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Why TinyML? Exploring the Reasons why TinyML is used for Real-World Problems
Signal and Image ProcessingMachine Learning (ML) and especially its application to cyber-physical systems is an uprising field of research. Many approaches on how to leverage the power of ML even in small devices have been published and applied in recent years, forming the field of TinyML. While TinyML has been promising several benefits such as cost-reduction, privacy and more,
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Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
Sensors, 2021Manuele Rusci +2 more
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An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity
Sensors, 2021Emiliano Sisinni +2 more
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A TinyML Deep Learning Approach for Indoor Tracking of Assets
Sensors, 2023Giancarlo Fortino +2 more
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An Adaptable and Unsupervised TinyML Anomaly Detection System for Extreme Industrial Environments
Sensors, 2023Fabio Antonelli +2 more
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Towards energy-aware tinyML on battery-less IoT devices
Internet of Things (Netherlands), 2023Jaron Fontaine +2 more
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A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions
Sensors, 2022Ivanovitch Silva +2 more
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Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs
Drones, 2021Anas Osman, Francesco De Natale
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Enhancing Food Supply Chain Security through the Use of Blockchain and TinyML
Information (Switzerland), 2022Athanasios Kakarountas +2 more
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