Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led […]
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Proactive Context Aware Task Offloading in Digital Twin Driven Federated IoT Systems with Large Language Models
This study considers the combination of Digital Twins (DT), Federated Learning (FL), and computation offloading to establish a context-aware framework for effective resource management in IoT networks. Although DT models can predict battery levels, CPU usage, and network delays to aid reinforcement learning (RL) agents, earlier RL-based controllers require significant training and are slow to […]
Pretraining and evaluation of BERT models for climate research
Motivated by the pressing issue of climate change and the growing volume of data, we pretrain three new language models using climate change research papers published in top-tier journals. Adaptation of existing domain-specific models based on Bidirectional Encoder Representations from Transformers (BERT) architecture is utilized for CliSciBERT (domain adaptation of SciBERT) and SciClimateBERT (domain adaptation […]
Invited lecture: “About the first GPS receiver on the Moon, and the other NASA space PNT stories” by James J. Miller (NASA)
Abstract: NASA’s contributions to the GNSS enterprise through science applications and space operations has played a vital role in the modernization and evolution of GPS capabilities for several decades. In the latest and recent developments, NASA has clearly demonstrated the success of the GPS+Galileo synergy by landing the first GPS+Galileo receiver on the lunar surface […]
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 […]
Agreement on collaboration between the Faculty of Engineering in Rijeka and the Shanghai Artificial Intelligence Research Institute
The University of Rijeka’s Faculty of Engineering signed a cooperation agreement with the Shanghai Artificial Intelligence Research Institute (SAIRI) to boost joint efforts in AI innovation and strengthen ties in cutting-edge digital technology research.
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, […]
Arian Skoki defended his doctoral thesis “Data-Driven Assessment of Player Performance and Recovery in Soccer”
Arian Skoki successfully defended his doctoral thesis “Data-Driven Assessment of Player Performance and Recovery in Soccer” (Procjena karakteristika i oporavka igrača u nogometu zasnovana na podatcima) on April 7, 2025. His research introduces innovative approaches to soccer analytics by leveraging minute-by-minute wearable sensor data, cognitive load measurements, and machine learning to uncover new insights into player fitness, […]
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 […]
