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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Talla Nana, Yannick Abel Liu, Gang Tienin, Bole Wilfried Southwest Jiaotong University School of Information Science and Technology Chengdu China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798331517786

This paper presents research on the use of multiple reconfigurable intelligent surfaces (RIS) in high-speed railway (HSR) tunnel communication systems for signal quality improvement and spectral efficiency (SE) maximization. This is done by placing multiple RISs on opposite sides of the tunnel, which then relay the signal to the onboard mobile relay (MR) of the high-speed train (HST), as it passes through the tunnel. To achieve our objective, a joint beamforming at Basestation (BS) and RISs phase shift SE optimization problem is formulated. The formulated problem is non-convex and hard to solve using existing conventional mathematical optimization techniques. We proposed a special class of deep reinforcement learning (DRL) called rank-based batch prioritization replay deep deterministic policy gradient (RBPR-DDPG) to solve the problem. The performance of the proposed solution is then compared with an adapted version of benchmark uniform random batch replay deep deterministic policy gradient (URBR-DDPG). Simulation results show that the placement of multiple RISs along the HSRtunnel line not only leads to signal quality improvement in the tunnel but also the maximization of the signal SE at the HST mobile relay. © 2024 IEEE.

关键词： Railroads

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Voxel-RCNN Improved Algorithm Based on Atrous Spatial Pyramid Pooling 4

Voxel-RCNN Improved Algorithm Based on Atrous Spatial Pyrami...

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4th International Conference on electronic information engineering and Computer science, EIECS 2024

作者： Yao, Kai Zhao, Haili Zhao, Dongjie Meng, Jiahe Liang, Meihui School of Electronic Information Engineering Changchun University of Science and Technology Changchun China School of Optoelectronic Engineering Changchun University of Science and Technology Changchun China

ISBN: (纸本)9798331531409

In order to solve the problem of poor bicycle and pedestrian detection under sparse point cloud conditions in autonomous driving, an improved Voxel-RCNN algorithm is proposed to optimize the performance by introducing a dilated spatial pyramid structure while minimizing the computational cost and model complexity. Voxel-RCNN uses 3D sparse convolution with a fixed-size kernel to extract target features, which may not be suitable for the detection of objects of different sizes and limits the information interaction of long-distance features. To this end, we design a sparse atrous spatial pyramid pooling (SASPP) module to expand the receptive field of the model and fuse multi-scale features. For the same purpose, a ASPP module is integrated in the 2D backbone network. The experimental results show that the improved algorithm has achieved significant improvement in the detection of bicycles and pedestrians, especially on difficult samples. The pedestrian detection accuracy is improved by 4.65%, and the bicycle detection accuracy is improved by 4.23%. These results demonstrate that our algorithm is effective for difficult-to-detect objects such as bicycles and pedestrians. © 2024 IEEE.

关键词： Bicycles

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Adaptive Q-Learning Trajectory Optimization for the Hybrid NOMA and OMA Assisted UAV Communications Network 1

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2nd International Conference on Internet of Things, Communication and Intelligent technology, IoTCIT 2023

作者： Feng, Simeng Zhang, Yunyi Liu, Kai Li, Baolong School of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China School of Electronic and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (数字)9789819727575

ISBN: (纸本)9789819727568

Benefit to the advantages of easy deployment and high flexibility, unmanned aerial vehicle (UAV) has been utilized to act as the aerial base station, providing communications service for target areas, such as remote regions and disaster areas. However, with the ever-increasing demand of high-speed and high-quality connections, the efficient multiple access constitutes the main challenge of the UAV communications network. Therefore, in this paper, we propose a hybrid multiple access strategy for UAV communications network, where both the non-orthogonal multiple access (NOMA) and the orthogonal multiple access (OMA) technology are invoked for the sake of efficiently handling the multi-users data-hungry connections. To expound, an adaptive Q-learning based trajectory optimization algorithm is conceived, which is capable of successively solving the problem of user clustering, power allocation and UAV’s trajectory, yielding the maximized achievable throughput. The numerical simulation results demonstrate that the proposed scheme has superiority in terms of average coverage and achievable rate, compared to that of the conventional OMA and NOMA schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Trajectories

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OCDM with Index Modulation for Autonomous Underwater Vehicles Communication

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-16页

作者： Jia, Zhenyu Zhang, Rongxin Chen, Zhong Yuan, Fei Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Xiamen University Xiamen China School of Electronic Science and Engineering Xiamen University Xiamen China

