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Spatial Super-channel Provisioning in Fiber Space Division Multiplexing (FSDM) Optical networks

Journal of Lightwave technology 2025年第10期43卷 4591-4606页

作者： Han, Shudan Li, Yongcheng Chang, Zeshan Li, Liangchuan Chang, Tianhai Shen, Gangxiang Soochow University Jiangsu New Optical Fiber Technology and Communication Network Engineering Research Center Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information Engineering Jiangsu215006 China Huawei Technology Shenzhen China

Space Division Multiplexing (SDM) and Spatial Super-Channel (Spa SCh) technology provide scalable solutions to meet the growing capacity demands of optical networks. This study focuses on multi-fiber optical networks integrated with innovative Spa SChs, enabling the creation of various Spa SCh granularities by spatially combining the same spectrum across multiple fibers, a transmission method referred to as Fiber-SDM (FSDM). To assess the performance of the FSDM-based network, we develop an analytical model to predict the blocking probability (BP) of Spa SChs with different granularities. Additionally, we propose an efficient adaptive Routing, Fiber, and Spectrum Assignment (RFSA) algorithm, based on the concepts of Spatial Spectrum Window (SSW) and the SSW plane (SSWP). Simulations are conducted to evaluate lightpath BP, considering the trade-off between system cost and power consumption. The results demonstrate the accuracy of the analytical model in predicting lightpath BP in the FSDM network. Furthermore, the Adaptive SSW Plane (A-SSWP) algorithm outperforms the benchmark RFSA algorithms, achieving the best performance under an optimal Spa SCh configuration. © 1983-2012 IEEE.

关键词： Blocking probability

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Improved reference-frame-independent quantum key distribution with intensity fluctuations

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Chinese Physics B 2025年第2期34卷 48-52页

作者： Zi-Qi Chen Hao-Bing Sun Ming-Shuo Sun Qin Wang Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China Broadband Wireless Communication and Sensor Network Technology Key Laboratory of Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China

Reference-frame-independent quantum key distribution(RFI-QKD) can avoid real-time calibration operation of reference frames and improve the efficiency of the communication process. However, due to imperfections of optical devices,there will inevitably exist intensity fluctuations in the source side of the QKD system, which will affect the final secure key rate. To reduce the influence of intensity fluctuations, an improved 3-intensity RFI-QKD scheme is proposed in this *** considering statistical fluctuations and implementing global parameter optimization, we conduct corresponding simulation analysis. The results show that our present work can present both higher key rate and a farther transmission distance than the standard method.

关键词： quantum key distribution intensity fluctuations decoy-state method

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Several Points are all It Takes: Saluting User-Assisted Single Image Reflection Removal

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IEEE Signal Processing Letters 2025年 32卷 1735-1739页

作者： He, Lingzhi Chang, Yakun Zhao, Yao Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Visual Intellgence +X International Cooperation Joint Laboratory of MOE Beijing100044 China

Reflection removal is essential for applications in photography, object detection, and augmented reality. Single-image reflection removal (SIRR) offers greater flexibility and applicability than multi-image methods, making it ideal for real-time scenarios. However, strong reflections obscure large portions of the transmission layer, limiting the performance of existing methods. We propose a novel user-assisted approach for SIRR, where users annotate occluded objects by selecting their categories and locations. This provides critical semantic information to guide accurate transmission layer recovery. Additionally, we design a hybrid CNN-Transformer network that leverages local feature extraction and global context modeling to address strong reflection challenges. Experiments on the strong reflection datasets demonstrate the effectiveness of our method, achieving significant improvements in transmission layer recovery and outperforming existing advanced methods across multiple metrics. © 1994-2012 IEEE.

