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In-Vehicle Communication Networks: A Historical Perspective and Review
Published in Richard Zurawski, Industrial Communication Technology Handbook, 2017
Nicolas Navet, Françoise Simonot-Lion
One of AUTOSAR’s main objectives is to improve the quality and the reliability of embedded systems. By using a well-suited abstraction, the reference model supports the separation between software and hardware, eases the mastering of the complexity, and allows the portability of application software components and therefore the flexibility for product modification, upgrade and update, as well as the scalability of solutions within and across product lines. Besides, AUTOSAR ensures a smooth integration process of components provided by different companies while protecting the industrial properties of each actor involved. The AUTOSAR reference architecture is schematically illustrated in Figure 50.8. An important issue is the automatic generation of an AUTOSAR MW that has to be done from the basic software components, generally provided by suppliers, and the specification of the application itself (description of applicative-level tasks, signals sent or received, events, alarms, etc.). The challenge is to realize such a generation so that the deployment of the MW layer can be optimized for each ECU.
Trends in Automotive Communication Systems
Published in Richard Zurawski, Networked Embedded Systems, 2017
Nicolas Navet, Francoise Simonot-Lion
One of AUTOSAR’s main objective is to improve the quality and the reliability of embedded systems. By using a well-suited abstraction, the reference model supports the separation between software and hardware, it eases the mastering of the complexity, allows the portability of application software components and therefore the flexibility for product modification, upgrade and update, as well as the scalability of solutions within and across product lines. The AUTOSAR reference architecture is schematically illustrated in Figure 13.8. An important issue is the automatic generation of an AUTOSAR MW that has to be done from the basic software components, generally provided by suppliers, and the specification of the application itself (description of applicative-level tasks, signals sent or received, events, alarms, etc.). The challenge is to realize such a generation so that the deployment of the MW layer can be optimized for each ECU.
Variability-intensive Software Systems
Published in Ivan Mistrik, Matthias Galster, Bruce R. Maxim, Software Engineering for Variability Intensive Systems, 2019
Matthias Galster, Ivan Mistrik, Bruce Maxim
Following Bass et al., we differentiate reference model and reference architecture [21]. A reference model is a “division of functionality together with data flow between the pieces,” i.e., a decomposition of a problem into parts that cooperatively solve the problem. A reference architecture, on the other hand, is a reference model mapped onto software elements that cooperatively implement the functionality of the reference model. This means, whereas reference modeling divides functionality, a reference architecture is the mapping of that functionality onto system decompositions [91].
M-Sweeps multi-target analysis of new category of adaptive schemes for detecting χ2-fluctuating targets
Published in Journal of Information and Telecommunication, 2020
CA technique is probably the most widely used CFAR detector, in which the decision threshold is computed adaptively through the traditional averaging procedure by adding the magnitudes of the reference cells and dividing the result by the size of the CFAR window. This is a complete and sufficient statistic for the noise power under the assumption of exponentially distributed and homogeneity background. It is commonly regarded as the reference model for comparing new implementations. In other words, it is optimum for homogenous environments. However, in the presence of extraneous targets, the assumption of homogeneity is no longer valid and therefore, its performance is seriously degraded under such conditions of operation. Additionally, it is incapable of maintaining its designed false alarm rate when facing clutter with statistical variations.
A Semantic Model for Enterprise Digital Transformation Analysis
Published in Journal of Computer Information Systems, 2023
Traditionally, systems analysts apply systems analysis and design methods and tools to analyze the system by specifying the requirements and to design the system by detailing the specifications for implementation. During the past decades of enterprise digital transformation, the roles of systems analysts have been shifted from “blueprint making” for systems construction to coordination for systems acquisition.60 Specifically, the major roles of systems analysts for enterprise digital transformation are to coordinate the development activities of all stakeholders involved in the digital transformation process and to assist the decision makers of the organization to monitor the gaps between the current system and the future system during the digital transformation process to ensure the implementation of transformation strategies. To support systems development coordination and supervision, systems analysts have used reference models.61 A reference model is an abstract framework or domain-specific ontology consisting of an interlinked set of clearly defined concepts to encourage clear communication. Our proposed semantic model is a reference model for enterprise digital transformation, and has the following advantages over the traditional systems development “blueprint making” tools such as UML. The semantic model integrates the semantic relationships between all organizational aspects of enterprise digital transformation. The model is able to represent key organizational aspects in enterprise digital transformation, including costs and benefits, system infrastructure, business process and business rules, data architecture, and users’ roles.The proposed enterprise digital transformation model can serve as a devise to integrate managerial requirements and technical specifications.The model is scalable and maintainable through adding, deleting, and modifying the organizational aspects and the relationships between them for a particular enterprise digital transformation.The semantic model can be computerized and readily incorporated into a web portal, as demonstrated in the next section of this paper.The semantic model is easy to understand for all decision makers and system developers in the organization to share knowledge about the enterprise digital transformation.