EZMac协议实现

AT91M5800a例子，很好用的！-AT91M5800a examples of good!

这个是针对pxa270s写的触摸屏的使用函数，还加了一个测试的程序。可以实现对触摸屏的几种操作的识别，上下划动，单击双击等。-This is written for pxa270s the use of touch-screen function, but also added a test procedure. Touch screen can be achieved on the identification of several operations, paddling up and down, double click and so on.

ADUC8XX代码-source code of ADUC8XX

Using Lightweight IP with the Nios II Processor_nios2_lwip_tutorial,This tutorial familiarizes you with the lightweight IP (lwIP) TCP/IP software component included in your Nios II development kit.

LCD 1602 显示，加简单的汉字显示，在51单片机上实现-LCD 1602 display

最近，越来越多的关注已被引导到语音信号的情感内容的研究，并且因此，许多系统已经提出了以识别讲话发音的情感内容。本文是语音情感分类处理语音情感识别系统设计的三个重要方面进行了调查。第一个是合适的特征选择用于语音表示。第二个问题是一个适当的分类方案的设计和第三个问题是适当的准备一个情感语音数据库，用于评估系统性能。有关性能的结论和目前语音情感识别系统的局限性在本次调查的最后一节讨论。本节还提出了提高语音情感识别系统的可能途径。

基于89S52的和菲利普收音模块5767的收音机程序。内含自动搜索，但是需要进一步的完善，可以学习和了解I2C的通讯方式。-89S52-based radio module and Philip 5767 radio program. Includes automatic search, but it needs further improvement can learn and understand the I2C communications.

英培特ARM教学平台EDUKIT-III，核心子板为s3c2410，在RH9下实现的sdram测试程序。-British培特ARM teaching platform EDUKIT-III, the core sub-board for s3c2410, in RH9 realize the SDRAM under test procedures.

LPC2148控制tea5767收音模块的收音机的完整源程序，ads下开发，网上多半是51和avr的，希望这个arm7的能对大家有用-LPC2148 controlled radio TEA5767 radio module complete source code, ads under development, on-line mostly 51 and avr, and hope that the ARM7 that can be useful for everyone

签名鉴别和笔记鉴别。In this paper, the local features obtained from the signature has been used to create the texture image based on the  spatio-temporal correlation matrix. The Gabor wavelets exhibit  desirable characteristics of spatial locality and orientation selectivity, and are optimally localized in the space and frequency  domains. So it has been chosen to extract the Gabor features of  the image. Signature is a small sample point problem and Support vector machine (SVM) is especially fit for two classes’ problem of small sample. The Gabor features vector has been imported to the SVM classifier and the classification result is obtained. The methods proposed in this paper are validated on the  S