Integrated photonic encoder for low power and high-speed image processing.

Modern lens designs are capable of resolving greater than 10 gigapixels, while advances in camera frame-rate and hyperspectral imaging have made data acquisition rates of Terapixel/second a real possibility. The main bottlenecks preventing such high data-rate systems are power consumption and data storage. In this work, we show that analog photonic encoders could address this challenge, enabling high-speed image compression using orders-of-magnitude lower power than digital electronics. Our approach relies on a silicon-photonics front-end to compress raw image data, foregoing energy-intensive image conditioning and reducing data storage requirements. The compression scheme uses a passive disordered photonic structure to perform kernel-type random projections of the raw image data with minimal power consumption and low latency. A back-end neural network can then reconstruct the original images with structural similarity exceeding 90%. This scheme has the potential to process data streams exceeding Terapixel/second using less than 100 fJ/pixel, providing a path to ultra-high-resolution data and image acquisition systems.

Iterative eigensolver using fixed-point photonic primitive.

Photonic computing has potential advantages in speed and energy consumption yet is subject to inaccuracy due to the limited equivalent bitwidth of the analog signal. In this Letter, we demonstrate a configurable, fixed-point coherent photonic iterative solver for numerical eigenvalue problems using shifted inverse iteration. The photonic primitive can accommodate arbitrarily sized sparse matrix-vector multiplication and is deployed to solve eigenmodes in a photonic waveguide structure. The photonic iterative eigensolver does not accumulate errors from each iteration, providing a path toward implementing scientific computing applications on photonic primitives.

Accelerated fixed-point iterative reconstruction for fiber borescope imaging.

Computational imaging systems with embedded processing have potential advantages in power consumption, computing speed, and cost. However, common processors in embedded vision systems have limited computing capacity and low level of parallelism. The widely used iterative algorithms for image reconstruction rely on floating-point processors to ensure calculation precision, which require more computing resources than fixed-point processors. Here we present a regularized Landweber fixed-point iterative solver for image reconstruction, implemented on a field programmable gated array (FPGA). Compared with floating-point embedded uniprocessors, iterative solvers implemented on the fixed-point FPGA gain 1 to 2 orders of magnitude acceleration, while achieving the same reconstruction accuracy in comparable number of effective iterations. Specifically, we have demonstrated the proposed fixed-point iterative solver in fiber borescope image reconstruction, successfully correcting the artifacts introduced by the lenses and fiber bundle.

Fixed-point iterative linear inverse solver with extended precision.

Solving linear systems, often accomplished by iterative algorithms, is a ubiquitous task in science and engineering. To accommodate the dynamic range and precision requirements, these iterative solvers are carried out on floating-point processing units, which are not efficient in handling large-scale matrix multiplications and inversions. Low-precision, fixed-point digital or analog processors consume only a fraction of the energy per operation than their floating-point counterparts, yet their current usages exclude iterative solvers due to the cumulative computational errors arising from fixed-point arithmetic. In this work, we show that for a simple iterative algorithm, such as Richardson iteration, using a fixed-point processor can provide the same convergence rate and achieve solutions beyond its native precision when combined with residual iteration. These results indicate that power-efficient computing platforms consisting of analog computing devices can be used to solve a broad range of problems without compromising the speed or precision.

Low-crosstalk mode-group demultiplexers based on Fabry-Perot thin-film filters.

Mode-group multiplexing (MGM) can increase the capacity of short-reach few-mode optical fiber communication links while avoiding complex digital signal processing. In this paper, we present the design and experimental demonstration of a novel mode-group demultiplexer (MG DeMux) using Fabry-Perot (FP) thin-film filters (TFFs). The MG DeMux supports low-crosstalk mode-group demultiplexing, with degeneracies commensurate with those of graded-index (GRIN) multimode fibers. We experimentally demonstrate this functionality by using a commercial six-cavity TFF that was intended for 100 GHz channel spaced wavelength-division multiplexing (WDM) system.

Analysis of the Antifungal Properties of Bacillus velezensis B-4 Through a Bioassay and Complete-Genome Sequencing.

