How to Build a Mixed-Criticality System in Industry?

In the last decade, the rapid evolution of diverse functionalities and execution platform led safety-critical systems towards integrating components/functions/applications with different ‘criticality’ in a shared hardware platform, i.e., Mixed-Criticality Systems (MCS)s. In academia, hundreds of publications has been proposed upon a commonly used model, i.e., Vestal’s model. Even so, because of the mismatched concepts between academia and industry, current academic models can not be exported to a real industrial system. This paper discusses the mismatched concepts from the system architecture perspective, with a potential solution being proposed

Deep Learning for Spatiotemporal Big Data: A Vision on Opportunities and Challenges

With advancements in GPS, remote sensing, and computational simulation, an enormous volume of spatiotemporal data is being collected at an increasing speed from various application domains, spanning Earth sciences, agriculture, smart cities, and public safety. Such emerging geospatial and spatiotemporal big data, coupled with recent advances in deep learning technologies, foster new opportunities to solve problems that have not been possible before. For instance, remote sensing researchers can potentially train a foundation model using Earth imagery big data for numerous land cover and land use modeling tasks. Coastal modelers can train AI surrogates to speed up numerical simulations. However, the distinctive characteristics of spatiotemporal big data pose new challenges for deep learning technologies. This vision paper introduces various types of spatiotemporal big data, discusses new research opportunities in the realm of deep learning applied to spatiotemporal big data, lists the unique challenges, and identifies several future research needs