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Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: deep neural network classifier
(Elsevier, 2021-04)
Advanced techniques in the recognition and classification of seismo-volcanic events are transcendental when studying active volcanoes, not only for their importance as an accurate real time seismic monitoring procedure but ...
MQTT based event detection system for structural health monitoring of buildings
(Springer Science y Business Media Deutschland GmbH, 2022)
Structural Health Monitoring (SHM) consists in a fundamental research field which aim to evaluate the current status of an infrastructure with the main purpose to identify damages and prevent catastrophic events. This paper ...
Predicting information credibility in time-sensitive social media
(Emerald Group Publishing, 2013)
Purpose – Twitter is a popular microblogging service which has proven, in recent years, its potential
for propagating news and information about developing events. The purpose of this paper is to focus
on the analysis ...
Learning and transfer of feature extractors for automatic anomaly detection in surveillance videos
(Universidade Tecnológica Federal do ParanáCuritibaBrasilPrograma de Pós-Graduação em Engenharia Elétrica e Informática IndustrialUTFPR, 2018-04-03)
Automatic video surveillance is becoming a topic of great importance in the current world. Surveillance cameras in private and public spaces greatly outnumber the humans available for performing the observation task. This ...
Enhanced fault characterization by using a conventional OTDR and DSP techniques
(Optical Society of America, 2018-10)
To plan a rapid response and minimize operational costs, passive optical network operators require to automatically detect and identify faults that may occur in the optical distribution network. In this work, we present ...
Automatic Identification of Interictal Epileptiform Discharges with the Use of Complex Networks
(Springer, 2019-01-01)
The identification of Interictal Epileptiform Discharges (IEDs), which are characterized by spikes and waves in electroencephalographic (EEG) data, is highly beneficial to the automated detection and prediction of epileptic ...