The strain B-4, isolated from a field in Changsha (China), presents strong antifungal activities, as identified by the Kirby-Bauer test, especially for pathogens that harm crops. Here, we obtained the complete genome sequence of the strain B-4 by Pacific Biosciences single-molecule real-time sequencing, making it well analyzed for understanding mechanisms and creating biological agents. Its 3,919-kb circular chromosome genome has 3,725 protein-coding genes [coding sequences (CDSs)] and 46.7% guanine-cytosine content. A comparative genome analysis of B-4 with other published strains (including Bacillus velezensis, Bacillus amyloliquefaciens, and Bacillus subtilis) revealed that the strain B-4 is a B. velezensis strain. These different strains have 2,889 CDSs in common, whereas 179 CDSs were found to be unique in the strain B-4, which is a far greater number than that in other strains. Regarding the antifungal activities of B-4, we were specifically concerned with the genes involved in the biosynthesis of secondary metabolites. In total, more than 19.56% of the genome was annotated to 12 gene clusters relating to synthesis of antimicrobial metabolites, which contained various enzyme-encoding operons for non-ribosomal peptide synthetases, polyketide synthases, and lantipeptide synthesis proteins. They were all considered to be related to the production of bacteriostatic substances or stimulation of induced systemic resistance by bacterial metabolites. These situations also present an advantage over those of other strains for biocontrol potential. We provide evidence that the biological control effect of the strain B-4, as demonstrated in antibacterial activity experiments and predicted from the complete genome sequence analysis, provides the basis for research promoting agricultural research on sustainable development, especially the contribution of biotechnology to agriculture.

Attosecond pulse retrieval from noisy streaking traces with conditional variational generative network.

Accurate characterization of an attosecond pulse from streaking trace is an indispensable step in studying the ultrafast electron dynamics on the attosecond scale. Conventional attosecond pulse retrieval methods face two major challenges: the ability to incorporate a complete physics model of the streaking process, and the ability to model the uncertainty of pulse reconstruction in the presence of noise. Here we propose a pulse retrieval method based on conditional variational generative network (CVGN) that can address both demands. Instead of learning the inverse mapping from a streaking trace to a pulse profile, the CVGN models the distribution of the pulse profile conditioned on a given streaking trace measurement, and is thus capable of assessing the uncertainty of the retrieved pulses. This capability is highly desirable for low-photon level measurement, which is typical in attosecond streaking experiments in the water window X-ray range. In addition, the proposed scheme incorporates a refined physics model that considers the Coulomb-laser coupling and photoelectron angular distribution in streaking trace generation. CVGN pulse retrievals under various simulated noise levels and experimental measurement have been demonstrated. The results showed high pulse reconstruction consistency for streaking traces when peak signal-to-noise ratio (SNR) exceeds 6, which could serve as a reference for future learning-based attosecond pulse retrieval.

Interior x-ray diffraction tomography with low-resolution exterior information.

X-ray diffraction tomography (XDT) resolves spatially-variant XRD profiles within macroscopic objects, and provides improved material contrast compared to the conventional transmission-based computed tomography (CT). However, due to the small diffraction cross-section, XDT suffers from long imaging acquisition time, which could take tens of hours for a full scan using a table-top x-ray tube. In medical and industrial imaging applications, oftentimes only the XRD measurement within a region-of-interest (ROI) is required, which, together with the demand to reduce imaging time and radiation dose to the sample, motivates the development of interior XDT systems that scan and reconstruct only an internal region within the sample. The interior problem does not have a unique solution, and a direct inversion on the truncated projection data often leads to large reconstruction errors in ROI. To reduce the truncation artifacts, conventional attenuation-based interior reconstruction problems rely on a known region or piecewise constant constraint within the ROI. Here we propose a quasi-interior XDT scheme that incorporates a small fraction of projection information from the exterior region to assist ROI reconstruction. In the phantom simulation, a small amount (17% of exterior region) of added exterior projection data improves the reconstruction quality by ~50%. The addition of exterior samplings in the experiment demonstrates improved spatial and XRD profile reconstructions compared to total-variation-based reconstruction or sinogram extrapolation. We expect our quasi-interior XDT to obviate the requirement on prior knowledge of the object or its support, and to allow the ROI reconstruction to be performed with the fast, widely-used filtered back-projection algorithm for easy integration into real-time XDT imaging modules.

Image Transport Through Meter-Long Randomly Disordered Silica-Air Optical Fiber.

