Architecture for one-shot compressive imaging using computer-generated holograms.

We propose a synchronous implementation of compressive imaging. This method is mathematically equivalent to prevailing sequential methods, but uses a static holographic optical element to create a spatially distributed spot array from which the image can be reconstructed with an instantaneous measurement. We present the holographic design requirements and demonstrate experimentally that the linear algebra of compressed imaging can be implemented with this technique. We believe this technique can be integrated with optical metasurfaces, which will allow the development of new compressive sensing methods.Engineering and Physical Sciences Research Council (EPSRC) (EP/G037256/1, EP/L015455/1)

An optical Fourier transform coprocessor with direct phase determination.

The Fourier transform is a ubiquitous mathematical operation which arises naturally in optics. We propose and demonstrate a practical method to optically evaluate a complex-to-complex discrete Fourier transform. By implementing the Fourier transform optically we can overcome the limiting O(nlogn) complexity of fast Fourier transform algorithms. Efficiently extracting the phase from the well-known optical Fourier transform is challenging. By appropriately decomposing the input and exploiting symmetries of the Fourier transform we are able to determine the phase directly from straightforward intensity measurements, creating an optical Fourier transform with O(n) apparent complexity. Performing larger optical Fourier transforms requires higher resolution spatial light modulators, but the execution time remains unchanged. This method could unlock the potential of the optical Fourier transform to permit 2D complex-to-complex discrete Fourier transforms with a performance that is currently untenable, with applications across information processing and computational physics

Full-field quantitative phase and polarisation-resolved imaging through an optical fibre bundle.

Flexible optical fibres, used in conventional medical endoscopy and industrial inspection, scramble phase and polarisation information, restricting users to amplitude-only imaging. Here, we exploit the near-diagonality of the multi-core fibre (MCF) transmission matrix in a parallelised fibre characterisation architecture, enabling accurate imaging of quantitative phase (error <0.3 rad) and polarisation-resolved (errors <10%) properties. We first demonstrate accurate recovery of optical amplitude and phase in two polarisations through the MCF by measuring and inverting the transmission matrix, and then present a robust Bayesian inference approach to resolving 5 polarimetric properties of samples. Our method produces high-resolution (9.0±2.6μm amplitude, phase; 36.0±10.4μm polarimetric) full-field images at working distances up to 1mm over a field-of-view up to 750×750μm 2 using an MCF with potential for flexible operation. We demonstrate the potential of using quantitative phase for computational image focusing and polarisation-resolved properties in imaging birefringence

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements

Research data supporting "Characterisation, design, and optimisation of a two-pass twisted nematic liquid crystal spatial light modulator system for arbitrary complex modulation"

This is data in support of the publication 'Characterisation, design, and optimisation of a two-pass twisted nematic liquid crystal spatial light modulator system for arbitrary complex modulation.' This directory contains the data pertaining to the paper 'Characterisation, design, and optimisation of a two-pass twisted nematic liquid crystal spatial light modulator system for arbitrary complex modulation', by Macfaden & Wilkinson (2016). Descriptions of these data files are as follows: FIG 2: These files contain sufficient data to determine the Jones matrices which represent the SLM. - One pass no SLM.xls This is the data corresponding to Figure 2(a1). - One pass with SLM off.xls This is the data corresponding to Figure 2(a2); the relative amplitude transmission with the SLM present but off. -Two pass with SLM.xlsx Each of two sheets contains the amplitude transmission under different SLM levels, polariser angles, and analyser angles for the two-pass scenario when the other half of the SLM is at level 0. A subset of this data corresponds to FIgure2(a3) - Two pass with SLM, Ronchi gratings.xlsx With the polariser and analser fixed at 0 degrees, the transmission through the SLM when Ronchi gratings are shown on the first and second half of the SLM. In each case, the other half is held at level 0, as is the static stripe composing the Ronchi grating FIG 3: - Measured Jones matrices.xlsx The determined Jones matrices shown in Figure 3(a). The column headings are for the labelled components A,B,C,D of the two sides 1,2. The labels x,y correspond to the real,imaginary parts of the matrix element rexpectively. - eigenvalues.mat & eigenvectors.mat The polarisation eigenvalues and eigenvectors --- from which Figure 3(b) was generated --- are complex numbers. Hence, they are saved as MATLAB .mat files. eigenvalues.mat is a 2x256x2 matrix. The dimensions correspond to [SLM half, SLM level, eigenvalue number] eigenvectors.mat is a 2x256x2x2 matrix. The dimensions correspond to [SLM half, SLM level, eigenvector number, eigenvector column vector]. FIG 4: - Figure4.fig The MATLAB figure file containing the raw figure. -Fig4 allStates.xlsx All of the accessible states in Figure 4(a) -Fig4 modulationPoints.xlsx On separate sheets, the target modulation points, actual modulation points, and the SLM levels required to achieve them. -Fig4 characterisation. On separate sheets, the amplitude only characterisation, theoretical and obtained Ronchi grating results. FIG 5: - Figure5.fig The MATLAB figure file containing the raw figure -Fig5 allStates.xlsx All of the accessible states in Figure 5(a) -Fig5 modulationPoints.xlsx On separate sheets, the actual nearest ,modulation points used, and the corresponding SLM levels. - Fig5 characterisation.xlsx The characterisation data in Figure 5(b). This contains the flat field and Ronchi characterisation data. FIG 6: - Figure6.fig The MATLAB figure file containing the raw figure. - Fig6 displacements.xlsx On separate sheets: the coefficients of the Taylor series approximation; and the characterisation data. FIG 7: - Figure7.fig The MATLAB figure file containing the raw figure. - Fig7 correction.xlsx On separate sheets: the coefficients of the Taylor series approximation; the amplitude characterisation; and the Ronchi amplitudes.EPSRC [EP/G037256/1], Optalysys Ltd, ICASE Studentshi

Dataset for: Quantitative phase and polarization imaging through an optical fiber applied to detection of early esophageal tumorigenesis

This folder contains data relating to the paper Gordon, G. S. D. et al. Quantitative phase and polarization imaging through an optical fiber applied to detection of early esophageal tumorigenesis. J. Biomed. Opt. 24, 1 (2019). doi: 10.1117/1.JBO.24.12.126004. ScatteringPhantoms.zip contains raw .mat files used to produce Figure 3 (scattering phantoms) BirefringentPhantoms.zip contains raw .mat files used to produce Figure 4 (birefringent phantoms) MouseTissue.zip contains raw .mat files used to produce Figures 5-7 (mouse oesophagus samples