Etalon Array Reconstructive Spectrometry.

Compact spectrometers are crucial in areas where size and weight may need to be minimized. These types of spectrometers often contain no moving parts, which makes for an instrument that can be highly durable. With the recent proliferation in low-cost and high-resolution cameras, camera-based spectrometry methods have the potential to make portable spectrometers small, ubiquitous, and cheap. Here, we demonstrate a novel method for compact spectrometry that uses an array of etalons to perform spectral encoding, and uses a reconstruction algorithm to recover the incident spectrum. This spectrometer has the unique capability for both high resolution and a large working bandwidth without sacrificing sensitivity, and we anticipate that its simplicity makes it an excellent candidate whenever a compact, robust, and flexible spectrometry solution is needed

Direct observation of plasmonic index ellipsoids on a deep-subwavelength metallic grating

We constructed a metallic grating on a deep-subwavelength scale and tested its plasmonic features in visible frequencies. The deep-subwavelength metallic grating effectively acts as an anisotropic homogeneous uniaxial form-birefringent metal, exhibiting different optical responses for polarizations along different optical axes. Therefore, this form-birefringent metal supports anisotropic surface plasmon polaritons that are characterized by directly imaging the generated plasmonic index ellipsoids in reciprocal space. The observed plasmonic index ellipsoids also show a rainbow effect, where different colors are dispersively distributed in reciprocal space

Theory of optical imaging beyond the diffraction limit with a far-field superlens

Recent theoretical and experimental studies have shown that imaging with resolution well beyond the diffraction limit can be obtained with so-called superlenses. Images formed by such superlenses are, however, in the near field only, or a fraction of wavelength away from the lens. In this paper, we propose a far-field superlens (FSL) device which is composed of a planar superlens with periodical corrugation. We show in theory that when an object is placed in close proximity of such a FSL, a unique image can be formed in far-field. As an example, we demonstrate numerically that images of 40 nm lines with a 30 nm gap can be obtained from far-field data with properly designed FSL working at 376nm wavelength.Comment: 6 pages, 3 figure

Isogeometric FEM-BEM coupled structural-acoustic analysis of shells using subdivision surfaces

We introduce a coupled finite and boundary element formulation for acoustic scattering analysis over thin shell structures. A triangular Loop subdivision surface discretisation is used for both geometry and analysis fields. The Kirchhoff-Love shell equation is discretised with the finite element method and the Helmholtz equation for the acoustic field with the boundary element method. The use of the boundary element formulation allows the elegant handling of infinite domains and precludes the need for volumetric meshing. In the present work the subdivision control meshes for the shell displacements and the acoustic pressures have the same resolution. The corresponding smooth subdivision basis functions have the $C^1$ continuity property required for the Kirchhoff-Love formulation and are highly efficient for the acoustic field computations. We validate the proposed isogeometric formulation through a closed-form solution of acoustic scattering over a thin shell sphere. Furthermore, we demonstrate the ability of the proposed approach to handle complex geometries with arbitrary topology that provides an integrated isogeometric design and analysis workflow for coupled structural-acoustic analysis of shells

Untrained, physics-informed neural networks for structured illumination microscopy

In recent years there has been great interest in using deep neural networks (DNN) for super-resolution image reconstruction including for structured illumination microscopy (SIM). While these methods have shown very promising results, they all rely on data-driven, supervised training strategies that need a large number of ground truth images, which is experimentally difficult to realize. For SIM imaging, there exists a need for a flexible, general, and open-source reconstruction method that can be readily adapted to different forms of structured illumination. We demonstrate that we can combine a deep neural network with the forward model of the structured illumination process to reconstruct sub-diffraction images without training data. The resulting physics-informed neural network (PINN) can be optimized on a single set of diffraction limited sub-images and thus doesn't require any training set. We show with simulated and experimental data that this PINN can be applied to a wide variety of SIM methods by simply changing the known illumination patterns used in the loss function and can achieve resolution improvements that match well with theoretical expectations.Comment: Preprint for journal submission. 21 Pages. 5 main text figures. 6 supplementary figure