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Mode-locked fiber lasers at 1064 and 910 nm wavelengths using Nd^(3+)-doped silica fiber

Chinese Optics Letters 2024年第9期22卷 102-107页

作者： He'nan Shen Xilong Zhao Fei Yu Yazhou Wang Yafei Wang Yan Sun Shikai Wang Yinggang Chen Zhongqing Jia Ruizhan Zhai Chunlei Yu Lili Hu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Institute of Optics and Fine Mechanics Chinese Academy of SciencesShanghai 201800China University of Chinese Academy of Sciences Beijing 100049China Laser Institute Qilu University of TechnologyQingdao 266000China Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou 310024China

Mode-locked lasing operations at 1064 and 910 nm wavelengths are demonstrated,respectively,in two all-fiber laser oscillators using our homemade Nd^(3+)-doped silica fiber(NDF)as the gain *** Al3+/Nd^(3+)co-doped silica core glass was fabricated by the modified sol-gel method with 18,300×10^(-6) Nd^(3+)doping *** NDF drawn by the rodin-tube method has a core of 4μm in diameter and a numerical aperture(NA)of *** 1064 nm,we measure an average laser output power of 18mWwith a pulse duration of 5.75 ps,a pulse energy of 1.14 nJ,and a slope efficiency of 7.2%.Using the same NDF gain fiber of a different length,a maximum average laser output power is 3.1 mW at 910 nm with a pulse duration of 877 ns,a pulse energy of 2.7 nJ,and a slope efficiency of 1.44%.

关键词： all-normal dispersion Nd3+-doped fiber filter bandwidth mode-locked fiber laser

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A Novel Visualization System of Using Augmented Reality in Knee Replacement Surgery: Enhanced Bidirectional Maximum CorrentropyAlgorithm

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International Journal of Medical Robotics and computer Assisted Surgery 2023年 e2154页

作者： Maharjan, Nitish Alsadoon, Abeer Prasad, P.W.C. Abdullah, Salma Rashid, Tarik A. School of Computing and Mathematics Charles Sturt University Sydney Campus Australia Department of Information Technology Study Group Australia Sydney Campus Australia Department of Computer Engineering University of Technology Baghdad Iraq Computer Science and Engineering University of Kurdistan Hewler KRG Erbil Iraq

Background and aim: Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. Methodology: markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction. Results: This study has improved video precision to 0.57 mm ∼ 0.61 mm alignment error. Furthermore, with the use of bidirectional points, i.e. Forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improved the processing time as well. The processing time of video frames was improved to 7.4 ∼11.74 fps. Conclusions: It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

关键词： Image registration

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DRL-Driven Localization With UAV in Near-field Communications

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IEEE Internet of Things Journal 2025年

作者： Khan, Muhammad Fawad Peng, Limei Ho, Pin-Han Chen, Yuguang Dong, FangJie Kyungpook National University School of Computer Science and Engineering Daegu41566 Korea Republic of University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Guangdong China University of Waterloo Department of Electrical and Computer Engineering ON Canada Shenzhen Unicom Innovation Business Capability Center China National Health Commission's Center for Statistics and Information China

In this paper, we propose a Deep Reinforcement Learning (DRL)-based Multi-Point Localization Scheme (MLS) to efficiently localize IoT devices using a single Unmanned Aerial Vehicle (UAV) equipped with a large-scale multi-antenna configuration in near-field communication (NFC). By utilizing the spherical wave-based near-field steering vector, the multi-antenna array on the UAV captures both the Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI) measurements from IoT devices to estimate their locations relative to the position of the UAV. This approach eliminates the need for multiple hovering points required by a single-antenna UAV (SA-UAV) or the deployment of multiple SA-UAVs. To enhance localization accuracy, key hovering points for the multi-antenna UAV (MA-UAV) are strategically selected, with weights assigned based on signal strength to prioritize stronger and more reliable signals. Furthermore, DRL dynamically adjusts the position of the MA-UAV to optimize the trade-off between localization accuracy and energy consumption. Extensive simulations conducted across rural, urban, and dense urban scenarios demonstrate that the proposed DRL-based MLS significantly improves localization accuracy while reducing the energy consumption of the UAV. © 2025 IEEE.

关键词： Deep reinforcement learning

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On a Piecewise Linear Function Approximation for Quantum Computation

On a Piecewise Linear Function Approximation for Quantum Com...

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IEEE Asia-Pacific Conference on Circuits and Systems

作者： Hideaki Okazaki Dept. of Informatics Faculty of Informatics Computer Science Course (or Field) Graduate school of Electrical and Information Engineering Shonan Institute of Technology Fujisawa Kanagawa Japan

In quantum computing, although correspondences between quantum circuits and polynomials have been explored, a correspondence between a quantum circuit and a low-degree polynomial, even a linear function has not fully clarified. In this paper, a quantum circuit approximating a linear function for building piecewise linear functions, is proposed. First, for qubit operations, a dimensionless form in $[0,1]$ of a linear function is provided. Secondly, a theorem of a parameterized quantum circuit approximating linear functions are provided. Thirdly, by using Yao: an open source Julia package, the quantum circuit simulation results to approximate the linear functions are presented. Finally, the principal results are summarized.

