Abstract

Abstract
Indoor Positioning Systems (IPSs) are essential for applications requiring accurate location awareness in indoor environments. However, achieving high precision remains challenging due to signal interference and environmental variability. This study proposes a multimodal IPS that integrates Bluetooth Received Signal Strength Indicator (RSSI) measurements and video imagery using machine learning (ML) and ensemble learning techniques. The system was implemented and deployed in the Hall of Biodiversity at the Natural History and Science Museum of the University of Porto. The venue presented significant deployment issues, namely restrictions on beacon placement and lighting conditions. We trained independent ML models on RSSI and video datasets, and combined them through ensemble learning methods. The experimental results from test scenarios, which included simulated visitor trajectories, showed that ensemble models consistently outperformed the RSSI-based and video-based models. These findings demonstrate that the use of multimodal data can significantly improve IPS accuracy despite constraints such as multipath interference, low lighting, and limited beacon infrastructure. Overall, they highlight the potential of multimodal data for deployments in complex indoor environments.

Abstract

Abstract
Developing safe, concurrent (and parallel) software systems is a hard task in multiple aspects, particularly the sharing of information and the synchronization among multiple participants of the system. In the message passing paradigm, this is achieved by sending and receiving messages among different participants, raising a number of verification problems. For instance, exchanging messages in a wrong order may prevent the system from progressing, causing a deadlock.MPI is the most commonly used protocol for high-performance, message-based parallel programs, and the need for formal verification approaches is well acknowledged by much recent work (e.g., see [1]). © 2012 Springer-Verlag.

Abstract
This paper describes the layered control architecture and its software implementation developed and used at the Underwater Systems and Technology Laboratory. The architecture is implemented as a toolchain which consists on three main entities: DUNE onboard software, Neptus command and control software and a common IMC message-based communication protocol. The LSTS software toolchain has been tested throughout various field deployments where it was used to control heterogeneous autonomous vehicles like AUVs, ASVs, UAVs and ROVs in both single and multi-vehicle operations.

Abstract
A viability algorithm is developed to compute the constrained minimum time function for general dynamical systems. The algorithm is instantiated for a specific dynamics (Dubin's vehicle forced by a flow field) in order to numerically solve the minimum time problem. With the specific dynamics considered, the framework of hybrid systems enables us to solve the problem efficiently. The algorithm is implemented in C using epigraphical techniques to reduce the dimension of the problem. The feasibility of this optimal trajectory algorithm is tested in an experiment with a Light Autonomous Underwater Vehicle (LAUV) system. The hydrodynamics of the LAUV are analyzed in order to develop a low-dimension vehicle model. Deployment results from experiments performed in the Sacramento River in California are presented, which show good performance of the algorithm.