output
20062024
most citedA Survey of Network-On-Chip Tools

44 citations

22 papers

cs.RO2024

Leveraging Event Streams with Deep Reinforcement Learning for End-to-End UAV Tracking

Ala Souissi, Hajer Fradi, Panagiotis Papadakis

In this paper, we present our proposed approach for active tracking to increase the autonomy of Unmanned Aerial Vehicles (UAVs) using event cameras, low-energy imaging sensors that…

cs.LG2021★ 17 cited

Predicting cognitive scores with graph neural networks through sample selection learning

Martin Hanik, Mehmet Arif Demirtaş, Mohammed Amine Gharsallaoui +1

Analyzing the relation between intelligence and neural activity is of the utmost importance in understanding the working principles of the human brain in health and disease. In exi…

cs.CV2020★ 34 cited

An original framework for Wheat Head Detection using Deep, Semi-supervised and Ensemble Learning within Global Wheat Head Detection (GWHD) Dataset

Fares Fourati, Wided Souidene, Rabah Attia

In this paper, we propose an original object detection methodology applied to Global Wheat Head Detection (GWHD) Dataset. We have been through two major architectures of object det…

nlin.AO2020

Analysis of self-equilibrated networks through cellular modeling

Omar Aloui, David Orden, Nizar Bel Hadj Ali +1

Network equilibrium models represent a versatile tool for the analysis of interconnected objects and their relationships. They have been widely employed in both science and enginee…

stat.AP2018

More investment in Research and Development for better Education in the future?

Rim Lahmandi-Ayed, Dhafer Malouche

The question in this paper is whether R&D efforts affect education performance in small classes. Merging two datasets collected from the PISA studies and the World Development Indi…

math.OC2017★ 3 cited

Exact Boundary Controllability for the Boussinesq Equation with Variable Coefficients

Jamel Ben Amara, Hedi Bouzidi

In this paper we study the exact boundary controllability for the following Boussinesq equation with variable physical parameters: \begin{array}{lll} ρ(x)y_{tt}=-(σ(x)y_{xx})_{xx}+…