图像的人员有很大的帮助。(&quot Hyperspectral Remote Sensing&quot is a book on hyperspectral remote sensing image processing and application of the English classics, hyperspectral remote sensing images for learning the staff is very helpful.)

最小均方误差自适应算法，简称LMS算法.LMS算法自适应实现 (MMSE adaptive algorithm, or LMS algorithm. LMS adaptive algorithm to achieve)

recnstitution重构相空间，在非线性系统分析中有重要的作用 (recnstitution phase space, in a nonlinear analysis of the important role of)

用matlab 实现bpsk的误码率蒙特卡洛仿真，可以作为编程的范例来用(Using matlab to achieve bpsk BER Monte Carlo simulation ，could be used as a programming paradigm)

对轨道不平顺的分布采用对数正态分布进行分布拟合(On the distribution of track irregularity conducted using lognormal distribution fitting)

Audio compression and recording

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.