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Real-time and ultrafast optical pulse quantization based on slicing the supercontinuum

Chinese Optics Letters 2021年第8期19卷 69-74页

作者： Ya Guo Qiang Cai Zhiwei Jia Bingjie Xu Zhensen Gao Qianwu Zhang Ruonan Zhang Adonis Bogris K.Alan Shore Yuncai Wang Pu Li Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of EducationTaiyuan University of TechnologyTaiyuan 030024China School of Information Engineering Guangdong University of TechnologyGuangzhou 510006China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China Science and Technology on Communication Security Laboratory Institute of Southwestern CommunicationChengdu 610041China Department of Informatics and Computer Engineering University of West AtticaAthens 12243Greece School of Electronic Engineering Bangor UniversityWales LL571UTUnited Kingdom

All-optical analog-to-digital conversion is a paramount issue in modern science. How to implement real-time and ultrafast quantization to optical pulses with different intensities in an all-optical domain is a central problem. Here, we report a realtime demonstration of an all-optical quantization scheme based on slicing the supercontinuum in a nonlinear fiber. In comparison with previous schemes through off-line analysis of the power of different optical spectral components in the supercontinuum, this, to the best of our knowledge, is the first demonstration of such functionality online in the time ***, the extinction ratio among the quantized outputs can exceed 10 d B, which further confirms the feasibility of the proposed quantization scheme. The current 3 bit resolution in the proof-of-principle experiment is limited by the current experimental condition, but it can be expected to be greatly enhanced through improving both the spectral width of the generated supercontinuum and the number of filtering channels used.

关键词： all-optical analog-to-digital conversion photonic quantization all-optical signal processing supercontinuum generation

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Personalized Video Recommendation with Attentive Multi-View Learning and Long-Term and Short-Term Interest Modeling

SSRN

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SSRN 2022年

作者： Wang, Chuangjian Zhang, Yuelin Yuan, Youwei Jiang, Peng Zhou, Qi Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China School of Management Huazhong University of Science and Technology Wuhan430081 China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan430081 China

Personalized video recommendations can be a good solution to the problem of massive video overload. However, most of the existing recommendation algorithms use ID as the core for feature design, which is far from enough. This paper proposes a new recommendation algorithm. This approach can use the features of multiple views to learn the representation of features in videos and users. In video encoder, this paper proposes an attentive multi-view learning model that can learn a uniform video representation for video type, title, content, and starring stars as different views. In the user encoder, this paper learns the long-term and short-term interests of users from multiple views. Firstly, the long-term interest of users is learned from two perspectives: the global behavior of users themselves and the neighbor behavior. Then, this paper designs an attentive Bi-LSTM model to learn users' short-term interests using recent users' viewing history behaviors. Extensive experiments are conducted on a large real dataset, and the experiments show that our approach can improve the effectiveness of video recommendation. © 2022, The Authors. All rights reserved.

关键词： Large dataset

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An Improved Lmmse Channel Estimation Algorithm for Mimo-Ofdm System

SSRN

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SSRN 2022年

作者： Tang, Wanwei Wang, Lixia Wang, Zedai Wei, Mingzhe Zhao, Jinlong Yue, Xinwei The Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan063000 China The School of Engineering Hong Kong University of Science and Technology 999077 Hong Kong The School of Electronic and Information Engineering Beihang University Beijing100191 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

This paper studies channel estimation techniques to recover the received signal more accurately and quickly. The classical Least Squares (LS) and Linear Minimum Mean-squared Error (LMMSE) estimation have the problems of poor estimation performance and high complexity. An Fast-LMMSE channel estimation algorithm is proposed to balance the performance and complexity. In order to apply it to the actual system, the corresponding pilot is also designed. Simulation results are provided and the comparison shows the performance gains achieved by the proposed technique over LS and MMSE estimation in SISO and MIMO communication systems. © 2022, The Authors. All rights reserved.

关键词： Channel estimation

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Interference Resource Scheduling Algorithm Based on Potential Game for UAV Cooperative Combat

Interference Resource Scheduling Algorithm Based on Potentia...

