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Asia communications and Photonics Conference and Exhibition (ACP)

作者： Shuailuo Huang Baolong Zhu Gang Qiao Yuyang Gao Mingqing Zuo Shikui Shen Zelin Wang Yongqi He Zhangyuan Chen Juhao Li China Unicom Research Institute Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Fundamental Network Technology China Mobile Research Institute Beijing China Pengcheng National Laboratory Shenzhen China

50-km weakly-coupled transmission based on a L-band 4-LP-mode FM-EDFA is experimentally demonstrated only adopting $\mathbf{2}\times \mathbf{2}$ or $\mathbf{4}\times \mathbf{4}$ MIMO-DSP.

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

50-km weakly-coupled transmission based on a L-band 4-LP-mode FM-EDFA is experimentally demonstrated only adopting $\mathbf{2}\times \mathbf{2}$ or $\mathbf{4}\times \mathbf{4}$ MIMO-DSP.

关键词： Asia Gain measurement Optical fiber communication MIMO L-band Photonics

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Large Scale Model Enabled Semantic communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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CALCULATION OF RAIN ATTENUATION AT MILLIMETER WAVELENGTHS BY MODELLING REALISTIC NON-ROTATIONAL SYMMETRIC RAIN DROPS DURING TURBULENT WEATHER SITUATIONS 27

CALCULATION OF RAIN ATTENUATION AT MILLIMETER WAVELENGTHS BY...

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27th Ka and Broadband communications Conference and the 39th International communications Satellite Systems Conference, ICSSC 2022

作者： Teschl, Franz Thurai, Merhala Steger, Sophie Schönhuber, Michael Graz University of Technology Institute of Communication Networks and Satellite Communications Graz8010 Austria Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States Graz University of Technology Graz8010 Austria JOANNEUM RESEARCH Forschungsgesellschaft MbH Graz8010 Austria

When determining scattering and attenuation of microwaves due to rain, so far, mainly rotationally symmetric rain drops are considered. This is true for remote sensing and communication applications, as weather radar meteorology as well as satellite and terrestrial communication. In turbulent weather situations, however, a significant number of drops exhibit no rotational symmetry. The reason can be found in the turbulent winds and in the collision of drops that can lead to asymmetric modes of drop oscillation. In recent years, therefore, several rain events during tropical depressions and storms were analyzed and the individual drops that were detected during those events with the use of disdrometers were fully reconstructed in 3D. By calculating the scattering parameters of the individual drops with the use of commercial electromagnetic solvers and by reconstructing their distribution, a good agreement between the determined measured polarimetric parameters and actual radar measurements could be archived. Those calculations were carried out at C- and S-band radar frequencies. The present study investigated if the approach is also suitable for the calculation of attenuation for sub-centimeter wavelengths and what influence the asymmetry can have. © 2022 FGM Events LLC. All rights reserved.

关键词： Drops

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5G New Radio Techniques for 3D networks in Connection-critical Scenarios

5G New Radio Techniques for 3D Networks in Connection-critic...

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IEEE Conference on Aerospace

作者： Luca Valcarenghi Justine Cris Borromeo Riccardo Bassoli Frank H.P. Fitzek Koteswararao Kondepu Nicola Andriolli Henok Berhanu Tsegaye Claudio Sacchi Scuola Superiore Sant’Anna Pisa Italy Deutsche Telekom Chair of Communication Networks Technische Universitaet at Dresden Dresden Germany Department of Computer Science and Engineering Indian Institute of Technology Karnataka India University of Pisa Pisa Italy Dept. of Information Engineering and Computer Science (DISI) University of Trento Trento Italy Department of Electrical and Computer Engineering The University of New Mexico Albuquerque (NM) NM

The recent natural disasters, such as the Earthquake in Morocco in September 2023, have demonstrated one more time the necessity of immediately restoring lost connectivity to rescue victims living in rural villages that are far from urban areas. In such a framework, three-dimensional (3D) Non-Terrestrial networks (NTNs) are expected to provide continuous wireless coverage at low Average Revenue Per User (ARPU), in disaster-hit and hotspot areas with the help of 5G New Radio (NR). This paper investigates the use of Unmanned Aerial Vehicles (UAV) as transparent aerial networks that perform signal amplification and frequency conversion between a satellite-based 5G network and a User Equipment (UE) using five different frequency bands (i.e., S-band, C-band, X-band, Ku-band, and Ka-band) supporting the satellite communication. The applications of the proposed technology concern connection-critical scenarios like emergency rescue and digital divide mitigation. Results show that 180Mbps to 4.85Gbps throughput can be achieved on the satellite-to-UAV link. Simulated transmission delay and average session time are also evaluated considering a delay budget of 2ms. The related results discussion confirms the viability and effectiveness of the proposed approach in the concerned case of study.

