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High-order Autler–Townes splitting in electrically tunable photonic molecules

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Photonics Research 2024年第8期12卷 1794-1801页

作者： YIHAO CHEN JUNTAO DUAN JIN LI YAN CHEN JIEWEN LI JIANAN DUAN XIAOCHUAN XU JIAWEI WANG School of Integrated Circuit Harbin Institute of Technology(Shenzhen)Shenzhen 518055China College of Advanced Interdisciplinary Studies National University of Defense TechnologyChangsha 410073China National key laboratory of Laser spatial information Harbin Institute of Technology(Shenzhen)Shenzhen 518055China

Whispering gallery mode optical microresonators represent a promising avenue for realizing optical analogs of coherent light–atom interactions,circumventing experimental ***-optical analogs of Autler–Townes splitting have been widely demonstrated,harnessing coupled optical microresonators,also known as photonic molecules,wherein the strong coupling between resonant fields enables energy level ***,we report the characterizations of Autler–Townes splitting in waveguide-coupled microring dimers featuring mismatched *** exploiting backscattering-induced coupling via Rayleigh and Mie scatterers in individual rings,high-order Autler–Townes splitting has been realized,yielding supermode hybridization in a multi-level *** resonance detuning using an integrated phase shifter,intra-cavity coupling-induced splitting becomes almost indistinguishable at the zero-detuning point where the strong inter-cavity coupling counteracts the imbalance of backscattering strengths in individual *** demonstrations on the maturing silicon photonics platform,our findings establish a framework of electrically tunable photonic molecules for coupling-mediated Autler–Townes splitting,offering promising prospects for on-chip signal generation and processing across classical and quantum regimes.

关键词： tunable tuning waveguide

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China key laboratory of Big data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China key laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of information and Communication Engineering Hainan UniversityHaikou 570228China State key laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

Geological modeling of coalbed methane reservoirs in the tectonically deformed coal seam group in the Dahebian block,western Guizhou,China

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Frontiers of Earth Science 2024年第1期18卷 44-67页

作者： Yong SHU Shuxun SANG Xiaozhi ZHOU key laboratory of Coalbed Methane Resources and Reservoir Formation Process(Ministry of Education) China University of Mining and TechnologyXuzhou 221116China Carbon Neutrality Institute China University of Mining and TechnologyXuzhou 221008China School of Resources and Geosciences China University of Mining and TechnologyXuzhou 221116China Jiangsu key laboratory of Coal-based Greenhouse Gas Control and Utilization China University of Mining and TechnologyXuzhou 221008China

The widely spread Carboniferous-Permian coal seam group in southern China has great potential for coalbed methane resources,but the extensively developed tectonically deformed coal seriously restricts its *** the Dahebian block in western Guizhou as the study area,the geological model of coalbed methane reservoirs in the tectonically deformed coal seam group was established,and the spatial distribution pattern of model parameters was clarified by clustering algorithms and factor *** facies model suggests that the main coal body structures in Nos.1,4,and 7 coal seams are cataclastic coal and granulated coal,whereas the No.11 coal seam is dominated by granulated coal,which has larger thicknesses and spreads more *** in situ permeability of primary undeformed coal,cataclastic coal,granulated coal,and mylonitized coal reservoirs are 0.333 mD,0.931 mD,0.146 mD,and 0.099 mD,respectively,according to the production performance analysis *** property model constructed by facies-controlled modeling reveals that Nos.1,4,and 7 coal seams have a wider high-permeability area,but the gas content is lower;the high-permeability area in the No.11 coal seam is more limited,but the gas content is *** results of the self-organizing map neural network and K-means clustering indicate that the geological model can be divided into 6 clusters,the model parameter characteristics of the 6 clusters are summarized by data analysis in combination with 6 factors extracted by factor analysis,and the application of data analysis results in multi-layer coalbed methane co-development is *** study provides ideas for the geological modeling in the tectonically deformed coal seam group and its data analysis.

关键词： geological modeling tectonically deformed coal coal seam group clustering algorithm Dahebian block,western Guizhou

Hybrid ITO Algorithm for Large-Scale Colored Traveling Salesman Problem

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Chinese Journal of Electronics 2024年

作者： Xueshi DONG College of Computer Science and Technology Qingdao University Beijing key laboratory of Urban spatial information Engineering School of Computer Science Wuhan University

In the fields of intelligent transportation and multi-task cooperation, many practical problems can be modeled by colored traveling salesman problem (CTSP). When solving large-scale CTSP with a scale of more than 1000dimensions, their convergence speed and the quality of their solutions are limited. This paper proposes a new hybrid ITO(HITO) algorithm, which integrates two new strategies, crossover operator and mutation strategy, into the standard ITO. In the iteration process of HITO, the feasible solution of CTSP is represented by the double chromosome coding, and the random drift and wave operators are used to explore and develop new unknown regions. In this process, the drift operator is executed by the improved crossover operator, and the wave operator is performed by the optimized mutation strategy. Experiments show that HITOis superior to the known comparison algorithms in term of the quality solution.

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Representation learning: serial-autoencoder for personalized recommendation

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Frontiers of Computer Science 2024年第4期18卷 61-72页

作者： Yi ZHU Yishuai GENG Yun LI Jipeng QIANG Xindong WU School of information Engineering Yangzhou UniversityYangzhou 225127China key laboratory of Knowledge Engineering with Big data(Ministry of Education of the People’s Republic of China) Hefei University of TechnologyHefei 230009China School of Computer Science and information Engineering Hefei University of TechnologyHefei 230009China

Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.