关键词： Feature extraction

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Improved calibration and 3D reconstruction for micro fringe projection profilometry

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Optics Express 2025年第6期33卷 13455-13471页

作者： Zhang, Chenfan Zhu, Dongchen Shi, Wenjun Wang, Lei Li, Jiamao Bionic Vision System Laboratory State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China

Micro fringe projection profilometry (MFPP) is a crucial three-dimensional measurement technology widely used in industrial inspection. As the system measurement accuracy is limited by calibration and reconstruction errors, this paper proposes a joint calibration of telecentric camera and projector, and develops noise-robust 3D reconstruction, ultimately forming a high-precision MFPP for microscopic object measurement. Specifically, the optimal parameters for the telecentric camera model are determined by constructing a virtual 3D calibration target, which circumvents the limitations that plane-based calibration methods often produce local optimal solutions. In response to the presence of phase errors, a method employing global homography is proposed to refine the projector pixel coordinates of feature points, thus enhancing the calibration accuracy of the projector. Furthermore, we integrate epipolar constraints into the reconstruction process, which helps eliminate noise points and optimizes the reconstruction results by constructing epipolar deviation. Extensive experiments demonstrate that our MFPP system can achieve micron-level measurement accuracy. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Profilometry

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DNN Task Assignment in UAV networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13340-13352页

作者： Tang, Xin Chen, Qian Weng, Wenjie Liao, Binhan Wang, Jiacheng Cao, Xianbin Li, Xiaohuan Guilin University of Electronic Technology Guangxi University Key Laboratory of Intelligent Networking and Scenario System School of Information and Communication Guilin541004 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Guilin University of Electronic Technology School of Architecture and Transportation Engineering Guilin541004 China Nanning530001 China Beihang University School of Electronic and Information Engineering Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beijing100191 China

Unmanned Aerial Vehicles (UAVs) offer high mobility and flexible deployment capabilities, making them ideal for Internet of Things (IoT) applications. However, the substantial amount of data generated by various applications within the existing low-altitude network requires processing through Deep Neural networks (DNN) on UAVs, which is challenging due to their limited computational resources. To address this issue, we propose a two-stage optimization method for flight path planning and task allocation based on a mother-child UAV swarm system. In the first stage, we employ a greedy algorithm to solve the path planning problem by considering the task size of the target area to be inspected and the shortest flight path as constraints. The goal is to minimize both the flight path of the UAV and the overall cost of the system. In the second stage, we introduce a novel DNN task assignment algorithm that combines Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Generative Diffusion Models (GDM), named GDM-MADDPG. This algorithm takes advantage of the reverse denoising process of GDM to replace the actor network in MADDPG. It enables UAVs to generate specific DNN task assignment actions based on agents' observations in a dynamic environment, thereby improving the efficiency of task assignment and overall system performance. The simulation results demonstrate that our algorithm outperforms the benchmarks in terms of path planning, Age of information (AoI), task completion rate, and system utility, demonstrating its effectiveness. © 2014 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Distributed Bundle Adjustment Based on Penalty Function Method

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IEEE Robotics and Automation Letters 2025年第6期10卷 5449-5456页

作者： Sun, Qixuan Zhang, Guanghui Zhu, Dongchen Wang, Lei Zhang, Xiaolin Li, Jiamao Chinese Academy of Sciences Bionic Vision System Laboratory State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China

Bundle Adjustment (BA) aims to estimate the camera poses and build maps utilizing the nonlinear optimization algorithm. The update step of the optimization is obtained by solving a linear system, which is the bottleneck of the BA efficiency. Many works perform bundle adjustment in a distributed manner to reduce the computational cost of solving the linear system and achieve outstanding performance. However, the distributed methods implement the constrained optimization method that involves the computation of the Lagrange multiplier, causing extra overhead. To improve the efficiency of BA, we propose a novel distributed algorithm based on the penalty method, dubbed PenBA, that formulates an unconstrained problem and hence saves the computation of extra variables. To solve the unconstrained linear system with low cost, we design a novel distributed Preconditioned Conjugate Gradient (PCG) which has complexity bounded by hyper-parameters. It can be proved that, given the parameter update strategy of the optimization method, the penalty linear system can be solved with fast convergence. Experimental results in the BAL dataset illustrate that our penalty method outperforms the Lagrange multiplier method and conventional BA. Compared with the most accurate conventional PCG-based BA solver, PenBA decreases the objective values by 3.8%, the time per iteration by 17% and the maximum memory consumption by 24%. Experimental results in the SfM and SLAM datasets prove that PenBA applies to both computer vision and robotics contexts. © 2016 IEEE.

