Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder. These nanostructures scatter light into lattice modes, which encode the wavefront direction and the polarization ellipticity in the linearly polarized components of the diffracted, interference patterns. Combining optical encoders with a neural network, the system predicts pointing and polarization when the interference patterns are adequately sampled. A comparison of "ordered" and "random" optical encoders shows that the latter both blurs the interference patterns and achieves higher resolution. Our work centers on the unexpected modulation and spatial multiplexing of incident light polarization by self-assembled hollow nanocavity arrays as a class of materials distinct from traditional metasurfaces that will not only enable encoding for polarization and optical computing but also for compressed sensing and imaging.
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http://dx.doi.org/10.1021/acsnano.4c09641 | DOI Listing |
ACS Nano
January 2025
Department of Mechanical Engineering, University of California at Riverside, Riverside, California 92521, United States.
Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Ernst Strüngmann Institute, Frankfurt am Main 60528, Germany.
The dynamics of neuronal systems are characterized by hallmark features such as oscillations and synchrony. However, it has remained unclear whether these characteristics are epiphenomena or are exploited for computation. Due to the challenge of selectively interfering with oscillatory network dynamics in neuronal systems, we simulated recurrent networks of damped harmonic oscillators in which oscillatory activity is enforced in each node, a choice well supported by experimental findings.
View Article and Find Full Text PDFAnal Bioanal Chem
January 2025
Department of Pathology, Immunology and Laboratory Medicine, University of Florida College of Medicine, Gainesville, FL, USA.
An increasing number of cannabis-related products have become available and entered the market, particularly those containing cannabidiol (CBD) and Δ-tetrahydrocannabinol (Δ-THC). Analytical methods for cannabinoids in urine have been described extensively in the literature. However, methods providing good resolution for distinguishing interferences from THC positional isomers are needed.
View Article and Find Full Text PDFMinerva Dent Oral Sci
January 2025
Department of Biomedical Sciences, Dentistry and Morphological and Functional Imaging, University of Messina, Messina, Italy.
Background: Cadaverine and hydrocinnamic acid are frequent metabolites in inflamed periodontal areas. Their role as a metabolite for plant growth inhibition has been established, but their relevance in humans has yet to be determined. Moreover, Vascular endothelial growth factor (VGEF) is a consistent growth factor in neo-angiogenesis in periodontal regeneration.
View Article and Find Full Text PDFJ Acoust Soc Am
January 2025
Department of Informatics, University of Oslo, 0316 Oslo, Norway.
In adaptive beamforming, the array signal processing adjusts its sensor delays and weights based on the incoming data. In conventional beamforming, these parameters are instead given from a predefined model. Adaptive beamformers can improve measurement precision by dynamically rejecting spatial interference.
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