Results 41 to 50 of about 2,597 (210)

TinyML for Speech Recognition

2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC)
We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on a highly resource-constrained IoT edge device. This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities, just to name a few examples.
Andrew Barovic, Armin Moin
openaire   +2 more sources

Developing a wireless distributed embedded machine learning application for the Arduino Portenta H7 [PDF]

, 2023
L'objectiu del projecte és desenvolupar una aplicació d'aprenentatge automàtic integrada distribuïda sense cables per a Arduino Portenta H7The aim of the project is to develop a wireless distributed embedded machine learning application for the Arduino ...
Ramos Domínguez, David
core   +3 more sources

A Machine Learning-Oriented Survey on Tiny Machine Learning

IEEE Access
The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures.
Luigi Capogrosso, Federico Cunico, Dong Seon Cheng, Franco Fummi, Marco Cristani   +4 more
doaj   +1 more source

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

, 2022
The rapid miniaturization of Machine Learning (ML) for low powered processing has opened gateways to provide cognition at the extreme edge (E.g., sensors and actuators).
Ahmed, Nadeem, Karunanayake, Ishan, Rajapakse, Visal   +2 more
core  

A Holistic Review of the TinyML Stack for Predictive Maintenance

IEEE Access
Downtime caused by failing equipment can be extremely costly for organizations. Predictive Maintenance (PdM), which uses data to predict when maintenance should be conducted, is an essential tool for increasing safety, maximizing uptime and minimizing ...
Emil Njor, Mohammad Amin Hasanpour, Jan Madsen, Xenofon Fafoutis   +3 more
doaj   +1 more source

A Systematic Review of State-of-the-Art TinyML Applications in Healthcare, Education, and Transportation

IEEE Access
Tiny Machine Learning (TinyML) has emerged as a transformative paradigm enabling machine learning inference directly on ultra-low-power microcontrollers and edge devices.
Chaymae Yahyati, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui, Ibrahim Ouahbi, May Almousa, Ahmed A. Abd El-Latif   +6 more
doaj   +1 more source

Real‐Time Data‐Driven Fault Diagnosis of Photovoltaic Arrays Using an Edge‐Server Machine‐Learning Framework

Energy Science &Engineering, EarlyView.
A real‐time, data‐driven framework detects and classifies photovoltaic array faults using edge sensing and server‐side machine learning. Ensemble tree models achieve near‐perfect accuracy with low latency, enabling practical, low‐cost deployment for reliable PV monitoring and intelligent maintenance.
Premkumar Manoharan, Sowmya Ravichandran, Deepak Kumar Sinha, Chandran Ramakrishnan, Omar Almomani   +4 more
wiley   +1 more source

Tiny Machine Learning (TinyML): Research trends and future application opportunities

Array
Tiny Machine Learning (TinyML) enables artificial intelligence on low-power edge devices, yet a quantitative understanding of TinyML research remains limited.
Hui Han, Silvana Trimi, Sang M. Lee
doaj   +1 more source

An autonomous network of acoustic detectors to map tiger risk by eavesdropping on prey alarm calls

Remote Sensing in Ecology and Conservation, EarlyView.
Tiger population recovery brings with it increased fatalities from human‐tiger conflict. We describe a network of autonomous intelligent passive acoustic sensors that monitor the forest for deer alarm calls as a proxy for tiger risk and provide a risk map to local communities in real‐time.
Arik Kershenbaum, Andrew Markham, Holly Root‐Gutteridge, Bethany Smith, Casey Anderson, Riley McClaughry, Ramjan Chaudhary, Amogh Vishwakarma, Stephen Cummins, Angela Dassow   +9 more
wiley   +1 more source

A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices

Engineering Proceedings
This study proposes a health monitoring system for snoring detection utilizing Tiny Machine Learning (TinyML) models, specifically designed for resource-constrained wearable Internet of Things (IoT) devices.
Timothy Malche, Sumegh Tharewal, Priti Maheshwary   +2 more
doaj   +1 more source