Self-optimizing neural networks for efficient person localization and tracking
Parole chiave LOCALIZZAZIONE, OTTIMIZZAZIONE STRUTTURALE, ALGORITMI EVOLUTIVI, RETI NEURALI, SISTEMI EMBEDDED
Riferimenti LUCIANO LAVAGNO, MIHAI TEODOR LAZARESCU
Gruppi di ricerca Sensing and processing, microelettronica
Tipo tesi RICERCA
Descrizione The broader project currently involves both Ph.D. and MS students and is aimed at the identification (out of a small set), localization, and tracking of persons indoors, using sensor data fusion from newly developed sensors within the project (e.g., long-range capacitive) and existing ones, operating on established sensing principles (e.g., very low-resolution infrared camera).
Specifically, the topic consists in searching the state-of-the-art for existing neural networks solutions that can either:
- optimize themselves during learning, to achieve an efficient implementation (minimize memory, processing) for inference;
- and/or are capable to tune and make use of “tools”, i.e., hard-coded algorithms (e.g., mathematical functions) in order to minimize the necessary resources (memory, processing) for inference.
The most promising NN solutions can then be implemented and evaluated using the experimental data that we have collected within the broader project for indoor human localization and tracking.
Vedi anche https://iot.det.polito.it/indoor-human-localization-and-identification-using-low-power-low-cost-long-distance-capacitive-sensors/
Scadenza validita proposta 04/10/2023 PROPONI LA TUA CANDIDATURA