Results 81 to 90 of about 2,210 (159)

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

TinyML Empowered Transfer Learning on the Edge

IEEE Open Journal of the Communications Society
Tiny machine learning (TinyML) is a promising approach to enable intelligent applications relying on Human Activity Recognition (HAR) on resource-limited and low-power Internet of Things (IoT) edge devices.
Ali M. Hayajneh, Maryam Hafeez, Syed Ali Raza Zaidi, Des McLernon   +3 more
doaj   +1 more source

TinyML-CoDE: A Co-Design and Evaluation Framework for Systematic TinyML Development

The current paper presents the key issues when developing Tiny Machine Learning in resource-constrained Internet of Things equipment. Based on the methodical research on 35 recent papers, we uncover five core research gaps that include standardized ...
Charith Lakpriya Jayathilake, Mohamed Mirshad Munawwer, Dhananjaya Lahiru Bandara   +2 more
core   +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

Towards efficient and intelligent tinyML : acceleration, architectures, and monitoring

Deploying Machine learning (ML) on the milliwatt-scale edge devices (TinyML) is gaining popularity due to recent breakthroughs in ML and Internet of Things (IoT), coupled with hardware and tooling innovations.
Pratap Ghanathe, Nikhil
core   +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

TinyML - Machine learning on microcontrollers on the edge of the cloud

, 2021
Diese Arbeit handelt von TinyML (Tiny Machine Learning) auf Mikrocontrollern, das 2021 zu einer der beliebtesten und aufstrebenden technologischen Innovationen gehört.
Bukvarevic, Mensur
core  

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

Програмна модель мікроконтролера Arduino Nano з використанням tinyml

, 2023
Науковий керівник: Бакуменко Ніна Станіславівна, кандидат технічних наук, доцент ЗВО кафедри теоретичної та прикладної системотехнікиМетою даної роботи є розширення можливостей використання малих пристроїв шляхом використання моделей штучного інтелекту ...
Andreiev, M. V., Андреєв , Максим Вячеславович   +1 more
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