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3D Object Detection Based on Spatially Adaptive Sparse Convolution

3D Object Detection Based on Spatially Adaptive Sparse Convo...

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Future Machine Learning and Data science (FMLDS), IEEE International Conference on

作者： Jing Jing Hu Yu Ling Fan Ping Zhang Chang Duan Chao Zhang Si Yu Chen Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China School of Optoelectronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Information Southwest Petroleum University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

Autonomous driving has gradually gained attention in recent years due to its potential to reduce driver burden and improve safety, in which 3D object detection is the basis of the autonomous driving perception system, aiming at predicting the location, size, and class of 3D objects in the vicinity of an autonomous vehicle, which is an important guide for subsequent path planning, motion prediction, and so on. In particular, 3D object detection has difficulties in input point cloud data processing and multimodal fusion. Unlike the image distribution where pixels are regularly distributed in the image plane, the point cloud is a sparse and irregular 3D representation, so it is difficult to apply the traditional convolution directly on the point cloud for detection. In this paper, we carry out a method for 3D object detection for point clouds, using deformable convolution and sparse convolution as a tool to explore the feature extraction method of sparse convolution for irregular point clouds and propose a spatially adaptive sparse convolution-based method.

关键词： Point cloud compression Convolutional codes Three-dimensional displays Adaptive systems Convolution Object detection Feature extraction Kernel Autonomous vehicles Vehicles

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Polarization-modulated population distribution of nitrogen molecular ions in a strong laser field

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Physical Review A 2024年第5期109卷 053522-053522页

作者： Yewei Chen Chaohui Zhou Xu Lu Liang Xu Qi Yuan Siyi He Rongyu Gong Ning Zhang Quanjun Wang Shunlin Huang Yuzhu Wu Yi Liu Jinping Yao School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai 201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing 100049 China School of Microelectronics Shanghai University Shanghai 200444 China

We investigate the dependence of N2+ lasing radiation on the polarization ellipticity of the pump laser with the 900- and 980-nm wavelengths. Our results show that the 391-nm lasing radiation is stronger with the elliptically polarized laser in the 900-nm pumping case, while the lasing signal decays monotonically with the increasing ellipticity in the 980-nm pumping case. In contrast, the strongest 428-nm lasing appears with the linear polarization laser for both pumping cases. The theoretical simulations reproduce key observations and reveal that the physical origin underlying the ellipticity-dependent behaviors is the sensitivity of photoexcitation of different channels on the wavelength and polarization of the pump laser. At the 900-nm pump wavelength, one-photon excitation from X2Σg+ to A2Πu of N2+ plays a dominant role in the population inversion. The elliptically polarized laser is beneficial to the perpendicular transition between the two states. In the 980-nm pumping case, the three-photon excitation from X2Σg+ to B2Σu+, which prefers linear polarization pumping, dominates the population inversion. The 428-nm lasing gain mainly depends on the population on the B2Σu+ state, thus the strongest radiation always occurs in the case of linear polarization. The polarization-dependent behaviors of N2+ lasing reflect the complex population inversion mechanism in the strong-field-prepared ionic system.

关键词： Air lasing Electronic excitation & ionization Multiphoton or tunneling ionization & excitation Ultrafast phenomena

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Context-CAM: Context-Level Weight-Based CAM with Sequential Denoising to Generate High-Quality Class Activation Maps

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IEEE Transactions on Image Processing 2025年 34卷 3431-3446页

作者： Du, Jie Chen, Wenbing Vong, Chi-Man Liu, Peng Wang, Tianfu Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Medical School Guangdong Shenzhen 518060 China University of Macau Department of Computer and Information Science 999078 Macao University of Electronic Science and Technology of China Artificial Intelligence Industrial Innovation Research Center Shenzhen Institute for Advanced Study Shenzhen 518110 China

Class activation mapping (CAM) methods have garnered considerable research attention because they can be used to interpret the decision-making of deep convolutional neural network (CNN) models and provide initial masks for weakly supervised semantic segmentation (WSSS) tasks. However, the class activation maps generated by most CAM methods usually have two limitations: 1) a lack of the ability to cover the whole object when using low-level features;and 2) introducing background noise. To mitigate these issues, an innovative Context-level weights-based CAM (Context-CAM) method is proposed, which guarantees: 1) the non-discriminative regions that have similar appearances and are located close to the discriminative regions can also be highlighted by the newly designed Region-Enhanced Mapping (REM) module using context-level weights;and 2) the background noises are gradually eliminated via a newly proposed Semantic-guided Reverse Sequence Fusion (SRSF) strategy that can sequentially denoise and fuse the region-enhanced maps from the last layer to the first layer. Extensive experimental results show that our Context-CAM can generate higher-quality class activation maps than classic and state-of-the-art (SOTA) CAM methods in terms of the Energy-Based Pointing Game (EBPG) score, and the improvements are up to 35.49% when compared to the second-best method. Moreover, for WSSS tasks, our Context-CAM can directly replace the CAM method used in existing WSSS methods without any architectural modification to further improve the segmentation performance. © 1992-2012 IEEE.

