The Operator as a Service (OaaS) vision is a novel paradigm where skilled remote operators are empowered with advanced digital technologies based on digital twins to remotely oversee and control multiple autonomous and flexible smart manufacturing lines. This paper proposes the application of Asset Administration Shell (AAS) to support a scalable deployment of the OaaS vision. Remote operators and flexible manufacturing lines are connected through an AAS dataspace, which provides connectivity and security and traceability functionalities. Furthermore, both operators and manufacturing lines can control with whom they collaborate. Once remote operators and manufacturing lines are connected, every time an unexpected situation occurs, the remote operator receives a notification to connect to the digital twin of the line to evaluate the situation and to solve it. This approach has been implemented based on Eclipse Basyx and AAS Dataspace for Everybody, extending an existing autonomous assembly line testbed and successfully validating it during the 2025 edition of the Hannover Messe.

The goal of the work presented in this paper is to develop an Intelligent Animated Agent with Natural Behaviour (IAAN). This agent is integrated into e-learning platforms in order to perform the role of an online tutor. The system stores into a database personalized information of each student regarding their level of education, their learning progress and their interaction with the platform. This information is then used by the 3D modeled virtual agent to give personalized feedback to each student; the purpose of the agent is to guide the students throughout the lectures taking into account their personal needs and interacting with them by means of verbal and non-verbal communication. To achieve this work a thorough study of natural behaviour has been held and a complex state machine is being developed in order to provide IAAN with the sufficient artificial intelligence as to enhance the students motivation and engagement with the learning process.

This paper addresses the problem of camera tracking and 3D reconstruction from image sequences, i.e., the monocular SLAM problem. Traditionally, this problem is solved using non-linear minimization techniques that are very accurate but hardly used in real time. This work presents a highly parallelizable random sampling approach based on Monte Carlo simulations that fits very well on the graphics hardware. The proposed algorithm achieves the same precision as non linear optimization, getting real time performance running on commodity graphics hardware. Both accuracy and performance are evaluated using synthetic data and real video sequences captured with a hand-held camera. Moreover, results are compared with an implementation of Bundle Adjustment showing that the presented method gets similar results in much less time.