| KEYWORD |
Microelectronics
Edge AI for Inertial Measurement Unit (IMU) event classification
Tesi esterna in azienda
Parole chiave MACHINE LEARNING, PATTERN RECOGNITION
Riferimenti LUCIANO LAVAGNO
Riferimenti esterni Marcello Babbi, Reply Torino
Gruppi di ricerca Microelectronics
Tipo tesi APPLIED RESEARCH
Descrizione Reply has established in its experimental laboratory in Area 42 a ML methodology for IMU pattern
recognition. The student will be involved in the development of embedded AI algorithm to detect
acceleration pattern for human activity and object motions. The thesis project aims to develop
ML/DL algorithms able to classify different IMU signals pattern enabling safety procedure in micromobility
use cases.
Conoscenze richieste Python programming, some knowledge of Machine Learning
Note The student will be involved in:
❑ SW requirements definition for edge deployment;
❑ ML Classification problem definition;
❑ Data pre-processing and feature engineering;
❑ AI algorithms (e.g. tCNN) devolopment and fine tuning for classification task;
❑ On-edge performance optimization and compression methods development (e.g. pruning, quantization);
❑ Support and development of communication protocols for gateway connectivity;
❑ Hardware in the Loop validation and on-field testing.
Scadenza validita proposta 11/10/2024
PROPONI LA TUA CANDIDATURA