smrsoftV10

关于阵列天线的资料，适合初学者，不错的资料(Information about the array antenna, suitable for beginners, good information)

粒子群优化算法，迭代选择函数最优值，修改不同的目标函数，应用较广(pso search the optima value)

里面包括几部分预测控制程序，程序本人都验证过，没有错误。(Includes several parts predictive control program, the program are verified, there is no error.)

基于SVPWM的三电平逆变的matlab仿真，阐述了负荷预测的应用研究,车牌识别定位程序的部分功能，LCMV优化设计阵列处理信号，Matlab实现界面友好，欢迎大家下载学习，光纤陀螺输出误差的allan方差分析。( Based on SVPWM three-level inverter matlab simulation, It describes the application of load forecasting, Part of the license plate recognition locator feature, LCMV optimization design array signal processing, Matlab to achieve user-friendly, Welcome to download the study, allan FOG output error variance analysis.)

To calculate the coupling partitial difference equation of heat transition and diffusion.

将低动态范围图像通过色调映射技术转换为高动态范围图像，对图像的亮度通道进行加强，该算法是reinhard代码进行实现的。(The low dynamic range image is transformed into high dynamic range image by hue mapping technology, and the brightness channel of the image is enhanced. The algorithm is implemented by Reinhard code.)

kalman filter this is the code which serves an example to work on with kalman filter

高分辨率雷达信号处理，PDF文档，送给感兴趣的朋友。(high-resolution radar signal processing, PDF files, as well as to interested friends.)

This paper deals with the problem of speech enhancement when a corrupted speech signal with an additive colored noise is the only information available for processing. Kalman filtering is known as an effective speech enhancement technique, in which speech signal is usually modeled as autoregressive (AR) process and represented in the state-space domain.(This paper deals with the problem of speech enhancement when a corrupted speech signal wit h an additive colored noise is the only informat ion available for processing. Kalman filterin g is known as an effective speech enhancement te chnique. in which speech signal is usually modeled as aut oregressive (AR) process and represented in th e state-space domain.)

介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。(Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.)