Maritime navigation safety is the basis for global logistics and marine ecosystems. The increasing availability of Automatic Identification System (AIS) data has opened new avenues for forecasting vessel trajectories with higher precision and robustness. This review presents a comprehensive survey of recent data-driven approaches, including probabilistic models, classical statistical methods, and deep learning (DL) architectures. […]
Laboratory for Information Processing and Pattern Recognition
Hybrid Deep Learning Models for Medium-Term Electricity Load Forecasting
This paper presents a hybrid model for medium- and long-term electricity demand forecasting, developed with the aim of achieving accurate forecasts on horizons ranging from a few weeks to several months. Univariate deep models show a significant drop in performance at higher time horizons and are unable to adequately model long-term nonlinearities and changes in […]
Machine Learning for Ship-Motion-Based Sea-State Estimation
Sea-state estimation (SSE) supports safe, efficient, and autonomous maritime operation. Conventional sources, including wave buoys, satellites, radar systems, and metocean products, are valuable but cannot provide continuous, local estimates. Ship-motion-based SSE offers a complementary solution by using the vessel as a wave-sensing platform through the wave buoy analogy (WBA). This review examines machine-learning (ML) approaches […]
LIVE Quantum – Development of an Integrated AI Platform for Multichannel Personalized Management of User Requests
The LIVE Quantum project focuses on the research and development of an advanced, integrated AI-based platform for multichannel, personalized management of user requests, primarily targeting operators of critical infrastructure systems such as energy, water, gas, telecommunications, and utilities. The project combines industrial research and experimental development activities to create a scalable and modular solution that […]
Digital Twin-Driven Federated Learning and Reinforcement Learning-Based Offloading for Energy-Efficient Distributed Intelligence in IoT Networks
Improved frameworks for delivering both intelligence and effectiveness under strict constraints on resources are required due to the Internet of Things’ (IoT) devices’ rapid expansion and the resulting increase in sensor-generated data. In response, this research considers a joint learning-offloading optimization approach and presents an improved framework for energy-efficient distributed intelligence in sensor networks. Our […]
Forecasting the Trajectory of Personal Watercrafts Using Models Based on Recurrent Neural Networks
Monitoring and predicting personal watercraft trajectories is a novel and largely unexplored research area where any development is valuable for various rental services. Unlike existing work focused on specific maritime routes, this study introduces a location-agnostic deep-learning approach capable of generalizing across diverse environments. This is achieved by using an innovative preprocessing approach including offset […]
A System for Real-Time Detection of Abandoned Luggage
In this paper, we propose a system for the real-time automatic detection of abandoned luggage in an airport recorded by surveillance cameras. To do this, we use an adapted YOLOv11-s model and a proposed algorithm for detecting unattended luggage. The system uses the OpenCV library for the video processing of the recorded footage, a detector, […]
Advanced Data Analysis Using Digital Signal Processing and Machine Learning Techniques
This project focuses on processing real-life digital signals (time-series and images), which often exhibit a non-stationary nature. We plan to utilize advanced signal processing techniques and artificial intelligence to analyze and classify such data. The project envisages the transformation of time-series into images (time-frequency representations providing simultaneous insight into signal characteristics in both domains). Special […]
Compound Flooding in Coastal Rivers in Present and Future Climate
The subject of this project is compound flooding in coastal areas due to high sea and river levels. Compound flooding is a global research priority due to the increasing occurrence in the context of climate change. The problem of compound flooding in coastal rivers is challenging because of a complex interaction between several factors, including […]
Data Processing on Graphs
Big data and the ever-growing need for their fast and efficient processing, aimed at obtaining information used in the automation of business processes, improving communication, enhancing efficiency and success in practically every segment of human activities, as well as better understanding of nature, humans, and society, are some of the main characteristics of computer technology […]
