Tiny Machine Learning Implementation for Guided Wave-Based Damage Localization [PDF]
SensorsThis 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 +2 more
doaj +4 more sources
Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring [PDF]
SensorsThe 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 +2 more
doaj +4 more sources
Edge intelligence for poultry welfare: Utilizing tiny machine learning neural network processors for vocalization analysis. [PDF]
PLoS ONEThe 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 +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]
DiseasesBackground/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 +3 more
doaj +2 more sources
Tiny Machine Learning Battery State-of-Charge Estimation Hardware Accelerated
Applied SciencesElectric mobility is pervasive and strongly affects everyone in everyday life. Motorbikes, bikes, cars, humanoid robots, etc., feature specific battery architectures composed of several lithium nickel oxide cells. Some of them are connected in series and
Danilo Pietro Pau, Alberto Aniballi
doaj +3 more sources
A Machine Learning-Oriented Survey on Tiny Machine Learning
IEEE AccessThe 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 +4 more
doaj +3 more sources
Computers, 2023 The technological step towards sensors’ miniaturization, low-cost platforms, and evolved communication paradigms is rapidly moving the monitoring and computation tasks to the edge, causing the joint use of the Internet of Things (IoT) and machine ...
Michele Vitelli +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 ReportsThis 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 +2 more
doaj +2 more sources
Contactless Glucose Sensing Using Miniature mm-Wave Radar and Tiny Machine Learning
IEEE Journal of MicrowavesIn this article, we present a contactless glucose sensing system called GlucoRadar, which leverages a miniature low-power mm-wave radar for data collection, data augmentation to boost the training data, and tiny machine learning (TinyML) for the ...
Reza Nikandish +4 more
doaj +3 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 +3 more
doaj +1 more source