3 documents found, page 1 of 1
Enabling dynamic indoor localization by employing intersection over union as a ...
Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Silva, Ivo Miguel Menezes; Pendão, Cristiano Gonçalves; Valkama, MikkoIn modern wireless networks evolving towards 6th generation, localization, and sensing in indoor environments play an increasingly critical role in ensuring reliability, security, and control over network users, including vehicular assets. Despite recent advancements in deep learning, using k-Nearest Neighbors (k-NN) as a positioning algorithm in Received Signal Strength Indicator (RSSI) fingerprinting-based lo...
EWOk: towards efficient multidimensional compression of indoor positioning data...
Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohanll, Elena Simona; Granell, Carlos; Nurmi, JariIndoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operat...
Autoencoder extreme learning machine for fingerprint-based positioning: A good ...
Gaibor, Darwin P. Quezada; Klus, Lucie; Klus, Roman; Lohan, Elena Simona; Nurmi, Jari; Valkama, Mikko; Huerta, Joaquín; Torres-Sospedra, JoaquínIndoor positioning based on machine-learning (ML) models has attracted widespread interest in the last few years, given its high performance and usability. Supervised, semisupervised, and unsupervised models have thus been widely used in this field, not only to estimate the user position, but also to compress, clean, and denoise fingerprinting datasets. Some scholars have focused on developing, improving, and o...