1 code implementation • 13 Jul 2022 • Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Jari Nurmi, Yevgeni Koucheryavy, Joaquín Huerta
Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted.
BIG-bench Machine Learning Generative Adversarial Network +1
no code implementations • 21 May 2022 • Pavel Pascacio, Joaquín Torres-Sospedra, Sven Casteleyn, Elena Simona Lohan
After this, the lateration is applied to collaboratively estimate the device position.
no code implementations • 4 May 2022 • Darwin Quezada-Gaibor, Lucie Klus, Joaquín Torres-Sospedra, Elena Simona Lohan, Jari Nurmi, Carlos Granell, Joaquín Huerta
We use those to compute the correlation among all samples in the dataset and remove fingerprints with low level of correlation from the dataset.
no code implementations • 21 Apr 2022 • Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Jari Nurmi, Yevgeni Koucheryavy, Joaquín Huerta
Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment.
no code implementations • 20 Sep 2021 • Joaquín Torres-Sospedra, Ivo Silva, Lucie Klus, Darwin Quezada-Gaibor, Antonino Crivello, Paolo Barsocchi, Cristiano Pendão, Elena Simona Lohan, Jari Nurmi, Adriano Moreira
The evaluation of Indoor Positioning Systems (IPS) mostly relies on local deployments in the researchers' or partners' facilities.
no code implementations • 29 Jul 2021 • Vladimir Bellavista-Parent, Joaquín Torres-Sospedra, Antoni Perez-Navarro
However, WiFi signals have been used in a large number of proposals to achieve the above positioning, many of which use machine learning to do so.