Model-based development tools are one possible solution to handle the increasing complexity of mechatronic systems. While traditional approaches often separate design of hardware and software, especially in mechatronic systems hardware/software interaction is the most critical component. Hence, both aspects must be considered in this context. The goal is a model-based development tool for software/hardware co-design including the generation of efficient code for the respective target platforms. EasyLab is a modular and easily expandable development tool especially suitable for such applications. Its objectives are to facilitate reusability and to accelerate the development process. It raises the level of abstraction and thus simplifies the development of mechatronic systems even for unexperienced users. A graphical user interface provides various modeling languages that are easy to use. By employing platform optimized generation of the code, efficiency of the resulting programs can be guaranteed, which we demonstrate on a set of experimental mechatronic systems.
Abstract:Mixed-criticality systems (MCS) aim at boosting the integration density in safety-critical systems, resulting into efficient systems, while simultaneously providing increased performance. The DREAMS project provides a cross-domain architectural style for MCS based on networked, virtualized multicores controlled by hierarchical resource managers. However, the availability of a platform is only one side of the coin: deploying mixed-critical applications to shared resources typically requires design-time configurations (e.g., to ensure real-time constraints or separation constraints mandated by safety regulations). These configurations are the outcome of complex optimization problems which are intractable in a manual process that also hardly can guarantee the consistency of all deployable artefacts nor their traceability to the requirements. However, existing toolchains lack support for MCS integration, and particularly DREAMS' advanced platform capabilities. We present an integrated model-driven toolchain and the underlying metamodels covering all relevant aspects of MCS including applications, timing, platforms, deployments, configurations and annotations for extra-functional properties such as safety. The approach focuses on the left branch of the V-cycle, and ranges from product-line and design space exploration to resource allocation and configuration generation. We report on the integration of exploration tools and a reconfiguration graph synthesizer, and evaluate the resulting toolchains in two use cases consisting of a product-line of wind power control applications and an avionic subsystem respectively
Abstract. Many approaches in computer vision require multiple retrievals of histograms for rectangular patches of an input image. In 2005 an algorithm to accelerate these retrievals was presented. The data structure utilized is called Integral Histogram, which was based on the well known Integral Image.In this paper we propose a novel approximating method to obtain these integral histograms that outperforms the original algorithm and reduces computational cost to more than a tenth. Alongside we will show that our adaptive approach still provides reasonable accuracywhich allows dramatic performance improvements for real-time applications while still being well suited for numerous computer vision tasks.
While robot systems become more and more elaborate, the need to simplify programming them grows as well. Regarding the high degree of internal heterogeneity in the sense that different microcontroller platforms, protocols and performance layers are used in a single robot application, it is not feasible to have specialists dedicated to each individual task. This motivates the need for tool support that allows an abstract view on a robot's sensors and actuators, means to program individual components as well as to define their interaction. In this work, we present how the model-based development and code generation tool EasyLab can be extended to support programming of all parts of a robot, including the main controller as well as peripheral devices like smart sensors. We show three typical use cases in the context of mobile platforms and highlight EasyLab's advantages in each domain.
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