Édler Lins de Albuquerque

dblp:150/6420 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0001-5982-5267ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
1 paper
Internet of things and sensor networks · 50% Network measurement and analytics · 50%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Energy-efficient computing · 100%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data mining
dimensionality reduction
0.212014
MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks · IEEE Trans. Computers 2014
Network measurement and analytics
sampling
0.212014
MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks · IEEE Trans. Computers 2014
Internet of things and sensor networks
wireless sensor network
0.212014
MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks · IEEE Trans. Computers 2014

Methods — techniques the papers use, named apart from their topics

data ranking · 0.6component analysis · 0.6
YearPublicationVenuePosition
2024 Self-organizing maps applied to the analysis and identification of characteristics related to air quality monitoring stations and its pollutants
Emanoel L. R. Costa, Taiane Braga, Leonardo Alves Dias, Édler Lins de Albuquerque, Marcelo A. C. Fernandes
Neural Comput. Appl.4
2014 MuSA: Multivariate Sampling Algorithmfor Wireless Sensor Networks
abstract
A wireless sensor network can be used to collect and process environmental data, which is often of multivariate nature. This work proposes a multivariate sampling algorithm based on component analysis techniques in wireless sensor networks. To improve the sampling, the algorithm uses component analysis techniques to rank the data. Once ranked, the most representative data is retained. Simulation results show that our technique reduces the data keeping its representativeness. In addition, the energy consumption and delay to deliver the data on the network are reduced.
André L. L. de Aquino, Orlando Silva Junior, Alejandro C. Frery, Édler Lins de Albuquerque, Raquel A. F. Mini
IEEE Trans. Computers4