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Adaptive optics in combination with multi-photon techniques is a powerful approach to image deep into a specimen. Remarkably, virtually all adaptive optics schemes today rely on wavefront modulators which are reflective, diffractive, or…
To go from (passive) process monitoring to active process control, an effective AI system must learn about the behavior of the complex system from very limited training data, forming an ad-hoc digital twin with respect to process inputs and…
We harness the momentum of light resonating inside a micro-droplet cavity, to experimentally generate micro-flows within the envelope of the drop. We 3D map these optically induced flows by using fluorescent nanoparticles; which reveals…
Tracking the evolution of an individual nanodroplet of liquid in real-time remains an outstanding challenge. Here a miniature optomechanical resonator detects a single nanodroplet landing on a surface and measures its subsequent evaporation…
The widespread adoption of machine learning and other matrix intensive computing algorithms has inspired renewed interest in analog optical computing, which has the potential to perform large-scale matrix multiplications with superior…
The shift in water security demands improvements in alternative solutions such as saltwater desalination. One of the most efficient technologies in this scope is the reverse osmosis systems, a technology based on a membrane separation…
Nanopores in solid state membranes are a tool able to probe nanofluidic phenomena or can act as a single molecular sensor. They also have diverse applications in filtration, desalination or osmotic power generation. Many of these…
Vanadium dioxide (VO2) is one of the most promising materials for developing hybrid photonic integrated devices (PICs). However, despite switching times as low as a few femtoseconds have been reported, the all-optical temporal dynamics of…
The rapid proliferation of AI-generated visual media has created an urgent need for efficient, trustworthy deepfake detection systems. However, existing deep learning-based detection methods rely on computationally intensive and…
Motion detection is a fundamental but challenging task for autonomous driving. In particular scenes like highway, remote objects have to be paid extra attention for better controlling decision. Aiming at distant vehicles, we train a neural…
Gathering data and identifying events in various traffic situations remains an essential challenge for the systematic evaluation of a perception system's performance. Analyzing large-scale, typically unstructured, multi-modal, time series…
The integration of machine learning and robotics into thin film deposition is transforming material discovery and optimization. However, challenges remain in achieving a fully autonomous cycle of deposition, characterization, and…
Optically levitated dielectric nanoparticles have become valuable tools for precision sensing and quantum optomechanical experiments. To predict the dynamic properties of a particle trapped in an optical tweezer with high fidelity, a tool…
We present an optofluidic sensor based on an elastomeric two-dimensional (2D) grating integrated inside a hemispherical fluid chamber. Laser beam is diffracted before (reflection) and after (transmission) going through the grating and…
In this work we review the coarse-to-fine spatial feature pyramid concept, which is used in state-of-the-art optical flow estimation networks to make exploration of the pixel flow search space computationally tractable and efficient. Within…
A major challenge for plasmonics as an enabling technology for quantum information processing is the realization of active spatio-temporal control of light on the nanoscale. The use of phase-shaped pulses or beams enforces specific…
Recent learning-based methods for event-based optical flow estimation utilize cost volumes for pixel matching but suffer from redundant computations and limited scalability to higher resolutions for flow refinement. In this work, we take…
Optical Remote Sensing Image Salient Object Detection (ORSI-SOD) remains challenging due to complex backgrounds, low contrast, irregular object shapes, and large variations in object scale. Existing discriminative methods directly regress…
The Optimal Power Flow (OPF) problem is pivotal for power system operations, guiding generator output and power distribution to meet demand at minimized costs, while adhering to physical and engineering constraints. The integration of…
Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides a more general perspective on generative modeling. Although…