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pro vyhledávání: '"Bihl, A"'
In this paper, a heuristic for a heterogeneous min-max multi-vehicle multi-depot traveling salesman problem is proposed, wherein heterogeneous vehicles start from given depot locations and need to cover a given set of targets. In the considered probl
Externí odkaz:
http://arxiv.org/abs/2410.23449
Autor:
Cominelli, Marco, Gringoli, Francesco, Kaplan, Lance M., Srivastava, Mani B., Bihl, Trevor, Blasch, Erik P., Iyer, Nandini, Cerutti, Federico
Wi-Fi devices, akin to passive radars, can discern human activities within indoor settings due to the human body's interaction with electromagnetic signals. Current Wi-Fi sensing applications predominantly employ data-driven learning techniques to as
Externí odkaz:
http://arxiv.org/abs/2407.04734
Publikováno v:
Journal of Defense Analytics and Logistics, 2024, Vol. 8, Issue 2, pp. 143-159.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JDAL-09-2023-0010
In this article, a heuristic is proposed for a min-max heterogeneous multi-vehicle multi-depot traveling salesman problem (TSP), wherein heterogeneous vehicles start from given depot positions and need to cover a given set of targets. The vehicles sh
Externí odkaz:
http://arxiv.org/abs/2312.17403
Publikováno v:
Journal of Defense Analytics and Logistics, Vol 8, Iss 2, Pp 143-159 (2024)
Purpose – Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers fro
Externí odkaz:
https://doaj.org/article/c1302ff3652444f6b64d80d52452c0dc
Autor:
Manna, Davide Liberato, Vicente-Sola, Alex, Kirkland, Paul, Bihl, Trevor Joseph, Di Caterina, Gaetano
Developing effective learning systems for Machine Learning (ML) applications in the Neuromorphic (NM) field requires extensive experimentation and simulation. Software frameworks aid and ease this process by providing a set of ready-to-use tools that
Externí odkaz:
http://arxiv.org/abs/2302.07624
Autor:
Yue, Ye, Baltes, Marc, Abujahar, Nidal, Sun, Tao, Smith, Charles D., Bihl, Trevor, Liu, Jundong
Over the past decade, artificial neural networks (ANNs) have made tremendous advances, in part due to the increased availability of annotated data. However, ANNs typically require significant power and memory consumptions to reach their full potentia
Externí odkaz:
http://arxiv.org/abs/2302.07328
Publikováno v:
Frontiers in Control Engineering, Vol 5 (2024)
When deploying agents to execute a mission with collective behavior, it is common for accidental malfunctions to occur in some agents. It is challenging to distinguish whether these malfunctions are due to actuator failures or sensor issues based sol
Externí odkaz:
https://doaj.org/article/c3136ab92da7418d8b85889532b40e09
Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive di
Externí odkaz:
http://arxiv.org/abs/2209.15087
Spiking Neural Networks for event-based action recognition: A new task to understand their advantage
Spiking Neural Networks (SNN) are characterised by their unique temporal dynamics, but the properties and advantages of such computations are still not well understood. In order to provide answers, in this work we demonstrate how Spiking neurons can
Externí odkaz:
http://arxiv.org/abs/2209.14915