Jasper and Astoria

ADO.NET团队在AtMix07会议上宣布启动两个孵化项目:Jasper和Astoria。Jasper基于ADO.NET实体框架,允许开发者仅通过连接字符串即可开始应用程序核心的编程工作,而无需自行构建数据层。Astoria旨在使应用程序能够作为数据服务暴露数据,通过HTTP访问,并使用URI标识信息资源。

At Mix 07 , ADO.NET Team has introduced two incubation project , Jasper and Astoria .

"Jasper"

Jasper is build on the ADO.NET Entity Framework,enables developers to start withmerely a connection string and immediately begin programming the core of application without buildingtheir own data layer ,writing queries andbinddata to UI

“Jasper” uses a set of new technologies to make this happen:

  • Dynamic generation of data classes so there is no configuration or design time code-gen to carry around.
  • Rich query and O/R capabilities because “Jasper” is built on top of the Entity Framework.
  • Auto-binding capabilities for ASP.NET, WinForms, and WPF to make binding data to a UI simple and automatic

Official Blog:http://blogs.msdn.com/adonet/archive/2007/04/30/project-codename-jasper-announced-at-mix-07.aspx

CTP downloading:http://www.microsoft.com/downloads/details.aspx?FamilyId=471BB3AC-B31A-49CD-A567-F2E286715C8F&displaylang=en

"Astoria"

"The goal of Microsoft Codename "Astoria" is to enable applications to expose data as a data service that can be consumed by web clients within a corporate network and across the internet. The data service is reachable over HTTP, and URIs are used to identify the various pieces of information available through the service. Interactions with the data service happens in terms of HTTP verbs such as GET, POST, PUT and DELETE, and the data exchanged in those interactions is represented in simple formats such as XML and JSON.

"

http://blogs.msdn.com/adonet/archive/2007/04/30/project-codename-astoria-announced-at-mix-07.aspx

【无人机】基于改进粒子群算法的无人机路径规划研究[和遗传算法、粒子群算法进行比较](Matlab代码实现)内容概要:本文围绕基于改进粒子群算法的无人机路径规划展开研究,重点探讨了在复杂环境中利用改进粒子群算法(PSO)实现无人机三维路径规划的方法,并将其与遗传算法(GA)、标准粒子群算法等传统优化算法进行对比分析。研究内容涵盖路径规划的多目标优化、避障策略、航路点约束以及算法收敛性和寻优能力的评估,所有实验均通过Matlab代码实现,提供了完整的仿真验证流程。文章还提到了多种智能优化算法在无人机路径规划中的应用比较,突出了改进PSO在收敛速度和全局寻优方面的优势。; 适合人群:具备一定Matlab编程基础和优化算法知识的研究生、科研人员及从事无人机路径规划、智能优化算法研究的相关技术人员。; 使用场景及目标:①用于无人机在复杂地形或动态环境下的三维路径规划仿真研究;②比较不同智能优化算法(如PSO、GA、蚁群算法、RRT等)在路径规划中的性能差异;③为多目标优化问题提供算法选型和改进思路。; 阅读建议:建议读者结合文中提供的Matlab代码进行实践操作,重点关注算法的参数设置、适应度函数设计及路径约束处理方式,同时可参考文中提到的多种算法对比思路,拓展到其他智能优化算法的研究与改进中。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值