Zobrazeno 11 - 20
of 53 206
pro vyhledávání: '"Anis, A. A."'
Autor:
Benyoub, Anis, Dupuy, Jonathan
A concurrent binary tree (CBT) is a GPU-friendly data-structure suitable for the generation of bisection based terrain tessellations, i.e., adaptive triangulations over square domains. In this paper, we expand the benefits of this data-structure in t
Externí odkaz:
http://arxiv.org/abs/2407.02215
Autor:
Paul, Ovi, Nayem, Abu Bakar Siddik, Sarker, Anis, Ali, Amin Ahsan, Amin, M Ashraful, Rahman, AKM Mahbubur
Land Use Land Cover (LULC) analysis on satellite images using deep learning-based methods is significantly helpful in understanding the geography, socio-economic conditions, poverty levels, and urban sprawl in developing countries. Recent works invol
Externí odkaz:
http://arxiv.org/abs/2406.05912
The Internet of Things (IoT) has been introduced as a breakthrough technology that integrates intelligence into everyday objects, enabling high levels of connectivity between them. As the IoT networks grow and expand, they become more susceptible to
Externí odkaz:
http://arxiv.org/abs/2406.02636
This paper introduces a novel reconfigurable and power-efficient FPGA (Field-Programmable Gate Array) implementation of an operator splitting algorithm for Non-Terrestial Network's (NTN) relay satellites model predictive orientation control (MPC). Ou
Externí odkaz:
http://arxiv.org/abs/2406.00402
The growing interest in satellite imagery has triggered the need for efficient mechanisms to extract valuable information from these vast data sources, providing deeper insights. Even though deep learning has shown significant progress in satellite i
Externí odkaz:
http://arxiv.org/abs/2406.00348
Autor:
Kane, Seyni, Bkakria, Anis
Graph encryption schemes play a crucial role in facilitating secure queries on encrypted graphs hosted on untrusted servers. With applications spanning navigation systems, network topology, and social networks, the need to safeguard sensitive data be
Externí odkaz:
http://arxiv.org/abs/2405.19259
Addressing uncertainty in Deep Learning (DL) is essential, as it enables the development of models that can make reliable predictions and informed decisions in complex, real-world environments where data may be incomplete or ambiguous. This paper int
Externí odkaz:
http://arxiv.org/abs/2405.20230
The expanding research on manifold-based self-supervised learning (SSL) builds on the manifold hypothesis, which suggests that the inherent complexity of high-dimensional data can be unraveled through lower-dimensional manifold embeddings. Capitalizi
Externí odkaz:
http://arxiv.org/abs/2405.13848
In this paper, we propose a new efficient mediated semi-quantum key distribution (MSQKD) protocol, facilitating the establishment of a shared secret key between two classical participants with the assistance of an untrusted third party (TP). Unlike e
Externí odkaz:
http://arxiv.org/abs/2404.17727
Autor:
Bkakria, Anis, Yaich, Reda
Privacy in Location-Based Services (LBS) has become a paramount concern with the ubiquity of mobile devices and the increasing integration of location data into various applications. In this paper, we present several novel contributions aimed at adva
Externí odkaz:
http://arxiv.org/abs/2404.13407