基于XMPP Protocol的开源套件

http://www.igniterealtime.org/downloads/source.jsp


Openfire Source
openfire_src_3_8_2.zip May 28, 2013 69.00 MB
openfire_src_3_8_2.tar.gz May 28, 2013 66.02 MB
Smack Source
smack_src_3_3_0.zip May 4, 2013 8.17 MB
smack_src_3_3_0.tar.gz May 4, 2013 6.51 MB
Tinder Source
tinder_src_1_2_2.zip August 21, 2010 0.12 MB
tinder_src_1_2_2.tar.gz August 21, 2010 0.08 MB
Whack Source
whack_src_1_0_0.zip July 14, 2008 2.71 MB
whack_src_1_0_0.tar.gz July 14, 2008 2.64 MB

There are many ways to view the source code for the Ignite Realtime Open Source projects:

Browse SVNOpenfire: openfire/trunk
Spark: spark/trunk
SparkWeb: sparkweb/trunk
XIFF: xiff/trunk
Smack: smack/trunk
Tinder: tinder/trunk
Whack: whack/trunk
Asterisk-IM: asterisk-im/trunk
SVN AccessOpenfire:
svn co http://svn.igniterealtime.org/svn/repos/openfire/trunk openfire
Spark:
svn co http://svn.igniterealtime.org/svn/repos/spark/trunk spark
SparkWeb:
svn co http://svn.igniterealtime.org/svn/repos/sparkweb/trunk sparkweb
XIFF:
svn co http://svn.igniterealtime.org/svn/repos/xiff/trunk xiff
Smack:
svn co http://svn.igniterealtime.org/svn/repos/smack/trunk smack
Tinder:
svn co http://svn.igniterealtime.org/svn/repos/tinder/trunk tinder
Whack:
svn co http://svn.igniterealtime.org/svn/repos/whack/trunk whack
Asterisk-IM:
svn co http://svn.igniterealtime.org/svn/repos/asterisk-im/trunk asterisk-im
OtherBinary Builds
Openfire Nightly Builds
Smack Nightly Builds
Bugs/Issues

To view and report bugs and issues, please view our issue tracker. Specific projects include: Openfire, Spark, SparkWeb, Smack API, Tinder API, Whack API, XIFF, Gateway Plugin, and Asterisk-IM.



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Openfire really impressed me and I find it really hard to come up with a negative when it comes to this great IM server... Installation was a breeze as was setup. Working with the browser-based administrative interface is a joy. In fact, Openfire is a beautiful, easy to use, configurable, customizable, extensible, and powerful instant messaging server.

– Marcel Gagné, Sys Admin Magazine
内容概要:该论文聚焦于T2WI核磁共振图像超分辨率问题,提出了一种利用T1WI模态作为辅助信息的跨模态解决方案。其主要贡献包括:提出基于高频信息约束的网络框架,通过主干特征提取分支和高频结构先验建模分支结合Transformer模块和注意力机制有效重建高频细节;设计渐进式特征匹配融合框架,采用多阶段相似特征匹配算法提高匹配鲁棒性;引入模型量化技术降低推理资源需求。实验结果表明,该方法不仅提高了超分辨率性能,还保持了图像质量。 适合人群:从事医学图像处理、计算机视觉领域的研究人员和工程师,尤其是对核磁共振图像超分辨率感兴趣的学者和技术开发者。 使用场景及目标:①适用于需要提升T2WI核磁共振图像分辨率的应用场景;②目标是通过跨模态信息融合提高图像质量,解决传统单模态方法难以克服的高频细节丢失问题;③为临床诊断提供更高质量的影像资料,帮助医生更准确地识别病灶。 其他说明:论文不仅提供了详细的网络架构设计与实现代码,还深入探讨了跨模态噪声的本质、高频信息约束的实现方式以及渐进式特征匹配的具体过程。此外,作者还对模型进行了量化处理,使得该方法可以在资源受限环境下高效运行。阅读时应重点关注论文中提到的技术创新点及其背后的原理,理解如何通过跨模态信息融合提升图像重建效果。
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