23 research outputs found
Parallel random LiDAR with spatial multiplexing of a many-mode laser
We propose and experimentally demonstrate parallel LiDAR using random
intensity fluctuations from a highly multimode laser. We optimize a degenerate
cavity to have many spatial modes lasing simultaneously with different
frequencies. Their spatio-temporal beating creates ultrafast random intensity
fluctuations, which are spatially demultiplexed to generate hundreds of
uncorrelated time traces for parallel ranging. The bandwidth of each channel
exceeds 10 GHz, leading to a ranging resolution better than 1 cm. Our parallel
random LiDAR is robust to cross-channel interference, and will facilitate
high-speed 3D sensing and imaging.Comment: 12 pages, 7 figure
Deep Learning with Passive Optical Nonlinear Mapping
Deep learning has fundamentally transformed artificial intelligence, but the
ever-increasing complexity in deep learning models calls for specialized
hardware accelerators. Optical accelerators can potentially offer enhanced
performance, scalability, and energy efficiency. However, achieving nonlinear
mapping, a critical component of neural networks, remains challenging
optically. Here, we introduce a design that leverages multiple scattering in a
reverberating cavity to passively induce optical nonlinear random mapping,
without the need for additional laser power. A key advantage emerging from our
work is that we show we can perform optical data compression, facilitated by
multiple scattering in the cavity, to efficiently compress and retain vital
information while also decreasing data dimensionality. This allows rapid
optical information processing and generation of low dimensional mixtures of
highly nonlinear features. These are particularly useful for applications
demanding high-speed analysis and responses such as in edge computing devices.
Utilizing rapid optical information processing capabilities, our optical
platforms could potentially offer more efficient and real-time processing
solutions for a broad range of applications. We demonstrate the efficacy of our
design in improving computational performance across tasks, including
classification, image reconstruction, key-point detection, and object
detection, all achieved through optical data compression combined with a
digital decoder. Notably, we observed high performance, at an extreme
compression ratio, for real-time pedestrian detection. Our findings pave the
way for novel algorithms and architectural designs for optical computing.Comment: 16 pages, 7 figure
Roadmap on Superoscillations
Superoscillations are band-limited functions with the counterintuitive property that they can vary arbitrarily faster than their fastest Fourier component, over arbitrarily long intervals. Modern studies originated in quantum theory, but there were anticipations in radar and optics. The mathematical understanding—still being explored—recognises that functions are extremely small where they superoscillate; this has implications for information theory. Applications to optical vortices, sub-wavelength microscopy and related areas of nanoscience are now moving from the theoretical and the demonstrative to the practical. This Roadmap surveys all these areas, providing background, current research, and anticipating future developments