Needs and Requirements Assessment of Physical Product Traceability within the Portuguese Textile and Clothing Industry in 2026 International Conference on ENTERprise Information Systems (CENTERIS)

Published by: Elsevier (Procedia Computer Science)

This paper investigates the needs and requirements of physical traceability in Portuguese Textile and Clothing Industry companies. By assessing IT companies that provide solutions across various supply chain sectors, the study reveals that no single solution encompasses all variables. IT companies offer multiple tracking and tracing solutions with differing levels of granularity, interoperability, and adoption. The paper also identifies areas of intervention and innovation, as well as the limitations that prevent their easy adoption.

Centralized Kalman Filters to Enhance Localization Accuracy Within Vehicular Networks Context in 2025 Innovations in Mechatronics Engineering IV

Published by: Springer

This paper addresses the challenge of achieving precise localization for Connected and Autonomous Vehicles (CAVs) and Vulnerable Road Users (VRUs), especially in sensor blind spots. It highlights the limitations of commercial GNSS devices, whose meter-level accuracy is insufficient for safety-critical applications. A centralized system using Kalman Filters is proposed to enhance positioning accuracy across heterogeneous devices. The study also evaluates different Kalman Filter types and compares their effectiveness in improving vehicular network localization.

Leveraging V2P communications to improve the VRU’s safety in 2024 International Wireless Communications and Mobile Computing (IWCMC)

Published by: IEEE

This paper explores how Vehicle-to-Pedestrian (V2P) communication can enhance safety for Vulnerable Road Users (VRUs) such as pedestrians and cyclists. It introduces a system that tracks both vehicles and VRUs, predicting potential collisions and alerting users of imminent danger. The study emphasizes the importance of reliable, low-latency communication and robust collision prediction algorithms. It proposes a balanced approach combining speed, accuracy, robustness, adaptability, and scalability to improve road safety in the context of connected and autonomous vehicles.