activity
20182022
collaborators

7 papers

cs.RO2022

A framework for robotic arm pose estimation and movement prediction based on deep and extreme learning models

Iago Richard Rodrigues, Marrone Dantas, Assis Oliveira Filho +7

Human-robot collaboration has gained a notable prominence in Industry 4.0, as the use of collaborative robots increases efficiency and productivity in the automation process. Howev…

cs.NI2020

The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption

Guto Leoni Santos, Theo Lynn, Judith Kelner +1

Software defined networking (SDN) and network functions virtualisation (NFV) are making networks programmable and consequently much more flexible and agile. To meet service level a…

cs.NI2020

Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks

Guto Leoni Santos, Pierangelo Rosati, Theo Lynn +3

Mobile network traffic prediction is an important input in to network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to acc…

cs.NI2020

Using Reinforcement Learning to Allocate and Manage Service Function Chains in Cellular Networks

Guto Leoni Santos, Patricia Takako Endo

It is expected that the next generation cellular networks provide a connected society with fully mobility to empower the socio-economic transformation. Several other technologies w…

cs.AI2018

A Summary Description of the A2RD Project

Juliao Braga, Joao Nuno Silva, Patricia Takako Endo +1

This paper describes the Autonomous Architecture Over Restricted Domains project. It begins with the description of the context upon which the project is focused, and in the sequen…

cs.AI2018

Theoretical Foundations of the A2RD Project: Part I

Juliao Braga, Joao Nuno Silva, Patricia Takako Endo +1

This article identifies and discusses the theoretical foundations that were considered in the design of the A2RD model. In addition to the points considered, references are made to…