Next-Gen Industry 4.0 Methodologies and Tools

The availability of inexpensive miniaturized wireless sensors with minimum energy consumption and installation impact has enabled the implementation of advanced monitoring infrastructures that are now ubiquitous in modern industrial applications. This is true also for large critical infrastructures, including energy, water, and communication networks. However, several challenges still need to be addressed to fully take advantage of the opportunities made possible by distributed industrial sensing. Within this framework, the activity carried out within the ELEDIA Research Center include

  • big data processing and anomaly detection in large energy, communications, and water networks, including trend analysis and performance evaluation;
  • exploitation of Artificial Intelligence (AI) for diagnostics and prognostics in industrial process analysis (including chemical processes, mechanical processes, etc.);
  • process evaluation and automated quality prediction in industrial production;
  • methodologies and algorithms for failure prediction in industrial machinery;
  • industrial digital twin development and application in Industry 4.0 scenarios.

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  • F. Viani, P. Rocca, M. Benedetti, G. Oliveri, and A. Massa, "Electromagnetic passive localization and tracking of moving targets in a WSN-infrastructured environment," Inverse Problems vol. 26, no. 7, pp. 074003, March 2010 (DOI: 10.1088/0266-5611/26/7/074003)
  • M. Donelli, F. Viani, P. Rocca, and A. Massa, "An Innovative Multiresolution Approach for DOA Estimation Based on a Support Vector Classification," IEEE Transactions on Antennas and Propagation vol. 57, no. 8, pp. 2279-2292, August 2009 (DOI: 10.1109/TAP.2009.2024485)
  • L. Lizzi, F. Viani, M. Benedetti, P. Rocca, and A. Massa, "The M-DSO-ESPRIT method for maximum likelihood DoA estimation," Progress In Electromagnetics Research vol. 80, pp. 477-497 2008 (DOI: 10.2528/PIER07121106)
  • F. Viani, L. Lizzi, P. Rocca, M. Benedetti, M. Donelli, and A. Massa, "Object tracking through RSSI measurements in wireless sensor networks," Electronics Letters vol. 44, no. 10, pp. 653 2008 (DOI: 10.1049/el:20080509)
  • M. Li, R. Guo, K. Zhang, Z. Lin, F. Yang, S. Xu, X. Chen, A. Massa, and A. Abubakar, "Machine Learning in Electromagnetics With Applications to Biomedical Imaging: A Review," IEEE Antennas and Propagation Magazine vol. 63, no. 3, pp. 39-51, June 2021 (DOI: 10.1109/MAP.2020.3043469)
  • M. Salucci, N. Anselmi, G. Oliveri, P. Rocca, S. Ahmed, P. Calmon, R. Miorelli, C. Reboud, and A. Massa, "A nonlinear Kernel-based adaptive learning-by-examples method for robust NDT/NDE of conductive tubes," Journal of Electromagnetic Waves and Applications vol. 33, no. 6, pp. 669-696, February 2019 (DOI: 10.1080/09205071.2019.1572546)