Results 1 to 10 of about 15,511 (143)

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   +6 more sources

Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring [PDF]

Sensors
The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge.
Chengjie Huang, Xinjuan Sun, Yuxuan Zhang   +2 more
doaj   +4 more sources

Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization [PDF]

Sensors
This work leverages ultrasonic guided waves (UGWs) to detect and localize damage in structures using lightweight Artificial Intelligence (AI) models. It investigates the use of machine learning (ML) to train the effects of the damage on UGWs to the model.
Jannik Henkmann, Vittorio Memmolo, Jochen Moll   +2 more
doaj   +4 more sources

Unlocking Edge Intelligence Through Tiny Machine Learning (TinyML)

IEEE Access, 2022
Machine Learning (ML) on the edge is key to enabling a new breed of IoT and autonomous system applications. The departure from the traditional cloud-centric architecture means that new deployments can be more power-efficient, provide better privacy and ...
Syed Ali Raza Zaidi, Ali M. Hayajneh, Maryam Hafeez, Q. Z. Ahmed   +3 more
doaj   +3 more sources

Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis. [PDF]

PLoS ONE
The health of poultry flock is crucial in sustainable farming. Recent advances in machine learning and speech analysis have opened up opportunities for real-time monitoring of the behavior and health of flock.
Ramasamy Srinivasagan, Mohammed Shawky El Sayed, Mohammed Ibrahim Al-Rasheed, Ali Saeed Alzahrani   +3 more
doaj   +2 more sources

Multi-Task Deep Learning Model for Automated Detection and Severity Grading of Lumbar Spinal Stenosis on MRI: Multi-Center External Validation [PDF]

Diseases
Background/Objectives: Accurate and reproducible grading of lumbar spinal stenosis (LSS) is clinically critical for guiding treatment decisions and patient management, yet manual assessment remains challenging due to imaging variability and inter ...
Phatcharapon Udomluck, Watcharaporn Cholamjiak, Jakkaphong Inpun, Waragunt Waratamrongpatai   +3 more
doaj   +2 more sources

TinyNS: Platform-Aware Neurosymbolic Auto Tiny Machine Learning. [PDF]

ACM Trans Embed Comput Syst
Machine learning at the extreme edge has enabled a plethora of intelligent, time-critical, and remote applications. However, deploying interpretable artificial intelligence systems that can perform high-level symbolic reasoning and satisfy the underlying system rules and physics within the tight platform resource constraints is challenging.
Saha SS, Sandha SS, Aggarwal M, Wang B, Han L, DE Gortari Briseno J, Srivastava M.   +6 more
europepmc   +5 more sources

From Tiny Machine Learning to Tiny Deep Learning: A Survey

ACM Computing Surveys
The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). While TinyML initially focused on enabling simple inference tasks on microcontrollers, the emergence of TinyDL marks a paradigm shift ...
Shriyank Somvanshi, Md Mehedi Islam, Mahmuda Sultana Mimi   +2 more
exaly   +3 more sources

A Tiny Machine Learning Model for Point Cloud Object Classification

APSIPA Transactions on Signal and Information Processing, 2023
The design of a tiny machine learning model which can be deployed in mobile and edge devices for point cloud object classification is investigated in this work. To achieve this objective, we replace the multi-scale representation of a point cloud object with a single-scale representation for complexity reduction, and exploit rich 3D geometric ...
Min Zhang, Jintang Xue, Pranav Kadam, Hardik Prajapati, Shan Liu, C.-C. Jay Kuo   +5 more
doaj   +3 more sources

Design and implementation of a 6-DoF robot arm control with object detection based on machine learning using mini microcontroller [PDF]

Scientific Reports
This research presents a novel approach to robotic manipulation by integrating an advanced machine learning-based object detection system on a resource-constrained AMB82-Mini microcontroller.
Hayder Hashim Almaliki, Amir Hooshang Mazinan, Seyed Mahmoud Modaresi   +2 more
doaj   +2 more sources