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The Effect of Mechanical Stress on the Sensitivity of Fiberoptic Polarization Temperature Sensor

The Effect of Mechanical Stress on the Sensitivity of Fibero...

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New Trends in Signal Processing (NTSP)

作者： Zdenek Vylezich Martin Kyselak Karel Slavicek David Grenar Zuzana Chalk Department of Electrical Engineerings Faculty of Military Technlogy Brno Czech Republic Institute of Computer Science Data and Technical Networks Brno Czech Republic Department of Telecommunication Faculty of Electrical Engineering and Communication Brno Czech Republic Department of Communication Technology Electronic Warfare and Radiolocation Faculty of Military Technology Brno Czech Republic

ISBN: (数字)9788080406370

ISBN: (纸本)9798350362688

The article analyses enhancing the sensitivity of fiber-optic polarization sensors to temperature changes via additional longitudinal strain. This multifunctional sensor is suitable for application as a sensing element for security purposes and has many possible biomedical applications. The testing method consists of stretching the fiber on a proof tester where it is possible to get an exact setting of the operating force, and then applying a container of water at a different temperature. Changes were captured by a laser power meter and plotted into graphs describing several sets of measurements to keep independence on manufacturing. The sensor sensitivity was increased and the proposed solution appeared to be suitable for in-position sensors with minimum additional outlays.

关键词： Temperature sensors Temperature measurement Optical fiber polarization Mechanical sensors Optical fiber sensors Sensitivity Force Sensor phenomena and characterization Optical fiber testing Stress

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Classification of Colorectal Cancer using ResNet and EfficientNet Models

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Open Biomedical engineering Journal 2024年 18卷

作者： Abhishek Ranjan, Abhishek Srivastva, Priyanshu Prabadevi, B. Rajagopal, Sivakumar Soangra, Rahul Subramaniam, Shamala K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Sensor and Biomedical Technology Vellore Institute of Technology Crean College of Health and Behavioral Sciences Chapman University Department of Communication Technology and Networks Universiti Putra Malaysia

Introduction: Cancer is one of the most prevalent diseases from children to elderly adults. This will be deadly if not detected at an earlier stage of the cancerous cell formation, thereby increasing the mortality rate. One such cancer is colorectal cancer, caused due to abnormal growth in the rectum or colon. Early screening of colorectal cancer helps to identify these abnormal growth and can exterminate them before they turn into cancerous cells. Aim: Therefore, this study aims to develop a robust and efficient classification system for colorectal cancer through Convolutional Neural networks (CNNs) on histological images. Methods: Despite challenges in optimizing model architectures, the improved CNN models like ResNet34 and EfficientNet34 could enhance Colorectal Cancer classification accuracy and efficiency, aiding doctors in early detection and diagnosis, ultimately leading to better patient outcomes. Results: ResNet34 outperforms the EfficientNet34. Conclusion: The results are compared with other models in the literature, and ResNet34 outperforms all the other models. © 2024 The Author(s).

关键词： CNN Colorectal cancer Deep learning EfficientNetB4 Histology Learning rate Loss function Optimizer ResNet34 ROC curve Transforms

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100 Gb/s NRZ OOK signal regeneration on a silicon chip

100 Gb/s NRZ OOK signal regeneration on a silicon chip

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CLEO: Science and Innovations, S and I 2022

作者： Wen, Hua Shun Cui, Jia Bin Zhou, Heng Chen, Yin Fang Jin, Ya Xu, Bo Rui Zhai, Kun Peng Sun, Jia Zheng Guo, Yuan Yuan Wu, Yan Ran Chen, Wen Chen, Wei Wang, Xin Zhu, Ning Hua Lu, Guo-Wei Ji, Gui Jun Zhou, Dennis Chi Cheng, Yiu-Kwok Yang, Da-Quan Li, Ming State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Optical Fiber Sensing and Communication Networks School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Division of Computer Engineering The University of Aizu Fukushima965-8580 Japan Ltd. Zhuhai519000 China