关键词： Class activation mapping context-level weights map fusion weakly supervised semantic segmentation

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A Digital Twin-Based Intelligent Network Architecture for Underwater Acoustic Sensor Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Song, Shanshan Huangfu, Bingwen Guo, Jiani Liu, Jun Cui, Junhong Shen, Xuemin Beihang University School of Electronic and Information Engineering Beijing100191 China Jilin University College of Computer Science and Technology Changchun130012 China UESTC Shenzhen Institute for Advanced Study Shenzhen China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs' environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks' demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation;(ii) greatly reduce the training time of AI algorithms;(iii) more rapidly obtain network status for multi-task scheduling at a low cost. © 2002-2012 IEEE.

关键词： Resource allocation

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Calibration-Free Raw Image Denoising via Fine-Grained Noise Estimation

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5368-5384页

作者： Yunhao Zou Ying Fu Yulun Zhang Tao Zhang Chenggang Yan Radu Timofte Beijing Laboratory of Intelligent Information Technology School of Computer Science and Technology Beijing Institute of Technology Beijing China MIIT Key Laboratory of Complex-field Intelligent Exploration Beijing Institute of Technology Beijing China Shanghai Jiao Tong University Shanghai China School of Communication Engineering Hangzhou Dianzi University Hangzhou China Lishui Institute of Hangzhou Dianzi University Lishui China Computer Vision Lab IFI and CAIDAS University of Würzburg Würzburg Germany

Image denoising has progressed significantly due to the development of effective deep denoisers. To improve the performance in real-world scenarios, recent trends prefer to formulate superior noise models to generate realistic training data, or estimate noise levels to steer non-blind denoisers. In this paper, we bridge both strategies by presenting an innovative noise estimation and realistic noise synthesis pipeline. Specifically, we integrates a fine-grained statistical noise model and contrastive learning strategy, with a unique data augmentation to enhance learning ability. Then, we use this model to estimate noise parameters on evaluation dataset, which are subsequently used to craft camera-specific noise distribution and synthesize realistic noise. One distinguishing feature of our methodology is its adaptability: our pre-trained model can directly estimate unknown cameras, making it possible to unfamiliar sensor noise modeling using only testing images, without calibration frames or paired training data. Another highlight is our attempt in estimating parameters for fine-grained noise models, which extends the applicability to even more challenging low-light conditions. Through empirical testing, our calibration-free pipeline demonstrates effectiveness in both normal and low-light scenarios, further solidifying its utility in real-world noise synthesis and denoising tasks.

关键词： Noise Data models Pipelines Estimation Noise reduction Image denoising Training Noise measurement Adaptation models Cameras

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S-INF: Towards Realistic Indoor Scene Synthesis via Scene Implicit Neural field 39

S-INF: Towards Realistic Indoor Scene Synthesis via Scene Im...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Zixi Xu, Guowei Wu, Haifeng Huang, Ye Li, Wen Duan, Lixin Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China Sichuan Provincial Key Laboratory for Human Disease Gene Study The Center for Medical Genetics Department of Laboratory Medicine Sichuan Academy of Medical Sciences Sichuan Provincial People’s Hospital UESTC China

ISBN: (纸本)157735897X

Learning-based methods have become increasingly popular in 3D indoor scene synthesis (ISS), showing superior performance over traditional optimization-based approaches. These learning-based methods typically model distributions on simple yet explicit scene representations using generative models. However, due to the oversimplified explicit representations that overlook detailed information and the lack of guidance from multimodal relationships within the scene, most learning-based methods struggle to generate indoor scenes with realistic object arrangements and styles. In this paper, we introduce a new method, Scene Implicit Neural field (S-INF), for indoor scene synthesis, aiming to learn meaningful representations of multimodal relationships, to enhance the realism of indoor scene synthesis. S-INF assumes that the scene layout is often related to the object-detailed information. It disentangles the multimodal relationships into scene layout relationships and detailed object relationships, fusing them later through implicit neural fields (INFs). By learning specialized scene layout relationships and projecting them into S-INF, we achieve a realistic generation of scene layout. Additionally, S-INF captures dense and detailed object relationships through differentiable rendering, ensuring stylistic consistency across objects. Through extensive experiments on the benchmark 3D-FRONT dataset, we demonstrate that our method consistently achieves state-of-the-art performance under different types of ISS. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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