Adaptive-optics Optical Coherence Tomography Processing Using a Graphics Processing Unit

Graphics processing units are increasingly being used for scientific computing for their powerful parallel processing abilities, and moderate price compared to super computers and computing grids. In this paper we have used a general purpose graphics processing unit to process adaptive-optics optical coherence tomography (AOOCT) images in real time. Increasing the processing speed of AOOCT is an essential step in moving the super high resolution technology closer to clinical viability

Efficient Volumetric Method of Moments for Modeling Plasmonic Thin-Film Solar Cells with Periodic Structures

Metallic nanoparticles (NPs) support localized surface plasmon resonances (LSPRs), which enable to concentrate sunlight at the active layer of solar cells. However, full-wave modeling of the plasmonic solar cells faces great challenges in terms of huge computational workload and bad matrix condition. It is tremendously difficult to accurately and efficiently simulate near-field multiple scattering effects from plasmonic NPs embedded into solar cells. In this work, a preconditioned volume integral equation (VIE) is proposed to model plasmonic organic solar cells (OSCs). The diagonal block preconditioner is applied to different material domains of the device structure. As a result, better convergence and higher computing efficiency are achieved. Moreover, the calculation is further accelerated by two-dimensional periodic Green's functions. Using the proposed method, the dependences of optical absorption on the wavelengths and incident angles are investigated. Angular responses of the plasmonic OSCs show the super-Lambertian absorption on the plasmon resonance but near-Lambertian absorption off the plasmon resonance. The volumetric method of moments and explored physical understanding are of great help to investigate the optical responses of OSCs.Comment: 11 pages, 6 figure

Amplification and generation of ultra-intense twisted laser pulses via stimulated Raman scattering

Twisted Laguerre-Gaussian lasers, with orbital angular momentum and characterised by doughnut shaped intensity profiles, provide a transformative set of tools and research directions in a growing range of fields and applications, from super-resolution microcopy and ultra-fast optical communications to quantum computing and astrophysics. The impact of twisted light is widening as recent numerical calculations provided solutions to long-standing challenges in plasma-based acceleration by allowing for high gradient positron acceleration. The production of ultrahigh intensity twisted laser pulses could then also have a broad influence on relativistic laser-matter interactions. Here we show theoretically and with ab-initio three-dimensional particle-in-cell simulations, that stimulated Raman backscattering can generate and amplify twisted lasers to Petawatt intensities in plasmas. This work may open new research directions in non-linear optics and high energy density science, compact plasma based accelerators and light sources.Comment: 18 pages, 4 figures, 1 tabl

cellSTORM - Cost-effective Super-Resolution on a Cellphone using dSTORM

Expensive scientific camera hardware is amongst the main cost factors in modern, high-performance microscopes. Recent technological advantages have, however, yielded consumer-grade camera devices that can provide surprisingly good performance. The camera sensors of smartphones in particular have benefited of this development. Combined with computing power and due to their ubiquity, smartphones provide a fantastic opportunity for "imaging on a budget". Here we show that a consumer cellphone is capable even of optical super-resolution imaging by (direct) Stochastic Optical Reconstruction Microscopy (dSTORM), achieving optical resolution better than 80 nm. In addition to the use of standard reconstruction algorithms, we investigated an approach by a trained image-to-image generative adversarial network (GAN). This not only serves as a versatile technique to reconstruct video sequences under conditions where traditional algorithms provide sub-optimal localization performance, but also allows processing directly on the smartphone. We believe that "cellSTORM" paves the way for affordable super-resolution microscopy suitable for research and education, expanding access to cutting edge research to a large community

Unified single-photon and single-electron counting statistics: from cavity-QED to electron transport

A key ingredient of cavity quantum-electrodynamics (QED) is the coupling between the discrete energy levels of an atom and photons in a single-mode cavity. The addition of periodic ultra-short laser pulses allows one to use such a system as a source of single photons; a vital ingredient in quantum information and optical computing schemes. Here, we analyze and ``time-adjust'' the photon-counting statistics of such a single-photon source, and show that the photon statistics can be described by a simple `transport-like' non-equilibrium model. We then show that there is a one-to-one correspondence of this model to that of non-equilibrium transport of electrons through a double quantum dot nanostructure. Then we prove that the statistics of the tunnelling electrons is equivalent to the statistics of the emitted photons. This represents a unification of the fields of photon counting statistics and electron transport statistics. This correspondence empowers us to adapt several tools previously used for detecting quantum behavior in electron transport systems (e.g., super-Poissonian shot noise, and an extension of the Leggett-Garg inequality) to single-photon-source experiments.Comment: 8 pages, 3 figure

A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR

$L_1$ regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing problems, such as image fusion, target detection, image super-resolution, and others and have led to promising results. However, solving such sparse reconstruction problems is computationally expensive and has limitations in its practical use. In this paper, we proposed a novel efficient algorithm for solving the complex-valued $L_1$ regularized least squares problem. Taking the high-dimensional tomographic synthetic aperture radar (TomoSAR) as a practical example, we carried out extensive experiments, both with simulation data and real data, to demonstrate that the proposed approach can retain the accuracy of second order methods while dramatically speeding up the processing by one or two orders. Although we have chosen TomoSAR as the example, the proposed method can be generally applied to any spectral estimation problems.Comment: 11 pages, IEEE Transactions on Geoscience and Remote Sensin