Autonomous underwater vehicles (AUVs) equipped with acoustic modems are currently one of the important means of obtaining underwater environment data such as sounds and images. They rely on high-speed underwater acoustic (UWA) communication technology for data transmission tasks. Orthogonal chirp division multiplexing (OCDM) is a multicarrier method based on chirp spread spectrum (CSS) which has better reliability in frequency selective fading channels than orthogonal frequency division multiplexing (OFDM). In this work, we combine index modulation (IM) and propose an OCDM-IM system that provides faster data rates when it activates the same number of subchirps as OCDM. On the other hand, it activates fewer subchirps when the data rate is the same, so it has better anti-interference capability. The simulation results under a measured UWA channel and random channels and the experimental results show that OCDM-IM has a better bit-error rate (BER) performance than that of OCDM and OFDM-IM systems when the subchirps are not fully loaded. In particular, when a small number of subchirps are activated, OCDM-IM is able to provide up to 2x bit rate with more than 5 dB improvement in BER performance. Our simulation and experimental results also show that IM has much better reliability than constellation mapping when Doppler effect is considered. Therefore, OCDM-IM is more suitable for the communication of AUVs. IEEE

关键词： Peak to average power ratio

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One stone,three birds:up-conversion,photothermal and p-n heterojunction to boost BiOBr:Yb^(3+),Er^(3+)/Cu_(3)Mo_(2)O_(9) full spectrum photodegradation

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Frontiers of Chemical science and engineering 2024年第10期18卷 119-131页

作者： Xintong Yao Dong Zhang Yupeng Liu Yanzhao Chen Dafeng Zhang Junchang Liu Xue-Yang Ji Hengshuai Li Peiqing Cai Xipeng Pu School of Materials Science and Engineering Shandong Provincial Key Laboratory of Chemical Energy Storage and Novel Cell TechnologyLiaocheng UniversityLiaocheng 252000China School of Physics Science and Information Technology Shandong Key Laboratory of Optical Communication Science and TechnologyLiaocheng UniversityLiaocheng 252000China College of Optical and Electronic Technology China Jiliang UniversityHangzhou 310018China

Broadening spectral response range to realize the full spectrum photocatalysis is crucial to develop photocatalysts with satisfactory light-energy conversion ability.A full-spectrum driven p-n heterojunction photocatalytic system was rationally designed through introducing the Er^(3+)/Yb^(3+)co-doped BiOBr with up-conversion effect as the collector of near infrared light and photocatalysts ***,Cu_(3)Mo_(2)O_(9) with the photothermal effect as a heat source to accelerate the reaction at the surface through absorbing the near infrared *** photocatalytic activity of BiOBr:Yb^(3+),Er^(3+)/Cu3Mo2O9 composite was markedly strengthened under visible and near infrared light irradiation,and the BiOBr:Yb^(3+),Er^(3+)/Cu_(3)Mo_(2)O_(9)-5 composite displayed the optimal photodegradation activities for 0.03372 min^(-1) and 0.058 h^(-1),being 2.3-folds and 2.4-folds than that of pure BiOBr:Yb^(3+),Er^(3+)under the visible and near infrared light,*** position of doped ions(Yb^(3+)and Er^(3+))in BiOBr:Yb^(3+),Er^(3+)was determined from the X-ray absorption fine structure *** the reasonable mechanism of p-n heterojunction was proposed base on the results of experimental and density functional theory *** work provides a rational strategy for the design and development of full-spectrum heterojunction photocatalysts with the up-conversion and photothermal effects to increase the photocatalytic performance.

关键词： photocatalyst full spectrum p-n heterojunction photothermal effect up-conversion

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Path planning for service robot based on improved A* algorithm 6

Path planning for service robot based on improved A* algorit...

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6th International Conference on information science, Electrical, and Automation engineering, ISEAE 2024

作者： Zhao, Fanghua Chai, Yunzhou Wang, Lihua School of Electronic Information Engineering Shandong University of Science and Technology Shandong266000 China

ISBN: (纸本)9781510682924

Aiming at the problems of high memory occupancy and too many inflection points and search points of A* algorithm in large-scale environment, an improved A* algorithm is proposed. Firstly, the adaptive adjustment of the heuristic function is realized by introducing dynamic weights to obtain the optimal path, and secondly, the cubic Bezier curve is used to smooth the path to reduce the number of inflection points. The simulation results show that the improved A* algorithm can significantly improve the path planning efficiency of service robots and reduce the task time of service robots. © 2024 SPIE.

