Spin-orbit interaction of light induced by transverse spin angular momentum engineering

We report the first demonstration of a direct interaction between the extraordinary transverse spin angular momentum in evanescent waves and the intrinsic orbital angular momentum in optical vortex beams. By tapping the evanescent wave of whispering gallery modes in a micro-ring-based optical vortex emitter and engineering the transverse spin state carried therein, a transverse-spin-to-orbital conversion of angular momentum is predicted in the emitted vortex beams. Numerical and experimental investigations are presented for the proof-of-principle demonstration of this unconventional interplay between the spin and orbital angular momenta, which could provide new possibilities and restrictions on the optical angular momentum manipulation techniques on the sub-wavelength scale. This phenomenon further gives rise to an enhanced spin-direction coupling effect in which waveguide or surface modes are unidirectional excited by incident optical vortex, with the directionality jointly controlled by spin-orbit states. Our results enrich the spin-orbit interaction phenomena by identifying a previously unknown pathway between the polarization and spatial degrees of freedom of light, and can enable a variety of functionalities employing spin and orbital angular momenta of light in applications such as communications and quantum information processing

Spiral Transformation for High-Resolution and Efficient Sorting of Optical Vortex Modes

Mode sorting is an essential function for optical multiplexing systems that exploit the orthogonality of the orbital angular momentum mode space. The familiar log-polar optical transformation provides a simple yet efficient approach whose resolution is, however, restricted by a considerable overlap between adjacent modes resulting from the limited excursion of the phase along a complete circle around the optical vortex axis. We propose and experimentally verify a new optical transformation that maps spirals (instead of concentric circles) to parallel lines. As the phase excursion along a spiral in the wave front of an optical vortex is theoretically unlimited, this new optical transformation can separate orbital angular momentum modes with superior resolution while maintaining unity efficiency

Compact and high-performance vortex mode sorter for multi-dimensional multiplexed fiber communication systems

With the amplitude, time, wavelength/frequency, phase, and polarization/spin parameter dimensions of the light wave/photon almost fully utilized in both classical and quantum photonic information systems, orbital angular momentum (OAM) carried by optical vortex modes is regarded as a new modal parameter dimension for further boosting the capacity and performance of the systems. To exploit the OAM mode space for such systems, stringent performance requirements on a pair of OAM mode multiplexer and demultiplexer (also known as mode sorters) must be met. In this work, we implement a newly discovered optical spiral transformation to achieve a low-cross-Talk, wide-opticalbandwidth, polarization-insensitive, compact, and robust OAM mode sorter that realizes the desired bidirectional conversion between seven co-Axial OAM modes carried by a ring-core fiber and seven linearly displaced Gaussian-like modes in parallel single-mode fiber channels. We further apply the device to successfully demonstrate high-spectralefficiency and high-capacity data transmission in a 50-km OAM fiber communication link for the first time, in which a multi-dimensional multiplexing scheme multiplexes eight orbital-spin vortex mode channels with each mode channel simultaneously carrying 10 wavelength-division multiplexing channels, demonstrating the promising potential of both the OAM mode sorter and the multi-dimensional multiplexed OAM fiber systems enabled by the device. Our results pave the way for futureOAM-based multi-dimensional communication systems

Fire Performance of High Strength Concrete using a Novel Fire Testing Method

Due to the degradation of high strength concrete performance(HSC) at elevated temperature, it is of great significance to have a detailed knowledge of the properties of HSC at elevated temperature. In conventional fire tests, furnaces are commonly employed to heat concrete. However, the thermal boundary condition provided by furnaces has been proven inconsistent and unreliable. The results achieved through conventional fire tests should be re-examined. In order to solve this problem, a novel fire testing method using radiant panel system is introduced. It is able to set up a consistent, reliable and uniform thermal boundary condition in fire resistance test of HSC. Furthermore, two digital information capturing methods: Digital Image Correlation(DIC) and actuator are applied to measure the deformation of HSC during the test. A series of solutions are proposed to deal with the limitations of these two methods and reliable measurement is achieved. Moreover, the fire performance of HSC is examined using this novel testing method under three conditions: ambient temperature test, unstressed-hot test and stress-hot test. The results demonstrate the compression strength, stress-strain curve and elastic modulus of HSC at ambient temperature and elevated temperature

