Flex 4 is on the way

Flex4(代号Gumbo)正处于积极开发阶段,其主要目标包括:设计师与开发者之间的无缝协作、加速应用开发流程、提升平台整体性能以及拓展应用范围。计划中的特性涵盖了从设计到部署的整个应用开发周期。

Flex 4

Flex 4, codenamed Gumbo, is now beginning active development. The product plan is not yet complete, but a few themes are under consideration:

  • Design in Mind: provide a framework meant for continuous collaboration between designer and developer. Probably involves an additional component model that integrates with the existing Halo components.
  • Accelerated Development: take application development from concept to reality quickly. Features could include application templates, architectural framework integration, binding improvements.
  • Horizontal Platform Improvements: features that benefit all application and user types. Features could include compiler performance, language enhancements, BiDi components, enhanced text.
  • Broadening Horizons: expand the range of applications and use-cases that can leverage Flex. Features could include finding a way to make the framework lighter, supporting more deployment runtimes, runtime MXML.

Follow the Flex 4 planning process

Download builds of Flex 4 from here

Milestones

These milestones are very much a work in progress:

MilestoneDate
Scope determinedApril 2008
Beta 1Late 2008
4.0 Final2009

Bugs for the upcoming Flex 4 milestone

Bugs for Trunk (Adobe Bug System) (1 issues)
T Key Summary Status Pr Updated
Bug SDK-11841 WebService fault details are hidden by Flex SDK ReopenedReopened C Mar 26, 2008

Fixed in Trunk (Adobe Bug System) (0 issues)
T Key Summary Updated

Planning 4.0

Coming soon!

 copied from: http://opensource.adobe.com/wiki/display/flexsdk/Flex+4
内容概要:本文介绍了一个基于MATLAB实现的无人机三维路径规划项目,采用蚁群算法(ACO)与多层感知机(MLP)相结合的混合模型(ACO-MLP)。该模型通过三维环境离散化建模,利用ACO进行全局路径搜索,并引入MLP对环境特征进行自适应学习与启发因子优化,实现路径的动态调整与多目标优化。项目解决了高维空间建模、动态障碍规避、局部最优陷阱、算法实时性及多目标权衡等关键技术难题,结合并行计算与参数自适应机制,提升了路径规划的智能性、安全性和工程适用性。文中提供了详细的模型架构、核心算法流程及MATLAB代码示例,涵盖空间建模、信息素更新、MLP训练与融合优化等关键步骤。; 适合人群:具备一定MATLAB编程基础,熟悉智能优化算法与神经网络的高校学生、科研人员及从事无人机路径规划相关工作的工程师;适合从事智能无人系统、自动驾驶、机器人导航等领域的研究人员; 使用场景及目标:①应用于复杂三维环境下的无人机路径规划,如城市物流、灾害救援、军事侦察等场景;②实现飞行安全、能耗优化、路径平滑与实时避障等多目标协同优化;③为智能无人系统的自主决策与环境适应能力提供算法支持; 阅读建议:此资源结合理论模型与MATLAB实践,建议读者在理解ACO与MLP基本原理的基础上,结合代码示例进行仿真调试,重点关注ACO-MLP融合机制、多目标优化函数设计及参数自适应策略的实现,以深入掌握混合智能算法在工程中的应用方法。
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