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International Conference on communication technology (ICCT)

作者： Min Deng Zhigao Wu Yongqi Chen School of Automatic and Electronic Information Xiangtan University Hunan National Center for Applied Mathematics Xiangtan China School of Automatic and Electronic Information Xiangtan University Xiangtan China Science and Technology on Communication Information Security Control Laboratory JiaxingChina

ISBN: (纸本)9781665432078

Interference resource scheduling is crucial in UAV (unmanned aerial vehicle) cooperative combats of all scales. In view of the simple constraints of the existing scheduling models and their small-scale application situations, a resource scheduling model with constraint of the minimum number of jammed targets is proposed to maximize the benefit of interference and minimize the cost. The analytic hierarchy process is used to weight the indicators of benefit and cost. And the interference resource scheduling model is constructed by the game theory. Besides, the feasibility and existence of the Nash equilibrium are proved. Then, a potential game theory algorithm combined with the tabu algorithm was proposed to solve the interference resource allocation problem. The simulation results show that the designed interference resource scheduling algorithm can achieve higher integration benefit than the other compared algorithms with less iterations.

关键词： Costs Scheduling algorithms Heuristic algorithms Simulation Interference Dynamic scheduling Autonomous aerial vehicles

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Impacts of Hardware Impairments on Mutualistic Cooperative Ambient Backscatter communications

arXiv

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arXiv 2022年

作者： Ye, Yinghui Shi, Liqin Chu, Xiaoli Lu, Guangyue Sun, Sumei Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications China Department of Electronic and Electrical Engineering The University of Sheffield United Kingdom Institute of Infocomm Research Agency for Science Technology and Research Singapore Singapore

In mutualistic cooperative ambient backscatter communications (AmBC), Internet-of-Things (IoT) device sends its information to a desired receiver by modulating and backscattering the primary signal, while providing beneficial multipath diversity to the primary receiver in return, thus forming a mutualism relationship between the AmBC and primary links. We note that the hardware impairments (HIs), which are unavoidable in practical systems and may significantly affect the transmission rates of the primary and AmBC links and their mutualism relationships, have been largely ignored in the study of mutualistic cooperative AmBC networks. In this paper, we consider a mutualistic cooperative AmBC network under HIs, and study the impacts of HIs on the achievable rates of the primary link and the AmBC link. In particular, we theoretically prove that although HIs degrades the rate of each link, the mutualism relationship between the AmBC and primary links is maintained, i.e., the rate of the primary link in the mutualistic cooperative AmBC network is still higher than that without the AmBC link. The closed-form rate expressions of both the AmBC and primary links are derived. Computer simulations are provided to validate our theoretical analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Backscattering

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Knowledge-aided covariance matrix estimation algorithm based on geometric mean in STAP for airborne radar

Knowledge-aided covariance matrix estimation algorithm based...

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IET International Radar Conference (IRC 2023)

作者： Yang Jing Xiaolin Du Guolong Cui Jibin Zheng Jianbo Li School of Computer and Control Engineering Yantai University Yantai People's Republic of China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu People's Republic of China National Key Laboratory of Radar Signal Processing Xidian University Xi’an People's Republic of China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing People's Republic of China

ISBN: (数字)9781837240982

The problem of knowledge-aided (KA) covariance matrix estimation in airborne radar space-time adaptive processing (STAP) is studied in this paper. In the traditional KA covariance matrix estimation methods, the insufficient number of available samples and inaccurate prior knowledge can cause serious deterioration of STAP performance. To tackle this problem, a new KA covariance matrix estimation algorithm based on geometric mean is proposed. Specifically, by utilizing the persymmetric structure information as well as the geometric mean in the space of positive definite matrices, a geometric mean estimator is constructed, which is then linearly combined with the prior covariance matrix to obtain a new type of CCM estimators. Simulation results illustrate that the proposed algorithm has good STAP performance with small samples and inaccurate prior knowledge.

