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
The reading of the T650 glyph has been a puzzle for decades. Here, we analyze the semantic contexts in which the glyph appears together with available phonetic evidence to arrive at a phonetic reading of JOM. We provide grammatical reconstructions of the lexical contexts and discuss the rebuses involved in non semantic contexts.

Abstract
Sensor networks can be viewed as a collection of tiny, low-cost devices programmed to sense the physical world and that communicate over radio links [12]. The devices are commonly called motes or smart dust [676], in allusion to their computational and sensing capabilities, as well as their increasingly small size. © 2009 Springer Berlin Heidelberg.

Abstract

Abstract