Concurrent Design of Embedded Control Software

Embedded software design for mechatronic systems is becoming an increasingly time-consuming and error-prone task. In order to cope with the heterogeneity and complexity, a systematic model-driven design approach is needed, where several parts of the system can be designed concurrently. There is however a trade-off between concurrency efficiency and integration efficiency. In this paper, we present a case study on the development of the embedded control software for a real-world mechatronic system in order to evaluate how we can integrate concurrent and largely independent designed embedded system software parts in an efficient way. The case study was executed using our embedded control system design methodology which employs a concurrent systematic model-based design approach that ensures a concurrent design process, while it still allows a fast integration phase by using automatic code synthesis. The result was a predictable concurrently designed embedded software realization with a short integration time

'Feeling' risk and seeing solutions: Predicting vaccination intention against Hepatitis B infection among men who have sex with men

This study assessed cognitive and affective predictors of intention to obtain vaccination against the hepatitis B virus (HBV) among men who have sex with men (MSM), based on leading social cognitive models of health behavior. The key predictors of vaccination intention were perceived risk of contracting HBV, expectancies regarding the outcome of vaccination, and the interaction between risk perception and outcome expectancies. Negative affect increased risk perceptions, which, in turn, positively affected vaccination intention. It is concluded that MSM should feel they are at risk for HBV, and see solutions to this risk. Copyright © 2008 SAGE Publications

Timing analysis of first-come first-served scheduled interval-timed directed acyclic graphs

Analyzing worst-case application timing for systems with shared resources is difficult, especially when non-monotonic arbitration policies like First-Come-First-Served (FCFS) scheduling are used in combination with varying task execution times. Analysis methods that conservatively analyze these systems are often based on state-space exploration, which is not scalable due to its inherent susceptibility to combinatorial explosion. We propose a scalable timing analysis method on periodically restarted Directed Acyclic Task Graphs, that can provide conservative bounds on task timing properties when shared resources with FCFS scheduling are used. By expressing task enabling and completion times in intervals, denoting best-case and worst-case timing properties, contention on the shared resources can be estimated using conservative approximations. With an industrial case study we show that our approach can easily analyze models with thousands of tasks in less than 10 seconds, and the worst-case bounds obtained show an average improvement of 46% compared to bounds obtained by static worst-case analysis