23 citations · 57 across the 4 of their papers we have counts for
5 papers
SURIMI: Supervised Radio Map Augmentation with Deep Learning and a Generative Adversarial Network for Fingerprint-based Indoor Positioning
Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Jari Nurmi +2
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.…
Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets
Darwin Quezada-Gaibor, Lucie Klus, Joaquín Torres-Sospedra +4
Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need…
Towards Accelerated Localization Performance Across Indoor Positioning Datasets
Lucie Klus, Darwin Quezada-Gaibor, Joaquın Torres-Sospedra +3
The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve compa…
Lightweight Hybrid CNN-ELM Model for Multi-building and Multi-floor Classification
Darwin Quezada-Gaibor, Joaquín Torres-Sospedra, Jari Nurmi +2
Machine learning models have become an essential tool in current indoor positioning solutions, given their high capabilities to extract meaningful information from the environment.…
Towards Ubiquitous Indoor Positioning: Comparing Systems across Heterogeneous Datasets
Joaquín Torres-Sospedra, Ivo Silva, Lucie Klus +7
The evaluation of Indoor Positioning Systems (IPS) mostly relies on local deployments in the researchers' or partners' facilities. The complexity of preparing comprehensive experim…