图7显示了我们刚刚描述的实现场景的目录与文件分层结构。
Figure 7 shows the directory and file hierarchy for the implementation scenario we just described.
MLSE采用层机制集成多元数据，实现场景绘制和交互。
By means of scene layer, MLSE enables multi variates integration, scene rendering and user interaction.
在部署一个应用软件时还有很多的工作需要做，这里没有时间继续实现场景。
There is enough work to do in deploying an application that there will not be time to continue implementing scenarios.
我将首先概述动态和静态横切，然后迅速切入一个实现场景来展示后一种技术。
I'll start with an overview of both dynamic and static crosscutting, and then move quickly into an implementation scenario that demonstrates the latter technique.
由于二维图像本身缺乏深度信息，现有的技术一直难以实现场景的任意平滑漫游。
Owing to the two dimension image itself being short of degree of depth information, the now available technology is hard to realize the walk through of continuously scene.
在这个实现场景中，设想系统在某一操作发生后，通过各不相同的通讯渠道通知客户。
For this implementation example, let's imagine a system that, upon some action taken, notifies customers via disparate communication channels.
我们将继续第1部分中采用的方法，它给出了最简单的实现场景来演示我们准备讨论的调节功能。
We continue with the approach adopted in Part 1, which involves the presentation of the bare minimum implementation scenario to illustrate our intended throttling capability.
这个小节解释在WebSphereDataPower上实现场景 #1和 #2 的一种方式。
This section explains one way for implementing scenarios #1 and #2 on WebSphere DataPower.
要实现场景 #1，我们使用一个HTTP绑定导出，以接受来自REST客户端的 HTTP请求。
To implement scenario #1, we use an export with HTTP binding to accept HTTP requests from REST clients.
不过，在一个实现场景中，可选的后续路径不能正确地捕获要实现的行为，这样就无法根据模型生成有意义的BPEL了。
However, in an implementation scenario, the alternative sequential paths do no correctly capture the behavior to implement, which makes it impossible to generate meaningful BPEL out of these models.
对场景图像中的显著区域采用梯度方向、二阶不变矩、归一化色调3种特征进行不变性表示，并根据其匹配率实现场景识别。
Those salient regions are represented by 3 invariant features of gradient orientation, moment and canonical hue. They are used for scene recognition in terms of their match ratio.
对场景图像中的显著区域采用梯度方向、二阶不变矩、归一化色调3种特征进行不变性表示，并根据其匹配率实现场景识别。
Those salient regions are represented by 3 invariant features of gradient orientation, moment and canonical hue. They are used for scene recognition in terms of their match ratio.
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