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Fast Fourier single-pixel imaging based on Sierra–Lite dithering algorithm

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Chinese Physics B 2019年第6期28卷 189-194页

作者： Zhen-Yu Liang Zheng-Dong Cheng Yan-Yan Liu Kuai-Kuai Yu Yang-Di Hu National University of Defense Technology State Key Laboratory of Pulsed Power Laser TechnologyHefei 230037China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300000China

The single-pixel imaging(SPI) technique is able to capture two-dimensional(2 D) images without conventional array sensors by using a photodiode. As a novel scheme, Fourier single-pixel imaging(FSI) has been proven capable of reconstructing high-quality images. Due to the fact that the Fourier basis patterns(also known as grayscale sinusoidal patterns)cannot be well displayed on the digital micromirror device(DMD), a fast FSI system is proposed to solve this problem by binarizing Fourier pattern through a dithering algorithm. However, the traditional dithering algorithm leads to low quality as the extra noise is inevitably induced in the reconstructed images. In this paper, we report a better dithering algorithm to binarize Fourier pattern, which utilizes the Sierra–Lite kernel function by a serpentine scanning method. Numerical simulation and experiment demonstrate that the proposed algorithm is able to achieve higher quality under different sampling ratios.

关键词： single-pixel imaging binary Fourier basis pattern the dithering algorithm

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Non-Markovian enhanced temperature sensing in a dipolar Bose-Einstein condensate

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Physical Review A 2023年第2期108卷 022608-022608页

作者： Lan Xu Ji-Bing Yuan Shi-Qing Tang Wei Wu Qing-Shou Tan Le-Man Kuang School of Physics and Chemistry Hunan First Normal University Changsha 410205 China Department of Physics and Electronic Information Science Hengyang Normal University Hengyang 421002 China Key Laboratory of Theoretical Physics of Gansu Province and Lanzhou Center for Theoretical Physics Lanzhou University Lanzhou 730000 China Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang 414000 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China Synergetic Innovation Academy for Quantum Science and Technology Zhengzhou University of Light Industry Zhengzhou 450002 China

We present a method for implementing quantum temperature sensing for extremely low temperatures in a quasi-1D dipolar Bose-Einstein condensate reservoir with a magnetic field-driven impurity atom acting as a quantum sensor. By analyzing the quantum signal-to-noise ratio (QSNR) as a metric for temperature sensing performance, we demonstrate that the presence of an attractive dipolar interaction in the reservoir, which includes the effects of non-Markovian dynamics on the sensor, significantly enhances estimation efficiency. We also investigate the steady-state estimation efficiency for long-encoding times through an analytical expression, which shows that the optimal QSNR depends on the driving magnetic field of the impurity atom. Our method can achieve high-efficiency temperature sensing for any low temperature by tuning the magnetic field. These findings suggest that our approach has potential applications in high-resolution quantum thermometry.

关键词： Open quantum systems & decoherence Quantum metrology Bose-Einstein condensates

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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Probing spatial effects on quantum reservoir sensing in quasi-one-dimensional Bose-Einstein condensates

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Physical Review Research 2025年第2期7卷 023100-023100页

作者： Lan Xu Ya Yang Yunlan Zuo Abdelâali Boudjemâa Qing-Shou Tan School of Physics and Chemistry Hunan First Normal University Changsha 410205 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of the Ministry of Education Hunan Normal University Changsha 410081 China Department of Physics Faculty of Exact Sciences and Informatics and Laboratory of Mechanics and Energy Hassiba Benbouali University of Chlef P.O. Box 78 02000 Chlef Algeria Key Laboratory of Hunan Province on Information Photonics and Freespace Optical Communication College of Physics and Electronics Hunan Institute of Science and Technology Yueyang 414000 China

We explore the impact of spatial separation between two sensor qubits on quantum sensing performance within a quasi-one-dimensional Bose-Einstein condensate reservoir. By analyzing the quantum Fisher information and quantum signal-to-noise ratio for sensor qubits initialized in both entangled and product states, we reveal distinct behaviors in estimating key reservoir parameters, including spectral density coupling strength and temperature. For coupling strength estimation, smaller spatial distances between the sensor qubits enhance sensing precision. In contrast, for temperature sensing, the spatial separation emerges as a valuable resource, enabling significant improvements in estimation accuracy. Additionally, we discover that product states may outperform entangled states for parameter estimation in quantum reservoir sensing. These findings provide insights into leveraging spatial configurations and state initialization for optimizing quantum sensing protocols in quantum reservoirs.

关键词： Quantum entanglement

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