Citizen participation: crowd-sensed sustainable indoor location services

In the present era of sustainable innovation, the circular economy paradigm dictates the optimal use and exploitation of existing finite resources. At the same time, the transition to smart infrastructures requires considerable investment in capital, resources and people. In this work, we present a general machine learning approach for offering indoor location awareness without the need to invest in additional and specialised hardware. We explore use cases where visitors equipped with their smart phone would interact with the available WiFi infrastructure to estimate their location, since the indoor requirement poses a limitation to standard GPS solutions. Results have shown that the proposed approach achieves a less than 2m accuracy and the model is resilient even in the case where a substantial number of BSSIDs are dropped.Comment: Preprint submitted to Elsevie

Prediction and Controlling of Auditory Perception in Augmented Environments. A Loudness-Based Dynamic Mixing Technique

At the core of augmented reality audio (ARA) technology lies the ARA mix, a process responsible for the assignment of a virtual environment to a real one. Legacy ARA mix models have focused on the natural reproduction of the real environment, whereas the virtual environment is simply mixed through fixed gain methods. This study presents a novel approach of a dynamic ARA mix that facilitates a smooth adaptation of the virtual environment to the real one, as well as dynamic control of the virtual audio engine, by taking into account the inherent characteristics of both ARA technology and binaural auditory perception. A prototype feature extraction technique of auditory perception characteristics through a real-time binaural loudness prediction method was used to upgrade the legacy ARA mix model into a dynamic model, which was evaluated through benchmarks and subjective tests and showed encouraging results in terms of functionality and acceptance