Two weeks left for submitting to IWMbD2017

The deadline for the submission of peer-reviewed papers to the Third International Workshop on Metamaterials-by-Design has been extended to September…

Ing. Ahmed won the "Student Competition Award" at QNDE-2017

The ELEDIA Research Center is pleased to announce that Ing. S. Ahmed won among all the applicants disciplines into "Student Competition…

IWMbD2017 Registration is Open!

The ELEDIA Research Center announces that the Registration to the "Third International Workshop on Metamaterials-by-Design" is open. The Workshop will…

IEEE-JMMCT Special Section on MbD

The ELEDIA Research Center is pleased to announce an upcoming Special Section of the IEEE Journal on Multiscale and Multiphysics…

R. J. Mailloux has joined ELEDIA

The ELEDIA Research Center is pleased to announce that Dr. R. J. Mailloux is member of the ELEDIA Teaching Staff.…

Objectives

The monitoring and diagnosing of complex structures like vehicles, buildings, water plants, etc., involve investigation strategies for the accurate detection of anomalies such as defects and cracks.This problem is usually recast as the solution of an inverse problem. In this framework, state of the art methods that provide high performance in terms of resolution exist, but they often require non‚Äźnegligible computational resources. As a consequence, real-time investigations are very complex and the monitoring can be performed only periodically. Undesired anomalies may occur in between two consecutive tests and this is unaccepptable in a wide set of applications.
Alternative solutions are required to make the structural health monitoring (SHM) fast and effective, especially if embedded/low-power/low-cost hardware is adopted for data acquisition. Self-learning methods, such as learning-by-example (LBE) methods like Neural Network (NN), Support Vector Machine (SVM), Gaussian Process (GP), provide a fast (even if suboptimal) evaluation of the structural health status, triggering more deep analysis only if required. Such approaches can be profitably integrated on top of sensing hardware platforms, like wireless sensor network (WSN) nodes.
 
 
The WSN technology represents a valid solution for low-cost SHM system, thanks to its properties of scalability, flexibility, low-power, multi-sensing, and adaptability to a large number of different application fields.
The ELEDIA@UniNAGA is involved in the study, design, and implementation of advanced SHM solutions, as well as in the testing and performance analysis of the investigated systems. Experimental setups and prototype are also under validation (check the Demos webpage to see an example of a wireless distributed system for the monitoring of the server room status).