Event Date
Event Date
Structural Health Monitoring (SHM) is required to maintain the safety and longevity of civil infrastructure. Here, we present a diffractive vibration monitoring system, integrating a jointly-optimized diffractive optical processor with a shallow neural network-based backend to remotely extract 3D structural vibration spectra, offering a low-power and cost-effective solution. This architecture eliminates the need for dense sensor-arrays or extensive data acquisition. It instead uses a spatially-optimized diffractive optical processor that encodes 3D structural displacements into modulated light, captured by a minimal number of detectors and decoded in real-time by shallow and low-power neural networks to reconstruct the 3D displacement spectra of structures. The diffractive system's efficacy was demonstrated both numerically and experimentally using millimeter-wave illumination on a laboratory-scale building model with a programmable shake table. Our system achieves more than an order-of-magnitude improvement in accuracy over conventional optics or separately trained modules, establishing a foundation for high-throughput 3D monitoring of structures.
Presenter
Yuntian Wang
Univ. of California, Los Angeles (United States)
Yuntian Wang is a Ph.D. student in the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA), where he conducts research as a member of Professor Aydogan Ozcan's laboratory. His research is centered on the intersection of deep learning and computational imaging. Mr. Wang earned his Bachelor of Engineering from the Southern University of Science and Technology in 2023 before commencing his doctoral studies at UCLA.