Ricardo Moura

Dr. Ricardo Moura

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Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2024 in Data in Brief
Verified
Journal Article
Open access
Nuno Pessanha SantosNuno Pessanha Santos
Nuno Pessanha Santos
RMRicardo Moura
Ricardo Moura
GTGonçalo Sampaio Torgal
Gonçalo Sampaio Torgal
VLVictor Lobo
Victor Lobo
MNMiguel de Castro Neto
Miguel de Castro Neto
Unmanned vehicles have become increasingly popular in the underwater domain in the last decade, as they provide better operation reliability by minimizing human involvement in most tasks. Perception of the environment is crucial for safety and other tasks, such as guidance and trajectory control, mainly when operating underwater. Mine detection is one of the riskiest operations since it involves systems that can easily damage vehicles and endanger human lives if manned. Automating mine detection from side-scan sonar images enhances safety while reducing false negatives. The collected dataset contains 1170 real sonar images taken between 2010 and 2021 using a Teledyne Marine Gavia Autonomous Underwater Vehicle (AUV), which includes enough information to classify its content objects as NOn-Mine-like BOttom Objects (NOMBO) and MIne-Like COntacts (MILCO). The dataset is annotated and can be quickly deployed for object detection, classification, or image segmentation tasks. Collecting a dataset of this type requires a significant amount of time and cost, which increases its rarity and relevance to research and industrial development.
1 January 2026 in Aquaculture and Fisheries
Verified
Journal Article
Open access
RMRicardo Moura
Ricardo Moura
Nuno Pessanha SantosNuno Pessanha Santos
Nuno Pessanha Santos
MCMaria Eduarda Catarino
Maria Eduarda Catarino
30 July 2025 in Scientific Data
Verified
Journal Article
Open access
PDF file
VLVictor Lobo
Victor Lobo
Nuno Pessanha SantosNuno Pessanha Santos
Nuno Pessanha Santos
RMRicardo Moura
Ricardo Moura
As data becomes increasingly available, relying on quality datasets for algorithm analysis and development is essential. However, data gathering can be expensive and time-consuming, and this process must be optimized to allow others to reuse data with simplicity and accuracy. The Wolfset is an acoustic dataset gathered using a Bruel & Kjaer type 8104 hydrophone in an anechoic tank usually used for ships’ sonar calibration. The name Wolfset is inspired by the Seawolf submarine class, renowned for its advanced sound source detection and classification capabilities. Using an anechoic tank, we can obtain a high-quality dataset representing acoustic sources without undesired external perturbations. In many operating conditions, several outboard motors and an electric motor from a basic remotely controlled ship model were used as sound sources, usually called targets. Then, external transients and noise sources were added to approximate the dataset to the sounds present in real-world conditions. This dataset uses a systematic approach to demonstrate the diversity and accuracy needed for effective algorithm development.
3 April 2021 in Journal of the American Statistical Association
Verified
Journal Article
Closed access
RMRicardo Moura
Ricardo Moura
MKMartin Klein
Martin Klein
JZJohn Zylstra
John Zylstra
Carlos CoelhoCarlos Coelho
Carlos Coelho
BSBimal Sinha
Bimal Sinha
In this article, the authors derive the likelihood-based exact inference for singly and multiply imputed synthetic data in the context of a multivariate regression model. The synthetic data are generated via the Plug-in Sampling method, where the unknown parameters in the model are set equal to the observed values of their point estimators based on the original data, and synthetic data are drawn from this estimated version of the model. Simulation studies are carried out in order to confirm the theoretical results. The authors provide exact test procedures, which in case multiple synthetic datasets are permissible, are compared with the asymptotic results of Reiter. An application using 2000 U.S. Current Population Survey public use data is discussed. Furthermore, properties of the proposed methodology are evaluated in scenarios where some of the conditions that were used to derive the methodology do not hold, namely for nonnormal and discrete distributed random variables, cases in which the inferential procedures developed still show very good performances.
28 February 2020 in Sankhya B
Verified
Journal Article
Closed access
PDF file
MKMartin Klein
Martin Klein
RMRicardo Moura
Ricardo Moura
BSBimal Sinha
Bimal Sinha
10 April 2024 in Scientific Data
Verified
Journal Article
Open access
PDF file
RMRicardo Moura
Ricardo Moura
Nuno Pessanha SantosNuno Pessanha Santos
Nuno Pessanha Santos
AVAlexandra Vala
Alexandra Vala
LMLeonor Mendes
Leonor Mendes
PSPaula Simões
Paula Simões
VLVictor Lobo
Victor Lobo
As a coastal state, Portugal must ensure active surveillance over its maritime area, ensuring its proper control and inspection. One of the most critical inspection activities is the fishery inspection. To protect biodiversity, we must ensure that all the ships comply with the existing safety regulations and respect the current fishing quotas. This georeferenced dataset describes the fisheries inspections done in Portuguese waters between 2015 and 2023. Since we are dealing with occurrences that may have originated some legal process to the ship’s owner, we have ensured data anonymization by pre-processing the dataset to maintain its accuracy while guaranteeing no unique identifiers exist. All the pre-processing performed to ensure data consistency and accuracy is described in detail to allow a quick analysis and implementation of new algorithms. The data containing the results of these inspections can be easily analyzed to implement data mining algorithms that can efficiently retrieve more knowledge and, e.g., suggest new areas of actuation or new strategies.