Difference between revisions of "Event:RTAS 2020"

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|has general chair=Björn Brandenburg
 
|has general chair=Björn Brandenburg
 
|has program chair=Gernot Heiser, Frank Mueller
 
|has program chair=Gernot Heiser, Frank Mueller
|Has PC member=Daniel Mossé, Peter User:Curator 83ubb, Hui Wu
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|Has PC member=Daniel Mossé, Peter Chubb, Hui Wu
 
|has Proceedings Bibliography=dblp.org/db/conf/rtas/index
 
|has Proceedings Bibliography=dblp.org/db/conf/rtas/index
 
|pageCreator=User:Curator 89
 
|pageCreator=User:Curator 89

Revision as of 12:43, 7 July 2022

RTAS is a top-tier conference with a focus on systems research related to embedded systems or timing issues. The broad scope of RTAS’20 ranges from traditional hard real-time systems to embedded systems without explicit timing requirements, including latency-sensitive systems with informal or soft real-time requirements.

RTAS’20 invites papers describing original systems and applications, case studies, methodologies, and applied algorithms that contribute to the state of practice in the design, implementation, verification, and validation of embedded systems and time-sensitive systems (of any size). The scope of RTAS’20 consists of two tracks:

Real-Time/Embedded Systems: Applications, Operating Systems, Run-Time Systems, Tools and Hardware Architectures Real-Time Applied Methodologies and Foundations.

Topics

Timing requirements of interest include not only classical hard real-time constraints, but also time-sensitive applications in a broader sense, including applications subject to probabilistic, soft real-time, quality-of-service (QoS), or latency requirements. For example, relevant application areas include (but are not limited to):

  • time-sensitive cloud/edge/fog computing systems (e.g., characterized by a focus on tail latency);
  • time-sensitive applications in the Internet of Things (IoT);
  • time-sensitive distributed event processing systems;
  • time-sensitive mobile computing apps;
  • timing aspects in robotics middlewares and frameworks;
  • machine learning in or for time-sensitive systems;
  • real-time control in smart cities and other large cyber-physical systems (CPS);
  • signal processing algorithms that must execute in real time; and
  • real-time healthcare solutions.
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