Contacts
Roles & Cluster
- Full Member
- Smart Internet of Things Ecosystems
Academic IDs
Saeid Alireza Zadeh. Completed the Doutoramento in Matemática in 2011 by Universidade do Porto Faculdade de Ciências. Is Auxiliary Researcher in Instituto Politécnico de Leiria. Published 11 articles in journals. Has 1 section(s) of books. Has received 1 award and/or honors. Works in the area(s) of Exact Sciences with emphasis on Mathematics with emphasis on Pure Mathematics, Exact Sciences with emphasis on Computer and Information Sciences with emphasis on Computer Sciences and Natural Sciences with emphasis on Biological Sciences with emphasis on Ecology. He is working on optimizing the performances of automated and unmanned vehicles used in fruit harvesting by reducing the localization and coordination errors, which are improvements on the adaptation of Project DBoidS "https://sciproj.ptcris.pt/171663PRJ" (PTDC/CCI-COM/2416/2021); developing strategies and metrics for optimizing dynamic task allocation in robotic network cloud systems; developing sensorless motion planning without collisions in deterministic environments; developing motion planning without collisions in dynamic environments; and developing a fast and efficient method to estimate the weights in training neural networks
Alirezazadeh, Saeid, and Luís A. Alexandre. 2025. “A Survey on Task Allocation and Scheduling in Robotic Network Systems.” IEEE Internet of Things Journal 12 (2): 1484–1508. https://doi.org/10.1109/jiot.2024.3491944.
DOIAlirezazadeh, Saeid, and Luís A. Alexandre. 2023. “Ordered Balancing: Load Balancing for Redundant Task Scheduling in Robotic Network Cloud Systems.” Cluster Computing 27 (2): 1185–1200. https://doi.org/10.1007/s10586-023-04013-x.
DOIAlirezazadeh, Saeid, and Luís A. Alexandre. 2023. “Static Algorithm Allocation with Duplication in Robotic Network Cloud Systems.” IEEE Transactions on Parallel and Distributed Systems, 1–11. https://doi.org/10.1109/tpds.2023.3267293.
DOILopes, Vasco, Saeid Alirezazadeh, and Luís A. Alexandre. “EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search.” Artificial Neural Networks and Machine Learning – ICANN 2021, 2021, 552–63. https://doi.org/10.1007/978-3-030-86383-8_44.
DOI
DUVOPS - DUVOPS – Digital Twins Heterogeneous Unmanned Vehicles Ocean Preservation System · Funded by FCT - Projetos de I&D
Total budget: 249.868,80€ Project details