Shadow–highlight feature matching automatic small crater recognition using high-resolution digital orthophoto map from Chang’E Missions

This paper introduces a new method of small lunar craters’ automatic identification, using digital orthophoto map(DOM) data. The core of the approach is the fact that the lunar exploration DOM data reveal contrasting highlight and shadow characteristics of small craters under sunlight irradiation. This research effort combines image processing and mathematical modeling. Overall it proposes a new planetary data processing approach, to segment and extract the highlight and shadow regions of small craters,using the image gray frequency(IGF) statistical method.IGF can also be applied to identify the coupling relationships between small craters’ shape and their relative features. This paper presents the highlight and shadow pair matching(HSPM) model which manages to perform highprecision automatic recognition of small lunar craters.Testing was performed using the DOM data of Chang’E-2(CE-2). The results have shown that the proposed method has a high level of successful detection rate. The proposed methodology that uses DOM data can complement the drawbacks of the digital elevation model(DEM) that has a relatively high false detection rate. A hybrid fusion model(FUM) that combines both DOM and DEM data, was carried out to simultaneously identify small, medium, and large-sized craters. It has been proven that the FUM generally shows stronger recognition ability compared to previous approaches and it can be adapted for high precision identification of craters on the whole lunar surface.The results meet the requirements for a reliable and accurate exploration of the Moon and the planets.

A Silicon Shallow-Ridge Waveguide Integrated Superconducting Nanowire Single Photon Detector Towards Quantum Photonic Circuits

A silicon shallow-ridge waveguide integrated superconducting nanowire single photon detector is designed and fabricated.At the bias current of 11.6μA,4% on-chip detection efficiency near 1550 nm wavelength is achieved with the dark count rate of 3 Hz and a timing jitter of 75 ps.This device shows the potential application in the integration of superconducting nanowire single photon detectors with a complex quantum photonic circuit.

3D Web Reconstruction of a Fibrous Filter Using Sequential Multi-Focus Images

A fibrous filtering material is a kind of fiber assembly whose structure exhibits a three-dimensional(3D)network with dense microscopic open channels.The geometrical/morphological attributes,such as orientations,curvatures and compactness,of fibers in the network is the key to the filtration performance of the material.However,most of the previous studies were based on materials’2D micro-images,which were unable to accurately measure these important 3D features of a filter’s structure.In this paper,we present an imaging method to reconstruct the 3D structure of a fibrous filter from its optical microscopic images.Firstly,a series of images of the fiber assembly were captured at different depth layers as the stage moved vertically.Then a fusion image was established by extracting fiber edges from each layered image.Thirdly,the 3D coordinates of the fiber edges were determined using the sharpness/clarity of each edge pixel in the layered images.Finally,the 3D structure the fiber system was reconstructed through distance transformation based on the locations of fiber edges.

A machine-learning-enabled approach for bridging multiscale simulations of CNTs/PDMS composites

Benefitting from the interlaced networking structure of carbon nanotubes(CNTs),the composites of CNTs/polydimethylsiloxane(PDMS)have found extensive applications in wearable electronics.While hierarchical multiscale simulation frameworks exist to optimize the structure parameters,their wide applications were hindered by the high computational cost.In this study,a machine learning model based on the artificial neural networks(ANN)embedded graph attention network,termed as AGAT,was proposed.The datasets collected from the micro-scale and the macro-scale simulations are utilized to train the model.The ANN layer within the model framework is trained to pass the information from micro-scale to macro-scale,while the whole model is aimed to predict the electro-mechanical behavior of the CNTs/PDMS composites.By comparing the AGAT model with the original multiscale simulation results,the data-driven strategy is shown to be promising with high accuracy,demonstrating the potential of the machine-learning-enabled approach for the structure optimization of CNT-based composites.

Hybrid waveguide scheme for silicon-based quantum photonic circuits with quantum light sources

We propose a hybrid silicon waveguide scheme to avoid the impact of noise photons induced by pump lights in application scenarios of quantum photonic circuits with quantum light sources.The scheme is composed of strip waveguide and shallow-ridge waveguide structures.It utilizes the difference of biphoton spectra generated by spontaneous four-wave mixing(SFWM)in these two waveguides.By proper pumping setting and signal/idler wavelength selection,the generation of desired photon pairs is confined in the strip waveguide.The impact of noise photons generated by SFWM in the shallow-ridge waveguide can be avoided.Hence,the shallowridge waveguide could be used to realize various linear operation devices for pump light and quantum state manipulations.The feasibility of this scheme is verified by theoretical analysis and a primary experiment.Two applications are proposed and analyzed,showing its great potential in silicon-based quantum photonic circuits.