关键词： Constrained optimization

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Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models 31

Chain-of-Specificity: Enhancing Task-Specific Constraint Adh...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wei, Kaiwen Zhong, Jiang Zhang, Hongzhi Zhang, Di Jin, Li Yu, Yue Zhang, Jingyuan College of Computer Science Chongqing University China Kuaishou Inc. China Key Laboratory of Network Information System Technology Aerospace Information Research Institute Chinese Academy of Sciences China

ISBN: (纸本)9798891761964

Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that LLMs sometimes struggle with adhering to specific constraints (e.g., in specific place or at specific time), at times even overlooking them, which leads to responses that are either too generic or not fully satisfactory. Existing approaches attempted to address this issue by decomposing, rewriting input instructions, or reflecting on prior failings, yet they fall short in adequately emphasizing specific constraints and in unlocking the underlying knowledge (e.g., programming within the context of software development). In response, this paper proposes a simple yet effective method Chain-of-Specificity (CoS). Specifically, CoS iteratively emphasizes the specific constraints in the input instructions, unlocks knowledge within LLMs, and refines responses. Experiments conducted on publicly available and self-build complex datasets demonstrate that CoS outperforms existing methods in enhancing generated content especially for the specificity. Besides, as the number of specific constraints increase, other baselines falter, while CoS still performs well. Moreover, we show that distilling responses generated by CoS effectively enhances the ability of smaller models to follow the constrained instructions. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Improving Sensing Performance of Plate-Type Fiber-Optic Accelerometers Using Noncircular Fiber-Optic Coils

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IEEE Sensors Journal 2025年第9期25卷 15080-15091页

作者： Lu, Tan Wang, Shun Wen, Kunhua Yang, Jun Mo, Haohao Jiang, Ruiyao Ping, Xinyang An, Ran Wang, Yuncai Qin, Yuwen Guangdong University of Technology School of Physics and Optoelectronic Engineering Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications Guangzhou510006 China Guangdong University of Technology Institute of Advanced Photonic Technology School of Information Engineering Key Laboratory of Photonic Technology for Integrated Sensing and Communication Ministry of Education of China Guangdong Provincial Key Laboratory of Information Photonics Technology Guangzhou510006 China Harbin Engineering University Key Laboratory of In-Fiber Integrated Optics Ministry of Education of China College of Physics and Optoelectronic Engineering Harbin150001 China

The commonly used mandrel-type or disk-type fiber-optic accelerometers (DT-FOAs) are inherently limited in their capacity to detect low-frequency signals due to their structural characteristics. The fiber-optic coil (FOC), as the core sensing component of an interferometric FOA, plays a crucial role in determining the sensor’s sensitivity through its strain distribution characteristics on the transducer plate. This article presents, for the first time, the novel introduction of noncircular FOCs (NC-FOCs), which are employed in the design of a rectangular thin-plate FOA and plate-tpye FOA (PT-FOA) to improve low-frequency detection performance. In this study, the finite element method (FEM) and tensor analysis theory are employed to establish a universal model for PT-FOA constructed with NC-FOCs. Sensors with FOCs of various shapes were designed, fabricated, and tested. Both theoretical and experimental results demonstrate that different shapes of FOCs can improve sensitivity by approximately 14.62%. The underlying mechanisms of these improvements are analyzed by studying the strain tensor distribution on the transducer plate. The proposed PT-FOA addresses the limitations of existing FOAs in low-frequency detection. The sensitivity enhancement method maintains the same resonant frequency and thermal noise levels while significantly improving the signal-to-noise ratio (SNR) and sensitivity of the sensor. This work provides a general modeling approach and valuable practical guidance for the design and optimization of high-sensitivity FOAs, with important implications for both research and application. © 2001-2012 IEEE.

