Zobrazeno 1 - 10
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pro vyhledávání: '"A Mehdi"'
This paper presents an autonomous method to address challenges arising from severe lighting conditions in machine vision applications that use event cameras. To manage these conditions, the research explores the built in potential of these cameras to
Externí odkaz:
http://arxiv.org/abs/2411.00729
Autor:
Shokri, Mehdi
The Bekenstein bound, inspired by the physics of black holes, is introduced to constrain the entropy growth of a physical system down to the quantum level in the context of a generalized second law of thermodynamics. We first show that the standard B
Externí odkaz:
http://arxiv.org/abs/2411.00694
Our future society will be increasingly digitalised, hyper-connected and globally data driven. The sixth generation (6G) and beyond 6G wireless networks are expected to bridge the digital and physical worlds by providing wireless connectivity as a se
Externí odkaz:
http://arxiv.org/abs/2410.23203
A naive classical representation of an n-qubit state requires specifying exponentially many amplitudes in the computational basis. Past works have demonstrated that classical neural networks can succinctly express these amplitudes for many physically
Externí odkaz:
http://arxiv.org/abs/2410.23152
Large language models (LLMs) have shown impressive capabilities across various tasks, but their performance on domain-specific tasks remains limited. While methods like retrieval augmented generation and fine-tuning can help to address this, they req
Externí odkaz:
http://arxiv.org/abs/2410.21868
Automated Facial Expression Recognition (FER) is challenging due to intra-class variations and inter-class similarities. FER can be especially difficult when facial expressions reflect a mixture of various emotions (aka compound expressions). Existin
Externí odkaz:
http://arxiv.org/abs/2410.22506
Autor:
Hosseinzadeh, Mehdi, Reid, Ian
In the field of autonomous driving and mobile robotics, there has been a significant shift in the methods used to create Bird's Eye View (BEV) representations. This shift is characterised by using transformers and learning to fuse measurements from d
Externí odkaz:
http://arxiv.org/abs/2410.20969
Deep reinforcement learning (DRL) is emerging as a promising method for adaptive robotic motion and complex task automation, effectively addressing the limitations of traditional control methods. However, ensuring safety throughout both the learning
Externí odkaz:
http://arxiv.org/abs/2410.20907
Fine-grained power estimation in multicore Systems on Chips (SoCs) is crucial for efficient thermal management. BPI (Blind Power Identification) is a recent approach that determines the power consumption of different cores and the thermal model of th
Externí odkaz:
http://arxiv.org/abs/2410.21261
In this paper, a novel approach for wireless localization is proposed and experimentally validated that leverages space-time coded reconfigurable intelligent surfaces (RIS). It is demonstrated that applying proper single-bit codes to each RIS element
Externí odkaz:
http://arxiv.org/abs/2410.20505