Results 61 to 70 of about 2,210 (159)

TinyML with Meta-Learning on Microcontrollers for Air Pollution Prediction

Proceedings
Tiny machine learning (tinyML) involves the application of ML algorithms on resource-constrained devices such as microcontrollers. It is possible to improve tinyML performance by using a meta-learning approach.
I Nyoman Kusuma Wardana, Suhaib A. Fahmy, Julian W. Gardner   +2 more
doaj   +1 more source

A Review of Photoplethysmography-Based Blood Pressure Monitoring: From Cloud-Based Machine Learning to TinyML Edge Deployment

IEEE Access
Cuffless continuous noninvasive blood pressure (cNIBP) monitoring based on photoplethysmography (PPG) has enjoyed great success through a wealth of high-performing machine learning (ML) algorithms.
Nour Faris Ali, Hasan Al-Nashash, Ibrahim M. Elfadel, Mohamed Atef   +3 more
doaj   +1 more source

A HW/SW co-design framework for TinyML acceleration

, 2022
LAUREA MAGISTRALELe reti neurali profonde (DNN) stanno ottenendo risultati impressionanti nel campo dell'Intelligenza Artificiale (AI), tra cui riconoscimento di immagini e discorsi, auto a guida autonoma, gestione delle attività, ottimizzazione dei ...
BRUNO, NUNZIO MARIA ALBERTO
core  

Widening Access to Applied Machine Learning With TinyML [PDF]

, 2022
Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our
Krishnan, Srivatsan, Reddi, Vijay, Warden, Pete, Lam, Maximilian, Tingley, Dustin, Plancher, Brian, Banzi, Massimo, Bennett, Matthew, Ghosal, Radhika, Smith, J. Evan, Ramaprasad, Dhilan, Chitlangia, Sharad, Pajak, Dominic, Banbury, Colby, Leiker, Daniel, Moroney, Laurence, Grafman, Sarah, Jaeger, Rupert, Agarwal, Anant, Kennedy, Susan, Mazumder, Mark, Stewart, Matthew, Brown, Benjamin, Suzuki, Lara, Mann, Cara   +24 more
core  

A Review of TinyML for Human Activity Recognition on Edge Devices

Machine Learning and Knowledge Extraction
The integration of Tiny Machine Learning (TinyML) into human activity recognition (HAR) represents a paradigm shift in artificial intelligence, enabling real-time, efficient, and privacy-preserving analysis on resource-constrained edge devices.
Ismail Lamaakal, Chaymae Yahyati, Yassine Maleh, Khalid El Makkaoui, Ibrahim Ouahbi   +4 more
doaj   +1 more source

A Review of the Transition from Industry 4.0 to Industry 5.0: Unlocking the Potential of TinyML in Industrial IoT Systems

Sci
The integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation.
Margarita Terziyska, Iliana Ilieva, Zhelyazko Terziyski, Nikolay Komitov   +3 more
doaj   +1 more source

A Joint Survey in Decentralized Federated Learning and TinyML: A Brief Introduction to Swarm Learning

Future Internet
TinyML/DL is a new subfield of ML that allows for the deployment of ML algorithms on low-power devices to process their own data. The lack of resources restricts the aforementioned devices to running only inference tasks (static TinyML), while training ...
Evangelia Fragkou, Dimitrios Katsaros
doaj   +1 more source

Voltage and Electromagnetic Fault Injectioin in TinyML: Attacks and Countermeasures

Tiny Machine Learning (TinyML) algorithms, designed to operate on constrained devices such as those found in Internet of Things (IoT) systems, are vulnerable to adversarial threats, including fault injection attacks.
Dustin Mazza, Srilalith Nampally, Anthony Etim, Ferhat Erata, Jakub Szefer, Tinghung Chiu, Leyla Nazhandali, Aubtin Rasouli, Wenjie Xiong   +8 more
core   +1 more source

A Review on Resource-Constrained Embedded Vision Systems-Based Tiny Machine Learning for Robotic Applications

Algorithms
The evolution of low-cost embedded systems is growing exponentially; likewise, their use in robotics applications aims to achieve critical task execution by implementing sophisticated control and computer vision algorithms. We review the state-of-the-art
Miguel Beltrán-Escobar, Teresa E. Alarcón, Jesse Y. Rumbo-Morales, Sonia López, Gerardo Ortiz-Torres, Felipe D. J. Sorcia-Vázquez   +5 more
doaj   +1 more source

TinyML-Based Swine Vocalization Pattern Recognition for Enhancing Animal Welfare in Embedded Systems

Inventions
The automatic recognition of animal vocalizations is a valuable tool for monitoring pigs’ behavior, health, and welfare. This study investigates the feasibility of implementing a convolutional neural network (CNN) model for classifying pig vocalizations ...
Tung Chiun Wen, Caroline Ferreira Freire, Luana Maria Benicio, Giselle Borges de Moura, Magno do Nascimento Amorim, Késia Oliveira da Silva-Miranda   +5 more
doaj   +1 more source