Electromagnetism, Electronics, Telecommunications and Interactive Cognitive Environments curriculum themes:

    • AI models for rehabilitation robotics (tutor Prof. P. Gastaldo)
    • Edge/cloud big data management and machine learning (tutor Prof. R. Berta)
    • Cybersecurity solutions for critical infrastructure (tutor Prof. M. Marchese)
    • Machine learning for resource allocation in satellite networks (tutor Prof. F. Patrone)
    • Energy Efficiency in the Beyond 5G edge-cloud continuum (tutor Prof. R. Bolla)
    • Advanced Orchestration techniques for 5/6G Service-Based Meshes (tutor Prof. R. Bruschi)
    • Simulations and experiments of electromagnetic problems involving biological tissues or materials in motion (tutor Prof. M. Raffetto)
    • Design and development of new generation haptic interfaces for virtual touch (tutor Prof. L. Seminara)
    • Novel inversion techniques for electromagnetic imaging and antenna diagnostics (tutor Prof. A. Randazzo) 
    • Human Machine Interface based on Electronic Skin for Upper-limb Prosthetics (tutor Prof. M. Valle)
    • Probabilistic graphical models and machine learning methods for remote sensing image analysis (tutor Prof. B. Serpico, Prof. G. Moser) 
    • Data driven self-awareness models for AI enabled wireless communications (tutor Prof. C. Regazzoni, Prof. L. Marcenaro)
    • Emergent and incremental collective-awareness in multiple heterogeneous autonomous agents (tutor Prof. C. Regazzoni, Prof. L. Marcenaro)
    • Integrated circuits with scalable power supply voltages for wireless sensor nodes (WSN) powered by energy harvesters (tutor Prof. D. Caviglia, Prof. O. Aiello) 
    • CSI and AI based techniques for radio signals analysis in ambient intelligence applications (tutor Prof. A. Sciarrone) 
    • IoT solutions in e-Health applications for home assistance and care (tutor Prof. F. Lavagetto)
    • Explainable Artificial Intelligence (tutor Prof. F. Bellotti)
    • Architectures to support Machine Learning (tutor Prof. A. De Gloria)

     

  • Computer Vision, Pattern Recognition and Machine Learning:

    • Theme A: 3D scene understanding with geometrical and deep learning reasoning
    • Theme B: Deep Learning for Multi-modal scene understanding
    • Theme C: Self-Supervised and Unsupervised Deep Learning
    • Theme D: Visual Reasoning with Knowledge and Graph Neural Networks for scene understanding