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Development trends for computing platforms moved from increasing the frequency of a single processor to increasing the parallelism with multiple cores on the same die. Multiple cores have strong potential to support cost-efficient fault tolerance due to their inherent spatial redundancy. This work makes a step towards software-only fault tolerance in the presence of permanent and transient hardware faults. Our approach utilizes software-based spatial triple modular redundancy and coded processing on a shared memory multi-core controller. We evaluate our approach on an Infineon AURIX TriBoard TC277 and provide experimental evidence for error resistance by fault injection campaigns with an iSystem iC5000 On-chip Analyzer.
Eye tracking is a powerful technique that helps reveal how people process visual information. This paper discusses a novel metric for indicating expertise in visual information processing. Named the Gaze
Relational Index (GRI), this metric is defined as the ratio of mean fixation duration to fixation count.
Data from two eye-tracking studies of professional vision and visual expertise in using 3D dynamic medical visualizations are presented as cases to illustrate the suitability and additional benefits of the
GRI. Calculated values of the GRI were higher for novices than for experts, and higher in non-representative, semi-familiar / unfamiliar task conditions than in domain-representative familiar tasks.
These differences in GRI suggest that, compared to novices, experts engaged in more knowledge-driven, top-down processing that was characterized by quick, exploratory visual search. We discuss future
research aiming to replicate the GRI in professional domains with complex visual stimuli and to identify the moderating role of cognitive ability on GRI estimates.
With the increase in demand of services in the automotive industry, automotive enterprises prefer to collaborate with other qualified cross-domain partners to provide complex automotive functions (or services), such as autonomous driving, OTA (Over The Air) vehicle update, V2X (Vehicle-to-Vehicle communication), etc. One key element in cross-domain enterprise collaboration is the mutual agreement between interfaces of software components. In this context, model-to-model mappings of software component models of heterogeneous frameworks for automotive services and to explore the synergies in their interface semantics, have become an essential factor in improving the interoperability among the automotive and other cross-domain enterprises. However, one of the challenges in achieving cross-domain component interface model-to-model mappings at an application level lies in detecting the interface semantics and the semantic relations that are conveyed in different component models in different frameworks. This paper addresses this challenge using a Model Driven Architecture (MDA) based analytical approach to explore interface semantic synergies in the cross-domain component meta-models that are used for automotive services. The approach applies manual semantic checking measurements at an application interface level to understand the meanings and relations between the different meta-model entities of cross-domain framework software components. In this research, we attempt to ensure that interface description models of software components from heterogeneous frameworks can be compared, correlated and re-used for automotive services based on semantic synergies. We have demonstrated our approach using component meta-models from cross-domain enterprises, that are used for the automotive application domain.