室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future

RSSOD-Bench: A large-scale benchmark dataset for Salient Object Detection in Optical Remote Sensing Imagery

We present the RSSOD-Bench dataset for salient object detection (SOD) in optical remote sensing imagery. While SOD has achieved success in natural scene images with deep learning, research in SOD for remote sensing imagery (RSSOD) is still in its early stages. Existing RSSOD datasets have limitations in terms of scale, and scene categories, which make them misaligned with real-world applications. To address these shortcomings, we construct the RSSOD-Bench dataset, which contains images from four different cities in the USA. The dataset provides annotations for various salient object categories, such as buildings, lakes, rivers, highways, bridges, aircraft, ships, athletic fields, and more. The salient objects in RSSOD-Bench exhibit large-scale variations, cluttered backgrounds, and different seasons. Unlike existing datasets, RSSOD-Bench offers uniform distribution across scene categories. We benchmark 23 different state-of-the-art approaches from both the computer vision and remote sensing communities. Experimental results demonstrate that more research efforts are required for the RSSOD task.Comment: IGARSS 2023, 4 page

Transmission of H7N9 influenza virus in mice by different infective routes.

BackgroundOn 19 February 2013, the first patient infected with a novel influenza A H7N9 virus from an avian source showed symptoms of sickness. More than 349 laboratory-confirmed cases and 109 deaths have been reported in mainland China since then. Laboratory-confirmed, human-to-human H7N9 virus transmission has not been documented between individuals having close contact; however, this transmission route could not be excluded for three families. To control the spread of the avian influenza H7N9 virus, we must better understand its pathogenesis, transmissibility, and transmission routes in mammals. Studies have shown that this particular virus is transmitted by aerosols among ferrets.MethodsTo study potential transmission routes in animals with direct or close contact to other animals, we investigated these factors in a murine model.ResultsViable H7N9 avian influenza virus was detected in the upper and lower respiratory tracts, intestine, and brain of model mice. The virus was transmissible between mice in close contact, with a higher concentration of virus found in pharyngeal and ocular secretions, and feces. All these biological materials were contagious for naïve mice.ConclusionsOur results suggest that the possible transmission routes for the H7N9 influenza virus were through mucosal secretions and feces

Bearing fault diagnosis method based on Hilbert envelope spectrum and deep belief network

When fault occurs in bearing, the frequency spectrum of vibration signal would change and it contains a considerable amount of fault information which can reflect the actual work condition and the fault type of bearing. Recently, the statistical features of the frequency spectrum have been widely used in bearing fault diagnosis. However, there are lots of statistical features with different sensitivity to fault identification. Selecting the most sensible statistical features for improving classification accuracy is often determined with experience, which will make great subjective influence on the fault diagnosis results. Deep belief network (DBN) is a deep neural network which can automatically find a latent hierarchical feature representation from the high dimension input data. In this study, a bearing fault diagnosis method based on Hilbert envelope spectrum and DBN is proposed. Firstly, the vibration signals under different test conditions are resampled. Secondly, the whole Hilbert envelope spectrum of the resampled signal is used directly as eigenvector to characterize the fault type of bearing. Finally, a DBN classifier model is established to recognize the fault type of bearing. DBN classifier model can be used as both an automatic feature extractor and a classifier for bearing fault diagnosis. Therefore, the process of fault diagnosis can be greatly simplified. The results of two different experiments demonstrate that the proposed method outperforms the competing methods and it can obtain a more excellent diagnostic performance