All-optical regeneration of 100 Gb/s NRZ OOK signal with 3.6 dB extinction ratio and 0.5 dB quality factor improvements is experimentally demonstrated using a silicon waveguide with reverse-biased p-i-n junction for t... 详细信息

ISBN: (纸本)9781557528209

关键词： Amplitude shift keying

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Lightdnn: A Lightweight Dual Neural Network Framework for Rapid Multiphysics Reconstruction in Hot Die Forging

SSRN

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SSRN 2025年

作者： Wang, Jue Xu, Xinyi Ye, Changxin Zhao, Fan Huangfu, Wei School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Laboratory of Metallic Materials and Processing for Modern Transportation Institute for Advanced Materials and Technology University of Science and Technology Beijing University of Science and Technology Beijing Beijing100083 China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China

Rapid and accurate reconstruction of multiphysics during hot die forging processes is essential for manufacturing optimization, quality assurance, and intelligent process control. While conventional finite element-based acceleration methods fail to meet sub-second requirements, existing sequence-based data-driven approaches suffer from two fundamental limitations: physically inconsistent predictions due to coordinate misalignment, and unsustainable computational costs from sequence-length-dependent parameter scaling. To address these challenges, we propose LightDNN, a lightweight dual-neural network framework that employs a coordinate-oriented paradigm to ensure physical consistency while maintaining computational efficiency for multiphysics reconstruction of hot die *** proposed framework operates through two components: a boundary nodes tracking network that describes the geometric deformation by predicting nodal coordinates' evolution, and a physical fields reconstruction network that reconstructs multiphysics distributions through coordinate-based physical quantities *** validation on 1,960 samples with different forging strategies demonstrates superior performance over existing sequential models in both geometric deformation prediction and 9 physical quantities distributions reconstruction, while simultaneously achieving a 96-98% reduction in number of parameters compared to conventional sequential models and millisecond-scale inference capability (62.68±3.79 ms on CPU and 2.91±0.26 ms on GPU).The proposed framework demonstrates strong potential for industrial deployment while establishing a novel paradigm for efficient multiphysics modeling in hot die forging processes. © 2025, The Authors. All rights reserved.

关键词： Forgings

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P-I2Prange: An Automatic Construction Architecture for Scenarios in I2P Ranges

P-I2Prange: An Automatic Construction Architecture for Scena...

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International Joint Conference on Neural networks (IJCNN)

作者： Runnan Tan Qingfeng Tan Haiyan Wang Yushun Xie Peng Zhang Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Department of New Networks PengCheng Laboratory Shenzhen China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Anonymous networks play a crucial role in preserving information privacy, yet simultaneously spark technical conflicts and gamesmanship between their maintainers and regulatory authorities. To solve the above conflicts and economic losses in real networks, it is imminent to realize adversarial exercises in anonymous networks and validate new technologies and scenarios in the cyber range. It is well known that the two dominant types of anonymity networks are Tor and Invisible Internet Project (I2P). This paper establishes a cyber range based on I2P to realize a task-driven automated scenario construction technique. The proposed task-driven automated scenario construction technique can solve the problem of decoupling between business scenarios and I2P cyber range network infrastructure. Specifically, our approach allows users to upload models/executable code to extend flexibly and fine-grained control I2P nodes, protocols, and traffic behavior characteristics. Then it empowers users to define application scenarios as needed, enabling them to handle intricate business scenarios programmatically. Therefore, it further supports diverse adversarial exercises and the validation of new technologies and scenarios within the dark web.

关键词： Measurement Privacy Costs Protocols Dark Web Scalability Neural networks

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Hao, Fengqi Zhu, Shulei Ma, Dexin Dong, Xiangjun Kong, Hoiio Liu, Xia Mu, Chunhua Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250014 China City University of Macau Faculty of Data Science 999078 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China Communication College Qingdao Agricultural University Qingdao266109 China Maize Research Institute Shandong Academy of Agricultural Sciences National Engineering Laboratory of Wheat and Maize Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Jinan250100 China

ISBN: (纸本)9781665410205

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction - receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings. © 2024 IEEE.

关键词： Deep learning Maize seedling detection Object detection UAV

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