assignmentutor-lab™ 为您的留学生涯保驾护航 在代写嵌入式网络系统Embedded Networked Systems方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写嵌入式网络系统Embedded Networked Systems方面经验极为丰富，各种代写嵌入式网络系统Embedded Networked Systems相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 电子工程代写|嵌入式网络系统代写Embedded Networked Systems代考|Model-Based Derivation of Key Performance Indicators

The modeling framework introduced provides rich capabilities to describe the system-under-design from different aspects, such as functionalities, hardware configuration, communication. The rigorous modeling allows for specifying the design, for communicating and documenting design choices but this is merely the first step. Our main goal is to characterize the design alternatives quantitatively in order to guide the designer along the design process: ideally, the design alternatives should be characterized in such a way that the derived properties should directly be comparable to key performance indicators.

Models used during the systems engineering process serve different purposes ranging from communication (among users and designers), documentation to evaluation, building and maintenance [11]. This section focuses on the use of models for design evaluation: along the design process the system designer has to make informed decisions when selecting the most “promising” design alternative. The selection should be driven by quantified properties of the design. These properties are originated in the design of components, compositions, parameters; and in the execution scenarios, i.e. the interactions between the systems designed and its embedding environment.

The model-based engineering approach formalizes all relevant aspects of the design in models and thus gives the formal foundation for deriving the emerging properties of the design. Frequently, the quantified design properties are aggregated in a “design quality measure” and used to guide a constrained design optimization process. The model based derivation of the design properties is just a manifestation of old and established engineering approach, namely use models to predict system behaviour [12].

The model based derivation of design properties and its use in “evolving” the system go beyond strictly design-time activities [13]. Generally speaking, the driving forces behind system evolution are “keeping operational” or “making it better” the system implemented as expressed in a quality measure. In runtime reconfigurable designs the calculation of the emerging system properties is carried out during the nominal operation of the systems to detect anomalies and consequently initiate and guide redesign (optimization) in runtime. Due to the possibly prohibitively large design space and the complexity of the design process the scope of the runtime redesign (i.e. the monitored set of key performance indicators and the investigated design alternatives) should be constrained [14]. For further details about the runtimedesign time trade-off see Sect. 2.2.

## 电子工程代写|嵌入式网络系统代写Embedded Networked Systems代考|Deriving the Key Performance Indicators

The model of a system is built from components defining particular elements of the design, e.g. tasks (functionalities), processors, communication interfaces, etc. All these components are annotated by attributes defining properties relevant for the implementation. Unfortunately these attributes in themselves do not say too much about the quality of the system as a whole. The attributes reflect very low level properties, which cannot directly be put side by side with the requirements set on system level. For example tasks are characterized by (among others) their computation demand (e.g. the number of floating point operations to be executed per invocation), but they do not determine directly the response time to the triggering event. The the task dependencies, the task allocation, scheduling, hardware characteristics, etc. the task dependencies, the task allocation, scheduling, hardware characteristics, etc. The models describing the design should be made executable, where the execution is of the (emerging) system level characteristics relevant to the design (i.e. the related application) at hand is called key performance indicators (KPIs).

Figure $1.8$ shows the process of the model-based support for system development. From the model execution, system level characteristics should be derived and compared to the requirements. If (some of) the requirements are not satisfied, the design should be modified. This is indicated as feedback in the figure. The modifications may target different aspects of the design, and accordingly adjustments in different models should be made. After the adjustments the KPI are recalculated (model execution) and design iteration starts.

# 嵌入式网络系统代考

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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