Glassfish V3特性预览

Glassfish V3 引入了组件化架构,支持 OSGi 模块管理和 JavaEE6 容器配置。它还提供了对动态脚本语言的支持,并允许在同一个应用服务器实例中部署不同平台的应用程序。此外,Glassfish V3 支持内置服务器模式部署,并引入了 Flashlight 监控框架以减轻服务器监控负担。

虽然目前Glassfish V3离正式发布还需要一段时日,但针对V3版这么长久的发布日期,就让人特想认识一下V3版将会给我带来怎么样的新特性。

组件化架构(OSGi Based Modular architecture)
  GlassFish V3 uses a modular architecture to address the emerging requirements in the Application server’s market and container profiles concept of the Java EE 6. GlassFish uses OSGI for module management, and inside the OSGI modules it uses HK2 module system for configuration and service management. So far, Administration console, CLI, and application types containers, can be extended using this modular system.  Java EE components can be replaced by any other compatible implantation using the OSGI modeling system

可插拔式容器与动态脚本语言支持:
    GlassFish V3 introduces new innovation in supporting different types of applications by letting the administrators and system managers to deploy different types of applications which are coming from different platforms and frameworks like Ruby On Rails, Grails, Quercus, and so on in the same applications server instance that they deploy the Java EE applications and therefore a unified administration infrastructure will be used to deal with all required administrative and management tasks. Containers load when they are required, for example when no PHP application is deployed, PHP container does not has any overhead over the application server.

增强后的命令行模式与管理控制平台:
      With GlassFish V3 it is possible to extend GlassFish CLI by developing new commands (extending an interface and using some HK2 services if required) as OSGI modules and simply putting them into GlassFish modules directory. The rest of the tasks of picking the module and responding to your command is what GlassFish module system does. Web based administration console can be extended using the same mechanism, but this time you can develop some JSF web pages to which you need to add to Administration console along with some descriptors which determine what is the page’s parent node in navigation tree and so on.

基于角色的安全策略支持:
      GlassFish V3 administration console is equipped with fine grained access management system which let the System Administrator to define new role with limited set of permission on using administration console.

支持用于内置服务器方式部署:
      GlassFish version 3 can be used as an embedded application server (run in the same JVM that the client application runs) with full support of Java EE 6. Embedded application server can be used for packaged software, unit testing, building new functionalities on top of Java EE 6 and so on.


提供让人耳目一新的问题剖析框架:
      Flashlight infrastructure introduced to remove the heavy burden of monitoring from application server and apply a very small percent of overhead whenever a client starts looking at the monitoring information. It let the developers and administrators to view variety of sever attributes in runtime and if they need to view any factor which is not provided by default (their own source code monitoring information) they can embed monitoring probes in sensitive parts of their code when they develop the software and later use the monitoring information that probes collect to see how that sensitive part of the application works.

REST 支持(Jersey):
    Glassfish Monitoring information can be received using RESTfull interfaces provided  in GlassFish V3, using this monitoring feature developers and administrators can gather statistics related to any attributes that they like using any programming language with REST or HTTP support.

本文来自:http://weblogs.java.net/blog/kal ... ne_pager_revie.html




(文/xmatthew  出处/BLOGJAVA)

本课题设计了一种利用Matlab平台开发的植物叶片健康状态识别方案,重点融合了色彩与纹理双重特征以实现对叶片病害的自动化判别。该系统构建了直观的图形操作界面,便于用户提交叶片影像并快速获得分析结论。Matlab作为具备高效数值计算与数据处理能力的工具,在图像分析与模式分类领域应用广泛,本项目正是借助其功能解决农业病害监测的实际问题。 在色彩特征分析方面,叶片影像的颜色分布常与其生理状态密切相关。通常,健康的叶片呈现绿色,而出现黄化、褐变等异常色彩往往指示病害或虫害的发生。Matlab提供了一系列图像处理函数,例如可通过色彩空间转换与直方图统计来量化颜色属性。通过计算各颜色通道的统计参数(如均值、标准差及主成分等),能够提取具有判别力的色彩特征,从而为不同病害类别的区分提供依据。 纹理特征则用于描述叶片表面的微观结构与形态变化,如病斑、皱缩或裂纹等。Matlab中的灰度共生矩阵计算函数可用于提取对比度、均匀性、相关性等纹理指标。此外,局部二值模式与Gabor滤波等方法也能从多尺度刻画纹理细节,进一步增强病害识别的鲁棒性。 系统的人机交互界面基于Matlab的图形用户界面开发环境实现。用户可通过该界面上传待检图像,系统将自动执行图像预处理、特征抽取与分类判断。采用的分类模型包括支持向量机、决策树等机器学习方法,通过对已标注样本的训练,模型能够依据新图像的特征向量预测其所属的病害类别。 此类课题设计有助于深化对Matlab编程、图像处理技术与模式识别原理的理解。通过完整实现从特征提取到分类决策的流程,学生能够将理论知识与实际应用相结合,提升解决复杂工程问题的能力。总体而言,该叶片病害检测系统涵盖了图像分析、特征融合、分类算法及界面开发等多个技术环节,为学习与掌握基于Matlab的智能检测技术提供了综合性实践案例。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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