We present a randomly disordered silica-air optical fiber featuring a 28.5% air filling fraction in the structured region, and low attenuation below 1 dB per meter at visible wavelengths. The quality of images transported through this fiber is shown to be comparable to, or even better than, that of images sent through commercial multicore imaging fiber. We demonstrate robust high-quality optical image transfer through 90 cm-long fibers with disordered silica-air structure, more than an order of magnitude improvement compared to previous disordered fiber imaging distances. The effects of variations of wavelength and feature size on transported image quality are investigated experimentally.

Three-dimensional reciprocal space x-ray coherent scattering tomography of two-dimensional object.

PURPOSE: X-ray coherent scattering tomography is a powerful tool in discriminating biological tissues and bio-compatible materials. Conventional x-ray scattering tomography framework can only resolve isotropic scattering profile under the assumption that the material is amorphous or in powder form, which is not true especially for biological samples with orientation-dependent structure. Previous tomography schemes based on x-ray coherent scattering failed to preserve the scattering pattern from samples with preferred orientations, or required elaborated data acquisition scheme, which could limit its application in practical settings. Here, we demonstrate a simple imaging modality to preserve the anisotropic scattering signal in three-dimensional reciprocal (momentum transfer) space of a two-dimensional sample layer. METHODS: By incorporating detector movement along the direction of x-ray beam, combined with a tomographic data acquisition scheme, we match the five dimensions of the measurements with the five dimensions (three in momentum transfer domain, and two in spatial domain) of the object. We employed a collimated pencil beam of a table-top copper-anode x-ray tube, along with a panel detector to investigate the feasibility of our method. RESULTS: We have demonstrated x-ray coherent scattering tomographic imaging at a spatial resolution ~2 mm and momentum transfer resolution 0.01 Å-1 for the rotation-invariant scattering direction. For any arbitrary, non-rotation-invariant direction, the same spatial and momentum transfer resolution can be achieved based on the spatial information from the rotation-invariant direction. The reconstructed scattering profile of each pixel from the experiment is consistent with the x-ray diffraction profile of each material. The three-dimensional scattering pattern recovered from the measurement reveals the partially ordered molecular structure of Teflon wrap in our sample. CONCLUSIONS: We extend the applicability of conventional x-ray coherent scattering tomography to the reconstruction of two-dimensional samples with anisotropic scattering profile by introducing additional degree of freedom on the detector. The presented method has the potential to achieve low-cost, high-specificity material discrimination based on x-ray coherent scattering.

X-ray diffraction tomography with limited projection information.

X-ray diffraction tomography (XDT) records the spatially-resolved X-ray diffraction profile of an extended object. Compared to conventional transmission-based tomography, XDT displays high intrinsic contrast among materials of similar electron density and improves the accuracy in material identification thanks to the molecular structural information carried by diffracted photons. However, due to the weak diffraction signal, a tomographic scan covering the entire object typically requires a synchrotron facility to make the acquisition time more manageable. Imaging applications in medical and industrial settings usually do not require the examination of the entire object. Therefore, a diffraction tomography modality covering only the region of interest (ROI) and subsequent image reconstruction techniques with truncated projections are highly desirable. Here we propose a table-top diffraction tomography system that can resolve the spatially-variant diffraction form factor from internal regions within extended samples. We demonstrate that the interior reconstruction maintains the material contrast while reducing the imaging time by 6 folds. The presented method could accelerate the acquisition of XDT and be applied in portable imaging applications with a reduced radiation dose.

Small-angle scatter tomography with a photon-counting detector array.

Small-angle x-ray scatter imaging has a high intrinsic contrast in cancer research and other applications, and provides information on molecular composition and micro-structure of the tissue. In general, the implementations of small-angle coherent scatter imaging can be divided into two main categories: direct tomography and angular dispersive computerized tomography. Based on the recent development of energy-discriminative photon-counting detector array, here we propose a computerized tomography setup based on energy-dispersive measurement with a photon-counting detector array. To show merits of the energy-dispersive approach, we have performed numerical tests with a phantom containing various tissue types, in comparison with the existing imaging approaches. The results show that with an energy resolution of ~6 keV, the energy dispersive tomography system with a broadband tabletop x-ray would outperform the angular dispersive system, which makes the x-ray small-angle scatter tomography promising for high-specificity tissue imaging.