Results 121 to 130 of about 2,597 (210)

Smart Energy Management in Agricultural Wireless Sensor Nodes Using TinyML-Based Adaptive Sampling. [PDF]

Sensors (Basel)
Hinostroza A, Tarrillo J, Nuñez M.
europepmc   +1 more source

TinyML with CTGAN based smart industry power load usage prediction with original and synthetic data visualization towards industry 5.0. [PDF]

Sci Rep
Muthusamy M, Sureshkumar A, Shanmugam P, Kaveri PR, Mohamed MYN, Sridhar S.   +5 more
europepmc   +1 more source

Correction: FastKAN-DDD: A novel fast Kolmogorov-Arnold network-based approach for driver drowsiness detection optimized for TinyML deployment. [PDF]

PLoS One
Essahraui S, Lamaakal I, Maleh Y, El Makkaoui K, Bouami MF, Ouahbi I, Elmannai H, Abd El-Latif AA.   +7 more
europepmc   +1 more source

Anomaly detection in elderly behavior using TinyML and UWB Radar [PDF]

LAUREA MAGISTRALENel contesto dell’assistenza agli anziani, sta emergendo la necessità di tecnologie avanzate che possano migliorare l’erogazione dell’assistenza sanitaria.
FIORE, ALESSANDRA
core  

A Cloud-Aware Scalable Architecture for Distributed Edge-Enabled BCI Biosensor System. [PDF]

Biosensors (Basel)
Ghosh S, Bhuvanakantham R, Sindhujaa P, Harishita PB, Mohan A, Gulyás B, Máthé D, Padmanabhan P.   +7 more
europepmc   +1 more source

Classification of gym exercises using tiny machine learning [PDF]

Aquesta tesi explora l'aplicació de Tiny Machine Learning (TinyML) per a la classificació d'exercicis del gimnàs utilitzant dispositius de baixa potència a la vora de la xarxa (dispositius Edge).
Graner Babià, Albert
core  

TinyML pipeline for efficient crack classification in UAV-based structural health inspections. [PDF]

Sci Rep
Zhang Y, Nürnberg A, Rau LSM, Vu QNP, Lu Y, Oelmann B, Bader S.   +6 more
europepmc   +1 more source

A TinyML-enabled approach to embed Machine Learning in Avionics Control Systems [PDF]

The incorporation of Machine Learning (ML) into aviation infrastructure signifies a pivotal advancement, unlocking exceptional possibilities for enhancing operational efficacy, facilitat- ing predictive maintenance, and improving instantaneous decision ...
IQBAL, ZAIN
core  

Lightweight Signal Processing and Edge AI for Real-Time Anomaly Detection in IoT Sensor Networks. [PDF]

Sensors (Basel)
Reis MJCS.
europepmc   +1 more source

David and Goliath: An Empirical Evaluation of Attacks and Defenses for QNNs at the Deep Edge

ML is shifting from the cloud to the edge. Edge computing reduces the surface exposing private data and enables reliable throughput guarantees in real-time applications. Of the panoply of devices deployed at the edge, resource-constrained MCUs, e.g., Arm
Costa, Miguel, Pinto, Sandro
core