As a key component of modern energy management systems, energy demand forecasting plays a crucial role in efficient resource allocation, grid stability, and the integration of renewables. Traditionally, classical techniques such as ARIMA and SARIMA have been used for energy demand prediction due to their simplicity and interpretability. However, these conventional methods often fail to […]
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LIVE Quantum project kick-off meeting
The project kick-off meeting for LIVE Quantum – Development of an integrated AI platform for multichannel, personalized management of customer requests was held on 20 February 2026 in Zagreb. The project is valued at EUR 2,418,416.98 and is implemented under the IRI S3 call – Increasing the development of new products and services arising from […]
Deep Unfolding ADMM Network for CS Image Reconstruction with Long-Short Term Residuals
Deep learning has demonstrated exceptional learning capabilities, leading to the development various deep unfolding networks for image reconstruction. However, current deep unfolding networks often replace certain steps of traditional optimization algorithms with neural networks, thereby compromising the interpretability of the optimization algorithms. Additionally, each iteration in the unfolding process may result in certain image information […]
XDT-FMARL: An Explainable Federated Multi-Agent Reinforcement Learning Framework for Energy-Efficient IoT Task Offloading
Effectively managing the vast data generated by sensor networks has become crucial with the rapid spread of IoT devices under strict resource constraints. This research introduces a framework that integrates explainable artificial intelligence (XAI), digital twins (DT), federated learning (FL), and multi-agent reinforcement learning (MARL) to optimise energy use and task distribution in distributed IoT […]
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 […]