关键词： personalized recommendation autoencoder representation learning collaborative filtering

Temperature-and Strain-Compensated Directional Curvature Sensor Based on Bragg Gratings in Three-Core Fiber with Bridged Waveguides

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中国激光 2024年第18期51卷 1-10页

作者： Wu Zhifang Wei Yefen Lin Jing Wang Hupo Huang Ruimin Wang Xiaoyan Pu Jixiong Fujian key laboratory of Light Propagation and Transformation College of Information Science and Engineering Huaqiao University

Fiber-based curvature sensors, especially those capable of discerning the direction of curvature, have attracted more and more interest due to their promising applications in structural health monitoring, high-precision measurement, medical and biological diagnosis-treat instruments, and so on. Here, we propose and demonstrate a compact directional curvature sensor that comprises two bridged waveguides and three Bragg gratings in a section of three-core fiber（TCF）. Both the waveguides and gratings are integrated by femtosecond laser micromachining method. The waveguides, connecting the TCF outer cores to the lead-in single-mode fiber core, function as beam couplers to realize simultaneous interrogation of all three gratings without any separate fan-in/out component. Owing to the spatial specificity, the outer-core gratings exhibit high and direction-dependent sensitivity to curvature, whereas the central-core grating is nearly insensitive to curvature but shows similar sensitivities to ambient temperature and axial strain as the outer-core gratings. It can be used to compensate the cross impact of temperature and strain when the outer-core gratings are applied for curvature detection. Moreover, the wavelength interval between two outer-core gratings is also proposed as an indicator for curvature sensing. It features with a much higher sensitivity to curvature and reduced sensitivities to temperature and axial strain. The corresponding maximum sensitivity to curvature is as high as 191.89 pm/m-1, while the sensitivities to temperature and strain are only 0.3 pm/℃ and 0.0218 pm/με, respectively. Therefore, our proposed device provides a compact and robust all-infiber solution for directional curvature sensing. It not only offers high sensitivity and accuracy but also immunity to temperature and axial strain fluctuations, making it a promising tool for a wide range of applications.

关键词： directional curvature sensing femtosecond laser micromachining fiber Bragg grating fiber optics waveguide integration

spatial Distribution Feature Extraction Network for Open Set Recognition of Electromagnetic Signal

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 279-296页

作者： Hui Zhang Huaji Zhou Li Wang Feng Zhou The Ministry key laboratory of Electronic information Countermeasure and Simulation Xidian UniversityXi’an710071China Science and Technology on Communication information Security Control laboratory Jiaxing314033China

This paper proposes a novel open set recognition method,the spatial Distribution Feature Extraction Network(SDFEN),to address the problem of electromagnetic signal recognition in an open *** spatial distribution feature extraction layer in SDFEN replaces convolutional output neural networks with the spatial distribution features that focus more on inter-sample information by incorporating class center *** designed hybrid loss function considers both intra-class distance and inter-class distance,thereby enhancing the similarity among samples of the same class and increasing the dissimilarity between samples of different classes during ***,this method allows unknown classes to occupy a larger space in the feature *** reduces the possibility of overlap with known class samples and makes the boundaries between known and unknown samples more ***,the feature comparator threshold can be used to reject unknown *** signal open set recognition,seven methods,including the proposed method,are applied to two kinds of electromagnetic signal data:modulation signal and real-world *** experimental results demonstrate that the proposed method outperforms the other six methods overall in a simulated open ***,compared to the state-of-the-art Openmax method,the novel method achieves up to 8.87%and 5.25%higher micro-F-measures,respectively.

关键词： Electromagnetic signal recognition deep learning feature extraction open set recognition

Broadband light-active optoelectronic FeFET memory for in-sensor non-volatile logic

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Science China(information Sciences) 2024年第9期67卷 346-347页

作者： Yong ZHANG Dongxin TAN Cizhe FANG Zheng-Dong LUO Qiyu YANG Qiao ZHANG Yu ZHANG Xuetao GAN Yan LIU Yue HAO Genquan HAN State key Discipline laboratory of Wide Band Gap Semiconductor Technology School of MicroelectronicsXidian University Hangzhou Institute of Technology Xidian University key laboratory of Light Field Manipulation and information Acquisition Ministry of Industry and Information Technologyand Shaanxi Key Laboratory of Optical Information Technology School of Physical Science and TechnologyNorthwestern Polytechnical University

Machine vision has become crucial in the modern intelligence era, especially in rapidly developing applications such as autonomous vehicles and robotic visual sensors [1].With the ever-increasing requirements of visua... 详细信息

关键词： Ferroelectric RAM

Exceptional point enhanced nanoparticle detection in deformed Reuleaux‑triangle microcavity

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Frontiers of Optoelectronics 2024年第3期17卷 83-91页

作者： Jinhao Fei Xiaobei Zhang Qi Zhang Yong Yang Zijie Wang Chuanlu Deng Yi Huang Tingyun Wang key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China

In this paper,we propose a deformed Reuleaux-triangle resonator(RTR)to form exceptional point(EP)which results in the detection sensitivity enhancement of *** introducing single nanoparticle to the deformed RTR at EP,frequency splitting obtains an enhancement of more than 6 times compared with non-deformed *** addition,EP induced a result that the far feld pattern of chiral mode responses signifcantly to external perturbation,corresponding to the change in internal ***,single nanoparticle with far distance of more than 4000 nm can be detected by measuring the variation of far feld directional *** to traditional frequency splitting,the far feld pattern produced in deformed RTR provides a cost-efective and convenient path to detect single nanoparticle at a long distance,without using tunable laser and external *** structure indicates great potential in high sensitivity sensor and label-free detector.

关键词： Exceptional point Deformed microcavity Nanoparticle detection Far-feld pattern