LOS ANGELES - eTradeWire -- A team of researchers at the University of California, Los Angeles (UCLA) has introduced a novel framework for monitoring structural vibrations using diffractive optical processors. This new technology uses artificial intelligence to co-optimize a passive diffractive layer and a shallow neural network, allowing the system to encode time-varying mechanical vibrations into distinct spatio-temporal optical patterns.

Structural Health Monitoring (SHM) systems are vital for assessing the condition of civil infrastructure, such as buildings and bridges, particularly after exposure to natural hazards like earthquakes. Traditional vibration-based methods rely on sensor networks of accelerometers and strain gauges, which demand significant power, generate large datasets requiring complex digital signal processing, and can be expensive to install and maintain. Furthermore, achieving high spatial resolution for accurate damage localization often requires a costly, dense sensor deployment.

More on eTradeWire News

• West Vancouver Facial Plastic Surgeon Expands Non-Surgical Hair Restoration and Aesthetics Clinic
• LLM Classroom Launches AI Prompt Packs for Business Professionals
• Eastern Adjustment Group, LLP Provides Property Loss Advocacy and Claim Support
• Marketplace Profile Launches the World's First Video Based B2B Supplier Directory
• Murk Daddy Unveils Bad Habit, A Bold New Single Engineered with One Mic Productions™ Precision

The new research, led by Professor Aydogan Ozcan of the UCLA Electrical and Computer Engineering Department, overcomes these challenges using a physical-digital co-integration. Instead of relying on traditional sensor networks that digitize raw physical signals, the new system uses a passive, optimized diffractive layer attached to the target structure. As the structure oscillates, this optimized diffractive surface moves, modulating an incoming illuminating wave to encode the structural displacements into light, which is then captured by a few optical detectors and rapidly decoded by a low-power neural network.

This approach marks a fundamental departure from conventional digital sensing paradigms by shifting a portion of the computational burden into the physical domain.

In collaboration with Professor Ertugrul Taciroglu's lab at UCLA's Civil and Environmental Engineering Department and Dr. Farid Ghahari of the California Geological Survey, Ozcan's team demonstrated the power of their platform through experimental validations using millimeter-wave illumination on a laboratory-scale building model with a programmable shake table. They successfully extracted 1D and 2D vibration spectra under various dynamic excitations, including seismic waveforms from an earthquake dataset. Additionally, they showcased a wavelength-multiplexed diffractive system capable of simultaneously monitoring multi-point structural vibrations using light sources with distinct wavelengths.

More on eTradeWire News

• TeleIQ Introduces AI Phone Assistant for Business Customer Support
• MOLTamp Launches Free Skinnable Shell for AI Coding Terminals
• Kunta Kinte Heritage Festival Presents 'Rooted Reception'
• Black cumin seed oil (Nigella sativa) Dr.Abhay Kumar Pati, An Ayurvedic and Integrated Physician,
• Red Wagon Agency and Marketing Leader Joe Whyte Announce Official HubSpot Partnership

One of the significant advantages of this technology is its scalability and energy efficiency. The diffractive surface functions as a completely passive encoder and consumes no energy during its operation. Furthermore, a design optimized for millimeter waves can be physically scaled to operate in other parts of the electromagnetic spectrum, such as the visible or infrared, by adjusting the dimensions of the diffractive features in proportion to the illumination wavelength.

This research was conducted by an interdisciplinary team from UCLA's Electrical and Computer Engineering Department, California NanoSystems Institute (CNSI), Civil and Environmental Engineering Department, and the California Geological Survey.

Publication: https://doi.org/10.1126/sciadv.aea1712