2 Search Results for "Dumitras, Tudor"

Document
DOI: 10.4230/LIPIcs.ECRTS.2025.13
Faster Classification of Time-Series Input Streams

Authors: Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao

Published in: LIPIcs, Volume 335, 37th Euromicro Conference on Real-Time Systems (ECRTS 2025)

Abstract
Deep learning–based classifiers are widely used for perception in autonomous Cyber-Physical Systems (CPS’s). However, such classifiers rarely offer guarantees of perfect accuracy while being optimized for efficiency. To support safety-critical perception, ensembles of multiple different classifiers working in concert are typically used. Since CPS’s interact with the physical world continuously, it is not unreasonable to expect dependencies among successive inputs in a stream of sensor data. Prior work introduced a classification technique that leverages these inter-input dependencies to reduce the average time to successful classification using classifier ensembles. In this paper, we propose generalizations to this classification technique, both in the improved generation of classifier cascades and the modeling of temporal dependencies. We demonstrate, through theoretical analysis and numerical evaluation, that our approach achieves further reductions in average classification latency compared to the prior methods.
Cite as

Kunal Agrawal, Sanjoy Baruah, Zhishan Guo, Jing Li, Federico Reghenzani, Kecheng Yang, and Jinhao Zhao. Faster Classification of Time-Series Input Streams. In 37th Euromicro Conference on Real-Time Systems (ECRTS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 335, pp. 13:1-13:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{agrawal_et_al:LIPIcs.ECRTS.2025.13,
  author =	{Agrawal, Kunal and Baruah, Sanjoy and Guo, Zhishan and Li, Jing and Reghenzani, Federico and Yang, Kecheng and Zhao, Jinhao},
  title =	{{Faster Classification of Time-Series Input Streams}},
  booktitle =	{37th Euromicro Conference on Real-Time Systems (ECRTS 2025)},
  pages =	{13:1--13:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-377-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{335},
  editor =	{Mancuso, Renato},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2025.13},
  URN =		{urn:nbn:de:0030-drops-235919},
  doi =		{10.4230/LIPIcs.ECRTS.2025.13},
  annote =	{Keywords: Classification, Deep Learning, Sensor data streams, IDK classifiers}
}
Document
DOI: 10.4230/DagSemProc.09201.6
Testing Run-time Evolving Systems

Authors: Tudor Dumitras, Frank Eliassen, Kurt Geihs, Henry Muccini, Andrea Polini, and Theo Ungerer

Published in: Dagstuhl Seminar Proceedings, Volume 9201, Self-Healing and Self-Adaptive Systems (2009)

Abstract
This document summarizes the results of the Working Group 4 - ``Testing'' - at the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems'' (organized by A. Andrzejak, K. Geihs, O. Shehory and J. Wilkes). The seminar was held from May 10th 2009 to May 15th 2009 in Schloss Dagstuhl~--~Leibniz Center for Informatics.
Cite as

Tudor Dumitras, Frank Eliassen, Kurt Geihs, Henry Muccini, Andrea Polini, and Theo Ungerer. Testing Run-time Evolving Systems. In Self-Healing and Self-Adaptive Systems. Dagstuhl Seminar Proceedings, Volume 9201, pp. 1-7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{dumitras_et_al:DagSemProc.09201.6,
  author =	{Dumitras, Tudor and Eliassen, Frank and Geihs, Kurt and Muccini, Henry and Polini, Andrea and Ungerer, Theo},
  title =	{{Testing Run-time Evolving Systems}},
  booktitle =	{Self-Healing and Self-Adaptive Systems},
  pages =	{1--7},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9201},
  editor =	{Artur Andrzejak and Kurt Geihs and Onn Shehory and John Wilkes},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09201.6},
  URN =		{urn:nbn:de:0030-drops-21065},
  doi =		{10.4230/DagSemProc.09201.6},
  annote =	{Keywords: Software Testing, Dynamically Evolving Systems}
}
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