| 1. |
- Elmroth, Erik, 1964-, et al.
(författare)
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Self-management challenges for multi-cloud architectures
- 2011
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Ingår i: Towards a Service-Based Internet. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642247545 - 9783642247552 ; , s. 38-49
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Konferensbidrag (refereegranskat)abstract
- Addressing the management challenges for a multitude of distributed cloud architectures, we focus on the three complementary cloud management problems of predictive elasticity, admission control, and placement (or scheduling) of virtual machines. As these problems are intrinsically intertwined we also propose an approach to optimize the overall system behavior by policy-tuning for the tools handling each of them. Moreover, in order to facilitate the execution of some of the management decisions, we also propose new algorithms for live migration of virtual machines with very high workload and/or over low-bandwidth networks, using techniques such as caching, compression, and prioritization of memory pages.
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| 2. |
- Ghanbarikarekani, Mina, et al.
(författare)
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Minimizing the stop time of private vehicles at intersections with LRT signal priority systems
- 2020
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Ingår i: Transportation Research Procedia. - : Elsevier BV. - 2352-1465 .- 2352-1457. ; 48:2020, s. 939-945
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Konferensbidrag (refereegranskat)abstract
- There are some strategies suggested to improve the performance of intersections and increase the demand for public vehicles by prioritizing them. To this end, several methods have been used such as Transit Signal Priority (TSP) system for Light Rail transit (LRT). LRT signal priority is a timing strategy that gives priority to LRTs at signalized intersections through changing the sequence of phases, extending green time and reducing red time at LRT's phase. In this paper, we propose a model to improve LRT signal priority systems. The developed model minimizes the green extension and red reduction of LRT's phase by estimating an optimal speed for LRTs reaching the stop line. Consequently, the priority of LRTs would be maintained while the performance of private vehicles would be improved by decreasing their stop time.
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| 3. |
- Zoljodi, Ali, et al.
(författare)
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3DLaneNAS : Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection
- 2022
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Ingår i: Artificial Neural Networks and Machine Learning – ICANN 2022. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031159183 ; , s. 404-415
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Konferensbidrag (refereegranskat)abstract
- Lane detection is one of the most fundamental tasks for autonomous driving. It plays a crucial role in the lateral control and the precise localization of autonomous vehicles. Monocular 3D lane detection methods provide state-of-the-art results for estimating the position of lanes in 3D world coordinates using only the information obtained from the front-view camera. Recent advances in Neural Architecture Search (NAS) facilitate automated optimization of various computer vision tasks. NAS can automatically optimize monocular 3D lane detection methods to enhance the extraction and combination of visual features, consequently reducing computation loads and increasing accuracy. This paper proposes 3DLaneNAS, a multi-objective method that enhances the accuracy of monocular 3D lane detection for both short- and long-distance scenarios while at the same time providing a fair amount of hardware acceleration. 3DLaneNAS utilizes a new multi-objective energy function to optimize the architecture of feature extraction and feature fusion modules simultaneously. Moreover, a transfer learning mechanism is used to improve the convergence of the search process. Experimental results reveal that 3DLaneNAS yields a minimum of 5.2% higher accuracy and ≈ 1.33 × lower latency over competing methods on the synthetic-3D-lanes dataset. Code is at https://github.com/alizoljodi/3DLaneNAS
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