64sift

这本书最近看的觉得挺有用的，希望能对做这方面的朋友有所帮助！谢谢！(This book recently read that Tingyou use, hoping to make friends in this area help! Thank you!)

Principal component analysis (PCA) is a popular tool for linear dimensionality reduction and feature extraction. Kernel PCA is the nonlinear form of PCA, which is promising in exposing the more complicated correlation between original high-dimensional features. In this paper, we first talk about the basic ideas of PCA and kernel PCA, and then focus on the reconstruction of pre-images for kernel PCA. We also give an introduction on how PCA is used in active shape models (ASMs), and discuss how kernel PCA can be applied to improve traditional ASMs. Then we show some experiment results to compare the performance of kernel PCA and traditional PCA for pattern classification. We also implement the kernel PCA-based ASMs, and use it to construct human face models.

Multi-Carrier Digital Communications Theory and Applications of OFDM Second Edition Multi-carrier modulation‚ Orthogonal Frequency Division Multiplexing (OFDM) particularly‚ has been successfully applied to a wide variety of digital communications applications over the past several years. Although OFDM has been chosen as the physical layer standard for a diversity of important systems‚ the theory‚ algorithms‚ and implementation techniques remain subjects of current interest. This is clear from the high volume of papers appearing in technical journals and conferences. Multi-carrier modulation continues to evolve rapidly. It is hoped that this book will remain a valuable summary of the technology‚ providing an understanding of new advances as well as the present core technology.

受激布里渊散射程序源码，泵浦光及散射光的Matlab模拟，很好很强大(Stimulated brillouin scattering program source code)

wolf-powell算法,运行在MATLAB环境中，用于搜索步长的计算(wolf powell algorithm)

说明：  Matlab小程序,其功能可以产生高斯色噪声的随机数等,很有用的喽!(Matlab small program, its function can generate a random number of Gaussian color noise, etc., very useful myself!)

In a multiple transmit antenna, single antenna per receiver downlink channel with limited channel state feedback, we consider the following question: given a constraint on the total system-wide feedback load, is it referable to get low-rate/coarse channel feedback from a large number of receivers or high-rate/high-quality feedback from a smaller number of receivers? Acquiring feedback from many receivers allows multi-user diversity to be exploited, while high-rate feedback allows for very precise selection of beamforming directions. We show that there is a strong preference for obtaining high-quality feedback, and that obtaining near-perfect channel information from as many receivers as possible provides a significantly larger sum rate than collecting a few feedback bits from a large number of users.

说明：  有监督的特征选择和优化程序MATLAB代码，基于最小二乘算法。内有测试数据，和详细程序说明(Least-Squares Feature Selection (LSFS) is a feature selection method for supervised regression and classification. LSFS orders input features according to their dependence on output values. Dependency between inputs and outputs is evaluated based on an estimator of squared-loss mutual information called LSMI)

this file includes the matlab code of Jensen similarity measure.

非负独立分量盲源信号的分离方法是一种很简单的盲源分离方法(Non-negative independent component blind source separation method is a very simple blind source separation methods)