Zobrazeno 1 - 10
of 85
pro vyhledávání: '"Hossny, Mohammed"'
Autor:
Hossny, Mohammed, Iskander, Julie
It is not until we become senior citizens do we recognise how much we took maintaining a simple standing posture for granted. It is truly fascinating to observe the magnitude of control the human brain exercises, in real time, to activate and deactiv
Externí odkaz:
http://arxiv.org/abs/2008.12210
Autor:
Iskander, Julie, Hossny, Mohammed
Reinforcement learning has been applied to human movement through physiologically-based biomechanical models to add insights into the neural control of these movements; it is also useful in the design of prosthetics and robotics. In this paper, we ex
Externí odkaz:
http://arxiv.org/abs/2008.05088
LiDAR data is becoming increasingly essential with the rise of autonomous vehicles. Its ability to provide 360deg horizontal field of view of point cloud, equips self-driving vehicles with enhanced situational awareness capabilities. While synthetic
Externí odkaz:
http://arxiv.org/abs/2006.04345
In this paper, we propose enhancing actor-critic reinforcement learning agents by parameterising the final actor layer which produces the actions in order to accommodate the behaviour discrepancy of different actuators, under different load condition
Externí odkaz:
http://arxiv.org/abs/2006.02818
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Saleh, Khaled, Abobakr, Ahmed, Attia, Mohammed, Iskander, Julie, Nahavandi, Darius, Hossny, Mohammed
Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, there
Externí odkaz:
http://arxiv.org/abs/1905.08955
Understanding the behaviors and intentions of humans are one of the main challenges autonomous ground vehicles still faced with. More specifically, when it comes to complex environments such as urban traffic scenes, inferring the intentions and actio
Externí odkaz:
http://arxiv.org/abs/1904.09862
In this presented work, we propose a realistic hair simulator using image blending for dermoscopic images. This hair simulator can be used for benchmarking and validation of the hair removal methods and in data augmentation for improving computer aid
Externí odkaz:
http://arxiv.org/abs/1904.09169
Deeper convolutional neural networks provide more capacity to approximate complex mapping functions. However, increasing network depth imposes difficulties on training and increases model complexity. This paper presents a new nonlinear computational
Externí odkaz:
http://arxiv.org/abs/1806.09152
