MIE

MATLAB及在电子信息中的应用：q707有限长序列z变换，z逆变换。708是逆变换(MATLAB and the application of electronic information: q707 finite length sequence of z transform, z inverse transformation. 708 is the inverse transform)

非常有用的程序，可以调用来处理循环平稳信号，用以产生三维自相关图形。(Very useful program, you can call to handle the cyclostationary signals to generate three-dimensional self-related graphics.)

２Ｄ非连续性力学分析，巴黎六大硕士课题.(2D non-mechanical analysis of continuity, the Paris Masters six topics.)

calibration image whith matlab

说明：  自适应遗传算法matlab代码 附件中的matlab程序是雷英杰课本中的第一个算例, 将其改为自适应的代码.(Adaptive genetic algorithm matlab code matlab annex LEI Ying-jie textbooks procedure is the first example, to read as adaptive code.)

说明：  图像变换 matlab语言编辑 包括灰度、旋转、放大、镜像(Image transformation)

智能天线技术的核心是自适应波束的形成，本文首先根据波束形成技术的不同，介绍了三大类自适应算法，基于来波方向的波束形成，基于参考信号的波束形成和基于信号结构特性的盲自适应算法，着重分析了基于参考信号的波束形成算法，并给出了数字波束形成的基本模型。(mart antenna technology is the core of the formation of adaptive beamforming, the paper first of all, according to the different beam-forming technology, introduced the three main types of adaptive algorithms, the DOA-based beamforming, based on the reference signal and the beam-forming characteristics based on the signal structure blind adaptive algorithm, focusing on analysis of the reference signal based on the beamforming algorithm, and digital beamforming are given the basic model.)

这里是一些VC与matlab混合编程的最新技术资料。有些是介绍了在VC ++调用Matlab的常用方法及各自特点，还有一些是介绍VC与matlab混合编程的实例。(Here are some VC mixed with matlab programming information on the latest technologies. Some are introduced in VC++ calling Matlab common methods and their own characteristics, and some is to introduce the VC with the matlab programming examples of mixing.)

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.

双闭环pi控制的svpwm仿真，simulink仿真模型(Svpwm pi-controlled double-loop simulation, simulink simulation model)