Leonardo Maria Millefiori
Orcid: 0000-0002-2242-0028
According to our database1,
Leonardo Maria Millefiori
authored at least 47 papers
between 2015 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2024
Detecting the Spatiotemporal Characteristics of the Supply-chain Disruption and Estimating its Short Term Effects.
Proceedings of the Workshops of the EDBT/ICDT 2024 Joint Conference co-located with the EDBT/ICDT 2024 Joint Conference, 2024
2023
Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction With Uncertainty Estimation.
IEEE Trans. Aerosp. Electron. Syst., June, 2023
Large Deviations for Classification Performance Analysis of Machine Learning Systems.
CoRR, 2023
Proceedings of the 26th International Conference on Information Fusion, 2023
Proceedings of the 9th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2023
2022
Maritime Anomaly Detection in a Real-World Scenario: Ever Given Grounding in the Suez Canal.
IEEE Trans. Intell. Transp. Syst., 2022
IEEE Trans. Aerosp. Electron. Syst., 2022
Next-Gen Intelligent Situational Awareness Systems for Maritime Surveillance and Autonomous Navigation [Point of View].
Proc. IEEE, 2022
CoRR, 2022
2021
Optimal Opponent Stealth Trajectory Planning Based on an Efficient Optimization Technique.
IEEE Trans. Signal Process., 2021
Deep Learning Methods for Vessel Trajectory Prediction Based on Recurrent Neural Networks.
IEEE Trans. Aerosp. Electron. Syst., 2021
IEEE Commun. Mag., 2021
Application of Hidden Markov Models to Analyze, Group and Visualize Spatio-Temporal COVID-19 Data.
IEEE Access, 2021
Sensors Localization and Target Tracking in Underwater Environment via Belief Propagation.
Proceedings of the 2021 IEEE Military Communications Conference, 2021
Uncertainty-Aware Recurrent Encoder-Decoder Networks for Vessel Trajectory Prediction.
Proceedings of the 24th IEEE International Conference on Information Fusion, 2021
MAST: A Quickest Detection Procedure for COVID-19 Epidemiological Data to Trigger Strategic Decisions.
Proceedings of the 29th European Signal Processing Conference, 2021
2020
IEEE Trans. Signal Process., 2020
Analytical Models for the Electromagnetic Scattering From Isolated Targets in Bistatic Configuration: Geometrical Optics Solution.
IEEE Trans. Geosci. Remote. Sens., 2020
Simulation-Based Feasibility Analysis of Ship Detection Using GNSS-R Delay-Doppler Maps.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2020
Adaptive Bayesian Learning and Forecasting of Epidemic Evolution - Data Analysis of the COVID-19 Outbreak.
IEEE Access, 2020
Prediction oof Vessel Trajectories From AIS Data Via Sequence-To-Sequence Recurrent Neural Networks.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
2019
Proceedings of the 5th IEEE International forum on Research and Technology for Society and Industry, 2019
Proceedings of the IEEE International Conference on Acoustics, 2019
Anomaly Detection and Tracking Based on Mean-Reverting Processes with Unknown Parameters.
Proceedings of the IEEE International Conference on Acoustics, 2019
2018
Detecting Anomalous Deviations From Standard Maritime Routes Using the Ornstein-Uhlenbeck Process.
IEEE Trans. Signal Process., 2018
IEEE Trans. Aerosp. Electron. Syst., 2018
Maritime Surveillance Using Spaceborne GNSS-Reflectometry: The Role of the Scattering Configuration and Receiving Polarization Channel.
Proceedings of the 4th IEEE International Forum on Research and Technology for Society and Industry, 2018
Spaceborne GNSS-Reflectometry for Ship-Detection Applications: Impact of Acquisition Geometry and Polarization.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Electromagnetic Modeling of Ships in Maritime Scenarios: Geometrical Optics Approximation.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Maritime Anomaly Detection Based on Mean-Reverting Stochastic Processes Applied to a Real-World Scenario.
Proceedings of the 21st International Conference on Information Fusion, 2018
Proceedings of the 21st International Conference on Information Fusion, 2018
Proceedings of the 21st International Conference on Information Fusion, 2018
Unsupervised Maritime Traffic Graph Learning with Mean-Reverting Stochastic Processes.
Proceedings of the 21st International Conference on Information Fusion, 2018
Proceedings of the 26th European Signal Processing Conference, 2018
Proceedings of the 26th European Signal Processing Conference, 2018
2017
Performance Assessment of Vessel Dynamic Models for Long-Term Prediction Using Heterogeneous Data.
IEEE Trans. Geosci. Remote. Sens., 2017
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017
2016
Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction.
IEEE Trans. Aerosp. Electron. Syst., 2016
Consistent Estimation of Randomly Sampled Ornstein-Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction.
IEEE Signal Process. Lett., 2016
Proceedings of the IEEE International Conference on Data Mining Workshops, 2016
Proceedings of the 19th International Conference on Information Fusion, 2016
The Mixed Ornstein-Uhlenbeck Process and context exploitation in multi-target tracking.
Proceedings of the 19th International Conference on Information Fusion, 2016
A distributed approach to estimating sea port operational regions from lots of AIS data.
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016
Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016
2015
Adaptive filtering of imprecisely time-stamped measurements with application to AIS networks.
Proceedings of the 18th International Conference on Information Fusion, 2015
A document-based data model for large scale computational maritime situational awareness.
Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015