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AI Methods for Predictive Maintenance in Power Electronics
Smart Trapping System for Dacus Flies
Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices
Introduction to EU-Funded AI Research Projects
Edge AI and TinyML: Concepts and Applications
Deep Learning for Computer Vision
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Displaying
36
contributions
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36
AI for Sustainable Development
Session:
AI-Enabled Semantic and Goal-Oriented Communications for the Internet of Things
Session:
Agent-Based Autonomous UAV Navigation in GPS-Denied Scenarios
Session:
Introduction to EU-Funded AI Research Projects
Collecting Data from Sensors for Pest Monitoring
Session:
Smart Trapping System for Dacus Flies
Deep Learning Techniques for Computer Vision
Session:
Deep Learning for Computer Vision
Deep learning use case
Session:
Deep Learning for Computer Vision
Designing an Image Classification Application with the Xiao Sense
Session:
Edge AI and TinyML: Concepts and Applications
Distribution of Certificates and Closing Remarks
Session:
Deep Learning for Computer Vision
Distribution of Certificates and Closing Remarks
Session:
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Edge AI for the Internet of Things: Insights from Real-World Projects
Session:
Estimating Remaining Useful Life (RUL) Using AI Techniques
Session:
AI Methods for Predictive Maintenance in Power Electronics
Fundamentals of Applied Machine Learning
Session:
Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices
Hardware Components of the Smart Trapping System
Session:
Smart Trapping System for Dacus Flies
Hardware Setup and Data Acquisition for Power Electronics Monitoring
Session:
AI Methods for Predictive Maintenance in Power Electronics
HyperFPGA: Heterogeneous Computing on MPSoC-FPGA
Session:
Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices
Introduction to Machine Learning and Edge AI
Session:
Introduction to TinyML and Data Ingestion in IoT
Session:
Edge AI and TinyML: Concepts and Applications
Lab 1: Training Machine Learning Models
Session:
Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices
Lab 2: Applying Model Compression Techniques
Session:
Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices
Lab 3: Deploying Machine Learning Models on Heterogeneous MPSoC-FPGA Devices
Session:
Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices
Machine Learning–Based Multi-Data Fusion on FPGA-SoC for Smart Agriculture
Session:
Introduction to EU-Funded AI Research Projects
Model Optimization and Compression Techniques
Session:
Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices
Opening Remarks & School Outline
Session:
smr4210-school_outline_2026-M.L._Crespo.pdf
Optical Identification of Dacus Flies
Session:
Smart Trapping System for Dacus Flies
Question and Answers
Session:
Introduction to EU-Funded AI Research Projects
R-PODID: AI Model for Motor Health Prediction and Network Anomaly Detection
Session:
Introduction to EU-Funded AI Research Projects
REMARKABLE Project Overview
Session:
Introduction to EU-Funded AI Research Projects
REMARKABLE Stellenbosch: Dendrometer-Based Tree Growth Analytics
Session:
Introduction to EU-Funded AI Research Projects
Registration formalities
Session:
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Responsible AIoT and TinyML
Session:
Understanding System Behavior and Failure Mechanisms in Power Electronics
Session:
AI Methods for Predictive Maintenance in Power Electronics
Using FPGAs to Accelerate Machine Learning Algorithms
Session:
Machine Learning and Model Compression on FPGA-Based Heterogeneous Devices
Visit to ICTP STI Labs
Session:
Deep Learning for Computer Vision
Visit to ICTP STI Labs
Session:
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When Learning Meets the Channel: Edge AI through Split and Semantic Design
Session:
Working with Xiao Sense: Hardware Basics and Initial Applications
Session:
Edge AI and TinyML: Concepts and Applications
Organizers
Paolo Calciati (PDM, Portugal), Luis Miguel Serra Da Costa Campos (PDM, Portugal), Marco Zennaro (ICTP, Italy), Local Organiser: Maria Liz Crespo (ICTP)
Co-sponsors
smr4210-school_outline_2026-M.L._Crespo.pdf
