The Future Internet will integrate large-scale systems constructed from the composition of thousands of distributed services, while interacting directly with the physical world via sensors and actuators, which compose the Internet of Things.
InterSCity researchers seek to apply novel Computer Science and Technology techniques to tackle urban social problems in underprivileged neighborhoods and low-income populations, leveraging existing data and collecting and analyzing new datasets to support Evidence-based Public Policymaking.
Journal of Systems and Software. Roberto Rodrigues-Filho, Iwens Sene, Barry Porter, Luiz F. Bittencourt, Fabio Kon and Fábio M. Costa, 2024.
In IEEE Transactions on Software Engineering, vol. 49, no. 4, pp. 1898-1911 (1 April 2023). Leonardo Leite, Nelson Lago, Claudia Melo, Fabio Kon, and Paulo Meirelles, 2023.
Public Transport. Fabio Kon, Éderson Cássio Ferreira, Higor Amario de Souza, Fábio Duarte, Paolo Santi, and Carlo Ratti, 2022.
Journal of Internet Services and Applications, vol 12. Tallys G. Martins, Nelson Lago, Eduardo F. Z. Santana, Alexandru Telea, Fabio Kon, and Higor A. de Souza, 2021.
Future Generation Computer Systems, v. 124. Fernanda Dallaqua, Álvaro Fazenda, and Fabio Faria, 2021.
A microservice-based, open-source smart city platform that aims at supporting collaborative, novel smart city research, development, and deployment initiatives.
An open-source, extensible, large-scale Traffic Simulator for Smart Cities, extensible to other Smart City domains.
A middleware for large-scale, low-latency processing of mobile data streams with support for mobile-mobile cooperation, context awareness, connection balancing, and cloud integration.
An interconnected network based on NFV to research aspects of network security in order to address the challenges posed by IoT technologies.
This project aims at developing sensor prototypes for monitoring the environment using free-design hardware and open source software. The project includes a base station and a sensor station including these sensors: LDR, DHT22, Soil Moisture, sap-flow, and leaf temperature.
The ForestEyes’ Project needs your help to track tropical forests’ deforestation. You can contribute analyzing and classifying remote sensing image tiles in Forest or Non-forest. Those remote sensing images are from LANDSAT-8’s bands, freely available at Earth Explorer. The pixels have 30m resolution which means that each pixel corresponds to an area of 900 square meters. Access https://www.zooniverse.org/projects/dallaqua/foresteyes/classify and contribute to the project!
No encerramento da Conference on Patterns, Graphics and Images (SIBGRAPI 2023), em Rio Grande – RS, o aluno da Unifesp Eduardo Bouhid Neto foi agraciado com o prêmio de melhor trabalho de graduação. O artigo é fruto da Iniciação Científica do aluno e contou com a coautoria de Paulo Roberto Costa Pedro e do Prof. Dr. Álvaro Luiz Fazenda, além da orientação do Prof. Dr. Fabio Augusto Faria.
O trabalho propôs um arcabouço de seleção de bandas de imagens multiespectrais de sensoriamento remoto utilizando o algoritmo de estimação de distribuição estocástico (UMDA).
Revista FAPESP:
Agência FAPESP: Exposicao apresenta resultados artisticos de pesquisa feita na cracolandia
