Results 61 to 70 of about 374 (124)

Tiny Neural Receiver: Enabling On-Device Learning for Scalable and Adaptive 6G Devices

AI
The evolution toward 6G communications requires integrating Tiny Machine Learning (TinyML) principles to enable intelligent, energy-efficient, and adaptable signal processing at the network edge. However, current receiver architectures face a fundamental
Iñigo Bilbao, Eneko Iradier, Jon Montalban, Marta Fernández, Iñaki Eizmendi, Pablo Angueira   +5 more
doaj   +1 more source

Optimized Tiny Machine Learning and Explainable AI for Trustable and Energy-Efficient Fog-Enabled Healthcare Decision Support System

International Journal of Computational Intelligence Systems
The Internet of things (IoT)-based healthcare decision support system plays a crucial role in modern medicine, especially with the rise in chronic illnesses and an aging population necessitating continuous remote health monitoring.
R. Arthi, S. Krishnaveni
doaj   +1 more source

Physics-Enhanced TinyML for Real- Time Detection of Ground Magnetic Anomalies

IEEE Access
Space weather phenomena like geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) pose significant risks to critical technological infrastructure.
Talha Siddique, Md. Shaad Mahmud
doaj   +1 more source

Development and evaluation of a TinyML-based sensor fusion system for medical waste classification on low-cost embedded devices

Medisains
Background: Medical waste management in resource-limited healthcare facilities remains dominated by manual segregation, which is error-prone and difficult to standardize.
Dini Afriani, Irfan Fadil
doaj   +1 more source

Understanding mushroom farm environment using TinyML-based monitoring devices

Environmental Research Communications
The optimization of environmental conditions in mushroom cultivation is pivotal for maximizing yield and quality. A Smart Environmental Monitoring System for Mushroom Farms is presented in this paper that makes use of advanced Tiny Machine Learning ...
Segun Adebayo, Halleluyah Oluwatobi Aworinde, Oluranti Olayinka Olufemi, Christian Okechukwu Osueke, Abidemi Emmanuel Adeniyi, Oluwasegun Julius Aroba   +5 more
doaj   +1 more source

Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data

Bioengineering
Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e ...
Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Jorge Ivan Cuevas-Chávez, Raquel Ochoa-Ornelas, Antonio Navarrete-Guzmán, Carlos Vidrios-Serrano, Daniel Alejandro Sánchez-Arias   +6 more
doaj   +1 more source

A TinyML Wearable System for Real-Time Cardio-Exercise Tracking

Engineering Proceedings
Cardiovascular exercise strengthens the heart and improves circulation, but most people struggle to fit regular workouts into their day. Short bursts of vigorous activity, sometimes called exercise snacks, can raise the heart rate and deliver meaningful ...
Timothy Malche
doaj   +1 more source

Exploring opportunities in TinyML

, 2022
Internet of Things (IoT) ha obtenido unas muy buenas y usables mejoras gracias a las implementaciones del Machine Learning (ML). Pero la implementación de Machine Learning en dispositivos IoT utilizando centros de datos conlleva una serie de problemas a tener en cuenta (privacidad de los datos, el cuello de botella de la red, etc.).
openaire   +1 more source

Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming

IET Smart Cities
Emerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices.
Ali M. Hayajneh, Sami A. Aldalahmeh, Feras Alasali, Haitham Al‐Obiedollah, Sayed Ali Zaidi, Des McLernon   +5 more
doaj   +1 more source

A Wearable System for Real-Time Fall Detection on Resource-Constrained Devices

Future Internet
In this study, we propose a wearable fall detection system that combines wearable sensors, TinyML model, and IoT-based communication for real-time monitoring and detection of falls. The system is designed for resource-constrained IoT devices where memory,
Timothy Malche, Govind Murari Upadhyay, Sumegh Tharewal, Vipin Balyan, Vikash Kumar Mishra, Gunjan Gupta, Pramod Kumar Soni   +6 more
doaj   +1 more source