关键词： Signal to noise ratio

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UDI-3DVT: A Novel network for 3-D Reconstruction via Uncertainty-Driven Sampling and Incremental Visual-Tactile Fusion

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IEEE Sensors Journal 2025年第6期25卷 10358-10369页

作者： Zhang, Chenyang Zhang, Shaohua Li, Yao Chen, Wenbai Gao, Guowei Wu, Peiliang Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province School of Information Science and Engineering Qinhuangdao066004 China Beijing Information Science and Technology University School of Automation Beijing100192 China

The fusion of visual and tactile senses allows robots to reconstruct and understand objects, aiding in downstream tasks such as object recognition and object grasping. However, most existing visual-tactile reconstruction methods typically overlook the role of visual point cloud processing in reconstruction, sampling points using generative-based methods rather than uncertainty-driven approaches. In addition, these methods only concatenate visual and tactile features, failing to fully leverage the complementary advantages of these two modalities, resulting in unsatisfactory reconstruction results. To address this issue, leveraging the open-source GelSight tactile sensor, this article proposes a novel method called UDI-3DVT, which reconstructs 3-D shapes by combining visual point clouds and multiple tactile images. Specifically, we explore the uncertainty of point clouds, performing additional sampling on high-uncertainty edge points, and propose an attention-based point cloud sampling method. Then, the framework leverages the efficiency of convolutional neural network (CNN) in image processing and the transformer's superiority in context modeling by using CNN for tactile features and transformer for visual features. A feature fusion module based on 1 × 1 convolution is designed to aggregate features from two branches. By gradually adding tactile features, we achieve incremental visual-tactile integration. Furthermore, since the contact-based perception of tactile sensors may cause slight deformations in objects, UDI-3DVT supports the reconstruction of both rigid and deformable objects. Extensive experiments demonstrate that our proposed framework outperforms current state-of-the-art (SOTA) methods, especially the Chamfer distance (CD) metric increased by 14.8%. © 2001-2012 IEEE.

关键词： Image reconstruction

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Turbulent drag reduction by sector-shaped counter-flow dielectric barrier discharge plasma actuator

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Chinese Physics B 2025年第2期34卷 362-370页

作者： Borui Zheng Shaojie Qi Minghao Yu Jianbo Zhang Linwu Wang Dongliang Bian School of Automation and Information Engineering Xi’an University of TechnologyXi’an 710048China School of Mechanical and Precision Instrument Engineering Xi’an University of TechnologyXi’an 710048China Science and Technology on Plasma Dynamics Laboratory Air Force Engineering UniversityXi’an 710038China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an 710048China Shaanxi University Key Laboratory of Photonic Power Devices and Discharge Regulation Xi’an 710048China

The primary objective in aircraft transportation is to minimize turbulent drag, thereby conserving energy and reducing emissions. We propose a sector-shaped counter-flow dielectric barrier discharge plasma actuator, which leverages jet synthesis for drag reduction. A drag control experiment was conducted in a low-speed wind tunnel with a controlled flow velocity of 9.6 m/s(Re = 1.445 × 10^(4)). This study investigated the effects of varying pulse frequencies and actuation voltages on the turbulent boundary layer. Using a hot-wire measurement system, we analyzed the pulsating and time-averaged velocity distributions within the boundary layer to evaluate the streamwise turbulent drag reduction. The results show that the local TDR decreases as the pulse frequency increases, reaching a maximum reduction of approximately 20.97% at a pulse frequency of 50 Hz. In addition, as the actuation voltage increases, the friction coefficient decreases, increasing the drag reduction rate. The maximum drag reduction of approximately 33.34% is achieved at an actuation voltage of 10 kV.

关键词： plasma flow control turbulent boundary layer turbulent drag reduction

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