Tarek Abdelzaher
University of Illinois at Urbana–Champaign
546 Papers
7.3K Citations
Tarek Abdelzaher is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 88, co-authored 517 publications. Previous affiliations of Tarek Abdelzaher include Urbana University & Hewlett-Packard.
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Papers
Planning and Resource Allocation for Hard Real-time, Fault-Tolerant Plan Execution
TL;DR: This work describes the interface between a real-time resource allocation system with an AI planner in order to create fault-tolerant plans that are guaranteed to execute in hardreal-time, and provides an example of an autonomous aircraft agent to illustrate how the planner-resource allocation interface improves CIRCA performance.
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A Delay Composition Theorem for Real-Time Pipelines
Praveen Jayachandran,Tarek Abdelzaher +1 more
- 04 Jul 2007
TL;DR: This paper bound the end-to-end delay of a job in a multistage pipeline as a function of higher-priority job execution times on different stages, so that the pipeline delay composition rule may be a step towards a general schedulability analysis foundation for large distributed systems.
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Diagnostic powertracing for sensor node failure analysis
Mohammad Maifi Hasan Khan,Hieu Le,Michael LeMay,Parya Moinzadeh,Lili Wang,Yong Yang,Dong Kun Noh,Tarek Abdelzaher,Carl A. Gunter,Jiawei Han,Xin Jin +10 more
- 12 Apr 2010
TL;DR: This paper introduces the tele-diagnostic powertracer, an in-situ troubleshooting tool that uses external power measurements to determine the internal health condition of an unresponsive host and the most likely cause of its failure.
A feasible region for meeting aperiodic end-to-end deadlines in resource pipelines
Tarek Abdelzaher,G. Thaker,P. Lardieri +2 more
- 24 Mar 2004
TL;DR: This paper generalizes the notion of utilization bounds for schedulability of aperiodic tasks to the case of distributed resource systems and evaluates the performance of admission control using simulation, as well as demonstrating the applicability of these results to task Schedulability analysis in the total ship computing environment envisioned by the US navy.
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Contrastive Self-Supervised Representation Learning for Sensing Signals from the Time-Frequency Perspective
Dongxin Liu,Tianshi Wang,Shengzhong Liu,Ruijie Wang,Shuochao Yao,Tarek Abdelzaher +5 more
- 19 Jul 2021
TL;DR: In this paper, a contrastive self-supervised representation learning framework was proposed for deep learning from frequency domain data, which takes both time-domain and frequency-domain features into consideration.
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