jbuilder9实用例子。

Java Applet 淡化效果-Java Applet downplay results

jsp简单程序，可以往数据库录入名字和年龄，然后可以通过网页读并显示显示出来-jsp simple procedures can go to the database entry name and age, which can then be read through the website and show displayed

java制作的播放音乐软件-play music production software

客户端浏览医学图像的jsp源代码。可用于临床工作站。-client View Medical Image jsp source code. Clinical workstations can be used.

实现多线程TCP传输，同时分成发送方和接受方-multi-threaded TCP, divided into transmit and receive side

采用java源码编写的Applet录音机-used to prepare the source java applet recorders

这是一个用Java写的数字签名的小程序。大家下载学习。-This is a digital signature to use Java to write a small program. You download the study.

本书赢得了全球程序员的广泛赞誉，即使是最晦涩的概念，在Bruce Eckel的文字亲和力和小而直接的编程示例面前也会化解于无形。从Java的基础语法到最高级特性（深入的面向对象概念、多线程、自动项目构建、单元测试和调试等），本书都能逐步指导你轻松掌握。 从本书获得的各项大奖以及来自世界各地的读者评论中，不难看出这是一本经典之作。本书的作者拥有多年教学经验，对C、C++以及Java语言都有独到、深入的见解，以通俗易懂及小而直接的示例解释了一个个晦涩抽象的概念(The legendary author Bruce Eckel brings Java to life with this extraordinarily insightful, opinionated and downright funny introduction. Thinking in Java introduces all of the language s fundamentals, one step at a time, using to-the-point code examples. More than virtually any other book, Thinking in Java helps you understand not just what to do-- but why. Eckel introduces all the basics of objects as Java uses them then walks carefully through the fundamental concepts underlying all Java programming-- including program flow, initialization and cleanup, hiding implementations, reusing classes and polymorphism. Using extensive, to-the-point examples, he introduces error handling, exceptions, Java I/O, run-time type identification, and passing and returning objects. He covers the Java AWT, multithreading, network programming with Java-- even design patterns. The best way to understand the real value of this book is to hear what readers of the online version have been saying about it: ")

风机在线监测系统,采用sqlserver数据库,有问题联系我-fan online monitoring system, using sqlserver database, a problem Contact

基于决策树实现的AdaBoost分类器，是一个由多个弱分类器进行训练生成强分类的过程。(Based on AdaBoost classifier tree implementation, is a plurality of weak classifiers in the training process generates a strong classifier.)