mensuales.m

This book is about how to make MATLAB-based Graphical User Interface (GUI) tools. It falls naturally into two parts: • GUI design, or how to make something that’s useful • GUI implementation, or how to make something that works The principles of good GUI design are, for the most part, timeless and universal. They apply in MATLAB as much as they apply anywhere else,though we consider them here with respect to specific examples. The guidelines for GUI implementation, on the other hand, tend to be much more specific—many of the implementation details we discuss here weren’t possible in MATLAB until recently. Accordingly, in this manualthere is a trend from the general to the specific. Part of the second half of the manual is devoted to the use of MATLAB’s GUI-building tool, Guide (Graphical User Interface Development Environment). A quick start example of how to use Guide also closes out this introductory chapter.

MATLAB 7.0从入门到精通：MATLAB 7.0从入门到精通随书光盘，书籍随书光盘的所有源码(MATLAB 7.0 from the Introduction to Mastery: MATLAB 7.0 from entry to the master CD with the book, books, CD-ROM with all the source books)

精通matlab图像处理的配套光盘下，内有程序代码(Proficient in matlab under the matching discs Image Processing)

Several modal identification techniques have been developed in the past few decades, and their use is rapidly expanding due to new focus on the instrumentation of major structures. This paper focuses on the expansion of the eigenvalue realization algorithm (ERA)–observer Kalman filter identification

this is regarding dstatcom

simulation to verify efficiency of Convolutional Coding

用自相关法求使信号s均方预测误差为最小的预测系数 (Autocorrelation and the signal s mean square prediction error for the smallest prediction coefficients)

数学建模案例MATLAB实用程序百例，其中包括了matlab的入门教学，与一些经典的例子（包括源代码）。(Mathematical modeling case MATLAB utility 100 cases, including teaching matlab entry, with some classic examples (including source code).)

Channel estimation including the LS and LMMSE algorithm,total simulation codes and the comparison of the two methods

Scale Invariant Feature Transform (SIFT) has shown to be very powerful for general object detection/recognition. And recently, it has been applied in face recognition. This is the matlab code.