关键词： Robot programming

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Deep Learning-Based Species Classification Using Joint ResNet and Data Augmentation 5th

Deep Learning-Based Species Classification Using Joint ResNe...

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5th International Conference on 3D Imaging Technologies-Multidimensional Signal Processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Zhang, Genpei School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819751839

Biodiversity conservation in modern society is of paramount importance, given the decline in species globally. This research delves into the realm of species identification, an essential element for effective biodiversity protection. By leveraging advanced image classification techniques, this study aims to automate and refine species classification. Specifically, a novel methodology integrating Residual Network (ResNet), particularly its ResNet50 and ResNet101 versions, with data augmentation techniques is introduced. The method capitalizes on ResNet's depth and robustness while harnessing data augmentation to enhance dataset versatility. This study is conducted on the Stanford Dogs dataset, a comprehensive collection of over 20,580 images spanning 120 dog breeds. The results highlight a significant improvement in classification accuracy, with ResNet models outperforming several benchmark models. Experimental results show that the combination of ResNet and data augmentation yields superior performance in species classification tasks. The findings of this research not only push the boundaries of image-based species identification but also offer valuable insights for real-world biodiversity conservation initiatives, fostering more informed and effective protection strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Abiotic

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Research on Small Target Detection Method for Industrial Safety Helmets Based on Improved YOLOv8

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Journal of Computing and information technology 2023年第2期31卷 123-136页

作者： Yuanshuai, Lan Mo, Chen Chuan, Li Qian, Wang Min, Liao School of Electronic Information Engineering Geely University Sichuan Chengdu China Chengdu College University of Electronic Science and Technology of China Sichuan Chengdu China

Industrial safety helmets are crucial personal protec-tive gear but detecting them as small targets in complex environments is challenging. This work proposes enhancements to the YOLOv8 object detection frame-work, specifically incorporating a spatial-to-depth (SPD) convolution module and a large selective kernel network (LSKNet). SPD-Conv combines spatial-to-depth layers and non-strided convolutions to retain fine-grained information when downscaling feature maps, while LSKNet introduces dynamic spatial selection and attention for refined context modeling. Our customized model is trained on a dataset of construction hardhat images captured via drones. Quantita-tive results showcase higher precision and recall over baseline YOLOv8, surpassing competing YOLOv5 versions. An optimized final model outcomes demonstrate accuracy exceeding 90% validation in mAP metric after 200 training rounds. By tackling limitations posed by small, obscured industrial safety gears, this enhanced real-time detection approach provides indispensable technological support for bolstering workplace hazard identification and prevention. © 2023, university of Zagreb Faculty of Electrical engineering and Computing. All rights reserved.

关键词： Accident prevention

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Accelerating Federated Learning with Adaptive Test-Driven Quantization at Edge Network 24

Accelerating Federated Learning with Adaptive Test-Driven Qu...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Liu, Yutao Fan, Chenyu Zhang, Xiaoning University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350363760

With the rapid development of 6G wireless communication and edge computing in recent years, mobile devices and terminals have collected vast amounts of data to train Machine Learning (ML) models. Since communication and computing resource constraints and data privacy exist for ML training at edge networks, the concept of Federated Learning (FL) is proposed to solve the above problems as a promising distributed ML paradigm. In FL, each participating client trains the local model and sends the local model upgrade to the central parameter server until the training process is completed. However, FL encounters a low bandwidth problem for parameter aggregation at network edges. To this end, in this paper, we propose the Test-Driven Adaptive Quantization (TAQ) method for FL. In TAQ, each client dynamically adopts a quantization level according to the training accuracy on a public testing dataset for the purpose of balancing communication overhead and training accuracy. In addition, we conduct a theoretical analysis of the convergence of TAQ. Detailed experimental results indicate that TAQ outperforms the state-of-the-art techniques, achieving a significant improvement in convergence speed ranging from 21% to 53%. © 2024 IEEE.

关键词： Federated learning

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Two-stage low-loss waveguide taper for O-band wavelength multiplexer

Two-stage low-loss waveguide taper for O-band wavelength mul...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Liu, Jiayu Zhang, Chongfu Chen, Jiahong Zeng, Wenjun Hu, Pengjv Liu, Qingran School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350379266

关键词： Wavelength division multiplexing

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